Download INVESTIGATING THE SOPHISTICATION OF LONG-LATENCY UPPER LIMB

INVESTIGATING THE SOPHISTICATION OF LONG-LATENCY
STRETCH RESPONSES DURING POSTURAL CONTROL OF THE
UPPER LIMB
by
Jedrzej (Andrew) Pruszynski
A thesis submitted to the Centre for Neuroscience Studies
In conformity with the requirements for
the degree of Doctor of Philosophy
Queen’s University
Kingston, Ontario, Canada
January, 2011
Copyright © Jedrzej (Andrew) Pruszynski, 2011
Abstract
A recent theory of motor control, based on optimal feedback control, posits that
voluntary motor behaviour involves the sophisticated manipulation of sensory feedback.
Although this theory can explain how people move in the world, it does not specifically
describe how this control process is implemented by the nervous system. In this thesis,
we propose and explore one physiological implication of this theory. Specifically, we
hypothesize that rapid feedback responses should possess the key functional attributes
of voluntary control because these two systems share a common neural pathway
through motor areas of cerebral cortex.
Our first four studies were designed to elaborate the functional attributes of the
long-latency stretch reflex, a fast feedback response which occurs 50-100ms following
the mechanical stretch of a muscle. Consistent with our hypothesis, we found that the
long-latency response possesses many attributes commonly reserved for voluntary
control: the long-latency response is continuously modulated by subject intent (Chapter
2), it compensates for the size-recruitment principle of the motoneuron pool (Chapter 3)
and it accounts for the mechanical properties of the upper-limb (Chapter 5). Further
investigation revealed that the long-latency response can be decomposed into two
functionally-independent processes (Chapter 4), and that one of these components
contributes all of the sophistication observed in Chapters 2 and 3.
The goal of our fifth study was to investigate the neural basis of the long-latency
response (Chapter 6). Our results provide strong evidence from both single-neuron
recordings in non-human primates and transcranial magnetic stimulation in humans that
primary motor cortex, which is known to be a critical node for voluntary control, also
contributes to the sophistication of the long-latency response.
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Taken together, the studies presented in this thesis demonstrate that the longlatency response possesses several functional attributes typically reserved for voluntary
control and that this sophistication likely arises via a transcortical pathway through
primary motor cortex.
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Co-Authorship
I conducted the research presented in this thesis under the supervision of Dr.
Stephen Scott. I was actively involved in all aspects (study conception, experimental
design, data collection, data analysis, and writing) of the presented work. I took the lead
on the work presented in Chapters 2, 3, 4, and 6 except for the muscle model presented
in Chapter 3 (Timothy Lillicrap) and the TMS portion of Chapter 6 (Isaac Kurtzer). Isaac
Kurtzer took the lead role on the study presented in Chapter 5 and my contribution
included helping with the study conception, experimental design and data analysis as
well as providing editorial advice on the manuscript.
The chapters presented in this dissertation are either about to be submitted
(Chapters 1 and 6), have already been submitted (Chapter 4) or are already published
(Chapters 2, 3, and 5). Wherever possible, please reference the published work:
Chapter 1: Pruszynski JA, Scott SH. Long-latency stretch responses as the earliest
output of the voluntary motor system.
Chapter 2: Pruszynski JA, Kurtzer I, Scott SH (2008) Rapid motor responses are
appropriately tuned to the metrics of a visuospatial task. J
Neurophysiology 100: 224-238.
Chapter 3: Pruszynski JA, Kurtzer I, Lillicrap TP, Scott SH (2009) Temporal Evolution
of "Automatic Gain-Scaling". Journal of Neurophysiology 102: 992-1003.
Chapter 4: Pruszynski JA, Kurtzer I, Scott SH (first submitted, August 2010). The longlatency reflex is composed of two functionally independent processes.
Chapter 5: Kurtzer IL, Pruszynski JA, Scott SH (2008) Long-latency reflexes of the
human arm reflect an internal model of limb dynamics. Current Biology
18: 449-453.
Chapter 6: Pruszynski JA, Kurtzer I, Nashed J, Omrani M, Brouwer B, Scott SH.
Primary motor cortex underlies multi-joint integration for fast feedback
control.
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Acknowledgements
Countless people have played an important role in making my PhD an enjoyable,
fruitful and rewarding experience. Thank you all, but because space is lacking, I want
highlight those people that were so critical to the entire endeavor that it is impossible to
imagine this period of my life without them.
• My wife, Kristen Wise
• Our baby boy, Olin Pruszynski
• My father and late mother, Przemyslaw and Leokadia Pruszynski
• My supervisor and mentor, Stephen Scott
• My committee, Randy Flanagan, Doug Munoz, Ken Rose and Niko Troje
• Our lab manager and animal technician, Kim Moore
• My closest colleague and great friend, Isaac Kurtzer
• Great folks, Scott, Connie, and the rest of the Selbie family
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Table of Contents
Abstract ............................................................................................................................. ii
Co-Authorship .................................................................................................................. iv
Acknowledgements ........................................................................................................... v
Table of Contents............................................................................................................. vi
List of Figures..................................................................................................................viii
Chapter 1 – General Introduction ..................................................................................1
1.1 – PREAMBLE ..........................................................................................................2
1.2 – MOTIVATION .......................................................................................................2
1.3 – OPTIMAL FEEDBACK CONTROL .......................................................................4
1.4 – OPTIMAL FEEDBACK CONTROL AND THE STRETCH RESPONSE ...............7
1.5 – TASK-DEPENDENCY OF STRETCH RESPONSES...........................................8
1.6 – SELECTIVE INTEGRATION OF SENSORY FEEDBACK .................................15
1.7 – SHORT-LATENCY RESPONSE: STEREOTYPED BUT NOT IMMUTABLE.....19
1.8 – NEURAL BASIS OF THE LONG-LATENCY STRETCH RESPONSE ...............21
1.9 – HYPOTHESIS AND OUTLINE OF EXPERIMENTS...........................................27
1.10 – REFERENCES .................................................................................................31
Chapter 2 – Rapid motor responses are modulated by visuospatial taskconstraints ........................................................................................................46
2.1 – ABSTRACT ........................................................................................................47
2.2 – INTRODUCTION ................................................................................................48
2.3 – MATERIALS AND METHODS............................................................................50
2.4 – RESULTS ...........................................................................................................62
2.5 – DISCUSSION .....................................................................................................82
2.6 – REFERENCES ...................................................................................................92
Chapter 3 – Sensitivity of rapid motor responses to pre-perturbation muscle
activity ...............................................................................................................99
3.1 – ABSTRACT ......................................................................................................100
3.2 – INTRODUCTION ..............................................................................................101
3.3 – METHODS........................................................................................................104
3.4 – RESULTS .........................................................................................................110
3.5 – DISCUSSION ...................................................................................................124
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3.6 – REFERENCES .................................................................................................133
Chapter 4 – The long-latency reflex is composed of two functionally independent
processes........................................................................................................141
4.1 – ABSTRACT ......................................................................................................142
4.2 – INTRODUCTION ..............................................................................................143
4.3 – MATERIALS AND METHODS..........................................................................144
4.4 – RESULTS .........................................................................................................150
4.5 – DISCUSSION ...................................................................................................163
4.6 – REFERENCES .................................................................................................168
Chapter 5 – Long-latency reflexes of the human arm reflect an internal model of
limb dynamics.................................................................................................172
5.1 – ABSTRACT ......................................................................................................173
5.2 – INTRODUCTION ..............................................................................................174
5.3 – RESULTS .........................................................................................................176
5.4 – DISCUSSION ...................................................................................................181
5.5 – METHODS........................................................................................................184
5.6 – REFERENCES .................................................................................................190
Chapter 6 – Primary motor cortex underlies multi-joint integration for fast
feedback control.............................................................................................194
6.1 – ABSTRACT ......................................................................................................195
6.2 – MAIN TEXT ......................................................................................................196
6.3 – METHODS SUMMARY ....................................................................................207
6.4 – REFERENCES .................................................................................................209
Chapter 7 – General Discussion ................................................................................212
7.1 – SUMMARY OF FINDINGS ...............................................................................213
7.2 – FUTURE DIRECTIONS ....................................................................................216
7.3 – CONCLUSIONS ...............................................................................................224
7.4 – REFERENCES .................................................................................................225
Appendix 1...................................................................................................................228
A1.1 – SPECIFICATION OF MODEL ........................................................................229
A1.2 – REFERENCES...............................................................................................231
Appendix 2...................................................................................................................232
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List of Figures
Figure 2.1 – Task apparatus and experimental paradigm...............................................52
Figure 2.2 – Determination of voluntary reaction time.....................................................55
Figure 2.3 – Exemplar hand kinematics and muscle activity for Experiment 1 ...............63
Figure 2.4 – EMG analysis without a priori assumptions about epochs..........................67
Figure 2.5 – EMG analysis under random trial order ......................................................69
Figure 2.6 – Task-dependency under different trial orders as a function of epoch .........70
Figure 2.7 – Behavioural performance and EMG in Experiment 2..................................72
Figure 2.8 – Comparison of models relating EMG and target position............................74
Figure 2.9 – Exemplar hand kinematics in Experiment 3 ................................................77
Figure 2.10 – Spatial tuning of EMG in Experiment 3 .....................................................78
Figure 2.11 – Directional tuning of rapid motor responses relative to the voluntary
response...............................................................................................................81
Figure 3.1 – Experimental apparatus and methods ......................................................106
Figure 3.2 – Spatial hand kinematics with different background and perturbation
magnitudes .......................................................................................................112
Figure 3.3 – Temporal joint kinematics from an exemplar subject ................................113
Figure 3.4 – Analysis of empirical and model kinematics..............................................115
Figure 3.5 – Exemplar patterns of muscle activity with different background loads and
perturbation magnitudes.....................................................................................118
Figure 3.6 – Group patterns of muscle activity..............................................................120
Figure 3.7 – Analysis of gain-scaling across epochs and in time..................................121
Figure 3.8 – Gain-scaling trends for each muscle group...............................................125
Figure 4.1 – Candidate models and experimental predictions ......................................145
Figure 4.2 – Apparatus and experimental paradigm .....................................................147
Figure 4.3 – Hand Kinematics, Experiment 1................................................................152
Figure 4.4 – Exemplar muscle activity, Experiment 1 ...................................................153
Figure 4.5 – Population muscle activity, Experiment 1 .................................................155
Figure 4.6 – Group analysis and trial-by-trial correlations.............................................156
Figure 4.7 – Group analysis, Experiment 2 ...................................................................160
Figure 4.8 – Group analysis, Experiment 3 ...................................................................162
Figure 5.1 – Reflex activity following single-joint torque perturbaitons..........................175
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Figure 5.2 – Reflex activity following multi-joint torque perturbations ...........................178
Figure 5.3 – Reflex tuning to shoulder/elbow torque.....................................................180
Figure 5.4 – Reflex activity for the same joint motion at two different limb
configurations ...................................................................................................182
Figure 6.1 – Experimental methods and neural population...........................................198
Figure 6.2 – Activity of neurons in primary motor cortex ...............................................199
Figure 6.3 – Population analysis of monkey neurons and muscles ..............................202
Figure 6.4 – Activity of monkey shoulder muscles ........................................................203
Figure 6.5 – Activity of human shoulder muscles evoked by torque perturbations and
cortical stimulation ............................................................................................205
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List of Tables
Table 2.1 – Assessment of target-dependent modulation by muscle..............................66
Table 2.2 – Results of model selection by muscle ..........................................................75
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Chapter 1
General Introduction
1
1.1 – PREAMBLE
In this thesis, we are motivated by a recent theory of motor control, based on
optimal feedback control, which posits that voluntary motor behaviour involves the
sophisticated manipulation of sensory feedback (Todorov, 2004; Todorov and Jordan,
2002). All the studies address one hypothesized physiological implication of this theory.
That is, that the long-latency stretch reflex should possess functional attributes similar to
voluntary control because the reflexive and voluntary motor systems form part of the
same control process passing through similar neural circuitry (Scott, 2004).
What follows in this opening chapter forms the core of an invited review. The goal
of the review as a whole is to introduce the theoretical framework of optimal feedback
control and elaborate how this theory motivates exploring the sophistication of the longlatency stretch response and its underlying neural implementation. What is included in
this dissertation focuses on what is known about the sophistication of stretch reflex
mechanisms and their neural basis. To ensure that this works as a standalone
introduction, I have selectively modified the text so that it does not preempt all the results
that follow in the dissertation.
1.2 – MOTIVATION
The nervous system counters mechanical perturbations applied to the arm with a
stereotypical sequence of muscle activity, starting with a short-latency stretch reflex (2050ms post-perturbation) and ending with a voluntary response (>100ms). Occurring
between these two events is the enigmatic and often studied long-latency stretch reflex
(50-100ms), which occurs earlier than standard metrics of voluntary reaction time yet
can sometimes be modified by a subject’s voluntary intent (Crago et al., 1976; Hagbarth,
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1967; Hammond, 1956; Rothwell et al., 1980). The duality of the long-latency reflex,
which is on the one hand fast, simple and automatic like the short-latency reflex, and on
the other hand, complex and capable like voluntary control has yielded a great deal of
debate about its functional role in motor behaviour and its underlying neural circuitry
(Marsden et al., 1983; Matthews and Miles, 1988; Shemmell et al., 2010).
Recent theories of motor control, based on optimal feedback control, posit that
voluntary motor behaviour involves the sophisticated manipulation of sensory feedback
(Todorov, 2004; Todorov and Jordan, 2002). We have previously suggested that one
physiological implication of this manipulation is that the long-latency reflex, like voluntary
control, should support a rich assortment of behaviours because this reflexive response
and the voluntary motor system are intimately linked (Scott, 2004). Reflexive and
voluntary responses are related because they both form part of the same feedback
control process and engage similar neural circuitry, including primary motor cortex. Such
a close link between reflexive and voluntary responses makes many experimental
predictions but belies the traditional distinctions made between reflexes and voluntary
control. Therefore, we try to avoid the term ‘reflex’, and its associated semantic
baggage, favouring instead a simple empirical distinction between relatively slow (i.e.
voluntary) and rapid motor responses (<105ms post-perturbation).
In this review, we briefly introduce optimal feedback control as it relates to
sensorimotor control and elaborate how this theory, which is formulated at the
behavioural level, can nevertheless help reconcile previous findings about the longlatency response and guide future experiments exploring its sophistication and neural
implementation.
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1.3 – OPTIMAL FEEDBACK CONTROL
The sensorimotor system is a product of evolution, development, and learning.
These processes act on different timescales to improve behavioural performance so it is
no surprise that many theories of motor behaviour are based on optimal control
principles and that these models are extremely successful at reproducing a wealth of
empirical observations (Fagg et al., 2002; Harris and Wolpert, 1998; Hogan, 1984; Kuo,
1995; Kurtzer et al., 2006; Scholz and Schoner, 1999; Smeets and Brenner, 1999;
Todorov and Jordan, 2002).
Optimal feedback control is a subfield of optimal control that computes control
signals based on the current state of the system and a cost-function which describes the
performance criteria of a given behaviour. The net result is a complex link between
sensory feedback signals and motor outputs that changes as a function of the task being
performed. Such controllers can reproduce a wide range of motor phenomena at the
behavioural level (Todorov, 2004) including muscle synergies, goal-directed corrections,
and apparent controlled variables (Todorov and Jordan, 2002). Given the inherent noise
in biological systems (Faisal et al., 2008), optimal feedback controllers designed with a
rational cost function (i.e. get to the target while penalizing control effort) predict goaldirected movements that are variable from trial-to-trial but remain successful at
achieving the behavioural goal. In fact, this variability adheres to a minimum intervention
principle, where only those errors that adversely affect the behavioural task are
corrected (Todorov and Jordan, 2002). Irrelevant errors are ignored because they play
no role in reducing the cost and because trying to correct these task-irrelevant errors
with a noisy sensorimotor system may actually produce more errors and possibly make
them task-relevant, impacting behavioural performance. Consistent with these
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expectations, many studies have demonstrated that patterns of variability are not
random but show considerable organization that is related to the behavioural goal of the
motor task (Scholz and Schoner, 1999). For example, in a pistol shooting task, variability
occurs predominantly in those directions where the pistol axis intersects the target so
that the bullet will successfully hit the target (Scholz et al., 2000).
An optimal feedback controller has several key features. First, such controllers
need an explicit definition of task performance (i.e. the cost function). The cost function
mathematically defines the relevant behavioural variables of the present task and
quantifies their relative weighting. For example, a rational cost function for upright stance
may include one term related to maintaining the body’s center-of-mass above its baseof-support and a second term that penalizes energy expenditure. In contrast, the cost
function for a 100-meter sprint would be heavily biased towards minimizing the time to
run the distance without a great deal of consideration for energy expenditure. Once the
cost function has been established, the complex mathematics of optimal feedback
control can be employed to find the control law which best satisfies the cost function
given the physical plant being controlled. Critically, the resulting mapping between
sensory inputs and motor outputs (i.e. feedback gains) may not be fixed since it is
adjusted based on the cost function, which can change according to the behavioural task
being performed.
Second, an optimal feedback controller receives multiple inputs which can be
mapped to its many outputs. In general, such a multiple-input, multiple-output (MIMO)
structure is critical for controlling a system with internal interactions, which are a
common feature of many systems (Brogan, 1991). When taking a shower, for example,
increasing the temperature is inherently linked to increasing the total flow of water.
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Keeping water flow constant requires knowledge of the interaction between the hot and
cold water valves (with respect to both temperature and flow) and is not trivial to solve
with simpler control schemes which control the valves independently. Biological systems
possess a myriad of complex interactions that could also be addressed by a MIMO
control structure. The flexibility afforded by MIMO control allows the nervous system to
account for the physical interactions which naturally occur between parts of the body and
may permit it to optimize complex high-level cost functions which hinge on such
interactions across parts of the body and across sensory modalities. As in the shower
example, controlling the shoulder joint will influence motion of the shoulder, elbow and
wrist because of intersegmental dynamics (Graham et al., 2003; Hollerbach and Flash,
1982). Furthermore, generating the optimal response at the shoulder joint may require
position information from the elbow when responding to a dog’s sudden pull on a leash,
force information from the foot when countering the unexpected movements of a bus
and visual information when reacting to catch a ball.
Lastly, an optimal feedback controller needs an accurate estimate of the state of
the system it is controlling. This is generated by combining delayed afferent feedback
from peripheral sensors and an estimate of current system state based on the
descending motor signals. This estimation process can predict changes in the periphery
before the corresponding sensory data has arrived, a process which requires knowledge
about the dynamical properties of the body – i.e. an internal model (Hwang and
Shadmehr, 2005; Kawato and Wolpert, 1998; Wolpert and Flanagan, 2001).
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1.4 – OPTIMAL FEEDBACK CONTROL AND THE STRETCH RESPONSE
Although optimal feedback controllers can reproduce a wide range of motor
behaviours, the theory does not specifically describe the physiological or neural basis for
such a control process. The formal mathematics of optimal feedback control are
incredibly complex (Stengel, 1994) and it is certain that the brain does not formally solve
these equations. In general, determining how a distributed neural network, which
includes multiple nested feedback loops, yields near-optimal motor behavior is an
important outstanding question in sensorimotor control (Scott, 2004; Shadmehr and
Krakauer, 2008).
Where can we start the process of unraveling the physiological mechanisms
which underlie optimal feedback control for voluntary movement? It has been wellestablished and is commonly appreciated that the voluntary motor system possesses an
incredible capacity to control direction, distance, speed, and accuracy of movement
(Shadmehr and Wise, 2005). Voluntary control mechanisms can also adjust for current
loads and can rapidly learn novel loads (Lackner and Dizio, 1994; Nozaki et al., 2006;
Shadmehr and Mussa-Ivaldi, 1994), visuomotor rotations (Cunningham, 1989; Krakauer,
2009; Pine et al., 1996) and even arbitrary sensorimotor mappings (Sailer et al., 2005).
Yet few studies on voluntary behaviour have linked their observations to online feedback
control. Optimal feedback control motivates exploring such a linkage because it
emphasizes the importance of using and manipulating sensory feedback to guide
voluntary motor behaviour. The implication is that feedback mechanisms, such as the
response of a muscle to its mechanical stretch (i.e. the stretch “reflex” response), should
exhibit a level of sophistication that is similar to voluntary movement because the two
systems are inherently linked as part of the same control process. In this context,
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probing the sophistication of the stretch response provides a window into understanding
voluntary control.
Here, we review both classical and recent findings which suggest that stretch
responses, and particularly the long-latency stretch response, possess many functional
attributes that are commonly reserved for voluntary movement and expected of an
optimal feedback control process: task-dependency, MIMO mappings, and knowledge of
limb dynamics. We then suggest that such functional similarity can be readily understood
if one appreciates that both long-latency responses and voluntary control share similar
neural substrates, including primary motor cortex.
1.5 – TASK-DEPENDENCY OF STRETCH RESPONSES
One expectation of an optimal feedback control process is that the control law
which maps sensory input to motor output should be sensitive to the ongoing behaviour.
Consider how a subject should respond to the same mechanical perturbation applied
during the maintenance of posture or in the middle of a reach. When in posture, the
subject needs to counter the applied perturbation and maintain their current position. But
when reaching, the subject should not maintain their current position. Rather, the
response should be directed towards the final reach target which they are trying to
attain. Depending on the behavioural context, the same sensory information may evoke
a robust motor correction, yield no response or even assist the perturbation (Hasan,
2005).
Consistent with the notion of modifiable feedback responses, over sixty years of
research has established that stretch responses are task-dependent in a variety of
situations. The task-dependent nature of the long-latency response has been studied in
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many different experimental settings using a range of devices and focusing on different
behaviours and muscle groups. Despite this wide array of approaches, most of the
experiments can be categorized into one of three different types of task-dependency.
One class of studies explores how the long-latency response is modulated by subject
intent. In these studies, subjects are given an explicit instruction on how they should
respond to an upcoming perturbation. A second class focuses on changing the
behavioural context and demonstrating that the demands of the task implicitly modify the
long-latency response. And a third class of studies investigates how the long-latency
response is modulated when the main goal of the task remains constant but the
surrounding environment is changed.
Explicit modulation by subject intent
Peter Hammond was the first researcher to describe the long-latency response
(Hammond, 1955). With a simple motor, pulley and clutch, he tethered a human
subject’s wrist with a cable and pulled the hand in such a way that extended the elbow
and caused a concomitant stretch of the biceps muscle. The imposed stretch evoked a
multi-peaked sequence of muscle activity. The first peak, occurring 20-50ms after the
perturbation, was the well-described short-latency response (Pierrot-Deseilligny and
Burke, 2005), whose monosynaptic contribution had been originally described in the
seminal work of Charles Sherrington (Liddell and Sherrington, 1924). The second peak,
which was substantially larger and occurred 50-100ms following the perturbation, was
termed the long-latency stretch response.
In quite literally the second experiment investigating this multi-peaked sequence
of muscle activity, Hammond probed its task-dependency by asking subjects to respond
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to an unpredictable perturbation based on a verbal instruction provided prior to each trial
(Hammond, 1956). The short-latency response evoked by the perturbation was not
sensitive to the instruction. Remarkably, however, the long-latency response was larger
when the subjects were told to “resist” the perturbation than when they were asked to
“let go”. In a control experiment, a very small perturbation was applied and subjects were
instructed to resist it as soon as possible. This small perturbation did not evoke the multiphasic sequence of muscle activity attributed to the short- and long-latency stretch
response mechanisms, but did yield a slowly-rising response at ~100ms which was
deemed a voluntary muscular response. These seminal studies led to the conclusion
that the long-latency stretch response was not voluntarily generated – because it
occurred prior to voluntary reaction time – but that it could be modulated by a subject’s
voluntary intent because it was sensitive to verbal instructions.
The finding that long-latency responses are modulated by subject intent is
incredibly robust. It has been investigated for a wide range of muscles at the elbow
(Colebatch et al., 1979; Crago et al., 1976; Evarts and Granit, 1976; Hagbarth, 1967;
Rothwell et al., 1980), wrist (Calancie and Bawa, 1985; Jaeger et al., 1982; Lee and
Tatton, 1982), finger (Capaday and Stein, 1987; Marsden et al., 1981; Rothwell et al.,
1980), ankle (Gottlieb and Agarwal, 1979; Gottlieb and Agarwal, 1980; Ludvig et al.,
2007), and jaw (Pearce et al., 2003). Furthermore, the phenomenon is readily
observable using a range of verbal instructions including “resist/let go” (Colebatch et al.,
1979; Rothwell et al., 1980), “flex/extend” (Hagbarth, 1967), and “compensate/do not
intervene” (Crago et al., 1976).
Although these pioneering studies have established that the long-latency
response is modulated by subject intent, their use of verbal instructions has limited the
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range of questions that could be asked to the small subset of behaviours where verbal
instructions have a reliable interpretation, generally at the extremes of behaviour. For
example, verbal instructions can only test for categorical changes in the long-latency
response. So it is possible to ask the subject to “resist as quickly as possible after the
perturbation” or “let go as quickly as possible after the perturbation” but it is unlikely that
subjects could reliably “resist 50%”, “resist 23%” or “resist 50% with the elbow and 75%
with the shoulder”. The limited range of verbal instructions is an impediment to exploring
the fine and nuanced capabilities expected of the long-latency response under the
optimal feedback control hypothesis.
Implicit modulation by behavioural context
Although subject intent robustly modifies the long-latency stretch response, such
manipulation is so direct that it may not reflect the natural state of affairs where a subject
changes from one behavior to another and does not explicitly focus on modulating their
rapid motor responses. In fact, there exists substantial evidence that changing the
functional goal of a task leads to changes in the long-latency response without explicit
instruction.
An elegant demonstration of such task-dependency occurs in the so-called ‘tea
cup’ experiment (Marsden et al., 1981). In that study, subjects were exposed to
mechanical perturbations that destabilized their body by pulling on their left hand while
crouching and either holding onto a table or grasping a tea cup with their right hand. The
principle result was that the long-latency response of right arm muscles was different
depending on what the right arm was doing. If it was gripping the table, the long-latency
response in extensor muscles was activated in response to the perturbation such that it
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would help stabilize the subject. If the subject was gripping an unsupported handle, the
long-latency response was absent, presumably because any activation in that muscle
would not help stabilize the body. And if they were holding a cup of tea, the long-latency
response was reversed to ensure that the liquid remained in the cup.
Another example of how behavioural context modulates the long-latency
response arises when subjects are engaged in maintaining the position of their hand
versus maintaining a set level of force/torque (Dietz et al., 1994; Doemges and Rack,
1992a; Doemges and Rack, 1992b; Hore et al., 1990). In these experiments, subjects
are presented with a display that indicates either their joint position or their joint torque.
Their task is to maintain this set level in the presence of mechanical perturbations.
Short-latency stretch responses are not modulated by the task-constraints, they are only
sensitive to the pre-perturbation muscle activity. In contrast, the long-latency response is
larger when perturbations are applied in the position control task than in the force control
task.
It is important to emphasize that task-dependent modulation is not limited to the
upper-limb. In fact, many of the original studies focused on the control of upright posture
(Jacobs and Horak, 2007; Nashner, 1977). For example, when a subject is standing on a
platform which can suddenly move, their long-latency response is governed by the
length of the platform (Horak and Nashner, 1986). When the platform is longer than the
foot, subjects recruit long-latency responses in ankle muscles to counter the perturbation
but when the platform is shorter than the foot, subjects utilize a hip strategy to counter
the same perturbation. Changing the response according to the platform length is critical
to task-success. Because the short platform was much shorter than the foot (9cm), using
the ankle strategy in this context would cause the feet to rotate off the support surface.
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In contrast, the hip-strategy allows the subject to move their center-of-mass relative to
their base-of-support, and thus stay upright, without generating torque around the ankle.
Environmental constraints
Generating appropriate feedback responses requires not only an appreciation of
the goal of the task but also the environment within which the task is taking place.
Indeed, several studies have demonstrated that changing the mechanical properties of
the environment leads to an appropriate change in the long-latency response (Akazawa
et al., 1983; Dietz et al., 1994; Perreault et al., 2008). In these studies, subjects typically
held on to a robot which simulated either a stiff or compliant environment and thus
affected how well they could maintain their hand at a given position. When the same
perturbation was applied in these two situations, the long-latency response was larger
for the compliant environment. The observation that long-latency responses are larger in
the compliant environment has led to the proposal that they act to regulate the stability of
the limb (Shemmell et al., 2010). However, it is important to recognize that a stiff
environment implies that feedback responses will be relatively ineffective. Therefore, the
long-latency response may be depressed because the nervous system recognizes that
responding will not change anything with respect to the task goal.
The long-latency response also accounts for interactions with external force
fields during reaching (Kimura et al., 2006). In that study, subjects made straight reaches
in a predictable force field that pushed their right hand away from its intended target.
When a perturbation was unexpectedly applied just before subjects entered the force
field, the long-latency response was modified to appropriately counter deviations
generated by the force field. Specifically, rightward force fields evoked increased activity
13
in flexor muscles and leftward force fields evoked increased activity in extensor muscles.
This demonstration of predictive modulation associated with a voluntary movement is
evidence that the long-latency stretch response is closely coupled to the voluntary motor
system.
Recent work has advanced the known capabilities of the long-latency response
by demonstrating that task-dependent regulation of limb stiffness is sensitive to
perturbation direction (Krutky et al., 2010). Subjects were exposed to destabilizing
environments which acted like springs with negative stiffness such that position errors in
a particular direction were amplified. These environments were oriented so that the
instability could be aligned or orthogonal to the direction of maximal endpoint stiffness of
the limb. Unlike the short-latency response, the long-latency response was preferentially
increased in those muscles which contribute to countering perturbations along the
direction of instability. Interestingly, this increase only occurred when the magnitude of
the environmental instability exceeded the mechanical stiffness of the limb in the
perturbation direction, a result which suggests that long-latency responses account for
the mechanical properties of both the environment and the limb. The directiondependence of the long-latency response is important since many tasks compromise
limb stability for particular directions and the mechanical properties of the upper-limb are
direction specific (Hogan, 1984). For example, when using a screwdriver, there is
substantial stability along the screw but a great deal of instability in off-axis directions. It
is important to emphasize that this sophisticated response observed during for the longlatency response is consistent with the known attributes of voluntary control where
patterns of muscular co-contraction account for the directional instability present in
various environments (Burdet et al., 2001; Franklin et al., 2003).
14
The environment has a powerful effect on the short-latency stretch response
because it is profoundly modulated by pre-perturbation muscle activity. The result of this
sensitivity is that the same perturbation in an environment that activates the muscle a
little or a lot will evoke a small or large short-latency response (Bedingham and Tatton,
1984; Matthews, 1986; Stein et al., 1995; Verrier, 1985). This gain-scaling phenomenon
was originally proposed as a mechanism to keep the short-latency response useful over
a range of load contexts (Marsden et al., 1976a). However, the presence of gain-scaling
may actually impede this goal because, under the assumption of a relatively linear
relationship between a muscle’s activity and its force production (Hof, 1984; Lawrence
and Deluca, 1983; Milner-Brown and Stein, 1975), the motor output should reflect the
magnitude of the perturbation regardless of the initial conditions. Since the gain-scaling
phenomenon is caused by the size-recruitment principle of the motoneuron pool
(Capaday and Stein, 1987; Kernell and Hultborn, 1990; Slot and Sinkjaer, 1994), its
presence in the short-latency epoch appears to reflect an inadequacy of the shortlatency response to account for the size-recruitment principle. However, no studies have
specifically examined whether the long-latency response, which typically demonstrates
much greater sophistication than the short-latency response, accounts for the state of
the motoneuron pool (i.e. shows less, little or no gain-scaling).
1.6 – SELECTIVE INTEGRATION OF SENSORY FEEDBACK
An important feature of an optimal feedback controller (and many other types of
modern control systems) is the ability to incorporate multiple sensory input signals into
each motor output (Brogan, 1991). The presence of a multiple-input, multiple-output
control structure would permit the sensorimotor control system to account for its many
15
internal interactions and to route sensory feedback to a variety of motor effectors as
appropriate for the task constraints. The ability of the motor system to adapt and use
sensory signals from various locations and modalities during feedback control is
exemplified by our ability to use cutaneous signals from light finger contact to stabilize
whole-body standing posture (Jeka and Lackner, 1994).
Flexible routing of sensory information
The short-latency stretch response can coordinate the contraction of multiple
muscles. At the simplest level, the monosynaptic response activates not just the muscle
being excited but also its functional synergists. An additional synapse via an inhibitory
interneuron means exciting one muscle will yield inhibition in its antagonists. More
complex coordination is possible at the spinal level. For example, when a noxious
stimulus is applied to the leg, cutaneous afferents yield activation of flexor muscles in the
stimulated leg and extensor muscles in the other leg (Kandel et al., 2000). This flexion
and crossed-extension reflex means that stepping on a nail results in a rapid response to
lift the stimulated leg and bear the weight of the body with the other leg.
The long-latency response can route sensory feedback in a much more flexible
manner. For example, in the tea cup experiment described previously, when the subject
was pulled forward by their left arm, a long-latency stretch response was also routed to
the muscles of the toe, which were not stretched by the perturbation but could contribute
to maintain the subject’s balance (Marsden et al., 1981). Long-latency responses are
also routed across finger muscles when subjects are exposed to perturbations during an
object manipulation task (Cole et al., 1984). If a perturbation is applied to the thumb, the
long-latency response appears on both thumb and forefinger muscles as they both
16
contribute to compensating for the load and thus help satisfy the goal of the task.
Interestingly, this coupling is influenced by task-constraints. If the subject is making
rhythmic movements that mimic a grasping motion but without the presence of an object
then the application of a perturbation does not result in a coordinated response across
the thumb and forefinger.
Recent work has demonstrated that feedback responses are also coordinated
across the two limbs (Diedrichsen, 2007; Mutha and Sainburg, 2009; Ohki and
Johansson, 1999). In a very elegant study, Diedrichsen (2007) asked subjects to reach
with two hands to two separate spatial targets (two-cursor condition) or make the same
bimanual movements to move a cursor presented at the spatial average location of the
two hands (single-cursor condition) to a single spatial target. When forces were applied
in the two-cursor condition, only the hand that received the force could ultimately counter
the applied load. In contrast, when the same perturbation was applied in the one cursor
condition, both hands could counter the disturbance applied to one of the hands. The
results indicate that subjects were optimal with respect to a simple cost function by
recruiting only one hand in the two cursor condition and efficiently splitting the response
between the hands in the one cursor condition. The work of Diedirchsen (2007) did not
record muscle activity and the behavioural responses occurred with a large enough
delay (~180ms with respect to velocity) that the phenomenon did not necessarily reflect
the action of the long-latency stretch response. However, further investigation in a
similar task designed specifically to elicit the long-latency stretch response showed that
it was present only in the muscles of the perturbed arm for the two-cursor task but
appears in muscles of both arms for the one-cursor condition (Mutha and Sainburg,
17
2009). Thus, long-latency responses appear to be routed across the limbs when such
routing contributes to task-success.
Accounting for properties of the musculoskeletal system
Optimal feedback control suggests that motor outputs should be crafted for the
physical plant they are controlling. This capability can be accomplished by flexibly
routing sensory information amongst multiple muscles (Gielen et al., 1988; Koshland et
al., 1991; Perreault et al., 2008; Soechting and Lacquaniti, 1988). For example, the longlatency stretch response accounts for the mechanical action of multi-articular muscles
(Gielen et al., 1988). If a subject’s task is to supinate their hand and a pronation
perturbation is applied, the coordinated response (at both short-and long-latencies)
includes a contribution from biceps which crosses the elbow and helps supinate the
hand. However, countering the pronation perturbations with the biceps muscle results in
unwanted elbow flexion. This elbow flexion ultimately needs to be countered to prevent
unwanted movement at the elbow. The short-latency response is only recruited by the
locally-stretched muscles, so it does not provide this compensation. In contrast, the longlatency response is evoked in the triceps muscles, which generates elbow extensor
torque. Furthermore, long-latency inhibition is present in brachialis, a monoarticular
elbow flexor. The flexibility of the system is incredible. For pronation perturbations of the
hand, the biceps and triceps act in concert as agonists and the briachialis is an
antagonist. For extension perturbations at the elbow, the biceps and briachialis are
coupled as agonists and the triceps act as antagonists.
The above example provides an elegant demonstration that the long-latency
response can intelligently coordinate the activity of multiple muscles of the limb. Such
18
coordination could allow the long-latency response to account for the complex
mechanical interactions that occur in multi-joint systems like the human arm. An
essential feature of the arm is that single-joint torque will cause multi-joint motion and
that single-joint motion can only result from multi-joint torque (Graham et al., 2003;
Hollerbach and Flash, 1982). A wealth of research has demonstrated that self-initiated
(i.e. voluntary) movements account for the complex mechanical properties of the limb, a
fact often cited as a hallmark of voluntary motor sophistication (Hwang and Shadmehr,
2005; Kawato and Wolpert, 1998; Wolpert and Flanagan, 2001). Demonstrating a similar
level of sophistication for the long-latency response would dramatically extend its known
capabilities.
1.7 – SHORT-LATENCY RESPONSE: STEREOTYPED BUT NOT IMMUTABLE
The short-latency stretch response is the fastest neuromuscular response to a
mechanical perturbation. Since this response occurs 20-50ms following a mechanical
perturbation, it must be mediated by mono- and oligo-synaptic pathways at the spinal
level (Kandel et al., 2000; Pierrot-Deseilligny and Burke, 2005). The largest contribution
to the short-latency response comes from the monosynaptic pathway (Burke et al.,
1984) where sensory fibres arising from the primary spindle endings (Group IA) in a
given muscle directly target motoneurons which innervate the same muscle and its
synergists (Liddell and Sherrington, 1924; Pierrot-Deseilligny and Burke, 2005). It is the
monosynaptic component of the short-latency response that is elicited by tapping a
tendon with a hammer, a valuable clinical tool for identifying neurological dysfunction.
The short-latency response is generally thought to be an extremely stereotyped
‘knee-jerk’ response that simply reflects the present state of spinal circuitry. Indeed, the
19
short-latency response displays limited sophistication under many experimental settings,
as described above, but it is not completely immutable and can be modulated under
some conditions. For example, the short-latency response is profoundly modulated at
the transition between posture and movement (Mortimer et al., 1981), or between stance
and walking/running (Duysens et al., 1993; Komiyama et al., 2000). In general, there is
substantial evidence that the short-latency response changes dramatically over the
course of cyclical movements such as gait (Akazawa et al., 1982; Capaday and Stein,
1986; Forssberg et al., 1975; Zehr et al., 2003), sinusoidal tracking (Dufresne et al.,
1980; Johnson et al., 1993) or hand cycling (Zehr and Chua, 2000).
The short-latency response can also be systematically modified by providing
extensive exposure to the same experimental condition and direct reinforcement of
response magnitude (Christakos et al., 1983; Wolf and Segal, 1996; Wolpaw et al.,
1983). These results suggest the short-latency response is modifiable with repeated
exposure to the same condition but that the timescale of such modulation dramatically
exceeds the typical length of an experimental session. This is in stark contrast to longlatency or voluntary responses, which can be modulated by naïve subjects within
seconds of the instruction and with minimal practice (Colebatch et al., 1979; Soechting
et al., 1981).
The general inflexibility of the short-latency response is surprising as there are
several mechanisms that can modulate the spinal circuitry. At the behavioral level,
performing the Jendrassik maneuver by interlocking the hands and pulling is known to
raise the sensitivity of the short-latency response that follows a tendon tap (Zehr and
Stein, 1999). Physiologically, it is well-established that the gamma motoneurons
modulate the sensitivity of muscle spindles (Hulliger, 1984) and that they can be
20
activated independently of alpha motoneurons which drive the extrafusal muscle fibres
and cause movement (Goodwin and Luschei, 1975; Loeb and Duysens, 1979; Taylor
and Cody, 1974). The nervous system could, in principle, use gamma activation to
regulate the sensitivity of feedback responses at all latencies without affecting motor
output (Prochazka, 1989).
Recently, it has been demonstrated that monkey spinal interneurons receive
descending input from cortical areas and exhibit task-specific preparatory activity (Prut
and Fetz, 1999). The implication is that cortical networks, which mediate a range of
sophisticated voluntary behaviour, have access to the spinal cord and should be able to
tune that circuitry to an upcoming perturbation. Presumably these signals could modify
the short-latency stretch response. However, in that study, the muscles were silent in the
preparatory period. This silence could allow for systematic sub-threshold changes in
muscle activity associated with the cortical preparation which would not be visible in the
recorded muscle activity (Capaday and Stein, 1987). The implication is that the shortlatency stretch response is tightly linked to the state of the motoneuron pool; if so, the
observed preparatory activity in spinal interneurons may disappear if the muscles were
activated.
1.8 – NEURAL BASIS OF THE LONG-LATENCY STRETCH RESPONSE
When Hammond first observed the long-latency response, he immediately
proposed that this phase of muscle activity could reflect one of two potential pathways
(Hammond, 1955; Hammond, 1956). One option was that it traversed the same spinal
pathway as the short-latency response but travelled along slower afferent fibres.
Alternatively, the long-latency response could travel along the same fast afferents but
21
traverse a longer route through the nervous system. This simple suggestion ignited a
long-standing debate about the neural origin of the long-latency response (Matthews,
1991). Below we review the evidence supporting spinal and cortical contributions to the
long-latency stretch response.
Spinal contributions to the long-latency response
The principle motivation for attributing the long-latency stretch response to a
spinal mechanism is that spinalized cats and monkeys still exhibit muscular activity in
the long-latency epoch (Ghez and Shinoda, 1978; Miller and Brooks, 1981; Tracey et al.,
1980). These results clearly demonstrate that a transcortical pathway is not required to
evoke long-latency activation, but it is unclear how similar this pathological response is
to that observed in intact animals and humans.
One possible spinal mechanism is that the sensory information which generates
the long-latency stretch response utilizes slower afferents originating from the secondary
spindle ending (Group II). Since these afferents have transmission speeds
approximately half that of Group IA afferents (Group I: 72-120m/s; Group II: 36-72m/s)
(Kandel et al., 2000), their timing would be appropriate for the generation of the longlatency response for at least some muscles of the upper-limb. Support for this
hypothesis comes from various sources. Muscle vibration fails to excite the long-latency
response as seen when the muscle is stretched by a mechanical perturbation
(Matthews, 1984; Matthews and Pickup, 1985). Since the primary muscle spindle is
robustly excited by the vibratory stimulus, the lack of a long-latency stretch response has
been taken as evidence that it does not use this type of sensory information, implicating
instead a spinal pathway mediated by Group II afferents. Group II transmission is further
22
supported by recent observations that the muscle relaxant tizanidine, which selectively
depresses transmission by Group II afferents, yields a reduction in the long-latency
response for muscles of the upper- and lower-limb (Grey et al., 2001; Meskers et al.,
2010).
Not all the reported evidence provides positive support for a Group II pathway.
For example, a smart controller would ignore that vibratory stimulus since it did not
evoke movement and interfere with task-success. Therefore, the lack of response may
reflect a control strategy rather than insensitivity to information originating from the
primary spindle afferent. Further contradictory results come from studies which
systematically slow the speed of afferent conduction by cooling the arm. Critically, the
absolute size of the slowing is proportional to the cross-sectional diameter of the fibres
and their pre-cooled conduction velocity (Paintal, 1965). This physical fact means that
cooling the arm will have a greater effect on the timing of the long-latency response if it
traverses a spinal pathway via slower afferents than a cortical pathway via faster
afferents. Indeed, in an elegant series of experiments, Peter Matthews found clear
evidence that cooling the arm slowed the long-latency response by an amount that could
not correspond to a spinal pathway (Matthews, 1989). Thus, at least for the muscles of
the hand, the vast majority of the long-latency response does not appear to traverse a
spinal pathway via Group II afferents.
Long-latency responses may also reflect the synchronization or reverberation of
the Group IA afferents which generate the short-latency stretch response (Hagbarth et
al., 1981; Schuurmans et al., 2009). Specifically, the application of a mechanical
perturbation causes robust excitation and synchronization of the primary spindle
endings. The synchronization at perturbation onset may then cause the afferent fibres to
23
enter their refractory period at around the same time and subsequently recover around
the same time. This second phase of activation may appear in the long-latency time
window suggesting that the long-latency stretch response merely reflects the ongoing
action of the short-latency response. Such an explanation is attractive and can account
for the phasic nature of the short- and long-latency responses, but it predicts that the
long-latency response should always be smaller than the short-latency response. This
suggestion is incompatible with the observation that the long-latency response is often
much larger than the short-latency response (Crago et al., 1976; Hammond, 1956;
Rothwell et al., 1980) and that the long-latency response can appear even in the total
absence of a short-latency response (Gielen et al., 1988; Soechting and Lacquaniti,
1988).
Cortical contributions to the long-latency response
There is substantial evidence suggesting that the long-latency response involves
a transcortical pathway through primary motor cortex. Philips first made this suggestion
based on anatomical observations that area 3a of primary somatosensory cortex
projects to primary motor cortex and that there exist direct projections from primary
motor cortex to spinal motoneurons (Phillips, 1969). Given the relatively direct route and
the speed of the fibres which mediate primary muscle spindle activation (Group IA,
conduction velocity ~100m/s), there is sufficient time for an afferent signal to reach
sensorimotor cortex and return to the periphery (a distance of 1-2m) before the onset of
the long-latency stretch response.
Several studies investigated the relative timing of the short- and long-latency
response as a test of the transcortical hypothesis. In an elegant series of experiments,
24
Marsden and colleagues hypothesized that if both the short-latency and long-latency
responses were mediated by the same fast afferent fibres, then the relative timing
between the short-latency and long-latency stretch response should be different across
muscles (Marsden et al., 1973; Marsden et al., 1976b). This prediction arises because
each muscle has a somewhat different relationship between its distance to the spinal
cord (which determines the short-latency timing) and its distance to the brain (which
would determine the long-latency timing). In contrast, if the long-latency response
traverses the same circuit as the short-latency response but using the slower afferents,
then the timing of both the responses should be proportional to the distance from that
muscle to the spinal cord. Their results were quite clear. The timing of the short-latency
response, elicited by a tendon tap, was 13 and 23ms for a muscle of the shoulder
(infraspinatus) and thumb (long-flexor), respectively. But the long-latency response
occurred 27 and 22ms later in the same muscles. This excess latency is consistent with
the distance between the given muscle and the brain rather than a simple (~2x) increase
that would occur if the afferent signals traversed slower (Group II) afferents to the spinal
cord and back.
Clinical studies have shown that lesions in the dorsal columns and in the
sensorimotor cortex can abolish the long-latency response (Lee and Tatton, 1978;
Marsden et al., 1977; Soechting and Lacquaniti, 1988) but these results cannot
unequivocally establish the presence of a transcortical pathway since the dysfunction
could be caused by a change in cortical modulation of spinal circuits. More interesting,
therefore, are results from subjects who suffer from Kippel-Fiel syndrome (Matthews et
al., 1990), a rare disease caused by a bilateral bifurcation of the descending motor
cortical projections whose prime symptom is the presence of undesired bilateral (mirror)
25
movements. When these subjects counter mechanical perturbations applied to the
forefinger, they demonstrate unilateral short-latency responses but bilateral long-latency
stretch responses. That is, stretching the left forefinger muscle (first dorsal interosseous)
muscle elicits the typical long-latency response in the left forefinger muscle and an
inappropriate evoked response in the unperturbed right forefinger muscle. Since the
bifurcation of motor pathways in these subjects occur at the cortical level, these results
imply that cortical structures at least partly mediate the long-latency response.
Further support for a transcortical pathway comes from single-cell recordings in
awake-behaving monkeys. Evarts demonstrated that pyramidal tract neurons in primary
motor cortex are very quickly modulated by a mechanical perturbation of the wrist
(Evarts, 1973). The activity of such neurons has been shown to precede and correlate
with long-latency responses in a variety of simple motor tasks (Evarts and Fromm, 1977;
Fromm and Evarts, 1977; Picard and Smith, 1992). More direct evidence comes from
the study of corticomotoneuronal cells, which project directly from primary motor cortex
to motoneurons and can be identified using spike-triggered averaging (Cheney and Fetz,
1984). Many corticomotoneuronal cells produce post-spike facilitation in their target
muscles prior to the onset of long-latency activity, indicating that they have total loop
times compatible with initiating the long-latency response. Furthermore, the observed
facilitation is stronger for spikes during a torque pulse than during a static hold, implying
that CM cells are casually involved in generating the long-latency response.
Neurophysiological evidence in human subjects also suggests a role for primary
motor cortex in generating the long-latency response. Electroencephalography has
revealed changes in scalp potentials prior to the long-latency response that are graded
according to the magnitude of the perturbation (Abbruzzese et al., 1985). Furthermore,
26
several groups have shown a supra-linear interaction between the long-latency response
elicited by a mechanical perturbation and transcranial magnetic stimulation applied over
primary motor cortex (Day et al., 1991; Palmer and Ashby, 1992). This interaction, which
does not occur for the short-latency response, is evidence that the long-latency
response and magnetic stimulation are physically co-localized at the site of stimulation,
primary motor cortex.
1.9 – HYPOTHESIS AND OUTLINE OF EXPERIMENTS
The studies presented in this thesis are all motivated by the concept that longlatency responses and voluntary control are inherently linked as part of the same control
process. This motivation comes from the theoretical framework of optimal feedback
control which stresses the importance of sensory feedback (Todorov, 2004; Todorov and
Jordan, 2002) and the fact that long-latency responses and voluntary control
mechanisms engage similar neural mechanisms, including motor cortex, somatosensory
cortex, cerebellum and a variety of subcortical and spinal structures (Scott, 2004). As
such, all the presented studies hypothesized that the long-latency response would
demonstrate functional attributes comparable to voluntary control.
A total of five studies were conducted in humans and/or monkeys to understand
the functional attributes of the long-latency response and the neural basis of this
sophistication. In all cases, the same apparatus was used and the basic experimental
paradigm was the same. Subjects sat in a robotic exoskeleton (KINARM, BKIN
Technologies Ltd., Kingston, ON) which permits combined flexion and extension
movements of the shoulder and elbow in the horizontal plane and can independently
apply mechanical loads to the shoulder and/or elbow (Scott, 1999). They were presented
27
with an initial start target and a final goal target (which were sometimes at the same
position) and were trained to place their hand in the initial target and respond to a
sudden mechanical perturbation by placing their hand inside the goal target. In essence,
the mechanical perturbation acted as a ‘go’ signal for moving their hand into the goal
target. The principle analysis in each paper compared the functional attributes of the
short-latency, the long-latency and the voluntary response and, where appropriate, the
corresponding neural activity in primary motor cortex.
In chapter 2, we developed a novel experimental paradigm that replaces verbal
instructions with visually-presented spatial targets. Verbal instructions are an
impediment to testing the sophistication of feedback responses because they are
inherently categorical and can only reliably address those situations at the extremes of
behaviour, where verbal instructions have a consistent interpretation. By adapting the
dominant paradigm for studying visually-guided reaching (Shadmehr and Wise, 2005),
we could test whether the long-latency response demonstrates the nuanced capabilities
commonly attributed to voluntary control. Specifically, we hypothesized that the longlatency response should be continuously modulated by target distance and direction, in
both one and two dimensions.
In chapter 3, we tested whether the long-latency response can account for the
state of the motoneuron pool. It has been previously shown that the short-latency
response is sensitive to pre-perturbation muscle activity such that the magnitude of the
perturbation response increases with pre-perturbation muscle activity (Bedingham and
Tatton, 1984; Matthews, 1986; Stein et al., 1995; Verrier, 1985). We argued that the
presence of such gain-scaling is a maladaptive byproduct of the motoneuron sizerecruitment principle. Therefore, we hypothesized that the long-latency response, like
28
voluntary control, should account for this complexity of the peripheral apparatus and
exhibit little or no gain-scaling.
In chapter 4, we investigated the functional organization of the long-latency
response. A great deal of historical debate has focused on determining whether the
long-response is of cortical or spinal origin. Recently, several studies have consolidated
these findings and suggested that both spinal and cortical circuits contribute to muscle
activity in the long-latency epoch (Kurtzer et al., 2010; Lourenco et al., 2006; Matthews
and Miles, 1988). In particular, the work of Lourenco and colleagues (2006)
unequivocally demonstrates that both spinal and cortical pathways can evoke muscular
responses in the long-latency response. Although this experiment demonstrates that
multiple neural mechanisms can contribute to the long-latency response in principle,
they could not demonstrate that this was the case in practice since the subjects were not
engaged in a meaningful motor task. We approached this problem from a novel
perspective. Specifically, we hypothesized that if multiple neural mechanisms contribute,
they should endow the long-latency response with some unique functional attributes.
This hypothesis was tested with a straightforward experimental protocol based on the
results from Chapters 2 and 3 and a mathematical model which could identify multiple
functional contributors to the long-latency response.
In chapter 5, we investigated whether the long-latency stretch response in human
shoulder muscles account for the mechanical interactions between the shoulder and
elbow joint. Our goal was to determine if stretch responses recognize the complex
mapping between the applied load and the resulting motion that occurs because of the
limb’s intersegmental dynamics (Graham et al., 2003; Hollerbach and Flash, 1982).
Unlike the few previous studies on this topic (Koshland et al., 1991; Soechting and
29
Lacquaniti, 1988), we applied known loads to the shoulder and elbow which yielded
particular motion patterns at these two joints. We could then directly test whether the
stretch responses were sensitive to local joint-motion or whether they integrate motion
across the joints in a manner appropriate to counter the underlying torque perturbation.
And finally, in Chapter 6, we tested whether the transcortical feedback pathway
through primary motor cortex contributes to the ability of the long-latency response to
account for the intersegmental dynamics of the limb. In a joint human and monkey study,
we searched for correlational evidence in single neurons and causal evidence via
transcranial magnetic stimulation to link processing in primary motor cortex with this
sophisticated feature of the long-latency response.
30
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45
Chapter 2
Rapid motor responses are modulated by visuospatial taskconstraints
46
2.1 – ABSTRACT
Considerable research has established that rapid motor responses (traditionally
called reflexes), can be modified by a subject’s voluntary goals. Here, we expand on
past observations using verbal instructions by defining the voluntary goal via visual
target position. This approach allowed us to objectively enforce task-adherence and
explore a richer set of variables, such as target direction and distance; metrics that
modify voluntary control and we hypothesize will influence rapid motor responses. Our
first experiment tested whether upper-limb responses are categorically modulated by
target direction by placing targets such that the same perturbation could push the hand
into one target and out of the other, a spatial analogue to “resist/yield” verbal
instructions. Consistent with these classical results, we found that the short-latency rapid
response (R1, 20-45ms) was not modulated by target direction whereas long-latency
rapid responses (R2/R3, 45-105ms) were modified in a manner approaching the
voluntary response (VOL, 120-180ms). Our second experiment tested whether upperlimb responses are continuously modulated by target distance by distributing five targets
along one axis centered on the hand. Here, the long-latency and voluntary response
mirrored the task demands by increasing activity in a graded fashion with increasing
target distance. Our final experiment explored how upper-limb responses incorporate
two-dimensional spatial information by placing targets radially around the hand. Notably,
long-latency responses exhibited smooth tuning functions to target direction that were
similar to those observed for the voluntary response. Taken together, these results
illustrate the flexibility of long-latency rapid responses and emphasize their similarity to
later voluntary responses.
47
2.2 – INTRODUCTION
Rapidly adjusting motor actions to account for changing conditions is a critical
component of successful movement. Consider a waitress holding a tray of drinks and
navigating a crowded restaurant. While she can plan her path to avoid potential
obstacles she must also respond to unexpected events such as a bump of the arm.
Clearly, the correct response is sensitive to context. If there is a busy table to the left and
an empty area on the right, corrective responses should be biased to the right. In
contrast, if the area to the right is filled with children it may be preferable to respond to
the left.
Many researchers have examined the flexibility of such rapid responses,
traditionally called reflexes, in the upper-limb by noting how they are altered by voluntary
goals (Hammond 1956; Lee and Tatton 1982; Evarts and Granit 1976; Colebatch et al.
1979; Jaeger et al. 1982a; Rothwell et al. 1980; Hagbarth 1967; Crago et al. 1976;
Capaday et al. 1994; Lewis et al. 2006). These studies typically issued a verbal
instruction such as "resist/let go" (Hammond 1956; Colebatch et al. 1979; Rothwell et al.
1980), "flex/extend" (Hagbarth 1967) or "compensate/do not intervene" (Crago et al.
1976) that indicated how a subject was to respond to a mechanical perturbation. Indeed,
many of these studies have shown that long-latency reflexes are substantially modulated
by voluntary goals. While these pioneering studies revealed that long-latency reflexes
are modifiable by subject intent, the use of verbal instructions severely limited the
breadth of conditions that could be explored to the subset of behaviors where verbal
cues have a reliable interpretation.
The limited range of reflex sophistication that can be explored via verbal
instructions is an impediment to testing recent theories of motor control. Specifically,
48
optimal feedback control posits that the extensive repertoire of behaviour exhibited by
the volitional motor system is accomplished via intelligent manipulation of sensory
feedback (Todorov and Jordan 2002; Todorov 2004). We have previously suggested
that one neurophysiological implication of such manipulation is that long-latency
reflexes, like volitional movement, should be capable of a rich assortment of behaviours
because reflexive and volitional control are intimately linked (Scott 2004; Kurtzer et al.
2008); effectively, they are part of the same ongoing control process. Such a close link
between reflexive and voluntary responses makes many experimental predictions but
belies the traditional distinctions made between reflexive and voluntary responses (see
Discussion). Therefore, we have discarded the term reflex in favor of a simple empirical
separation between slow (voluntary) and rapid motor responses that fits better within our
framework (see Methods).
Our general hypothesis is that these rapid motor responses are capable of all the
sophistication attributed to voluntary control within the constraints of their limited
processing time. To test for such extensive sophistication, we created a paradigm that
can examine these responses under a wide range of behaviors akin to the prevalent
methodology used to study the volitional motor system (Shadmehr and Wise 2005).
Briefly, subjects are shown a target while they maintain posture at the start position and
are trained to respond to an unpredictable perturbation by quickly placing their hand
inside the target allowing us to quantify how rapid responses change when targets are
placed at various locations. Unlike the ambiguity of verbal instructions, target metrics
such as position, size and shape, along with timing constraints explicitly define the goal
of the task and can be monitored and controlled. In the present study, we tested whether
rapid responses of muscles spanning the shoulder and elbow were sensitive to the
49
direction and distance of spatial targets in one- and two- dimensions. These factors are
known to modify voluntary control and we hypothesized the same metrics would modify
rapid responses.
In our first experiment, we placed targets such that the same perturbation pushed
the arm toward one of the targets and away from the other allowing us to establish
whether rapid responses are categorically sensitive to target direction, analogous to
previous studies using verbal instructions. In the second experiment we placed targets at
five positions along one axis to determine whether rapid responses scale continuously
with target distance. Lastly, we placed targets radially around the hand to establish how
rapid responses are spatially tuned to target direction. Our results indicate that there is a
clear distinction between short-latency and long-latency rapid responses for all the
muscles we examined including shoulder flexors, shoulder extensors, elbow flexors and
elbow extensors. Short-latency rapid responses were never modulated by target position
even in conditions of relatively highly stimulus predictability. In contrast, long-latency
rapid responses were robustly modified in all three experiments even in conditions of
relatively low stimulus predictability and the changes they expressed progressively
approached the voluntary response. This work has been presented previously in
abstract form (Pruszynski et al. 2007).
2.3 – MATERIALS AND METHODS
Subjects
A total of 18 subjects (11 males and 7 females, aged 21-35, 17 right-handed)
participated in 37 experimental sessions. All subjects were neurologically unimpaired,
50
had normal or corrected vision and gave informed consent according to a protocol
approved by the Queen's University Research Ethics Board.
Apparatus and Experimental Paradigm
Subjects performed the experiments with a robotic exoskeleton (KINARM, BKIN
Technologies Ltd., Kingston, ON) that permits combined flexion and extension
movements of the shoulder and elbow to move the hand in the horizontal plane (Scott
1999). Furthermore, KINARM can independently apply mechanical loads to the shoulder
and/or elbow and record kinematic variables of the joints and hand. Target lights and
simulated hand feedback were presented to the subject in the horizontal plane via a
heads-up display composed of an overhead projector and semi-transparent mirror.
Throughout the experiment, direct vision of the entire arm and hand was occluded and
hand feedback was removed prior to perturbation onset so that responses were guided
only by proprioception.
EXPERIMENT 1: CATEGORICAL MODULATION BY TARGET DIRECTION. Subjects
(n = 11) maintained their hand in a small central area (radius = 0.3 cm) against a
background load (±2 Nm) that activated either the elbow flexors or extensors. The
central area was positioned such the shoulder and elbow angles were 45° and 90°,
respectively. Subjects were then presented with a peripheral target (PT, radius = 20 cm)
located on either the left or right side of their hand and thus requiring predominantly
elbow flexion or extension movements (Figure 2.1A). After a random hold (1-4 seconds)
at the central area, a rapid torque perturbation (±2 Nm at elbow) displaced the hand
51
B
C
D
Applied Torque (Nm)
A
d
4
2
Flexors
b
a
c
Extensors
e
Extensors
0
-2
Flexors
-4
Time (ms)
E
Figure 2.1. Task apparatus and experimental paradigm. A. In Experiment 1, subjects were
presented with one of two large peripheral targets (PT, radius = 20 cm) located on the left or
right side of their hand's starting position (CT, radius = 0.3 cm). B. The targets in Experiment
1 were chosen such that flexion/extension perturbations of the elbow pushed that hand into
(IN) or out of (OUT) the target. Applied background loads, perturbations and locations of the
IN and OUT targets were different for flexors and extensors. C. Timeline for an exemplar trial.
(a) Subjects shown the CT and PT while the background load was introduced that primed the
muscle of interest; (b) Hand feedback was extinguished while subjects maintained posture at
the CT; (c,d) After a random 1-4s period, a rapid step perturbation was applied that
stretched/lengthened the muscle of interest; (e) After 1250 ms, the load was removed while
performance feedback was given to the subject. Note that for the random condition trials
were added (dashed lines) to ensure that subjects could not predict the direction of
perturbation based on the background load. D. In Experiment 2, subjects were presented
with one of five PTs (radius = 5 cm) centered 0, 3, or 6 cm to the left or right of their hand
requiring predominantly flexion or extension of the elbow. E. In Experiment 3, four potential
PTs (radius = 5 cm) were presented at locations to the left, right, in front and behind the hand
such that obtaining the targets required various degrees of shoulder and elbow motion.
52
either towards (IN condition) or away (OUT condition) from the center of the displayed
PT (Figure 2.1B). Subjects were instructed not to anticipate the perturbation and to place
their hand in the PT as quickly as possible after perturbation onset. Upon completing the
trial, subjects were given feedback to indicate success (PT filled green) or failure (PT
filled red and an auditory tone) based on preset speed and accuracy criteria (hand within
PT for 1000 of the 1300 ms after perturbation onset). A detailed timeline of each trial is
presented in 2.1C. Fifteen repeats of each condition (2 muscle groups and 2 targets)
were performed in a blocked sequence, randomized across subjects, for a total of 60
trials.
In addition to the above experiment, a subset of the subjects (N = 8) performed
the same task (during the same session) except that trials were presented in random
order so that subjects could not anticipate either perturbation onset or direction. This
was accomplished by adding trials such that the background load was not predictive of
the perturbation direction (Figure 2.1C, dashed lines). Spatial target location was also
randomly chosen for each trial. Subjects performed 15 repeats of each condition (2
muscle groups, 2 targets, 2 perturbation directions) for a total of 120 trials.
EXPERIMENT 2: GRADED MODULATION BY TARGET DISTANCE. The experimental
protocol is similar to Experiment 1 with the exception of PT placement and size. Subjects
(N = 9) were presented with one of five potential PTs (radius = 5 cm) located at 0, 3, and
6 cm to the left and right of the hand and thus requiring predominantly elbow flexion or
extension (Figure 2.1D). Twenty-five repeats of each condition (2 muscle groups and 5
targets) were performed in a blocked sequence, randomized across subjects, for a total
of 250 trials.
53
EXPERIMENT 3: SPATIAL TUNING OF RAPID RESPONSES. The experimental
protocol is similar to Experiment 1. Subjects (N = 6) were shown one of four potential
PTs (radius = 5 cm) that were equally distributed around the CT at a constant distance of
6 cm and placed at locations requiring predominantly elbow flexion, elbow extension,
shoulder flexion or shoulder extension (Figure 2.1E). Twenty repeats were collected in
each condition (4 muscle groups and 4 targets) in a blocked sequence, randomized
across subjects, for a total of 320 trials.
CONTROL EXPERIMENT: DETERMINING VOLUNTARY ONSET. A common problem
in previous experiments that investigate reflexes is deciding what components of
muscular activity are reflexive. In fact, the philosophical and semantic legacy of the term
has made it unclear what responses (if any) qualify (see Discussion). Further, recent
theoretical advances suggest that the distinction between reflex and voluntary is
obsolete because the underlying processes are inherently linked (Scott 2004). We
wished to explicitly avoid these historical considerations so we chose a simple
operational definition based on a control experiment that empirically determined
voluntary muscle onset; any muscle activity occurring prior to voluntary muscle onset is
then termed a rapid motor response.
Voluntary muscle onset was determined by applying perturbations that did not
engage significant early phasic responses (Hammond 1956; Evarts and Vaughn 1978;
Jaeger et al. 1982b). In the absence of these early phasic responses, we could clearly
identify the large and prolonged burst activity typically associated with voluntary muscle
activity (compare traces in Figure 2.2A). Subjects (N = 9) performed the same protocol
54
Rapid and Voluntary Activity Across Muscles
Main
Reaction Time
1
Figure 2.2. Determination of voluntary reaction time. A. Comparison of EMG responses
collapsed across all subjects and muscles for the main (from Experiment 1, black line) and
reaction time tasks (Control Experiment, gray line). The horizontal axis indicates time after
perturbation onset and the dashed lines are divisions within our pre-defined rapid response
epochs (R1: 20-45 ms, R2: 45-75 ms, R3: 75-105 ms). The vertical axis represents EMG
activity after normalization. B-E. Histograms showing the single-trial onset times for each
muscle sample. EMG onset was determined by finding the first sample of muscle activity that
was 3 SD above baseline on a trial-by-trial basis. The vertical solid and dashed lines indicate
perturbation onset and the end of the beginning of the voluntary epoch (105 ms). Markers
and associated times indicate the median onset time for that muscle.
55
as in Experiment 1 with perturbations reduced to ±0.5 Nm and delivered at the shoulder
joint. Importantly, though the perturbation was applied at the shoulder, background loads
were applied at the elbow (±2 Nm) as in Experiment 1 to tonically activate the muscle of
interest. We applied the perturbation at the shoulder since it allowed us to generate a
perceptually robust perturbation without evoking substantial responses at the elbow.
Unlike our other experiments, no formal speed and accuracy criteria were used. Rather,
subjects were encouraged to move to the PT as quickly as possible after perturbation
onset. Twenty-five repeats were completed in each condition for a total of 50 trials.
We determined muscle-onset on a trial-by-trial basis as the point where EMG
activity first exceeded 3 standard deviations from baseline for 5 consecutive samples (5
ms). The resulting distributions were rightward skewed with median onset times of 161,
157, 136 and 143 ms for Brachioradialis, Biceps, Triceps Lateral and Triceps Long,
respectively (Figure 2.2B-E). Based on these distributions and our own qualitative
observations we defined the end of the rapid response window at 105 ms, overlapping
with only ~8% of all collected voluntary muscle onset times and consistent with many
previous reports of voluntary onset (Lee and Tatton 1975; Rothwell et al. 1980; Kimura
et al. 2006; Hammond 1956; Jaeger et al. 1982b; Tatton and Lee 1975; Matthews 1986;
Calancie and Bawa 1985).
Muscle Activity
Surface electromyographic (EMG) recordings were obtained from six upper limb
muscles involved with flexion or extension at the elbow and/or shoulder: Brachioradialis
(Br, monoarticular elbow flexor), Biceps Long (Bi, biarticular flexor), Triceps Lateral
56
(TLat, monoarticular elbow extensor), Triceps Long (TLo, biarticular extensor), Deltoid
Posterior (DP, monoarticular shoulder extensor) and Pectoralis Major (PM,
monoarticular shoulder flexor). Prior to electrode placement, the skin was cleaned and
abrased with rubbing alcohol and the electrode contacts were covered with conductive
gel. Electrodes (DE-2.1, Delsys Inc., Boston, MA) were placed on the belly of the muscle
oriented along the muscle fiber and the reference electrode (Dermatrode, American
Imex Corp., Irvine, CA) was attached to the ankle. To assess the quality of each EMG
signal, we performed a set of maneuvers found to elicit high levels of activation for each
muscle in the plane of the task. EMG signals were amplified (gain = 103 or 104) and
bandpass filtered (20-450 Hz) by a commercially available system (Bagnoli, Delsys Inc.,
Boston, MA) then digitally sampled at 1000 Hz.
Rapid Response Epochs
We were primarily interested in comparing different epochs of muscle activity
occurring before our operationally defined voluntary muscle onset (see Control
Experiment); such responses are referred to as rapid motor responses. Taking into
account previous reports (Lee and Tatton 1975; Rothwell et al. 1980; Crago et al. 1976;
Nakazawa et al. 1997; Bonnet 1983; Mortimer et al. 1981) and our own observations of
reliably timed phasic events across subjects and muscles, we defined three distinct
epochs of the activity in temporal order. Response 1 (R1, 20-45 ms), classically referred
to as the short-latency or spinal stretch reflex (Pierrot-Deseilligny and Burke 2005).
Response 2 (R2, 45-75 ms), often referred to as the long-latency reflex (Hammond
1954; Matthews 1986). Response 3 (R3, 75-105 ms), sometimes referred to as longlatency reflex or triggered response (Rothwell et al. 1980; Crago et al. 1976). Note that
57
the timing of our epochs most closely mirror M1, M2 and M3 as proposed by Lee and
Tatton (1975) and our choice of R1, R2 and R3 is largely to avoid confusion with the
common abbreviation for primary motor cortex (M1). For some analyses, we included
time epochs between -100-0 and 120-180 ms which were defined as baseline (PRE) and
voluntary (VOL), respectively. Note that our results were not qualitatively changed by
modestly altering epoch onset or duration.
Data Analysis
FILTERING AND NORMALIZATION. The combined compliance of the robot and the
subject's arm introduces a shift between the commanded perturbation by the motors and
the corresponding start of limb motion. We determined this delay by tightly coupling an
accelerometer (EGA Series, Entran Corp., Montreal, Canada) to the arm of several
subjects while they performed a perturbation task in the KINARM. The time between
issuing the perturbation command and the onset of arm acceleration was approximately
10 ms and was similar across several subjects and load conditions. Thus, all data was
aligned on perturbation onset as determined by the KINARM and then shifted by a fixed
delay of 10 ms. Joint and hand position were obtained directly from the KINARM and
then lowpass filtered (20 Hz, 2-pass, 6th order Butterworth). EMG signals were
bandpass filtered (25-250 Hz, 2-pass, 6th order Butterworth), full-wave rectified and
normalized by their mean activity for a 2 Nm load.
BEHAVIOUR AND KINEMATICS. We were most interested in comparing kinematic and
muscle responses for the same imposed perturbation but different target positions. To
quantify the static changes in behaviour as a function of target position, we analyzed the
58
hand's final position (350 ms after perturbation onset). We used principle component
analysis to generate 95% confidence ellipses of final hand position. If these ellipses did
not overlap, we deemed the positions to be significantly different. Furthermore, we used
the receiver-operator characteristic (ROC) technique to determine when these
differences in position were significant (Green and Swets 1966). For each time step
(1ms) we generated an ROC curve representing the probability that an ideal observer
could discriminate the PT based on the joint position for the same perturbation. Values
of zero and one indicate perfect discrimination whereas a value of 0.5 indicates
performance at chance. Discrimination was deemed significant when the ROC area
remained below 0.25 or above 0.75 for five consecutive samples (Corneil et al. 2004).
Note that changing threshold criteria did not qualitatively alter our results.
MUSCLE ACTIVITY. When comparing responses across pre-defined epochs of muscle
activity (PRE, R1, R2, R3, VOL) we calculated the mean level of activity for each muscle
in that epoch on a trial-by-trial basis. For each muscle, we used the Wilcoxon ranksum
test (also known as a Mann-Whitney U-Test), a non-parametric comparison of medians,
to evaluate the hypothesis that the median muscle activity differed with target position.
Similar results were found using t-tests.
In order to identify when responses differed across target conditions without a
priori assumptions about epochs, we generated an ROC curve for every 1 ms sample to
calculate the probability that an ideal observer could discriminate the target position
based on the EMG response for the same perturbation but different PTs (ROC < 0.25 or
> 0.75). We also calculated the point when the ROC curve began to deviate from chance
(Thompson et al. 1996), termed the ‘knee’, by regressing the ROC values located 15 ms
59
around the discrimination point then calculating the time when this line intersected the
pre-perturbation ROC results. Note that calculating the ROC knee is a formalized
method of determining when two signals initially diverge and replaces previous attempts
to identify this point by visual inspection (Crago et al. 1976; Marsden et al. 1976; Evarts
and Vaughn 1978).
In Experiment 3, we determined how responses were tuned to two-dimensional
target positions by fitting the data with a plane (Kurtzer et al. 2005; Kurtzer et al. 2006).
The resulting plane coefficients (X,Y) describe how the EMG activity is related to spatial
target position and can be used to calculate the preferred target direction (PD), where
PD = atan2(Y,X) and 0o, 90o, 180o and 270o represent right, ahead, left and behind in
external space. A Rayleigh test was applied across the population of PDs (for a
particular muscle and epoch) to determine whether the net tuning was unimodally
distributed (Batschelet 1981).
AKAIKE’S INFORMATION CRITERIA. For Experiment 2, we used Akaike's Information
Criterion (AIC) to judge the merits of potential models relating muscle activity within our
pre-defined epochs to target position. AIC is a principled technique for choosing a
parsimonious model from a set of candidates by providing a metric of model quality that
balances fit and complexity (Burnham and Anderson 2002). In AIC, model quality is
proportional to the likelihood (£) of a candidate model (θ') given the experimental data (x)
and complexity is accounted for by K, the number of free parameters in the candidate
model (equation 1):
AIC
= -2log(£(θ'|x)) + 2K,
(1)
60
By making an assumption of Gaussian error of equal variance, finding the likelihood was
reduced to calculating the square root of the sum of squares (RSS) which was done via
either linear regression or constrained non-linear optimization (fmincon in Matlab, The
Mathworks, Boston, MA). Note that when non-linear optimization was used, the
procedure was restarted 10,000 times from random locations in an attempt to locate the
global best fit. We compared the potential models in two ways. First, we found the model
that resulted in the smallest AIC score, which was deemed the best candidate. Second,
we found how often a potential candidate was acceptable for explaining the data by
calculating the difference between its AIC score and that of the best model (ΔAIC = AICmin(AIC)). If this difference was less than an arbitrarily chosen threshold (ΔAIC<2), the
model was deemed acceptable (Burnham and Anderson 2002).
We compared four possible models relating EMG activity to target position (for
each muscle sample and epoch). 1) A constant function which had one free variable, f(p)
= b, where p is the target position (defined as 1 to 5) and b is a positive constant. 2) A
step function defined as two piecewise continuous constant functions with three free
variables, two constant levels and the switch point, f(p) = b, if p<=x and f(p)=c, otherwise
and constrained such that c > a and that x was within the range of tested positions. 3) A
linear function; f(p) = ap + b, where a is a positive constant and b unconstrained. 4) A
sigmoid which had four free variables, f(p) = a / (1+exp(-(p-x)/c)) + d and constrained
such that a > 0, x was within the range of positions. Note that as c approaches 0, a
sigmoid approaches a step function, to avoid this we constrained the sigmoid to rise over
a minimum of three consecutive target positions (c > 0.34). In some analyses the
constant function is referred to as ‘no modulation’, the step function is referred to as
‘discrete modulation’ and the grouping of linear and sigmoidal functions is termed
61
‘graded modulation’. While our candidates for constant and discrete representation are
fundamentally limited to a constant and step function, in principle, any smooth and
monotonically increasing function could have been included to describe graded
modulation. We included both linear and sigmoidal functions to permit the possibility of
no saturation, or saturation at either or both target extremes.
2.4 – RESULTS
Experiment 1: Categorical Modulation by Target Direction
FEATURES OF BEHAVIOUR. This experiment examined whether subjects categorically
modify their rapid motor responses as a function of spatial target direction when subjects
are presented target and load conditions in blocked order. Subjects learned the task
quickly and had little difficulty reaching the goal target within the imposed speed and
accuracy constraints, performing with a mean success rate of 99% (SD 1). Figure 2.3A
shows the mean hand trajectories and endpoint variability for the IN (gray) and OUT
(black) conditions from a representative subject. Note that the IN and OUT correspond to
those conditions where the perturbation displaces the hand into or out of the presented
target, respectively. The initial segments of the IN and OUT conditions were overlapping,
as expected because they were generated by the same perturbation. However, hand
paths rapidly deviated towards the PT (Figure 2.3A) which resulted in significant
differences in final hand position. We used ROC analysis (see Methods) to determine
when elbow and shoulder kinematics (Figure 2.3B,C) were modified by target position.
For this subject, target-dependent differences in elbow and shoulder kinematics were
significant 130 ms and 121 ms after perturbation onset, respectively (see where traces
cross 0.75 or 0.25 in Figure 2.3D,E). These results were typical across the population
62
B
C
D
E
ROC Area
Angle (deg)
A
F
G
R1
R2
R3
H
Figure 2.3. Exemplar hand kinematics and EMG for Experiment 1. A. Spatial hand position for
a representative subject. Traces correspond to the mean response from the IN (gray) and OUT
(black) conditions for both the flexor (dashed) and extensor (solid) muscle groups. Endpoint
ellipses represent the standard deviation of hand position 350 ms after perturbation onset. The
arrows point to the approximate separation of the kinematic responses as a function of target
location. B,C. Temporal joint kinematics for the elbow and shoulder. The horizontal axes
represent time after perturbation onset and the vertical axes represent the joint angle which is
initially 45o and 90o for the shoulder and elbow, respectively. D,E. Temporal ROC analysis of
elbow and shoulder kinematics (see Methods) between target locations. Vertical axes represent
the ROC area which indicates the probability that an ideal observer can identify the target
position based on the kinematics. The horizontal lines at 0.25 and 0.75 are arbitrary significance
levels where an ideal observer could discriminate between conditions in ¾ of the trials. F,G.
Traces represent mean muscle activity from the IN (gray) and OUT (black) conditions for an
exemplar muscle. Panel G is a zoomed in version of F focusing on the rapid motor response
epochs. H. Each bar represents the EMG responses (mean + SEM) of the exemplar muscle
within the given epoch for the IN (gray) and OUT (black) targets. Symbols indicate significant
increase from the baseline epoch (Ranksum, †, P < 0.01) and differences as a function of target
position (*, P < 0.01).
63
with target-dependent effects becoming significantly different at 139 ms (SD 24) for the
elbow and 136 ms (SD 22) for the shoulder.
MUSCLE ACTIVITY. We applied background loads to activate the relevant muscle prior
to perturbation onset allowing us to observe both increases and decreases in response
to the applied perturbation. When a muscle was stretched by the mechanical
perturbation, we consistently observed a robust multi-peaked EMG response for all
subjects even on single-trial recordings. A typical mean response is shown in Figure
2.3F,G for a single muscle sample.
The mean response exhibits increasing sensitivity to target position, that is, the
difference between IN and OUT conditions increased with time after perturbation onset.
The earliest rapid response (R1, 20-45 ms) was indistinguishable for the two target
positions; medium-latency rapid responses (R2, 45-75 ms) were excited by the
perturbation and additionally scaled by target position and late rapid responses (R3, 75105 ms) were mostly evoked or suppressed by target position. For the exemplar muscle,
binned analysis reveals that while all epochs are significantly elevated from baseline
(Wilcoxon ranksum, P < 0.01) there is no target-dependent differences in either the
baseline (Ranksum statistic, Rs(15,15) = 240, P = 0.77) or R1 epochs (Rs(15,15) = 226, P =
0.81). In contrast, target-dependent modulation of muscle activity was significant in the
R2 (Rs(15,15) = 173, P = 0.01), R3 (Rs(15,15) = 120, P < 10-6) and VOL epochs (Rs(15,15) =
120, P < 10-6, Figure 2.3H). As in the exemplar muscle, target-dependent modulation
across the population of muscle samples was rare for early periods and typical to
universal for later periods (Wilcoxon ranksum, P < 0.01): PRE (2 of 44 muscle samples,
5%), R1 (5%), R2 (32%), R3 (86%), VOL (100%). Across the population of collected
64
muscles, only the R2, R3 and VOL epochs show a significant change in activity as a
function of target position (paired t-test, P < 0.01). Similar results were found when
analyzing the individual muscles (Table 1).
ROC analysis, which does not assume any underlying epochs, also reveals
increasing target-dependency. For the exemplar muscle, significant differences were
found 79 ms after perturbation onset and a trend towards significance (knee) began at
58 ms (Figure 2.4A). This result is visually evident when looking at the ROC response
for each muscle collapsed across subjects; the ROC curves begin to deviate from
chance approximately 60 ms post-perturbation and cross the threshold at approximately
85 ms after perturbation onset (Figure 2.4B). Across the population, significant targetdependent differences were present at 87 ms (SD 16, Figure 2.4C, Black) with a trends
began 61 ms (SD 18) after perturbation onset (Figure 2.4C, Red).
EFFECT OF STIMULUS PREDICTABILITY. The analysis in the previous sections
focused exclusively on data collected when the perturbation direction was predictable
based on the background load (blocked). Here, we examine data collected when
perturbation direction was unpredictable and spatial target position was chosen
randomly from trial-to-trial. Subject behaviour was very similar between blocked and
random conditions. Though subjects reported that the random condition was more
difficult, they performed extremely well with a mean success rate of 98% (SD 1). We
noted only a marginal increase in subject variability which was quantified by the area of
the confidence ellipse for their hand position 350 ms after perturbation onset (paired ttest, t31 = 2.35, P = 0.03). In addition, only two subjects showed increased variability in all
65
Muscle
R1
R2
R3
VOL
Brachioradialis (Br)
t-statistic / p-value (df = 10)
0.7 / 0.5
4.9 / 0.001
-2.1 / 0.06
Biceps (Bi)
-0.13 / 0.90
2.4 / 0.03
Triceps Lateral (Tlat)
0.38 / 0.71
5.1 / 10
Triceps Long (Tlo)
0.19 / 0.85
3.8 / 0.004
Population (df = 43)
-1.19 / 0.24
5.4 / 10
-4
-6
-5
5.7 / 10
8.1 / 10
-5
13.7 / 10
4.4 / 0.001
3.4 / 0.006
6.5 / 10
-14
11.1 / 10
Table 2.1. Assessment of target-dependent modulation by muscle.
66
-4
6.1 / 10
-4
10.2 / 10
-7
-14
A
B
C
Figure 2.4. EMG analysis without a priori assumptions about epochs. A. ROC analysis from 25
ms before to 350 ms after perturbation onset for the exemplar muscle in Figure 3F,G. The
vertical axis represents the probability that an ideal observer could discriminate between IN and
OUT conditions based on the EMG data. The thick horizontal line drawn at 0.75 represents an
arbitrary significance threshold and the red line is the regression used to determine the
beginning of this trend (Knee, see Methods). For this example, significant differences occurred
79 ms after perturbation onset and the trend began 58 ms after perturbation onset. B. Mean
ROC response for each muscle group. Layout is similar to A with the vertical dashed lines
indicating the pre-determined epochs. C. ROC discrimination time for each collected muscle
sample: Brachioradialis (Br), Biceps (Bi), Triceps Lateral (TLat), Triceps Long (TLo). Black dots
indicate the time that each sample reaches significance and red dots represent the beginning of
the trend. The mean for each muscle is shown with a diamond and the vertical line represents
the mean across muscles.
67
random conditions (IN and OUT, Flexors and Extensors) suggesting that changes in
variability with predictability were not particularly strong.
As in the blocked conditions, upper-limb muscle responses exhibited an
increasing sensitivity to target position. Figure 2.5A,B presents the same exemplar
muscle as in Figure 2.3F,G; again, there is little difference in R1 and an increasing
difference in the R2, R3 and VOL epochs. Across the population of muscle samples only
the R2, R3 and VOL epochs show a significant change in activity as a function of target
position (paired t-test, P < 0.01). ROC analysis reveals that the knee of the curve for
random conditions, which occurs at 64 ms (SD 18) is not significantly different to the
blocked condition (t-test, t59 = -1.7, P = 0.1). In contrast, there was a significant delay in
the time of significance which was pushed back to 95ms (t59 = -3.6, P < 10-3).
Random and blocked conditions were more directly compared by plotting the
amount of target-dependency (OUT–IN) for each muscle sample and epoch of activity
(Figure 2.6). In this plot, points in the upper-right quadrant represent muscle samples
that showed target-dependency in the correct direction (OUT > IN) for both random and
blocked conditions; samples located along the line of unity had equal levels of targetdependent modulation. The R1 response shows no target-dependent modulation in
either blocked or random trial orders. In contrast, there is a clear similarity between the
random and blocked responses for the later epochs in that nearly all muscles exhibit
target-dependency in both random and blocked conditions. Though the responses are
similar, across the population blocked order responses exhibit significantly more targetdependency for each of the R2 (paired t-test, t31 = 3.6, P < 10-3), R3 (t31 = 4.0, P < 10-3)
and VOL (t31 = 5.78, P < 10-5) epochs as seen by the systematic bias away from line of
unity towards the horizontal axis. On average, the amount of task-dependency in the
68
A
B
R1
25
R2
R3
EMG (au)
20
15
10
5
0
0
C
100
200
Time (ms)
0
300
45
75
105
Time (ms)
D
Mean ROC
1
20
ROC Discrimination Time
ROC Area
Br
Muscle
0.75
Bi
TLat
TLo
0.5
0
100
200
0
300
50
100
150
Time (ms)
Time (ms)
Figure 2.5. Analysis of EMG under random trial order. A,B. Same format as Figure 3F,G. C.
Same format as Figure 4B. D. Same format as Figure 4C.
69
R2
R1
2
5
0
0
-2
-5
Random (EMGout - EMGin, au)
-2
0
2
-5
0
R3
5
VOL
20
50
0
0
-50
-20
-20
0
20
-50
0
50
Blocked (EMGout - EMGin, au)
Figure 2.6. Task-dependency under different trial orders as a function of epoch. The horizontal
and vertical axes represent the mean difference in activity (OUT - IN) for each muscle sample
under blocked and random trial orders, respectively. Samples in the upper-right quadrant
indicate the predicted target-dependent modulation (OUT > IN). Samples that lie close to the
diagonal exhibit a similar amount of target-dependency for both blocked and random trial
orders. Note the different scales for each epoch.
70
random condition was 44, 75 and 80% the size of the blocked condition in the R2, R3
and VOL epochs, respectively. Thus, randomly presenting the perturbations resulted in
subtle changes including a reduction in the amount of target-dependency but no
significant change in its onset.
Finally, we compared the effect of repeating the same perturbation condition by
sub-dividing the blocked experiment into the first and last five trials of each block. If the
target-dependency we observed in the blocked condition was the result of scaling with
repeated performance we should observe a systematic increase in target-dependency
for the last five trials. In fact, no epochs exhibited such a systematic increase in targetdependency (one-sided t-test, P > 0.3).
Experiment 2: Graded Modulation by Target Distance
FEATURES OF BEHAVIOUR. Here, we assessed whether rapid motor responses are
modulated in a graded fashion as a function of target distance or whether they are
limited to categorical changes according to target direction (as shown in Experiment 1).
Subjects had no difficulty learning the task and were extremely successful with a
success rate of 96% (SD 3). Population mean hand trajectories for each target position
in both flexor and extensor conditions are shown in Figure 2.7A and B, respectively. All
subjects showed systematic differences in final hand position for the five target
conditions and temporal differences in elbow kinematics between the extreme targets
were significant 135 ms (SD 30) after perturbation onset (Figure 2.7C). Similar temporal
trends are found at the shoulder (Figure 2.7D). Furthermore, these trajectories reveal a
visible rank order trend according to target position as early as any separation.
71
A
B
C
D
E
F
R1
R2
R3
Figure 2.7. Behavioural Performance and EMG in Experiment 2. A and B. Each plot represents
exemplar hand kinematics for extensor (A) and flexor (B) trials as a function of target position.
Subjects began each trial at the filled black circle and the black diamond indicates the hand
position 350 ms after perturbation onset. Small arrows indicate the approximate direction of
movement caused by the perturbation. Note that the extrinsic target positions requiring greatest
agonist activity (green traces) are reversed for the elbow flexors and extensors. C. Temporal
kinematics for the elbow joint aligned on perturbation onset. Color and line style represent the
target position and perturbation conditions shown in A and B. D. Temporal kinematics for the
shoulder joint, same format as B. E,F. Pooled EMG showing graded modulation of activity with
target position. Data is collapsed across all available muscle samples and targets reversed for
the flexors and extensors. F is a zoomed in version of G focusing on the rapid motor response
epochs.
72
MUSCLE ACTIVITY. Figure 2.7E,F presents the pooled response to the same
perturbation and targets located at five distances; note that target order is reversed for
flexors and extensors. Qualitatively (and in agreement with Experiment 1), the earliest
response (R1) was not scaled by target distance and thus exhibited a constant
relationship with target position. In contrast, the R2, R3 and VOL epochs show a
monotonic/graded increase in activity as a function of target distance.
Since the qualitative observation of graded modulation across muscles and
subjects may simply reflect the temporal smearing of discrete responses, we used
Akaike’s Information Criterion (AIC, see methods) to determine the best candidate model
for each collected muscle sample (Figure 2.8A). In the R1 epoch, almost all muscles (29
of 34, 85%) were best described by a constant function, indicating no modulation as a
function of target position. The R2 epoch was generally best described by a constant
function (59%) with some muscles exhibiting discrete (18%) or graded (linear or
sigmoidal) modulation by target position (24%). R3 was most commonly described by a
linear function (53%) with a total of 21 muscles that exhibited graded modulation by
target position. Lastly, the VOL epoch was best described by graded modulation (74%)
with most muscles exhibiting a sigmoidal (50%) or linear (24%) relationship. A similar
progression from constant to graded modulation was found when inspecting individual
muscles (Table 2).
Similar trends were found when we calculated relative AIC scores (ΔAIC) to
assess the adequacy of the candidate models (Figure 2.8B). Based on this analysis, R1
activity was adequately explained by the constant function for 31 of 34 (91%) muscles.
Furthermore, activity in the R2, R3, and VOL epochs were adequately explained by a
linear or sigmoidal function (graded modulation) for 59, 82, and 88% of muscle samples,
73
Epoch
Epoch
Figure 2.8. Comparison of models relating EMG and target position. A. Results of best model
selection according to AIC. The vertical axis presents percentage of muscles that are best
described by a particular model and the horizontal axis represents the epoch of interest. Line
style represents different models (long dashes = constant, short dashes = discrete, solid =
graded). B. Same layout as above for acceptable models.
74
Muscle
Candidate Model
Constant
Discrete
Graded
R1 / R2 / R3 / Vol
Brachioradialis (Br)
9/8/1/1
0/0/1/2
0/1/7/6
Biceps (Bi)
5/4/1/0
0/4/0/3
4/1/8/6
Triceps Lateral (Tlat)
8/5/2/0
0/1/4/0
0/2/2/8
Triceps Long (Tlo)
7/3/2/0
1/1/2/3
0/4/4/5
Table 2.2. Results of model selection by muscle. Values represent the number of times the
given candidate model was chosen as best by AIC.
75
respectively. Taken together, these results suggest a progression of target selectivity,
with R1 exhibiting constant or no modulation by target position while R2, R3 and VOL
epochs were modulated by target position in a graded, rather than discrete, fashion.
Experiment 3: Spatial Tuning of Rapid Responses
FEATURES OF BEHAVIOUR. We distributed four targets radially around the central
area to determine whether rapid motor responses could be spatially modulated as a
function of two-dimensional target position. As in previous experiments, subjects had
little difficulty performing the task with an average success rate of 93% (SD 8) and no
substantive differences in performance across the targets or load conditions. Figure
2.9A-D shows hand kinematics for an exemplar subject in all four load conditions. Note
that the target locations are identical for all the load conditions and that the only
difference was the background and perturbation loads which were chosen for each
muscle group (elbow flexor, elbow extensor, shoulder flexor, shoulder extensor). As in
previous experiments, spatial analysis of hand position reveals that every subject
achieved significantly different hand endpoints for all targets under all load conditions.
Temporal analysis of shoulder and elbow angles revealed rapid target-dependent
changes (Figure 2.9E-H). ROC analysis between IN and OUT targets reveals significant
differences between elbow and shoulder kinematics at 143 ms (SD 21) and 129 ms (SD
19) after perturbation onset, respectively.
MUSCLE ACTIVITY. Figure 2.10A shows pooled EMG responses that demonstrate how
muscle responses change for the same perturbation but different target positions well
before the voluntary epoch. To summarize these differences, we calculated tuning
76
A
IN
E
OUT
B
OUT
F
IN
C
G
OUT
IN
D
H
IN
Shoulder
Elbow
Angle (deg)
10
OUT
5
0
-5
-10
0
5cm
100 200 300
Time (ms)
Figure 2.9. Exemplar hand kinematics in Experiment 3. A-D. Each plot shows exemplar hand
kinematics of a single subject for elbow flexor (A), elbow extensor (B), shoulder flexor (C) and
shoulder extensor (D) trials. Subjects began each trial at the black filled circle and the black
diamond indicates the hand position 350 ms after perturbation onset. E-H. Panels represent
the temporal change in joint position aligned on perturbation onset. The shoulder (solid) and
elbow (dashed) traces are colored according to extrinsic targets shown in A-D.
77
A
Pooled EMG
B
R1
R2
20
Br
10
0
10
Bi
5
0
15
10
Tlat
5
0
10
Tlo
5
0
8
6
PM
4
2
DP
EMG Activity (au)
0
10
5
0
0 100 200 300
Time (ms)
78
R3
Voluntary
Figure 2.10. Spatial tuning of EMG in Experiment 3. A. Pooled muscle activity showing
modulation as a function of two-dimensional target position. Same format as Figure 2A and
the color of each trace corresponds to Figure 9A-D. B. Tuning function for rapid response
(R1, R2, R3) and voluntary (VOL) epochs. Radial distance from the center represents the
mean EMG activity for each of the four target positions and error bars denote standard error
(±SEM) across subjects. Lines connecting each data point are the result of linear
interpolation in radial coordinates. The gray circle represents the normalized EMG activity in
the IN condition in that epoch. The thin vectors pointing from the center of the circle represent
the calculated preferred direction (PD) for each muscle sample, note that the line is solid
when the fit was significant (plane, P < 0.01). Large arrows indicate the group PD after
passing a Rayleight test (P < 0.05).
79
functions (EMG versus the four target positions) by a plane-fit for each epoch of activity.
Note that for all analyses we used only the background load and perturbation condition
that tonically activated and stretched the muscle of interest. Across all collected muscle
samples, plane fits for the R1 epoch were universally insignificant (plane fit, P < 0.01 for
0 of 36 muscle samples) and highly variable with resulting PDs showing no consistent
trend (Figure 2.10B, R1). R2 responses also typically resulted in insignificant plane fits
for individual muscle samples (P < 0.01 for 3 of 36) but the calculated PDs exhibited
trends that qualitatively match results in later periods (Figure 2.10B, R2). Lastly, both R3
(29 of 36) and VOL (35 of 36) epochs exhibited significant fits and showed consistent
trends in their preferred directions (Figure 2.10B, R3 and Voluntary). The similarity of
preferred directions was tested by using a Rayleigh test to determine whether the data
was unimodally tuned. In fact, we noted a progression of similarity, with 0, 3, 6, and 6 of
6 muscles exhibiting unimodally-tuned preferred directions in the R1, R2, R3 and VOL
epochs (Rayleigh test, P < 0.05).
The progression of target-dependence found in the tuning functions is similar to
the increasing levels of task-dependency we found in Experiment 1 and 2. To
summarize how the directionality of rapid responses tend toward the voluntary response,
we calculated the angular difference between preferred directions calculated in each
rapid response epoch and the voluntary response (R1-VOL, R2-VOL, R3-VOL, Figure
2.11). There is a clear progression of directionality with R1-VOL being randomly
assigned and R2-VOL and R3-VOL clustering around 0o, indicating more similarity with
the voluntary response. This trend was quantified by calculating the mean absolute
angular difference which can range from 0 to 180o, with a random distribution resulting in
90o and perfect correspondence resulting in 0o. In our data set, the mean absolute
80
R1-VOL
R2-VOL
R3-VOL
90o
180o
0o
270o
Figure 2.11. Directional tuning of rapid motor responses relative to the voluntary response.
Each panel presents a polar histogram of angular difference in preferred directions between
a pre-defined epoch (R1, R2, R3) and the voluntary (VOL) response. Dots represent
individual muscle sample and are filled if the plane fit for that muscle yielded a significant
preferred direction (P < 0.01) in that epoch and the voluntary epoch. A difference approaching
0o indicates greater similarity between the preferred direction of that epoch and the voluntary
81
difference between R1, R2 and R3 was 93o (SD 47), 45o (SD 40) and 19o (SD 20),
respectively.
In Experiment 1 we compared rapid responses between IN and OUT targets for
muscles which span the elbow. Here, we extend this analysis to shoulder muscles
because IN/OUT targets are a subset of conditions in this experiment (compare Figure
2.3 and Figure 2.9). In general we found similar results at the shoulder for R1 with no
muscle samples (0 of 12) differentially modulated by IN/OUT conditions (Wilcoxon
ranksum, P > 0.2) and for R3 and VOL epochs where nearly all muscle samples (R3: 12
of 12, VOL: 11 of 12) exhibited significant differences (P < 0.05). A potential difference
may exist in the R2 epoch where 1 of 12 shoulder muscle samples exhibited significant
target-dependent differences (compared to 14 of 44 for the elbow) although this was not
statistically significant (z-test of proportions, z = -1.62, P = 0.11).
2.5 – DISCUSSION
Summary
In the present study, we examined how rapid motor responses of the human
upper-limb are modulated by the metrics of a visuo-spatial task. In all of these
experiments we noted a progression of motor sophistication whereby earliest responses
showed little or no sensitivity to the spatial goal while the later responses progressively
approached the voluntary response. In Experiment 1 we showed that long-latency rapid
responses (R2: 45-75, R3: 75-105 ms post-perturbation) were categorically scaled up
and down by the direction to the spatial target. This pattern of scaling was robust across
both subjects and muscles with 86% of muscle samples showing significant targetdependent modulation. In contrast, only 5% of samples were modulated within the short82
latency rapid response epoch (R1: 20-45ms). Comparable results were found when load
and targets were presented in random order though the effect size was reduced
suggesting that R2 and R3 epochs incorporate both prior information and current
sensory evidence in generating their response. In Experiment 2, we established that
modulation of long-latency rapid responses, particularly R3, is not merely a categorical
switch that is turned 'on' for distant targets and 'off' for close ones. Rather, the response
is graded based on the distance to the spatial target. Lastly, in Experiment 3, we showed
that the R2/3 responses are spatially tuned to two-dimensional target position indicating
that they incorporate the multi-joint requirements of the task in their response.
While many studies have investigated whether rapid motor responses can be
modulated by subject intent at the elbow (Hammond 1956; Hagbarth 1967; Crago et al.
1976; Colebatch et al. 1979; Evarts and Granit 1976; Rothwell et al. 1980), wrist (Lee
and Tatton 1982; Calancie and Bawa 1985; Jaeger et al. 1982a), finger (Marsden et al.
1981; Rothwell et al. 1980; Capaday et al. 1994), ankle (Gottlieb and Agarwal 1979;
Gottlieb and Agarwal 1980; Ludvig et al. 2007) and jaw (Pearce et al. 2003), most of
these have focused on individual muscles at that joint. An important feature of the
present study is that our experiments were designed to test a large subset of muscles
acting to move the arm in the plane of the task including mono-articulars, bi-articulars,
flexors and extensors of the shoulder and elbow. Accordingly, our results demonstrate
that while muscles exhibit some differences in detail, they all showed the same general
pattern of modulation as a function of spatial goal suggesting that modulation of rapid
motor responses is a common feature for upper-limb muscles.
83
Advantages of our visuo-spatial perturbation paradigm
Our paradigm builds upon a prominent methodology which uses verbal
instructions to instruct a subject how to respond to an imposed perturbation (Hammond
1956; Hagbarth 1967; Crago et al. 1976; Rothwell et al. 1980; Colebatch et al. 1979;
Capaday et al. 1994; Lewis et al. 2006). Although verbal instructions have varied widely
across researchers and experiments, including "resist/let go" (Colebatch et al. 1979;
Hammond 1956; Rothwell et al. 1980), "flex/extend" (Hagbarth 1967), "compensate/do
not intervene" (Crago et al. 1976), they are inherently categorical and restricted to the
ambiguities of natural language. They are also not amenable to animal models that
could explore the neural substrates of sophisticated rapid motor responses. In the
present study we minimized the dependence on verbal instructions by specifying and
enforcing an explicit spatial goal. In our first experiment we replaced the categorical
verbal instructions above with two potential targets placed such that the perturbation
displaced the hand directly into one target and directly out of the other. Such an
approach is advantageous because the task is explicitly defined and performance can
be directly measured allowing us to explore the flexibility of rapid motor responses under
conditions that would be difficult with verbal cues. For example, we established that the
modulation is graded by simply distributing visual targets at various distances from the
start position. Furthermore, we showed that rapid responses could be adjusted to
varying multi-joint requirements at the shoulder and elbow by distributing targets in two
dimensions. The analogous verbal cues for these experiments, perhaps "increase /
decrease resistance by 25, 50 and 75%” for the gradation experiment, and “respond to
the elbow perturbation by flexing your shoulder and elbow” for the multi-joint experiment,
would be difficult for subjects to adhere to and likely impossible to monitor or enforce.
84
It is important to note that a few researchers have used spatial targets to study
the modulation of rapid motor responses (Day et al. 1983; Brown and Cooke 1986;
Koshland and Hasan 2000). However, these studies differ from the present experiments
because they provided precise information about when to generate a specific movement
and thus tested how rapid motor responses change during the initiation of a reach. For
example, Koshland and Hasan (2000) report that R2 and R3 responses depend on the
impending direction of arm movement. In that study, subjects were instructed to reach a
target in response to either a ‘small’ or ‘large’ perturbation in the same direction, one of
which always followed 1s after an auditory warning. Notably, both perturbations used
were small and transient resulting in similar displacements (both < 1°) relative to the
required movement (~20°). As a result, subjects could reasonably prepare and initiate
the same motor commands prior to the mechanical perturbation. However, our recent
work illustrates a profound change in neural processing in primary motor cortex during
the transition from posture to movement (Kurtzer et al. 2005). This abrupt change in the
state of neural control makes it difficult to ascertain whether their applied perturbations
were probing a postural, movement or a transition control state. Further, rapid motor
responses are known to be modulated by motor-neuronal excitability (Matthews 1986)
which is likely changing during the initiation of movement. Our study tried to control both
of these factors by introducing a long and random hold period and/or randomizing the
perturbation direction. This manipulation made it impossible for subjects to consistently
anticipate perturbation onset and initiate a movement before perturbation onset. Rather,
they had to remain in posture until perturbation onset. Thus, our study establishes that
changes in rapid motor responses as a function of target position do not require the
85
overt initiation of a reaching movement; appropriate long-latency rapid responses can be
chosen and implemented after perturbation onset.
What is a reflex?
Reflexes are broadly defined as a stereotyped transformation from a sensory
stimulus to a motor event (Kandel et al. 2000). However, past research in physiology,
psychology and philosophy has yielded little agreement about what specifically
constitutes a reflex response (Prochazka et al. 2000). One class of definitions offered by
Prochazka and colleagues (2000) is that reflexes are those responses that are
automatic, that is, they are transformations that cannot be consciously modified or
suppressed. However, given that even the mono-synaptic stretch reflex is modifiable
under certain conditions (Stein and Capaday 1988), it is unclear that any muscular
responses can be characterized as a reflex by this definition. A second class of
definitions is based on the anatomical or physiological nature of the mapping between
the input and output. When the mapping is simple and well defined, the response is a
reflex and when the pathway is complex, the response is voluntary. However, the
demarcation of complexity is inherently subjective and changes with time as pathways
become better characterized.
In the present experiment we wanted to avoid the philosophical and semantic
debate surrounding the term reflex as it is unclear that such a debate is fruitful in
furthering our understanding about the underlying neurophysiological processes. In fact,
our own experience suggests that the term is a substantial obstacle. Therefore, we
turned to an operational definition whereby we empirically defined a set of rapid
muscular responses because they appear prior to the earliest muscle activity in a control
86
experiment designed to determine kinesthetic reaction time (Hammond 1956; Jaeger et
al. 1982b; Evarts and Vaughn 1978). Our reaction time manipulation eliminated the
earliest phasic muscle responses by reducing perturbation magnitude, leaving only a
large and maintained pattern of muscle activity commonly attributed to a voluntary
response. The earliest of these responses occurred ~100ms after perturbation onset, a
result that is consistent with many other studies (Lee and Tatton 1975; Rothwell et al.
1980; Kimura et al. 2006; Hammond 1956; Jaeger et al. 1982b; Tatton and Lee 1975;
Matthews 1986; Calancie and Bawa 1985) and corresponds to a similarly timed volley of
activity in our main experimental tasks (Figures 2.3, 2.5, 2.7, 2.10). It should be noted
that while we labeled this maintained volley of activity as voluntary, we could just as well
have chosen R4. In summary, we specifically avoided the term reflex and replaced it
rapid motor response which refers only to a temporal window of events occurring prior to
kinesthetic muscle onset as determined empirically.
Short-latency (R1) rapid responses are not modulated by target position
In agreement with many previous studies, our results demonstrate that R1
responses of the upper-limb during postural control are not quickly modified by subject
intent (Hammond 1956; Marsden et al. 1972b; Crago et al. 1976; Rothwell et al. 1980).
Such inflexibility is surprising given that spinal interneurons receive descending input
from cortical areas and exhibit task-specific preparatory activity (Prut and Fetz 1999).
However, in that study the muscles were silent in the preparatory period which may have
allowed subthreshold changes in muscle activity that would not be visible in the recorded
EMG (Capaday and Stein 1987). As such changes are known to modify R1 responses
(Matthews 1986; Marsden et al. 1972a; Capaday et al. 1994) it would be informative to
87
establish whether changes in preparatory activity are present without changes in EMG
when the muscles are above threshold.
The lack of R1 modulation within our postural task is contrasted by changes
observed across behaviours such as the profound modulation of all rapid motor
responses in the upper-limb between posture and movement (Mortimer et al. 1981) or in
the lower-limb between stance and walking (Komiyama et al. 2000) or running (Duysens
et al. 1993). In fact, rapid responses of both upper and lower-limb have been shown to
change over the course of a cyclical movement such as gait (Forssberg et al. 1975;
Akazawa et al. 1982; Capaday and Stein 1986; Zehr et al. 2003), sinusoidal tracking
(Dufresne et al. 1980; Johnson et al. 1993) or hand cycling (Zehr and Chua 2000)
illustrating that spinal circuitry can generate sophisticated and flexible responses under
certain conditions.
In addition to changes across tasks, several studies have shown systematic
changes in R1 responses over long training periods in a single task (Christakos et al.
1983), particularly when R1 magnitude is directly reinforced (Wolpaw et al. 1983;
Wolpaw 1983; Wolf and Segal 1996). While our experiments could have reasonably
elicited modulation of the R1 response we did not find such modulation. In fact, two of
the authors (AP and IK) have performed the task over several thousands of trials and
neither shows modulation of R1 responses. This is does not imply that R1 is completely
immutable; rather, it is consistent with past observations that changes require extensive
practice within a consistent training regime. We suspect that we could have observed R1
target-dependency by providing subjects with a very long set of trials under the same
perturbation and target condition. In contrast, naive subjects were able to modify their R2
88
and R3 responses within seconds of the instruction and with minimal practice (Colebatch
et al. 1979; Soechting et al. 1981).
Effect of stimulus predictability on long-latency (R2 and R3) rapid responses
Our three main experiments demonstrate that R2 and R3 responses are
modulated in a sophisticated fashion as a function of spatial target position even when
perturbation onset is unpredictable. However, in these experiments the perturbation
direction was known in advance and it is possible that our observed modulation was due
to the release of pre-planned motor output in response to the perturbation such as those
observed under startle conditions (Valls-Sole et al. 1999; Carlsen et al. 2004). We tested
this possibility by having subjects perform Experiment 1 in random order such that both
the perturbation direction and onset were unpredictable. If the responses merely
reflected a pre-planned output then we would observe no mean target-dependency and
highly variable kinematic behavior as on half the trials subjects would release the
inappropriate plan for the current perturbation. In fact, subjects showed only a small and
inconsistent increase in variability when perturbation direction was unpredictable and
never responded with the wrong movement. That said, there were subtle differences in
the R2 and R3 responses which exhibited similarly-timed but systematically smaller
target-dependency. These results are consistent with previous findings demonstrating
that task-dependency of R2/3 responses is decreased but not eliminated by reducing the
predictability of perturbation onset (Rothwell et al. 1980) or direction (Crago et al. 1976;
Evarts and Vaughn 1978; Gottlieb and Agarwal 1980; Oriain et al. 1979). However,
unlike the reports of some previous authors (Crago et al. 1976; Evarts and Vaughn
1978) we did not find a significant delay in the onset of target-dependency (ROC Knee)
89
indicating that sophisticated rapid responses are present at a fixed time after
perturbation onset regardless of predictability (Gottlieb and Agarwal 1980; Rothwell et al.
1980; Oriain et al. 1979). Taken together, our present results suggest that R2 and R3
responses are not the result of a simple triggered response where one motor output is
pre-programmed and released based on the occurrence of a sensory stimulus. Rather
R2 and R3 responses combine both current sensory information and prior perturbation
history (in addition to the spatial goal) to form an appropriate response; additional work
is needed to establish the rules of how these factors are combined.
How are R2/3 responses related to the volitional motor system?
The volitional motor system possesses an incredible capacity to control direction,
distance, speed, accuracy, load and many other parameters of movement. Our results
demonstrate that R2 and R3 responses of multiple upper-limb muscles share some of
the key functional attributes of volitional control. For example, we have recently
demonstrated that R2/3 responses possess an internal model of limb dynamics (Kurtzer
et al. 2008) and here we show that these responses are appropriately tuned to the
metrics of a spatial task. We suspect that such similarities are not accidental and can be
readily understood if one appreciates that R2/3 responses and volitional control share
similar neural circuitry including somatosensory cortex, motor cortex, cerebellum and
other sub-cortical structures (Matthews 1991; Evarts 1973; Cheney and Fetz 1984;
Strick 1983; Lewis et al. 2004). Accordingly, the sophistication of R2/3 responses would
reflect the earliest volley of activity through the same neural circuit that is later engaged
by voluntary control (Scott 2004). This concept of an evolving sensorimotor
approximation through the same neural structures fits well within recent theories of
90
volitional motor control that emphasize the importance of intelligently using feedback
(Todorov and Jordan 2002; Todorov 2004; Scott 2004) and is related to previous work
which attributed the task-dependency of R2/3 responses to a hastened voluntary
response (Rothwell et al. 1980; Crago et al. 1976; Hasan 2005). It may also explain why
a precise definition of reflex has proven difficult to establish .
91
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Chapter 3
Sensitivity of rapid motor responses to pre-perturbation muscle
activity
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3.1 – ABSTRACT
The earliest neural response to a mechanical perturbation, the short-latency
stretch response (R1: 20-45ms), is known to exhibit ‘automatic gain-scaling’ whereby its
magnitude is proportional to pre-perturbation muscle activity. Because gain-scaling likely
reflects an intrinsic property of the motoneuron pool (via the size-recruitment principle),
counteracting this property poses a fundamental challenge for the nervous system which
must ultimately counter the absolute change in load regardless of the initial muscle
activity (i.e. show no gain-scaling). Here we explore the temporal evolution of gainscaling in a simple behavioural task where subjects stabilize their arm against different
background loads and randomly occurring torque perturbations. We quantified gainscaling in four elbow muscles (Brachioradialis, Biceps Long, Triceps Lateral, Triceps
Long) over the entire sequence of muscle activity following perturbation onset – the
short-latency response, long-latency response (R2: 50-75ms; R3: 75-105ms), early
voluntary corrections (120-180ms) and steady-state activity (750-1250ms). In agreement
with previous observations, we found that the short-latency response demonstrated
substantial gain-scaling with a three-fold increase in background load resulting in an
approximately two-fold increase in muscle activity for the same perturbation. Following
the short-latency response, we found a rapid decrease in gain-scaling starting in the
long-latency epoch (~75ms post-perturbation) such that no significant gain-scaling was
observed for the early voluntary corrections or steady-state activity. The rapid decrease
in gain-scaling supports our recent suggestion that long-latency responses and voluntary
control are inherently linked as part of an evolving sensorimotor control process through
similar neural circuitry.
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3.2 – INTRODUCTION
A prominent and well-studied feature of the short-latency stretch response (R1:
20-45ms post-perturbation) is ‘automatic gain-scaling’ whereby the same muscle-stretch
will elicit larger responses when pre-perturbation muscle activity is increased
(Bedingham and Tatton 1984; Marsden et al. 1976; Matthews 1986; Stein et al. 1995;
Verrier 1985). This modulation is generally attributed to the intrinsic organization of the
motoneuron pool (Capaday and Stein 1987; Houk et al. 1970; Kernell and Hultborn
1990; Marsden et al. 1976; Matthews 1986; Slot and Sinkjaer 1994) where motor units
are recruited in order of their force-generating capability and resilience to fatigue (Cope
and Clark 1991; Henneman 1957), a phenomenon termed the size-recruitment principle.
Although gain-scaling may be a useful short-term strategy (Bedingham and Tatton 1984;
Marsden et al. 1976; Matthews 1986), ultimately the steady-state response to an
additional load must be independent of pre-perturbation muscle activity (i.e. not show
gain-scaling) given the largely linear relationship between load and muscle activity for
low to moderate loads (Hof 1984; Lawrence and Deluca 1983; Milner-Brown and Stein
1975). This poses a substantial challenge for the motor system which must compensate
for automatic gain-scaling to generate appropriate motor commands across different
background load conditions.
Although many studies have established that short-latency responses
demonstrate gain-scaling (Bedingham and Tatton 1984; Marsden et al. 1976; Matthews
1986; Stein et al. 1995; Verrier 1985) and that steady-state muscle activity does not (Hof
1984; Lawrence and Deluca 1983; Milner-Brown and Stein 1975), it is unknown how
gain-scaling evolves between these two temporal extremes. Specifically, it is unclear
how quickly gain-scaling is reduced and to what extent phasic muscle activity in the
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long-latency (R2: 50-75ms; R3: 75-105ms) and early voluntary (VOL: 120-180ms)
epochs demonstrate gain-scaling. Most previous studies did not address this issue
because their experimental methods (i.e. brief servo-controlled displacements, electrical
stimulation, ‘do not intervene’ instructions) elicited robust short-latency responses but
negligible muscle activity in the long-latency and/or voluntary epochs (Matthews 1986;
Ruegg et al. 1990; Stein et al. 1995; Verrier 1985).
For example, Bedingham and Tatton (1984) demonstrated that gain-scaling is
present for both the short- and long-latency (combined R2 and R3 epoch) response in a
wrist flexor muscle but did not elicit substantial muscle activity past these epochs. The
absence of later responses, likely due to a ‘do not intervene’ instruction (Asatryan and
Feldman 1965; Crago et al. 1976), fundamentally precluded a complete characterization
of gain-scaling. In principle, the authors could have partially characterized the evolution
of gain-scaling over the first 100ms following perturbation onset, in the R1, R2 and R3
epochs, but no such quantitative analysis was provided and confidently determining
these changes from the presented figures is difficult. To our knowledge, only two studies
have quantified the evolution of gain-scaling over a prolonged sequence of activity,
focusing on the R1, R2, and R3 epochs in muscles spanning the ankle joint (Toft et al.
1989; Toft et al. 1991). Both of these studies used a ‘do not intervene’ instruction
coupled with a brief servo-controlled displacement and did not find gain-scaling for the
short-latency response as commonly seen for upper-limb muscles, making it difficult to
generalize from their results.
How might gain-scaling evolve over time and subsequent epochs of activity? One
possibility is that gain-scaling is nearly constant across the early phasic response
(<180ms) and that compensation for the size-recruitment principle takes place over a
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much longer timescale as in previous studies of steady-state muscle activity (Hof 1984;
Lawrence and Deluca 1983; Milner-Brown and Stein 1975). An alternative hypothesis is
that the gain-scaling of the phasic epochs quickly approaches the steady-state response
via systematic decreases in gain-scaling following the short-latency epoch. Such a
systematic change across epochs would not be surprising given that many studies have
shown significant functional and physiological distinctions between activity in the shortand long-latency epochs (Cole et al. 1984; Crago et al. 1976; Evarts and Vaughn 1978;
Gielen et al. 1988; Grey et al. 2001; Hammond 1956; Kimura et al. 2006; Lewis et al.
2006; Rothwell et al. 1980; Tsuji and Rothwell 2002). For example, we have recently
reported that the R2, R3 and VOL epochs increasingly incorporate an internal model of
limb dynamics (Kurtzer et al. 2008) and are increasingly sensitive to spatial task
constraints (Pruszynski et al. 2008a). Although these previous reports of sophistication
do not a priori imply a reduction in gain-scaling, they lead to the natural prediction that
gain-scaling will also be systematically and progressively modified over the R2, R3 and
VOL epochs. Such a decrease in gain-scaling would provide another example of
sophistication within the long-latency epoch and support our recent suggestion, based
on optimal feedback control (Todorov 2004; Todorov and Jordan 2002), that long-latency
responses and voluntary control are inherently linked as part of the same control
process through similar neural circuitry (Scott 2004).
To test between these two possibilities, we utilized a straightforward task
whereby subjects maintained their hand within a central target in the presence of various
background loads and randomly occurring step-torque perturbations at the elbow.
Importantly, this approach yielded a prolonged multi-phasic muscle response in all four
elbow muscles of interest with clearly identifiable activity in the short-latency, long103
latency and early voluntary epochs. Since subjects were required to stabilize their limb at
the same position both before and after the perturbation, we could also determine
changes in steady-state muscle activity. Consistent with previous results, we found that
the short-latency response, but not the steady-state activity, demonstrated significant
gain-scaling and that the total limb displacement was reduced with larger background
loads. With respect to the two competing hypotheses, we noted that gain-scaling was
quickly reduced following perturbation onset, with most muscle samples showing
decreases within the R3 epoch. In addition to our main experimental findings we report
two notable observations. First, gain-scaling in all epochs was greater for large
perturbations than for small ones. Second, increases in intrinsic muscle stiffness
associated with increased tonic muscle activity appeared to play a modest role in
countering the applied mechanical perturbations, a result that was well reproduced by a
realistic model of the muscle and limb.
3.3 – METHODS
Subjects. A total of 8 subjects participated in the experiments. All were neurologically
unimpaired, gave informed consent according to a protocol approved by the Queen's
University Research Ethics Board and were paid for their participation.
Apparatus. Subjects performed the tasks with a robotic exoskeleton (KINARM, BKIN
Technologies Ltd., Kingston, ON) which supports the limb in the horizontal plane while
permitting flexion and extension motions at the shoulder and elbow (Scott 1999). The
device can monitor motion of the two joints and independently apply torque loads to the
shoulder and/or elbow. Target lights and simulated hand feedback were presented to the
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subject in the plane of the task using an overhead projector and a semi-transparent
mirror. Direct vision of the arm and hand was occluded throughout the experiment and
hand feedback was provided only between trials to ensure all corrective responses were
guided by proprioception.
Protocol. Each trial began with subjects placing their index finger near the center (< 0.3
cm) of a target which required shoulder and elbow angles of 45° and 90°, respectively
(Figure 3.1A). A background load (±1, ±2, ±3 Nm) was then slowly introduced (rise time
= 500 ms) that required compensatory activation of the agonist muscles of interest,
either the elbow flexors or extensors. After a random hold time of 1-4 seconds, a steptorque perturbation (an addition of 1.25 or 2.5 Nm in the same direction as the
background load) stretched the pre-activated agonist muscles (Figure 3.1B). Subjects
were instructed to avoid co-contracting or anticipating the perturbation but to return to
the displayed spatial target (radius = 1 cm) quickly and accurately after perturbation
onset. Upon completion of the trial, visual feedback indicated success or failure based
on speed and accuracy criteria (return to the central target within 500ms of perturbation
onset and then remain inside the target for 1000ms). Before beginning the main
experiment, subjects were presented with approximately five repeats of each condition
to familiarize them with the apparatus and task constraints. Subjects performed 20
successful repeats in each condition for a total of 240 correct trials (3 background loads,
2 magnitudes x 2 muscle groups x 20 repeats) blocked by muscle group but randomized
otherwise. Breaks of approximately 3 minutes were enforced every 60 correct trials,
though subjects could rest at anytime. The entire experimental session lasted ~2 hours.
105
B
tbg
tp
Applied Torque (Nm)
A
6
5
4
3
2
1
0
BG
Perturbation
Figure 3.1. Experimental Apparatus and Methods. A. Subjects were presented with a visual
target (radius = 1 cm) at the tip of their index finger when their shoulder and elbow angles
were 45° and 90°, respectively. B. The experimental conditions included three levels of
background load/torque (1, 2, 3 Nm) coupled with two perturbation magnitudes (1.25, 2.5 Nm)
for both the elbow flexors and extensors. Elbow flexor and extensor trials were collected in
separate blocks.
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Muscle Recording, Filtering and Normalization. Detailed electromyographic (EMG)
procedures have been reported previously (Pruszynski et al. 2008a). In the present
experiments, surface recordings were obtained from four muscles acting to flex and
extend the elbow joint: Biceps Long (Bi), Brachioradialis (Br), Triceps Long (TLo),
Triceps Lateral (TLa). One sample of Brachioradialis (Subject #1) and Biceps Long
(Subject #4) were excluded from analysis because of poor signal quality, leaving a total
of 30 muscle samples. EMG data were filtered (two-pass, 6th-order Butterworth, 20-250
Hz), rectified and then normalized such that a value of one represents the mean
agonist activation while countering a 2Nm background load.
Taking into account many previous reports (Bonnet 1983; Crago et al. 1976;
Kurtzer et al. 2008; Lee and Tatton 1975; Mortimer et al. 1981; Nakazawa et al. 1997;
Pruszynski et al. 2008a; Rothwell et al. 1980), we defined three distinct epochs of rapid
post-perturbation muscle activity in temporal order of their appearance: Response 1 (R1,
20-45 ms), classically referred to as the short-latency or spinal stretch reflex (PierrotDeseilligny and Burke 2005); Response 2 (R2, 50-75 ms), often referred to as the longlatency reflex (Hammond 1954; Matthews 1986); Response 3 (R3, 75-105 ms),
sometimes referred to as long-latency reflex or triggered response (Crago et al. 1976;
Rothwell et al. 1980). Within our nomenclature, activity within the R1 window is
alternately referred to as the short-latency response and the combination of the R2 and
R3 epochs is alternately called the long-latency response. As previously discussed
(Pruszynski et al. 2008a), we specifically chose to discard the term ‘reflex’ in favor of
labeling events in their temporal order to avoid the substantial philosophical and
historical legacy of the term (Prochazka et al. 2000). Furthermore, the timing of our
epochs closely mirror M1, M2 and M3 as proposed by Lee and Tatton (1975) and our
107
choice of R1, R2 and R3 is largely to avoid confusion with the common abbreviation for
primary motor cortex (M1). In addition to the rapid response epochs, we included time
epochs between -100-0ms, 120-180ms and 750-1250ms which were defined as
baseline (PRE), early voluntary (VOL) and steady-state (POS), respectively.
Calculating gain-scaling and correcting for joint kinematics. Gain-scaling is defined as
the change in evoked muscle activity for the same perturbation magnitude but different
background loads normalized by the change in pre-perturbation muscle activity across
these background loads (Equation 1). In the present manuscript, we calculated the gainscaling across the small (1Nm) and large (3Nm) background loads for both small
(1.25Nm) and large (2.5Nm) perturbations.
Gain-scaling = (A(t)bg3,m – A(t)bg1,m) / (A(pre)bg3,m – A(pre)bg1,m)
(1)
Where A(t) is the muscle activity at a particular time (or the average activity over an
epoch as in the denominator) and the subscripts refer to the background load level (bg)
and perturbation magnitude (m), either large or small. Note that gain-scaling of 1
signifies no multiplicative effect of background load on the evoked perturbation activity,
that is, no gain-scaling.
In the present experiment, we applied step-torque perturbations as opposed to
servo-controlling a particular kinematic trajectory. Step-torque perturbations have the
advantage of better approximating the types of perturbations that motor responses tend
to counter; that is, perturbations that are influenced by their action rather than servotrajectories which will occur regardless. However, using step-torque perturbations
required us to account for any systematic changes in joint-velocity which may occur as a
function of background load such as those caused by increases in muscle stiffness
108
(Cannon and Zahalak 1982; Franklin and Milner 2003; Gomi and Osu 1998; Hunter et al.
1982) or active changes in muscle activity vis-à-vis gain-scaling. Systematic changes in
kinematics are problematic because the phasic responses are strongly related to joint
velocity (Jaeger et al. 1982; Lenz et al. 1983; Tatton and Bawa 1979) and could lead us
to incorrectly attribute changes in muscle activity to decreases in gain-scaling rather than
decreases in joint-velocity. We estimated the sensitivity to joint-velocity by performing
the experiment at two perturbation magnitudes and relating the change in evoked activity
to the change in observed position kinematics (Equation 2). Note that the second term,
describing the change in position kinematics (Δθ(t)), is effectively a metric of jointvelocity over the time window (t) and that velocity sensitivity was calculated at the
medium background load (2Nm).
Velocity Sensitivity = (A(t)bg2,m2 – A(t)bg2,m1) / (Δθ(t)bg2,m2 – Δθ(t)bg2,m1)
(2).
This sensitivity was then used to correct the observed gain-scaling based on the
observed difference in motion as a function of background load (Equations 3 and 4).
C = Velocity Sensitivity*(Δθ(t)bg3,m – Δθ(t)bg1,m) (3)
Corrected Gain-scaling = (A(t)bg3,m – A(t)bg1,m + C) / (A(pre)bg3 – A(pre)bg1)
(4)
Gain-scaling was calculated for each pre-defined epoch (R1, R2, R3, VOL, POS). In
addition, we calculated gain-scaling as a function of time by applying a 10ms moving
window to the dataset.
Modeling intrinsic muscle stiffness. To better understand the influence of intrinsic muscle
properties on our observations, we modeled a single-link manipulator with an actuator
based on a realistic muscle model (Brown et al. 1999; Brown and Loeb 1999; Brown and
Loeb 2000a; Brown and Loeb 2000b; Li and Todorov 2004). A detailed description of the
109
model is provided in Appendix 1 but it is important to mention that all the parameters in
the model were independent of our experimental dataset. The resulting model was then
exposed to a single-joint version of the experimental conditions previously described.
Accordingly, a background torque was applied to the manipulator and the manipulator
was programmed to counter this external load near the center of its workspace (90°).
Once the manipulator had countered the load and stopped moving, the compensatory
activation required to maintain this position was fixed and an additional step-torque
perturbation was applied. The resulting kinematics were calculated and compared to our
empirical results. Since the activation was kept constant while the perturbation was
applied we could directly attribute changes in kinematics to changes in the intrinsic
muscle properties via pre-perturbation muscle activation.
We also used the model to estimate intrinsic muscle stiffness (k = Δτ/Δθ = (τf-τo) /
(θf-θo)), which is the spring-like tendency of muscle to resist changes in position. Since
the model does not include a simple term which represents stiffness, we estimated this
value by modeling a commonly used empirical technique (Mussa-Ivaldi et al. 1985). In
short, the manipulator was programmed to counter a set load (τo) near the center of its
workspace (θo = 90°). We then displaced the manipulator to a new position (θf = 89°; Δθ
= 1°) while keeping muscle activation constant and noted the new level of joint-torque
generation (τf) after the manipulator reaches a steady-state (joint-velocity returns to zero)
and restorative torque production saturates.
3.4 – RESULTS
Behaviour and Kinematics.
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For each muscle group (elbow flexors and extensors) we introduced one of three
background loads that required increased muscle activity and then applied one of two
perturbation magnitudes that stretched the excited muscle group (Figure 3.1). Subjects
were required to return to the central target within 500ms of perturbation onset and then
remain inside the target for 1000ms. They learned this task quickly and had little difficulty
meeting the imposed speed and accuracy constraints, performing with an average
success rate of 90% (SD 3).
Both perturbation magnitude and background loads altered the resulting
kinematics (Figure 3.2). These changes were quantified with respect to the elbow joint
as our elbow torque perturbations resulted in predominately (though not exclusively)
elbow motion (Figure 3.3). Not surprisingly, increases in perturbation magnitude led to
increased maximal joint-displacement (three-way ANOVA, background load vs
perturbation magnitude vs perturbation direction, main-effect of perturbation magnitude,
F1,91 = 354, P < 10-6) whereby a two-fold increase in perturbation magnitude resulted in a
2.4x increase in maximal elbow displacement (small/1.25Nm: 5.1 ± 1.1°; large/2.5Nm:
12.2 ± 2.2°; mean ± SD); we noted no significant difference in maximal excursion as a
function of perturbation direction (F1,91 = 2.2, P = 0.14). In contrast to the increased
motion induced by larger perturbation magnitudes, increases in background load
significantly reduced maximal elbow excursion (F2,91 = 33.1, P < 10-6) with a three-fold
increase in background load resulting in a 35% decrease in joint-displacement.
Interestingly, while changes in perturbation magnitude (small/1.25N: 178 ± 22ms;
large/2.5Nm: 184 ± 15ms; mean ± SD; F1,91 = 2.4, P = 0.12) and direction (F1,91 = 0.95, P
= 0.33) did not significantly alter the timing of maximal excursion, increases in
background load resulted in significantly
111
Small
Perturbation
Large
Perturbation
Extension
Perturbation
Flexion
Perturbation
1cm
Figure 3.2. Spatial hand kinematics with different background and perturbation conditions
from an exemplar subject. A. Hand kinematics resulting from the imposed background loads
(light gray = 1Nm; dark gray = 2Nm; black = 3Nm) and perturbations (solid = 1.25Nm; dashed
= 2.5Nm). The circle in each panel indicates the spatial target that subjects were required to
achieve quickly and accurately following perturbation onset.
112
B
DShoulder Angle (°)
DElbow Angle (°)
0
-5
-10
-15
0
100
200
100
D
Shoulder Velocity (°/s)
C
Elbow Velocity (°/s)
Extension Perturbation
A
50
0
-50
-100
0
F
DShoulder Angle (°)
DElbow Angle (°)
10
5
0
0
100
200
100
H
Shoulder Velocity (°/s)
G
50
0
-50
-100
0
2
0
100
200
Time (ms)
0
100
200
0
100
200
0
100
200
0
100
200
30
15
0
-15
-30
200
15
Elbow Velocity (°/s)
Flexion Perturbation
E
100
4
0
-2
-4
30
15
0
-15
-30
Figure 3.3. Temporal joint kinematics from an exemplar subject. A. Elbow angle following an
elbow extension perturbation. The horizontal axis represents time relative to perturbation onset
and the vertical axis shows the change in elbow angle relative to the initial configuration
(Shoulder = 45°, Elbow = 90°). Solid (1.25Nm) and dashed (2.5Nm) lines denote perturbation
magnitude and line shade denotes background load (light gray = 1Nm, dark gray = 2Nm, black
= 3Nm). B. Resultant shoulder angle following an elbow extension perturbation. C. Elbow
velocity trajectory following an elbow extension perturbation. D. Shoulder velocity trajectory
following an elbow extension perturbation. E-H. Same format as A-D except for an elbow
flexion perturbation (which stretches the elbow extensor muscles).
113
earlier reversal times (1Nm BG: 193 ± 16ms; 3Nm BG: 170 ± 22ms; F2,91 = 11.1, P < 103
).
In addition to large-scale changes in kinematic behavior as described above, we
were interested in quantifying how changes in background load modified intrinsic muscle
properties such as stiffness. To quantify the effect of background load, we calculated the
elbow displacement 50ms after perturbation onset (Figure 3.4A), a time at which
changes in kinematics could be attributed almost exclusively to changes in intrinsic
muscle stiffness because of the delay between EMG activity and the generation of
muscle force (Brown et al. 1999). As expected, even at 50ms post-perturbation there
was a highly significant effect of perturbation magnitude for elbow flexion perturbations
across the three background loads (paired t-test, t47 = 49.4, P < 10-6) with an
approximately two-fold increase in joint displacement for a two-fold increase in
perturbation magnitude (small/1.25Nm: -1.3 ± 0.2°; large/2.5Nm: -2.7 ± 0.4°; mean ±
SD). In contrast to the large kinematic changes associated with perturbation magnitude,
initial changes in joint-displacement were very modestly affected by a three-fold increase
in background load. We noted a very small though significant decrease in induced
motion between the small and large background loads (small perturbation: t7 = -5.5, P <
10-3, mean difference ± SD = -0.06 ± 0.03°; large perturbation: t7 = -4.2, P < 0.01, 0.12 ±
0.08°). This small effect of background load can be clearly seen in Figure 3.4A where
the near-zero slopes of the plotted lines indicate that the change in joint-angle is only
modestly effected by changes in background load. In fact, the effect of background load
did not reach 5% of the total elbow displacement until 55ms (SD 10) and 54ms (SD 8)
after perturbation onset for small and large perturbation, respectively. This modest effect
114
DJoint Angle @ 50ms (°)
A -1
-2
DJoint Angle (°)
Large
Perturbation
-3
-4
B
Small
Perturbation
1
2
3
Background Load (Nm)
0
-5
-10
-15
0
100
Time (ms)
200
Figure 3.4. Analysis of Empirical and Model Kinematics. A. This panel presents the resultant
change in joint-angle 50ms following the mechanical perturbation for each level of background
load. Each thin line shows the elbow angle of an individual subject for the elbow flexion
perturbation and the thick-dashed line is from the single-joint of the model. Note that the model
data falls within the range of the collected data and shows a similar trend whereby larger
background loads result in only modest decreases in joint-displacement as witnessed by the
small positive slope of each line. B. Same format as Figure 3A but data is taken from the
model.
115
was consistent with ROC analysis which quantifies the probability that an ideal observer
can distinguish between background loads based on only the elbow kinematics
(Pruszynski et al. 2008a); ROC values of 0 or 1 indicate perfect discrimination and 0.5
indicates discrimination at chance level. Such an analysis reveals a clear effect of
background load immediately after perturbation onset that reaches significance (ROC <
0.25 for 5ms) at 52ms (SD 11) and 44ms (SD 17) for the small and large perturbations,
respectively.
Model of Intrinsic Muscle Properties
Our empirical results indicate that increases in intrinsic muscle properties, as
determined by how such changes influence the resultant joint-kinematics, are modest
over the range of background loads used in the present experiment (Figure 3.4A).
Initially surprised by the small effect, we compared our empirical kinematics to a
mathematical model of the limb and muscle. The model shows striking similarity to the
empirical results for the earliest portion (<50 ms) of the joint kinematics (compare Figure
3.3A and 3.4B) even though all free parameters in the model are set based on previous
literature (see Appendix 1). Specifically, the resultant joint-displacement predicted by the
model at 50ms for all background load and perturbation conditions falls well within the
observed range of observed elbow-kinematics (Figure 3.4A). As with the empirical
results, the effect of background load was present immediately after perturbation onset
but resulted in modest changes in initial kinematics, remaining under 5% until 53 and 55
ms for the small and large perturbations, respectively. We also used the model to
estimate the restorative forces attributable to intrinsic muscle stiffness at each
background load. By displacing the model arm by a known amount (1°) while
116
keeping muscle activation constant, we could determine the intrinsic restoring forces
attributable to changes in position (i.e. muscle stiffness). For both small and large
displacements, our model predicts that intrinsic stiffness is modulated with initial muscle
activation reaching steady-state values of 1.0, 1.7 and 2.3Nm/rad for the 1, 2, and 3Nm
initial background loads studied empirically.
Muscle Activity
Our primary interest was to investigate the evolution of gain-scaling over the
entire multi-phasic sequence of activity that follows muscle stretch. Every background
load and perturbation magnitude evoked a clear sequence of phasic activity whose
timing was similar across subjects, muscles and conditions. The timing of the bursts
largely corresponded to the pre-defined epochs though we rarely found a clear
separation between the R2 and R3 epochs. In Figure 3.5, the effect of background load
on pre-perturbation activity is visible for an exemplar muscle sample; with increased
background load the absolute magnitude of muscle activity was increased over the
entire response. We were particularly interested in determining whether there was any
gain-scaling of the phasic responses, that is, an increase in the magnitude of rapid
responses over and above the constant shift introduced by the background load. This is
revealed in Figures 3.5B and 3.5D where the offset due to the initial background load is
removed. Consistent with many previous results, the R1 response is clearly larger for the
same perturbation when the background load is increased. However, the difference as a
function of background load is systematically reduced in later epochs even though the
overall magnitude of muscle activity is substantially larger. Although each muscle
sample displayed a unique pattern of activity, the general trend with respect to gain117
A
Small Perturbation
EMG (norm)
8
DEMG (norm)
B
C
Large Perturbation
8
R1 R2 R3
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Figure 3.5. Exemplar patterns of muscle activity with different background and perturbation
loads. A. EMG activity from an exemplar muscle sample (Triceps Lateral, Subject #2) for the
small perturbation (1.25Nm) but different background loads (light grey = 1 Nm, black = 3 Nm).
Note that the middle (2Nm) background load condition is omitted for clarity. The horizontal axis
denotes time and is aligned to perturbation onset (black vertical line). The dashed vertical lines
designate intervals for the R1, R2 and R3 epochs and are based on average responses
across muscle samples (see Methods). The vertical axis represents normalized EMG activity
where a value of 1 represents EMG activity used to counter a 2 Nm background load. B. Same
data and format as in A except that the offset associated with background load activation has
been removed. C. Same format as A except for the large perturbation (2.5Nm). D. Same
format and data as C with offset removed.
118
scaling was qualitatively similar for all collected muscles as well as the grand mean
(Figure 3.6).
The increase in steady-state muscle activity for the same change in mechanical
load was independent of the initial background load (two-way ANOVA, background load
vs perturbation magnitude, main-effect of background load, F2,176 = 0.27, P = 0.76) and
highly sensitive to the perturbation magnitude (main-effect of perturbation magnitude,
F1,176 = 54.6, P < 10-6). Hence, steady-state muscle activity did not exhibit any gainscaling. This result is shown in Figure 3.7A which plots the change in muscle activity for
the small and large perturbation torque at each of the three initial background loads used
in this experiment. Note that the increase in muscle activity for the applied perturbations
is the same regardless of the initial background load.
We quantified the progression of gain-scaling over the sequence of activity that
makes up the early corrective response both within pre-defined epochs of muscle activity
(Figure 3.7B) and with respect to time without a priori assumptions about underlying
epochs (Figure 3.7C). For our principle analysis we combined results across muscle
samples because visual and statistical inspection of individual muscle groups did not
reveal substantial differences with respect to gain-scaling (three way ANOVA, gainscaling vs. perturbation magnitude vs. muscle group, main effect of muscle group, F3,236
= 0.42, P = 0.74).
Figure 3.7B shows the corrected gain-scaling (see Methods) for each epoch of
activity under both small and large perturbation magnitudes. The short-latency response
is approximately doubled for a unit increase in the background load and the same
mechanical perturbation (gain-scaling = ΔR1small/ΔBG = 1.91; ΔR1large/ΔBG = 2.38). As in
the R1 epoch, gain-scaling was statistically present in many later epochs (t-test, gain119
Small Perturbation
Biceps
4
4
3
3
2
2
1
1
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Brachioradialis
4
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Mean
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Lateral
100
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Long
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0
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0
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Figure 3.6. Group patterns of muscle activity. The layout is the same as in Figure 5B and 5D.
The left column presents the data for small (1Nm) and large (3Nm) background loads and the
small perturbation (1.25Nm). The right column is for the large perturbation (2.5Nm). The
bottom row is the grand mean across all muscle samples.
120
1.5
1
0.5
0
1Nm
2Nm
3Nm
1.25 2.5
DPerturbation (Nm)
3
C
*
*
*
*
Gain-Scaling (DEMG / DBG)
B
Gain-Scaling (DEMG / DBG)
DEMG from Baseline (norm)
A
*
2
1
0
R1
R2
R3 VOL POS
Epoch
3
Large
Small
2
1
0
0
50
100 150 200 250
Time (ms)
Figure 3.7. Analysis of gain-scaling across epochs and in time. A. Linearity of steady-state
muscle activity (750-1250ms post-perturbation) associated with increased loads. The
horizontal axis represents the change in load between background and perturbation and the
vertical axis represents the resulting change in steady-state EMG. The three symbols denote
the three initial background loads (square = 1Nm; circle = 2Nm; triangle = 3Nm) and error bars
represent standard error across muscle samples. B. Population data showing the sensitivity of
muscle responses in each epoch to the background load. Results are shown for both the small
(1.25Nm, solid) and large (2.5Nm, dashed) perturbations. The bars represent gain-scaling
after correction for reduced joint motion (see methods). Dots within each bar show uncorrected
gain-scaling. Symbols (*) above each bar indicate significant gain-scaling (t-test, P < 0.05,
gain-scaling > 1). C. Sensitivity of muscle responses to background load with respect to time.
The horizontal axis represents time relative to perturbation onset and the vertical axis
represents gain-scaling at each point in time. Thin and thick lines denote the small and large
perturbations, respectively. Note that gain-scaling at each instant in time (1ms) is smoothed
with a 10ms sliding window.
121
scaling > 1, P < 0.05), though there is a notable effect of epoch (R1, R2, R3, VOL) on
gain-scaling (ANOVA, F3,236 = 9.6, P < 10-3) whereby later responses exhibit
systematically decreased gain-scaling for both perturbation magnitudes (slope of linear
regression between epoch timing and gain-scaling < 0; paired t-test, small: t29 = -6.0, P
< 10-3; large: t29 = -5.7, P < 10-3). A more direct examination of gain-scaling revealed that
the R2 epoch was not significantly less gain-scaled than the R1 epoch for either the
small or large perturbation (paired t-test, P > 0.1) with just over half of the collected
samples decreasing their gain-scaling when compared to gain-scaling in R1 (small: 53%;
large: 53%). In contrast, the majority of muscle samples demonstrated reduced gainscaling in the R3 (small: 80%; large: 70%), VOL (97%; 80%) and POS (93%, 100%)
epochs. Indeed, the VOL response exhibited no statistically significant gain modulation
(ΔVOLsmall/ΔBG = 0.68; ΔVOLlarge/ΔBG = 0.92) for either perturbation magnitude (gainscaling = 1, small: t29 = -0.58, P = 0.56; large: P = 0.61).
Although gain-scaling was progressively reduced for both small and large
perturbation magnitudes, there was a significant effect of perturbation magnitude on
gain-scaling whereby the large perturbation resulted in greater gain-scaling in each
epoch (ANOVA, F1,238 = 4.9, P = 0.03). A within-epoch comparison showed a similar
trend (paired t-test, gain-scaling (small) < gain-scaling (large), R1: t29 = -2.2, P = 0.02;
R2: t29 = -1.3, P = 0.1; R3: t29 = -3.2, P < 0.001).
The binned results were not qualitatively changed by modestly altering either the
onset or duration of the epochs. For example, when bins were narrowed by 10ms to limit
potential overlap between epochs (R1: 25-40; R2: 55-70; R3: 80-100; VOL: 125-175),
we continued to find no significant decrease in gain-scaling for the R2 epoch when
compared to the R1 epoch for either the large or small perturbation (P > 0.1). The
122
majority of muscle samples still showed reduced gain-scaling in the R3 (small: 83%
large: 73%) and VOL (small: 97% large: 83%) epochs and the VOL epoch exhibited no
significant gain-scaling for either perturbation magnitude (small: P = 0.56; large: P =
0.64). We continued to find a trend towards greater gain-scaling with larger perturbation
magnitude (R1: P = 0.04; R2: P = 0.2; R3: P = 0.003).
Similar results are found when gain-scaling is calculated in time without a priori
assumptions about epochs (Figure 3.7C). This view of gain-scaling revealed two phases
of increased activity that correspond roughly with the phasic bursts of muscle activity
associated with the R1 and R2 epochs (Figure 3.6, Grand Mean). The first phase of
activity is modestly larger that the second phase and this is quickly followed by a
dramatic drop in gain-scaling that occurs approximately 75ms post-perturbation,
corresponding to the boundary between the R2 and R3 epoch.
In the present experiment, we applied step-torque perturbations rather than
enforcing a particular kinematic trajectory. Since we did not explicitly control joint motion
it is possible that the observed difference in background load scaling results from smaller
kinematic displacements caused by increases in muscle stiffness. However, the changes
in initial kinematics were small and would, on average, account for < 5% of the reduction
in background load scaling in each of the epochs of interest. This is shown in Figure
3.7B where the bars and variability present the gain-scaling after we corrected by the
reduced displacement and the small filled dots within each bar present the uncorrected
gain-scaling. Note that no correction was carried out on the steady-state epoch and that
time-series data in Figure 3.7C is corrected at every sample.
Although our principle analysis collapsed across muscle groups, each individual
muscle group demonstrated similar trends. Most importantly, we found that later
123
responses exhibited systematically decreased gain-scaling for each muscle group and
both perturbation magnitudes as measured by calculating the slope of the linear
regression between epoch timing and gain-scaling in that epoch (Figure 3.8A). This
finding was statistically significant (paired t-test, slope of regression < 0, P < 0.05) for 7
of 8 experimental conditions (4 muscle groups x 2 perturbation magnitudes) with the
other condition (Biceps, Large Perturbation) showing the same trend (P = 0.09). In fact,
all the muscle groups showed significant gain-scaling in the R1 epoch for both
perturbation magnitudes (t-test, gain-scaling > 0, P < 0.05) and none of the muscle
groups showed significant gain-scaling in the VOL epoch (t-test, gain-scaling ≠ 0, P >
0.1). We also determined the extent to which individual muscle groups showed a
decrease in gain-scaling from the R1 to the R3 epoch. Again, each muscle group
demonstrated a trend that is consistent with our principle findings whereby gain-scaling
in the R3 epoch is reduced from that seen during R1, though these reductions were
statistically significant (paired t-test, P < 0.05) in only two conditions (Figure 3.8B,C).
3.5 – DISCUSSION
The goal of this study was to elucidate the temporal evolution of ‘automatic gainscaling’ in human elbow muscles during postural maintenance of the upper-limb. Unlike
most previous work which focused on the short-latency response (Cathers et al. 2004;
Matthews 1986; Ruegg et al. 1990; Stein et al. 1995; Toft et al. 1989; Verrier 1985), we
specifically designed our experiment to robustly elicit a prolonged sequence of muscle
activity by requiring subjects to maintain their hand at a central spatial target in the
presence of unpredictable torque perturbations applied at the elbow (Kurtzer et al. 2008;
Pruszynski et al. 2008a). In agreement with many previous studies of human
124
0
B
S L
-10
-20
*
*
*
*
*
-30
-40
*
*
Brachio Biceps Triceps Triceps
radialis
Lateral Long
Gain-Scaling (DEMG / DBG)
DGain-Scaling / Second
A
3
C
Small Perturbation
4
*
*
3
2
2
1
1
0
R1 R3
Brachio
radialis
Biceps
Triceps
Lateral
Triceps
Long
Large Perturbation
4
0
R1 R3
Brachio
radialis
Biceps
Triceps
Lateral
Triceps
Long
Muscle
Figure 3.8. Gain-scaling trends for each muscle group. A. Summary of regression slopes
relating gain-scaling and the timing of subsequent epochs of phasic muscle activity (R1-VOL).
Gain-scaling was calculated within each epoch and the regression was done with respect to
the mean time for that epoch (R1 = 0.0325s (32.5ms); R2 = 0.0625s; R3 = 0.09s; VOL =
0.15s). Each bar represents the regression slope (mean and SEM) for the indicated muscle
sample. White bars are from the small perturbation (1.25Nm) condition and grey bars are from
the large perturbation (2.5Nm). Symbols (*) below each bar indicate a significant (t-test, P <
0.05, slope of regression < 0) negative relationship between subsequent epochs of activity and
gain-scaling. B. Direct comparison of gain-scaling (mean and SEM) in R1 and R3 for each
muscle group in the small perturbation condition. White bars indicate gain-scaling in the R1
epoch and black bars indicate gain-scaling in the R3 epoch. Note that all muscle samples
demonstrate a trend towards decreased gain-scaling in the R3 epoch though only two of the
conditions (*) demonstrate significant decreases (paired t-test, P < 0.05, gain-scaling in R3 <
gain-scaling in R1). C. Same format as B except for the large perturbation condition.
125
upper-limb muscles, we found that the short-latency response (R1: 20-45ms) is robustly
scaled by pre-perturbation muscle activity (Bedingham and Tatton
1984; Cathers et al. 2004; Marsden et al. 1976; Matthews 1986; Stein et al. 1995) and
that steady-state muscle activity (750-1250ms) is not (Hof 1984; Lawrence and Deluca
1983; Milner-Brown and Stein 1975). Our results are also consistent with previous
reports of gain-scaling within the long-latency epoch for a wrist flexor muscle
(Bedingham and Tatton 1984).
We extend previous work by characterizing gain-scaling over the entire sequence
of muscle activity that follows a mechanical perturbation. Our results demonstrate a
rapid decrease in gain-scaling that begins approximately 75ms after perturbation onset
and quickly approaches the steady-state response (Figure 3.7B,C). Gain-scaling in the
R3 (75-105ms) epoch was significantly smaller than for the short-latency response and
was statistically absent in both the early voluntary (VOL: 120-180ms) steady-state
epochs. This pattern was apparent for both small and large perturbation magnitudes and
when analyzing each individual muscle group suggesting that rapid reductions in gainscaling may be a common feature of perturbation responses of elbow muscles.
Modest influence of intrinsic muscle stiffness
Previous authors have suggested that activity-dependent changes in intrinsic
muscle stiffness play an important role in stabilizing the upper-limb by providing
substantial restorative forces at zero-latency (Franklin and Milner 2003; Gomi and Osu
1998; Mussa-Ivaldi et al. 1985). However, our present experimental results suggest a
modest effect of activity-dependent muscle stiffness on initial elbow motion, whereby a
300% increase in background load and a concomitant increase in muscle activity yielded
126
only a ~5% reduction in joint motion 50ms after perturbation onset (Figure 3.4A). These
empirical findings were well matched by a realistic model of the muscle and limb. The
model suggests that although stiffness is systematically increased with initial activation,
the overall contribution is small for the range of activations used in the present study.
Our estimates of stiffness, ranging from 1.2 to 2.3Nm/rad, is within the range of previous
empirical reports that measured intrinsic stiffness of isolated muscle preparations,
particularly those estimated made at low initial forces/activations with stretch magnitudes
that surpass the influence of short-range stiffness mechanisms (Hoffer and Andreassen
1981; Huyghues-Despointes et al. 2003; Nichols and Houk 1976; Rack and Westbury
1974).
A reasonable criticism of the present study is that our background loads and
muscle activations were not large enough to cause appreciable changes in muscle
stiffness and that at large initial activations, the role of intrinsic muscle stiffness would
increase substantially (Hoffer and Andreassen 1981; Joyce and Rack 1969). However,
the loads we used represented a substantial portion of what subjects could perform
under our non-isometric conditions over the duration of the experiment making further
empirical increases difficult (Bedingham and Tatton 1984; Franklin and Milner 2003).
Furthermore, when the model was used to simulate larger initial activations (approaching
MVC), estimates of intrinsic stiffness increased relatively linearly with muscle activation
(Hoffer and Andreassen 1981; Joyce and Rack 1969) and saturated at approximately
10Nm/rad. Perhaps more importantly, even the highest activation levels resulted in
modest reductions of joint-displacement. Another possibility is that the limbs’ inertial
properties dominate initially but that stiffness plays a substantial role at greater
displacements. Again, this is difficult to address empirically as greater displacements
127
occur at times where evoked muscle activity is present, making it difficult to separate
changes in intrinsic stiffness from changes in muscle activity. In our simulations, we
clamped active changes in muscle activity and found that the effect of stiffness clearly
increased but was still limited (<15% reduction in joint-kinematics) even at 250ms postperturbation when displacements are substantially larger (~30° and 60° for small and
large perturbations, respectively) than the peak excursions found experimentally (~5°
and 12°, compare Figure 3.3A and 3.4B).
While our present work suggests a modest contribution of intrinsic stiffness at low
to moderate background load levels for the upper-limb, additional work is needed to fully
explore this important issue. Furthermore, it should be stressed that our empirical results
demonstrate that the total effect of gain-scaling which includes both intrinsic muscle
properties and scaling of muscle activity does result in substantial behavioural
consequences including reductions in peak displacement and reversal time for a given
perturbation magnitude (Figures 3.2 and 3.3).
Physiological implications of decreases in gain-scaling
It is well-established that short-latency responses and H-reflexes are often
modulated by pre-perturbation muscle activity and thus demonstrate gain-scaling
(Bedingham and Tatton 1984; Marsden et al. 1976; Matthews 1986; Stein et al. 1995;
Verrier 1985). This modulation likely reflects the intrinsic organization of the motoneuron
pool whereby motor units are recruited in strict order of their force-generating capability
and resilience to fatigue (Cope and Clark 1991; Henneman 1957), a phenomenon
termed the size-recruitment principle. Indeed, previous work has convincingly described
(Marsden et al. 1976; Matthews 1986) and formalized (Capaday and Stein 1987; Houk
128
et al. 1970; Kernell and Hultborn 1990; Slot and Sinkjaer 1994) how the size-recruitment
principle specifically leads to gain-scaling and empirical studies have demonstrated that
the size-recruitment principle likely applies to motor unit recruitment at all latencies
(short, long and voluntary) following a mechanical perturbation (Calancie and Bawa
1985a; Calancie and Bawa 1985b).
In summary, many previous studies suggest that gain-scaling is caused by the
size-recruitment principle which is likely an unavoidable mapping between inputs to the
motoneuron pool (i.e. motor commands) and the resulting muscle activity (Bawa 2002;
Cope and Sokoloff 1999; Kandel et al. 2000). Therefore, it is important to reconcile this
well-explored constraint with our observation that gain-scaling is reduced in the longlatency epoch and that early voluntary and steady-state muscle activity is not gainscaled; how can gain-scaling be reduced/eliminated over time given the size-recruitment
principle? Ultimately, the elimination of gain-scaling within this constraint requires that
synaptic input to the motoneuron pool from a fixed perturbation be reduced in proportion
to the background load. Such a reduction could be caused by a number of different
mechanisms such as a reduction in the efficacy of descending motor commands at the
level of the motoneuron pool. For example, the ‘tonic-vibration reflex’ (Matthews 1966) is
generally absent in spinalized cats but can be facilitated to levels observed in a
decerebrate cat by intravenous injection of DOPA, presumably by activating a
descending system of mono-aminergic fibers (Goodwin et al. 1973). Similar low-level
circuitry could modulate the excitability of rapid-response epochs (Houk 1979) by
removing facilitation in proportion to background load and time following perturbation. A
second possible mechanism is a reduction of descending motor commands in proportion
to background load. Of particular interest is activity in primary motor cortex (M1)
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because it likely provides an important contribution to descending motor commands in
both the long-latency (Matthews 1991) and voluntary epochs; to our knowledge, no one
has reported the effect of background load on perturbation responses in M1.
It should be emphasized that not all recruitment rules (for example, random
selection of motor units) would lead to gain-scaling. An alternative and more speculative
explanation for a reduction in gain-scaling is that later motor commands can increasingly
circumvent the size-recruitment principle. Several studies have observed apparent
violations of the size-recruitment principle during voluntary contractions under a variety
of experimental conditions and for several muscles of the upper- and lower-limb
(Hodson-Tole and Wakeling 2008). For example, studies have shown that sizerecruitment likely occurs over smaller muscle compartments rather then the entire
muscle for the human upper-limb during isometric contractions (Vanzuylen et al. 1988)
and cat lower-limb during gait (Hoffer et al. 1987). Such an organization would not
eliminate gain-scaling because each compartment locally obeys the size-recruitment
principle, but it could lead to substantial changes in gain-scaling if descending motor
commands selectively recruited different muscle compartments. Others have reported
violations of size-recruitment without finding such discrete compartments, suggesting
instead that motor units are activated as a function of their mechanical action rather than
as a function of their size (Herrmann and Flanders 1998). In this scenario there is no
need for descending motor commands to compensate for background load activation as
their influence is not subject to the size-recruitment principle. Of course, the
simplification gained from not having to compensate for size-recruitment may well be
overshadowed by the complexity of selecting specific motor units. Again, it may be
fruitful to explore the effect of background load on M1 perturbation responses as an
130
organization that avoids the size-recruitment principle predicts no gain-scaling in M1
responses.
Intermediate gain-scaling in the long-latency epoch
An important result in the present study is a rapid decrease in gain-scaling such
that gain-scaling in the long-latency epoch, specifically R3, is smaller than the shortlatency response but greater than the voluntary response (SL > LL > VOL). This
progressive decrease may provide an important clue about the neural substrates of
muscle activity within the long-latency epoch for the upper-limb. One possibility is that
activity within each epoch reflects largely independent neural circuits each with
progressively greater correction for the size-recruitment principle (see above). In
contrast, the decrease in gain-scaling may occur because long-latency activity is
comprised of two components that overlap in time (Crago et al. 1976; Rothwell et al.
1980), a transient component with no correction for gain-scaling and an increasingly
dominant component whose contribution completely corrects for gain-scaling. The net
result of this arrangement as measured via surface electromyography would be a
progressive reduction in gain-scaling. In fact, while it is well-established that the longlatency response for the upper-limb includes a cortical contribution from M1 (Matthews
1991) it has also been shown that phasic-activity within the long-latency epoch is
present in decerebrate cats (Ghez and Shinoda 1978) and in both decerebrate (Miller
and Brooks 1981) and spinalized monkeys (Tracey et al. 1980). Taken together with
these previous observations, our present results are consistent with the notion that longlatency activity is comprised of at least two separate circuits which overlap in time
(Pruszynski et al. 2008b). We speculate that, if isolated, the spinal component would
131
demonstrate gain-scaling similar to the short-latency response (R1) whereas the supraspinal contribution would show no gain-scaling, similar to the early voluntary response
(VOL).
In summary, the size-recruitment principle is a rational rule for recruiting motor
units insofar as it tends to minimize fatigue and neuromuscular noise (Kandel et al.
2000; Mendell 2005), two important considerations for motor control. It also introduces
substantial complexity for motor commands when trying to generate similar changes in
force at various levels of background/tonic load. In that sense, the size-recruitment
principle is another complexity of the neuromuscular periphery, like muscle-mechanics
and limb-dynamics, which must be taken into account by descending motor commands
(Kurtzer and Scott 2007). What is striking about the present results is that correction for
the size-recruitment principle begins very rapidly following a mechanical perturbation,
within 75ms of perturbation onset. This similarity between long-latency responses and
voluntary corrections in the human upper-limb is consistent with our recent suggestions
that activity within these epochs is inherently linked as part of the same control process
through similar neural circuitry (Scott 2004).
132
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Chapter 4
The long-latency reflex is composed of two functionally
independent processes
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4.1 – ABSTRACT
The nervous system counters mechanical perturbations applied to the arm with a
stereotypical sequence of muscle activity, starting with the short-latency stretch reflex
and ending with a voluntary response. Occurring between these two events is the
enigmatic long-latency reflex. Although researchers have been fascinated by the longlatency reflex for over sixty years, some of the most basic questions about this response
remain unresolved and often debated. In the present study he help resolve one such
question by clearly demonstrating that the human long-latency reflex during a naturalistic
motor task is not a single discrete response; rather, it appears to reflect the output of (at
least) two functionally-independent processes that overlap in time and sum linearly. One
of these components shares an important attribute of the short-latency reflex (i.e.
automatic gain-scaling) and the other shares a defining feature of voluntary control (i.e.
task-dependency). We further show that the task-dependent component of long-latency
activity reflects a feedback control process rather than the simplest ‘triggered reaction’ to
a mechanical stimulus.
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4.2 – INTRODUCTION
Recent theories of motor control propose that coordinated movement is
generated by the intelligent manipulation of sensory feedback (Todorov and Jordan,
2002). This suggestion has reignited interest in the long-latency reflex, a natural
candidate to mediate an intelligent feedback signal because it occurs very quickly in
response to a mechanical perturbation and exhibits a wide range of sophistication
(Diedrichsen et al., 2010; Scott, 2004). Although researchers have been fascinated by
the long-latency reflex for over sixty years, some fundamental questions remain
unresolved. One such question is whether the long-latency reflex is a discrete event,
either via a transcortical pathway (Capaday et al., 1991; Cheney and Fetz, 1984; Evarts,
1973; MacKinnon et al., 2000; Marsden et al., 1977; Matthews et al., 1990; Phillips,
1969) or a spinal mechanism (Cody et al., 1986; Eklund et al., 1982; Ghez and Shinoda,
1978; Matthews, 1984; Miller and Brooks, 1981; Schuurmans et al., 2009; Tracey et al.,
1980) or whether it reflects the temporal overlap of multiple contributors at various levels
of the neuraxis (Gomi and Osu, 1998; Kimura et al., 2006; Kurtzer et al., 2010; Lewis et
al., 2004; Lourenco et al., 2006; Matthews and Miles, 1988; Shemmell et al., 2009).
In the present paper, we tackled this problem from a novel perspective inspired
by Peter Matthews, who recently asked: “what physiological contribution does each of
several pathways provide to human reflex motor control in each particular situation?”
(Matthews, 2006). By turning his question around, we posited that if multiple contributors
do exist, they should endow long-latency activity with different functional capabilities.
Our approach was built on previous experiments which showed that the sequence of
muscle activity following a mechanical perturbation is decreasingly sensitive to preperturbation muscle activation (short-latency > long-latency > voluntary = none)
143
(Pruszynski et al., 2009) but increasingly sensitive to task-constraints (short-latency =
none < long-latency < voluntary) (Pruszynski et al., 2008). Based on these results, it is
reasonable to suggest that the intermediate sensitivity of the long-latency reflex reflects
the pre-setting of feedback gains within a discrete neural circuit that is somewhat
sensitive to both parameters (Fig. 4.1A) (Colebatch et al., 1979; Evarts and Granit, 1976;
Hammond, 1956; Lee and Tatton, 1975). Alternatively, we suspected that the
intermediate nature of long-latency activity occurs because it reflects the temporal
overlap of two processes (Crago et al., 1976; Rothwell et al., 1980), one sensitive only to
pre-perturbation muscle activity and another sensitive only to task-constraints (Fig.
4.1B).
We can test between these possibilities because, under reasonable
assumptions, they make different predictions about how the sensitivity to preperturbation muscle activity and task-constraints should interact. The discrete hypothesis
predicts that larger pre-perturbation muscle activity would yield greater sensitivity to
task-constraints (Fig. 4.1C) because it assumes that both factors act to pre-set the
sensitivity of a single circuit. In contrast, the overlap hypothesis predicts that taskdependent activity is independent of pre-perturbation muscle activity because it assumes
they are processed by two independent circuits (Fig. 4.1D) whose outputs sum linearly
(Day et al., 1983).
4.3 – MATERIALS AND METHODS
Subjects and Apparatus
A total of 17 subjects (11 males and 6 females, aged 21-36) participated in the
three experiments. All were neurologically unimpaired, had normal/corrected vision and
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A
B
Discrete Model
P1
P2
f(bg)
Overlap Model
P3
f(bg,T)
f(T)
f(bg)
Muscle Activity
+
=
S
L
VOL
105
S
0 20 50
time (ms)
D
Long-Latency Response: Discrete
L
105
VOL
time (ms)
Long-Latency Response: Overlap
Task 1
Muscle Activity
f(T)
+
=
0 20 50
C
P2
P1
Task 1
Task 2
Task 2
0
0
Background Load
Background Load
Figure 4.1. Candidate Models and Experimental Predictions. A. Schematic showing the typical
evoked muscle activity following a mechanical perturbation. The vertical black line indicates
perturbation onset and the multi-phasic trace (the black line) represents muscle activity that is
recorded by a surface electrode. The color coding underneath the trace represents the
processes which actually contribute to the observed EMG activity. In the discrete model, three
processes uniquely contribute to one of the three principal epochs of evoked muscle activity the short-latency reflex (S), the long-latency response (L) and voluntary response (VOL). To
match previous empirical findings (see results), P1 is sensitive to pre-perturbation muscle
activity as manipulated by background load but not task constraints as manipulated by spatial
target position, P2 is somewhat sensitive to both background load and task constraints and P3
is sensitive only to task constraints. B. The layout of this schematic and, critically, the multiphasic trace which represents observed EMG activity is the same as in panel A. However, in
the temporal overlap model, the phasic events are caused by two processes that overlap
during the long-latency epoch: P1 which is sensitive only to background load and P2 which is
sensitive only to task constraints. Because these two processes are superimposed in the longlatency epoch, they yield intermediate levels of sensitivity to both background load and taskconstraints in that epoch. C. Predictions made by the discrete model when simultaneously
manipulating background load and task constraints. Note the multiplicative interaction between
the two parameters whereby larger background loads induce greater sensitivity to taskconstraints. D. Predictions made by the overlap model. Note that the amount of modulation
associated with task-constraints is independent of background load.
145
gave informed consent according to a protocol approved by the Queen’s
University Research Ethics Board.
All experiments were performed in a robotic exoskeleton (KINARM, BKIN
Technologies Ltd., Kingston, ON) which permits combined flexion and extension
movements of the shoulder and elbow in the horizontal plane and can independently
apply mechanical loads to the shoulder and/or elbow (Scott, 1999). Target lights and
simulated hand feedback were provided in the horizontal plane. Direct vision of the hand
was occluded and hand feedback was removed prior to perturbation onset so that all
perturbation responses were guided entirely by proprioception.
Experiment 1: Two Target Task
Subjects (N = 11) maintained their hand in a small central area (radius = 0.3 cm)
while countering a background load (±2 Nm at elbow) that activated either the elbow
flexors (negative background loads) or extensors (positive background loads) prior to
perturbation onset (Fig. 4.2A-E). Subjects were then shown a peripheral target (radius =
20cm) located on either the medial or lateral aspect of their hand and thus requiring
predominantly elbow flexion or extension movements, respectively. After a 1-4s random
hold period, a rapid step-torque perturbation (±2 Nm at the elbow) displaced the hand
either towards (IN condition) or away from (OUT condition) the peripheral target.
Subjects were required to quickly place their hand into the displayed target following the
perturbation. After completing the trial, subjects were provided visual feedback to
indicate success (target filled green) or failure (target filled red) based on set speed and
accuracy criteria. A successful trial required the subject to place their hand inside the
peripheral target within 300ms of perturbation onset and then remain inside the target for
146
C
Flexors
Applied Torque (Nm)
A
4
pre-excited
2
d
e
b
a
0
c
-2
B
Extensors
D
Background Perturbation
(BG)
(P)
Applied Torque (Nm)
10cm
Time (ms)
pre-inhibited
-4
4
pre-inhibited
2
0
-2
pre-excited
-4
Applied Torque (Nm)
E
Exp 1
4
2
0
-2
-4
F
G
Exp 2
Exp 3
extensor
pre-excited
flexor
pre-inhibited
extensor
pre-inhibited
flexor
pre-excited
BG
P
Epoch
BG
P
BG
P
Figure 4.2. Apparatus and Experimental Paradigm. A,B. In experiment 1 and 2, subjects were
presented with one of two large peripheral targets (PTs, 20 cm) located on the medial or lateral
aspect of their hand when it was at the central starting position (CT). Target location was
chosen such that the applied flexion/extension perturbations displaced the hand either into (IN)
our out of (OUT) the target. The central starting position was placed at the tip of the index
finger when shoulder (?s) and elbow (?e) angles were 45° and 90°, respectively. Applied
perturbations, background loads and target positions were different for studying elbow flexors
(A) and extensors (B). In Experiment 3, only the OUT target was used and only the elbow
flexors were investigated. C,D. Timeline for an individual trial for studying elbow flexors (C)
and extensors (D). (a) Both the CT and PT appeared while a background load (BG) was slowly
introduced (rise time = 500ms) that could either pre-activate or pre-inhibit the principle muscle
group of interest. (b) Hand feedback was removed while subjects maintained their hand at the
CT in the presence of the background load. (c,d) After 1-4s, a rapid step (1250ms duration)
perturbation (P) was introduced that stretched/lengthened the muscle group of interest. (e)
The background load was slowly removed (fall time = 1s) while performance feedback was
provided to the subject by filling the PT green or red if the trial was successful or unsuccessful,
respectively. E,F,G. Schematic representation of conditions in Experiment 1, 2 and 3,
respectively. The horizontal axis represents loads applied at the elbow joint during the
background hold (BG) and perturbation (P) epochs. Positive and negative loads in the BG
epoch pre-activate extensor and flexor muscles, respectively. Positive and negative changes
in load between the BG and P epochs stretch the extensor and flexor muscles, respectively.
147
the next 1000ms. Fifteen successful repeats were performed in each condition (2
background loads, 2 perturbation loads, 2 targets) for a total of 120 trials.
Experiment 2: Two Targets and Multiple Background Loads
The protocol is similar to that in Experiment 1. Subjects (N = 8) were presented
with one of seven initial background loads (±2.25, ±1.5, ±0.75, 0 Nm at the elbow) and a
perturbation which always stretched the elbow flexor muscles (-2.25Nm) (Fig. 4.2F). A
total of 20 successful repeats were performed in each condition (7 background loads, 1
perturbation loads, 2 target locations) for a total of 280 trials.
Experiment 3: Two Perturbation Magnitudes
The protocol is similar to Experiment 1. Subjects (N = 8) countered a background
load that inhibited the elbow flexor muscles (+2.5Nm) and were presented with one of
two perturbation magnitudes that stretched the elbow flexors (-1.25, -2.5Nm) (Fig. 4.2G).
Only the OUT target was presented. Forty successful repeats were performed in each
condition (1 background load, 2 perturbation loads, 1 target location) for a total of 80
trials. At the end of the experiment, we collected 10 trials with a background load
exciting the elbow flexor muscles (+2.5Nm) for the purposes of normalization (see Signal
Processing).
Electromyography
Surface electromyography (EMG) was collected according to previously reported
procedures (Kurtzer et al., 2008; Pruszynski et al., 2008; Pruszynski et al., 2010). In
Experiment 1 and 2, EMG was recorded from four muscles involved with flexion or
148
extension at the elbow joint: Brachioradialis (Br, monoarticular flexor), Biceps Long Head
(Bi, biarticular flexor), Triceps Lateral Head (TLat, monoarticular extensor) and Triceps
Long Head (TLo, biarticular extensor). Note that one muscle sample in Experiment 1
was not included in the analysis because it yielded no observable signal. In Experiment
3, EMG was recorded only from Biceps. After cleaning the skin, electrodes (DE-2.1,
Delsys, Boston, MA) were placed on the muscle belly parallel to the muscle fibers; the
reference electrode was placed on the ankle. EMG signals were amplified (gain = 103104), band-pass filtered (20-450Hz) and then digitized at 1kHz.
We defined three separate epochs of rapid muscle activity, the short-latency reflex (2045 ms), the long-latency response (50-105 ms) and the voluntary response (120180ms). We also calculated mean muscle activation in a baseline epoch prior to
perturbation onset (-100-0ms).
Signal Processing
Joint and hand position signals were obtained directly from the KINARM device
with a sampling rate of 1kHz. These signals were then low-pass filtered (20Hz, two-pass,
sixth-order Butterworth). After digitization, EMG signals were band-pass filtered (25250Hz, two-pass, sixth-order Butterworth), full-wave rectified, and normalized by their
mean activity during the baseline epoch in the most excitatory background load condition
available in each experiment (2Nm in Experiments 1; 2.25Nm in Experiment 2 and 3).
Akaike’s Information Criterion
In Experiment 2, we used Akaike’s Information Criterion (AIC) to judge the merits
of potential models relating muscles activity within the long-latency epoch to various
149
background loads for both spatial target positions. AIC is a technique for choosing a
parsimonious model from a set of candidates by balancing how well the model fits the
data and the complexity of a given model (Burnham and Anderson, 2002). Model quality
is proportional to the likelihood (£) of a candidate model (θ') given the experimental data
(x) and complexity is accounted for by K, the number of free parameters in the candidate
model (AIC = -2log(£(θ'|x)) + 2K). Under the assumption that model errors were
Gaussian with equal variance, the model quality term was simplified to the square root of
the sum of squares (RSS), which was calculated either via linear regression or
constrained non-linear optimization (fmincon in Matlab, The Mathworks, Boston, MA).
When non-linear optimization was used, the procedure was restarted 1,000 times from
random initial locations in an attempt to locate the global best fit.
We compared four models relating muscle activity to background load for each muscle
sample: (1) a constant function, (2) a linear function, (3) two linear functions that were
piecewise continuous and (4) a sigmoid. Candidate models were compared in two ways.
First, the candidate model with the lowest AIC score was deemed the best candidate
model. Second, we determined how often a candidate model was acceptable by
calculating the difference between its AIC score and that of the best candidate model
(ΔAIC = AIC-min(AIC)). If this difference was less than a typically chosen threshold
(ΔAIC<4), the model was deemed acceptable (Burnham and Anderson, 2002).
4.4 – RESULTS
Target-dependent responses persist when load-dependent activity is fully suppressed
Our first experiment required subjects to maintain their hand at a central location
while countering a background load and then respond to a sudden mechanical
150
perturbation by moving their hand to a visually-defined spatial target (Fig. 4.2). Critically,
background loads could either excite or inhibit the muscle prior to the perturbation
(Pruszynski et al., 2009) and targets were positioned such that the same perturbation
could displace the hand either into the target (IN) or away from the target (OUT)
(Pruszynski et al., 2008). Within this straightforward two-by-two experimental design (2
target positions, 2 background loads), the difference in evoked muscle activity for the
same background load but different spatial targets was termed target-dependent activity
(OUT – IN) and the difference in evoked muscle activity for the same spatial target but
different background loads was termed load-dependent activity (Pre-Excited – PreInhibited).
Although the differences in background load were notable, they did not have a
large effect on subject behaviour (Fig. 4.3). No subjects showed a significant difference
in final hand position as a function of background load for either the elbow flexor or
elbow extensor conditions (for exemplar, see overlap of 95% confidence intervals, Fig.
4.3A,D). Similarly, an across-subject ANOVA revealed no statistical difference in initial
elbow displacement (50ms post-perturbation) as a function of background load, spatial
target position of muscle group (P > 0.05).
As expected from many previous experiments that studied the same factors in
isolation, each of the four experimental conditions yielded a qualitatively different pattern
of activity (Fig. 4.4A). When the muscle was pre-inhibited and the perturbation displaced
the hand into the target (pre-inhibited / IN), we observed no substantial muscle activity in
the short-latency, long-latency or voluntary epochs. In contrast, pre-exciting the muscle
for the same target location (pre-excited / IN) yielded distinct phasic events in the shortlatency and long-latency epochs but no substantial voluntary response. When the
151
A
D
OUT
IN
IN
OUT
5cm
Shoulder Angle (deg)
C
E
120
Elbow Angle (deg)
B
90
60
F
60
45
30
0
100 200 300
Time (ms)
0
100 200 300
Time (ms)
Figure 4.3. Hand Kinematics, Experiment 1. A. Spatial hand position from a representative
subject for a perturbation which stretched the elbow extensor muscles. Traces correspond to
the mean response for the IN (blue) and OUT (red) targets, under both excitatory (solid) and
inhibitory (dashed) background loads. Black (pre-excited) and white (pre-inhibited) dots
represent the mean hand position 300ms after perturbation onset and the surrounding ellipses
indicate the 95% confidence intervals of these endpoints. B,C: Temporal kinematics of the
elbow and shoulder. The horizontal axis represents time after perturbation onset and the
vertical axis represents the joint angle, which is initially 45° and 90° for the shoulder and
elbow, respectively. The vertical lines to the right of each trace represent the 95% confidence
interval of the corresponding trajectory 300ms after perturbation onset. They are offset from
one another for clarity. D,E,F. Same format as for A,B,C for perturbations which stretched the
elbow flexor muscles.
152
A
TASK 1:
OUT Target
L
OUT
minus
IN
VOL
B
Target-Dependent Component
S
L
VOL
Load-Dependent Component
S
EMG (au)
pre-excited
minus
pre-inhibited
LOAD 2:
pre-inhibited
LOAD 1:
pre-excited
S
TASK 2:
IN Target
L
VOL
100ms
Figure 4.4. Exemplar Muscle Activity, Experiment 1. A. Muscle activity from a single,
representative subject and muscle (Subject 8, Triceps Long). The horizontal axis represents
time aligned on perturbation onset (black vertical line). Thin grey lines within each panel
approximate divisions between the response epochs (short-latency (S): 20-45ms; long-latency
(L): 50-105ms; Voluntary (VOL): 120-180ms). The four panels with a light grey background are
actual experimental conditions (see Figure 2E). Panels to the right (in the solid box) depict the
difference in activity for the same background load condition but different target locations
(OUT - IN) and thus represent target-dependent activity. Panels on the bottom (dashed box)
depict the difference in activity for the same target locations but different background load
condition (Pre-Excited - Pre-Inhibited) and thus represent background load-dependent activity.
B. An overlay of the load-dependent and task-dependent components. Same data as in A
placed on the same axes to highlight their similarity.
153
muscle was pre-excited and the perturbation displaced the hand away from the target
(pre-excited / OUT), evoked muscle activity appeared phasic as for the pre-excited / IN
condition but was substantially more robust, especially in the long-latency and voluntary
epochs. And when the muscle was pre-inhibited prior to a perturbation which displaced
the hand away from the target (pre-inhibited / OUT), the evoked activity appeared as a
slowly-growing response beginning near the onset of the long-latency epoch and
continuing through the voluntary response with no discernable separation.
Despite the differences in evoked activity for each experimental condition, targetdependent activity was strikingly constant for both background loads and loaddependent activity was strikingly constant for both target positions (Fig. 4.4B). Similar
results were observed across the population of collected elbow muscle samples that act
to flex or extend the elbow joint (Fig. 4.5). In fact, the only notable difference was a small
temporal delay when the muscle was inhibited prior to the perturbation. On average,
muscles reached half of their peak activity ~5ms earlier when they were pre-excited,
which is consistent with previous studies and likely reflects the additional slack present
in muscle fibres when they are inhibited prior to the stretch (Proske et al., 1993). Based
on this result, all subsequent analyses were performed after shifting the pre-inhibited
responses by 5ms. Note that all figures showing raw EMG signals in time are not shifted.
The discrete hypothesis predicts that target-dependent activity should be modified by the
background load. In contrast, the overlap hypothesis predicts that target-dependent
activity should be the same for both background loads. Consistent with the overlap
hypothesis, we found no statistical difference in target-dependent activity as a function of
background load for the long-latency epoch (t42 = 1.5, P = 0.13) or during the voluntary
response (t42 = 0.62, P = 0.5, Fig. 4.6A). At the level of individual muscles, a linear
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Pre-Excited
Pre-Excited
Pre-Inhibited
DEMG (norm)
EMG (norm)
Target-Dependency (OUT-IN)
OUT
30
Biceps
Pre-Inhibited
20
10
IN
DEMG (norm)
40
EMG (norm)
Brachioradialis
0
30
20
10
0
DEMG (norm)
EMG (norm)
Triceps Long
40
30
20
10
0
20
DEMG (norm)
EMG (norm)
Triceps Lateral
25
15
10
5
0
0
50
100 150 200
Time (ms)
0
50
100 150 200
Time (ms)
0
50
100 150 200
Time (ms)
Figure 4.5. Population Muscle Activation, Experiment 1. Each row presents the average
muscle activation across subjects aligned on perturbation onset (black vertical line). The left
and middle columns represent activation patterns when the muscle was pre-excited and preinhibited, respectively. The rightmost column plots the mean target-dependent activity
calculated by subtracting the evoked response for the two target locations (OUT - IN) for both
the excitatory (solid) and inhibitory (dashed) background loads. Note that target-dependency is
remarkably similar regardless of initial background load.
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Long-Latency Voluntary
1
t42 = 0.6, p = 0.5
0.8
0.6
t42 = 1.5, p = 0.14
0.4
0.2
0
PE PI
PE PI
B
Correlation Coefficient (r)
Target-Dependency (norm)
A
1
0.8
Long
Vol
C
Short
Long
t42 = 3.9; p < 0.001
t42 = -1.1; p = 0.3
0.6
t42 = 2.1; p = 0.045
0.4
t42 = 0.2; p = 0.8
0.2
0
PE/OUT
PI/OUT
PE/OUT
PE/IN
Figure 4.6. Group Analysis and Trial-by-Trial Correlations, Experiment 1. A. Comparison of
target-dependent activation in the epochs of interest normalized to their overall magnitude of
activation; the normalization factor is mean muscle activity in that epoch for the OUT target
condition and Pre-Excited background load. A value of 0 indicates no target-dependency and a
value of 1 indicates that target-dependency was as large as the overall level of activity when
the muscle was pre-excited for the OUT target. Error bars represent the SEM. B. Each bar
represents one of the experimental conditions, including a particular background load and
target position (PE = pre-excited; PI = pre-inhibited; IN = in target; OUT = out target). The
vertical axis represents the average trial-by-trial correlation between the long-latency and
voluntary epochs. The horizontal bar represents the correlation in a control analysis comparing
two sub-components of the voluntary response in the PI/OUT condition (i.e. best-case
scenario). All error bars indicate the SEM. C. Same format as B except that the comparison is
done between the short- and long-latency epochs and different conditions are contrasted. The
horizontal bar represents the correlation between two sub-components of the long-latenxy
epoch in the PE/IN condition.
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regression comparing target-dependent activity for the two background loads yielded
slope terms that were not significantly different than unity (long-latency: mean slope =
1.01, 95% confidence interval = [0.87 1.15]; voluntary: 1.01, [0.94 1.09]) and we found a
robust correlation between target-dependent activity in the pre-excited and pre-inhibited
conditions (long-latency: r = 0.69; voluntary: r = 0.91). Analogous statistical results were
found when comparing load-dependent activation as a function of target position.
Evidence for the overlap hypothesis provided by trial-by-trial correlations across epochs
An interesting prediction of the overlap hypothesis is that trial-by-trial correlations
between the short-latency, long-latency and voluntary epochs should be systematically
related to the background load and target condition presented to the subject.
Specifically, correlations between the long-latency and voluntary epochs should be
higher when the muscle is pre-inhibited and the subject is pushed away from the target
(pre-inhibited / OUT) than when the muscle is pre-excited and pushed away from the
target (pre-excited / OUT). This is because, when the muscle is pre-inhibited, the longlatency response is putatively dominated by a single (target-dependent) component
which also dominates the voluntary epoch (see Fig. 4.1B). In contrast, when the muscle
is pre-excited, long-latency activity is composed of two components, one of which (loaddependent) is essentially absent during the voluntary response and thus would reduce
any correlation between these epochs. Note that the discrete hypothesis does not
predict any particular pattern of correlations across conditions because each of the
epochs is independent of one another.
The empirical results were strikingly consistent with the predictions made by the
overlap hypothesis. Trial-by-trial correlations between the long-latency and voluntary
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epochs were significantly higher when the muscle was pre-inhibited and pushed away
(pre-inhibited / OUT) from the target than when it was pre-excited and pushed away
(pre-excited / OUT) from the target (mean r = 0.44 vs. 0.18, t42 = 3.9, P < 0.001; Fig.
4.6B). Note that the resultant correlation coefficient is impressively high given the noisy
nature of EMG signals. A control analysis between two subdivisions of the voluntary
epoch (spaced 15ms apart, like that between the long-latency and voluntary epoch, 120143ms and 158-180ms) yielded a mean trial-by-trial correlation coefficient that was not
significantly higher (r = 0.51, t42 = -1.1, P = 0.3).
A related prediction can be made about the relationship between short-latency
and long-latency activity. Higher correlations should occur when the muscle is preexcited and pushed into the target (pre-excited / IN) than when the muscle is pre-excited
and pushed out of the target (pre-excited / OUT). This is because the former condition
results in a unitary contribution to the long-latency epoch from the load-dependent
component, which putatively dominates the short-latency reflex. Our results were again
compatible with the overlap hypothesis. Trial-by-trial correlations were significantly
higher for the pre-excited / IN condition than the pre-excited / OUT condition (mean r =
0.13 vs. 0.005, t42 = 2.1, P < 0.05; Fig. 4.6C). A control analysis revealed that the
correlations found in the pre-excited / OUT condition were not significantly lower than
trial-by-trial correlations between two subdivisions of the long-latency epoch (spaced
5ms apart, 50-73ms and 78-105ms, r = 0.12, t42 = 0.2, P = 0.8).
Target-dependent activity is constant across a wide range of background loads
The results of our first experiment support the overlap hypothesis by
demonstrating that target-dependent activity in the long-latency epoch is independent of
158
background load. However, this observation may reflect our choice of a particular
inhibitory background load, which yielded no phasic activation in the long-latency epoch
for the IN target. To address this limitation, our second experiment tested whether
target-dependent activity is constant across a wide range of background loads. The
experimental protocol was identical to Experiment 1 except that subjects were presented
with seven potential background loads rather than two (Fig. 4.2F).
As expected, we observed a systematic increase in the long-latency response for
both the IN and OUT targets as the background load was increased (Fig. 4.7A). We also
found that for the same background load, activity in the long-latency epoch was larger
for the OUT target than for the IN target. Despite these changes in overall activity,
qualitative inspection of target-dependent activation suggested that it was constant
across background loads (Fig. 4.7B).
We quantified the constancy of target-dependent activity by comparing the ability
of four candidate models to relate long-latency activity to background load. For each
muscle sample, we fit the muscle activity in the long-latency epoch with four candidate
models – (1) constant, (2) linear, (3) piecewise linear, (4) sigmoid – and evaluated their
ability to represent target-dependent activity. We then ranked each model for every
muscle sample using the Akaike Information Criterion (AIC), an information-theoretic
approach for choosing a parsimonious model that takes into account both quality of fit
and model complexity (Burnham and Anderson, 2002) (see Experimental Procedures).
Matching our qualitative observations, both the IN and OUT target conditions yielded
robustly increasing responses that were rarely well described by a constant model (best
model, IN = 0%, OUT = 17%; adequate model, IN = 3%, OUT = 31%) (Fig. 4.7C).
Strikingly, target-dependent activity (OUT – IN, Fig. 4.4C) was almost always well
159
B
OUT and IN Activity
EMG Activity (norm)
4
OUT
3
2
IN
1
DEMG Activity (norm)
A
-2.25 -1.5 -.75 0 .75 1.5 2.25
Background Load (Nm)
% Models Adequate (AIC < 4)
C
OUT-IN Activity
4
3
2
1
-2.25 -1.5 -.75 0 .75 1.5 2.25
Background Load (Nm)
Constant Model Performance
100
50
0
OUT
IN
OUT-IN
Target Condition
Figure 4.7. Group Analysis, Experiment 2. A. This figure presents the change in long-latency
activity for the same mechanical perturbation as a function of background load (horizontal
axis) for two target locations (thick = OUT target; thin = IN target). Error bars indicate the SEM.
B. Same format as in A except for target-dependent activation (OUT - IN). The error bars
represent the average SEM when bootstrapping the difference calculation. C. Results of AIC
analysis on each individual muscle sample demonstrating the proportion of times that a
constant model was adequate for explaining the relationship between background load and
muscle activation in the long-latency epoch. The three bars are for muscle activity for the OUT
target, IN target and the difference between the two (OUT - IN). Again, the error bars represent
average SEM and arise because the procedure (OUT-IN calculation) was bootstrapped to
estimate the variability.
160
explained by a constant model (best = 61%; adequate = 91%), suggesting that despite
substantial and systematic changes in the overall response, target-dependent activity
was constant across background loads.
Leveraging our approach: target-dependent activity is not a simple ‘triggered reaction’
Our first two experiments demonstrated that long-latency activity in human
subjects performing a simple motor task reflects the temporal overlap of two
independent components. In our third experiment, we show that the present
experimental approach can answer a long-standing question about long-latency activity.
Specifically, previous authors have suggested that task-dependent activity reflects a
‘triggered reaction’ whereby a motor plan is formulated in advance of the perturbation
and then released by its occurrence (Crago et al., 1976). As such, a triggered reaction
would reflect an open-loop, startle-like process (Carlsen et al., 2004; Valls-Sole et al.,
1999) rather than a closed-loop feedback response that is adjusted as a function of
current sensory information. Testing this hypothesis is straightforward with our approach.
We used an inhibitory background load and the OUT target to isolate target-dependent
activity and then randomly applied one of two perturbation magnitudes (small: 1.25Nm,
Large: 2.5Nm, Fig. 4.2G). Since the subjects could not predict which perturbation
magnitude would occur, the triggered reaction hypothesis would predict that, on
average, the muscle response would be the same for both perturbation magnitudes
because the nervous system would not know which response to prepare and trigger. In
contrast, a more sophisticated mechanism would evaluate the sensory information about
the magnitude of the perturbation on every trial and scale the target-dependent activity
appropriately (i.e. generate larger response for the larger perturbation). Our results
161
A
120
Long
1.25Nm
2.5Nm
Muscle Activity (norm)
100
80
60
40
20
0
EMG in Long-Latency (norm)
B
0
50
100 150
Time (ms)
200
250
t7 = 3.6; p = 0.008
12
10
*
8
6
4
2
1
0
*
1.25
2.5
Perturbation Magnitude (Nm)
Figure 4.8. Group Analysis, Experiment 3. A. Average muscle activity when the muscle is preinhibited but perturbation magnitude is unpredictable (small/1.25Nm = grey; large/2.5Nm =
black). The horizontal axis represents time relative to perturbation onset (black vertical line)
and the vertical axis depicts normalized muscle activity. B. Mean activation in the long-latency
epoch across subjects. Each thin grey line represents a single-subjects data and error bars
represent SEM. The stars above each bar indicate that both conditions yielded significant
evoked activity within the long-latency epoch (one-sided t-test > 1, P < 0.05).
162
clearly reject the notion of the simplest triggered reaction that can only plan one
response as all subjects significantly scaled the magnitude of long-latency activity
(paired t-test, t7 = 3.6, P < 0.01; Fig. 4.8A). In fact, doubling the perturbation magnitude
(1.25Nm to 2.5Nm) yielded, an approximately two-fold increase in target-dependent
muscle activity (mean = 2.2, 95% confidence interval = [1.6, 2.8]), suggesting that targetdependent activity accurately reflected the perturbation magnitude on the current trial
(Fig. 4.8B).
4.5 – DISCUSSION
To our knowledge, the present work is the first to demonstrate that taskdependent muscle activity in the long-latency epoch (as modulated by target position) is
independent of pre-perturbation muscle activity and, conversely, that load-dependent
activity (as modulate by pre-perturbation muscle activity) is independent of target
position. These observations provide the clearest demonstration to date that longlatency activity is not a discrete event as commonly assumed when it is referred to as
the long-latency or transcortical reflex. Rather, long-latency muscle activity in an awake
human performing a naturalistic motor task appears to reflect the temporal overlap of (at
least) two functionally-independent components.
It is important to emphasize that our results not only demonstrate the presence of
two independent components, but also provide a powerful experimental paradigm for
manipulating their contribution. We expect that future studies will use our approach to
‘dissect’ the long-latency response into its constituent parts, which will further our
understanding of the functional and structural organization of rapid feedback responses.
For example, a straightforward application of our approach allowed us isolate target163
dependent activity. We could then directly show that target-dependent activity does not
reflect the simplest pre-planned ‘triggered reaction’ (Crago et al., 1976), a long-standing
hypothesis in the field, because it was clearly sensitive to perturbation magnitude on a
trial-by-trial basis even though the perturbation magnitude was unpredictable. This result
implies that target-dependent activity rapidly incorporates available sensory information
when generating a response and therefore reflects the output of a relatively fast
feedback control process.
Our paradigm may resolve other prominent findings in the literature. For
example, Rothwell and colleagues (1980) found that long-latency activity, like the
voluntary response, was sensitive to the predictability of the mechanical perturbation.
This result led them to speculate that the long-latency response reflects an “interaction
between a reflex of long-latency and a subsequent very rapid voluntary event”. Our
results demonstrate that their speculation was correct insofar as target-dependent
activation in the long-latency epoch was well correlated with target-dependent activation
in the voluntary epoch. Further experiments using our approach can isolate targetdependent activity, determine whether it is the source of task-dependency in a host of
experimental situations (Diedrichsen et al., 2010; Kurtzer et al., 2008; Marsden et al.,
1981) and empirically establish its similarity to voluntary control.
Our experimental approach can also be used to isolate the load-dependent
component of long-latency activity. On this front, the overlap model indicates that loaddependent activity consists to two distinct phases of activation, an initial period of
excitation in the short-latency epoch and a second peak in the long-latency epoch (Fig.
4.1B). This is consistent with past suggestions that the long-latency response is caused
by the re-activation of primary spindle afferents following their synchronization during the
164
short-latency reflex and subsequent refractory period (Schuurmans et al., 2009).
Alternatively, it may be that the load-dependent component of long-latency activity is
mediated by slower secondary muscle afferents which yield a distinct phasic burst of
activation at a longer latency (Grey et al., 2001; Matthews, 1984). Although our present
results cannot distinguish between these possibilities, future experiments could use our
approach to focus directly on the load-dependent component when testing between
these hypotheses. The results of such studies may well demonstrate that the loaddependent component actually reflects the overlap of multiple contributors. For example,
it is rational to assume that both factors above contribute to long-latency activity. Our
findings do not rule out the possibility that the two functionally-independent components
we identified can be further subdivided. They do, however, predict that all the potential
sub-components of load-dependent activity will not be sensitive to task-constraints and
that all potential sub-components of task-dependent activity will not be sensitive to preperturbation muscle activity.
As highlighted by Peter Matthews (2006), several previous studies have
demonstrated that multiple neural generators can contribute to long-latency activity in
principle but have left open the important question of whether/when they do contribute in
practice (Gomi and Osu, 1998; Kurtzer et al., 2010; Lourenco et al., 2006; Matthews and
Miles, 1988). For example, in a particularly elegant sequence of experiments, Lourenco
and colleagues (2006) clearly showed that electrical stimuli applied to a mixed nerve can
excite wrist muscles at two different latencies within the long-latency epoch. Further
experimental manipulations led the authors to conclude that the two phases of excitation
are likely caused by a spinal pathway via group II afferents and a cortical pathway via
group I afferents. Although their physiological evidence is convincing, the functional
165
contribution of each pathway could not be tested because the constraints of their
experimental approach meant that their subjects were not engaged in a meaningful
motor task. Coupling their results with our own, which explicitly control behaviour but do
not directly test the underlying neural circuits, it is natural to speculate that loaddependent activity reflects a spinal circuit, like the short-latency reflex, and that targetdependent activity involves a cortical pathway, like voluntary control.
The few experiments that have linked neural structures to the functional
capabilities of long-latency activity have used transcranial magnetic stimulation (TMS) to
systematically interfere with cortical processing (Kimura et al., 2006; Shemmell et al.,
2009). For example, Kimura and Gomi (2006) showed that disrupting sensorimotor
cortex did not eliminate long-latency activity but did impair its predictive modulation
according to the directionality of a force field that subjects were about to reach through.
Similarly, Shemmel and colleagues (2009) used the same technique to demonstrate that
interfering with primary motor cortex affects long-latency activity associated with the
stability of the environment but not the verbal task-instructions given to the subject,
suggesting that only the former functionality relies on a circuit including primary motor
cortex. Although both of these studies demonstrated an important neuroanatomical
segregation between various aspects of long-latency activity, they were fundamentally
limited because their TMS intervention only acted as categorical probe of a given neural
circuit. In contrast, our behavioural paradigm did not test the neural circuitry, but it did
permit much greater control over each functional component based on simple changes
in background load and spatial target position. Accordingly, we were able to clearly
demonstrate the presence of independent functional components, show how these
components interact, determine how their activity is related across epochs and establish
166
the features of a single component in isolation. Despite these differences, it is important
to emphasize that the TMS findings are complementary to our own. Together they form
a foundation for understanding long-latency activity as a temporal designation that
incorporates the output of multiple neural generators, some of which contribute unique
functional properties. It would be fruitful for future experiments to couple our paradigm
with TMS to isolate the individual functional components and determine their underlying
neural circuits.
167
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Chapter 5
Long-latency reflexes of the human arm reflect an internal model
of limb dynamics
172
5.1 – ABSTRACT
A key feature of successful motor control is the ability to counter unexpected
perturbations. This process is complicated in multi-joint systems, like the human arm, by
the fact that loads applied at one joint will create motion at remote joints (Craig 2005;
Graham et al. 2003). Here we test whether our most rapid corrections, i.e. reflexes,
address this complexity through an internal model of the limb’s mechanical properties.
By selectively applying torque perturbations to the subject’s shoulder and/or elbow we
revealed a qualitative difference between the arm’s short-latency/spinal reflexes and
long-latency/cortical reflexes. Short-latency reflexes of shoulder muscles were linked
exclusively to shoulder motion whereas their long-latency reflexes were sensitive to both
shoulder and elbow motion. In fact, a long-latency reflex could be evoked without even
stretching or lengthening the shoulder muscle but by displacing just the elbow joint. This
ability to incorporate elbow motion into a shoulder muscle reflex was appropriate to
decode and counter the underlying shoulder torque perturbations given the mechanical
interactions across the two joints. In addition, the shoulder’s long-latency reflexes were
appropriately modified across the workspace to account for changes in limb geometry
that affect the transformation between joint torque and joint motion. These results
provide clear evidence that long-latency reflexes possess an internal model of limb
dynamics, a degree of motor intelligence previously reserved for voluntary motor control
(Hollerbach and Flash 1982; Kawato 1999; Wolpert and Flanagan 2001). The use of
internal models for both voluntary and reflex control is consistent with substantial overlap
in their neural substrates and current notions of intelligent feedback control (Desmurget
and Grafton 2000; Todorov and Jordon 2002; Scott 2004).
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5.2 – INTRODUCTION
One of the most influential concepts in the field of motor control is that our
nervous system possesses neural structures that mimic the properties of our limbs and
our interactions with the world (Kawato 1999). Such internal models would allow us to
achieve rapid and accurate voluntary behavior despite the difficulties present with motor
noise, delayed sensory feedback, and a complex musculoskeletal apparatus. Although
the existence of internal models has been strongly established for voluntary limb control
(Hollerbach and Flash 1982; Shadmehr and Mussa-Ivaldi 1994; Lackner and DiZio 1994;
Flanagan and Wing 1997; Singh and Scott 2003) their involvement in reflex limb control
has been less clear. This issue is particularly compelling since countering unexpected
and external perturbations is a ubiquitous occurrence of everyday life. Furthermore,
recent theories of motor control imply a more intimate link between the strategies and
mechanisms employed for feedforward and feedback control of movement (Todorov and
Jordan 2002).
The present experiments examined the rapid compensatory responses of a
shoulder extensor (posterior deltoid) to multi-joint perturbations while the subjects
maintained a constant limb posture. If the reflexes of this muscle (20-100 ms postperturbation) include an internal model of the limb dynamics then its compensatory
response should mirror the multi-joint mechanical interactions that naturally occur across
the shoulder and elbow joints, such as flexor torque at one joint causing extensor motion
at the other joint and vise versa (Fig. 5.1a,b). In contrast, the nervous system could base
its reflex responses on sensory information that is local to each muscle followed by a
more integrated (but delayed) voluntary response (>100 ms post-perturbation). A third
possibility is that the reflexes comprise distinct functional networks with an increasing
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A
C
te
75o
ts
EMG (au)
12
R1 R2 R3
8
4
45o
1
-50
B
D
shoulder
elbow
50
100
Time (ms)
150
7
R1 R2 R3
4
EMG (au)
D Joint Angle (deg)
8
0
0
5
3
-4
1
-8
-25
0
50
Time (ms)
100
-50
0
50
100
Time (ms)
150
Figure 5.1. Reflex activity following single-joint torque perturbations to induce equal shoulder
motion but different elbow motion: Experiment 1. A. Depiction of a subject's limb
configuration following the shoulder torque (red) or an elbow torque perturbation (blue).
Initial limb configuration has a shoulder angle = 45º and elbow angle = 75º, full elbowextension is 0º. Data taken from a representative subject at 50 ms post-perturbation (scaled
by 15X for clarity). B. The subject's joint displacement is shown over time revealing that
shoulder motion is highly similar across the two conditions while the elbow motion is
substantially different. Both the shoulder (solid lines) and elbow (dashed lines) angles are
relative to the initial limb configuration with flexion and extension motion being positive and
negative, respectively. C. Evoked muscle activity from the subject's posterior deltoid (a
shoulder extensor muscle) normalized to the pre-perturbation baseline (mean ± sem across
trials). Vertical lines delineate the reflex periods (see Methods). D. Group data for the same
muscle, same format (mean ± sem across subjects).
175
degree of sophistication; short-latency reflexes would depend on local information
whereas more long-latency reflexes would depend on an internal model similar to that
during voluntary control.
5.3 – RESULTS
Our first experiment involved perturbing the limb with either a shoulder torque or
an elbow torque (Fig. 5.1a,b). In both cases, the single-joint torque induced motion at
both joints due to the limb’s intersegmental dynamics. Moreover, we set the magnitude
of the two perturbations to induce an equal amount of shoulder motion but different
amounts of elbow flexion. If the shoulder reflexes only depend on local shoulder motion
then similar activity will occur in the two conditions. Instead, if the reflexes include an
internal model of limb dynamics then greater activity should occur for the shoulder
torque than elbow torque perturbation as the shoulder muscle contributes to
compensatory shoulder torque.
In fact, we observed a qualitative difference between posterior deltoid’s earliest
and later reflex periods (Fig. 5.1c,d). The earliest burst of activity (R1) occurred around
20-45 ms and was similar between conditions (t(9) = -1.8, P = 0.11) though significantly
above baseline (Shoulder torque t(9) = 5.8, P < 0.001; Elbow torque t(9) = 6.7, P < 0.001).
This indicates that the earliest reflex activity did not utilize an internal model. In contrast,
later reflex periods were influenced by elbow motion in a manner consistent with an
internal model of limb dynamics. Greater sensitivity to the shoulder torque perturbation
began around 55ms, achieved significance within the R2 interval of 45-75 ms (t(9) = 4.2,
P < 0.005), the R3 interval of 75-100 ms (t(9) = 4.5, P < 0.005), and the subsequent
voluntary period (t(9) = 4.7, P < 0.005). Note that this differential effect was not merely a
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consequence of greater limb motion since less elbow motion was induced by the
shoulder torque than elbow torque perturbation (Fig. 5.1b). Identical results were
observed for the shoulder flexor (pectoralis major) in the complementary conditions.
To further examine whether the R2 and R3 reflex periods (also called longlatency reflexes) possess an internal model of limb dynamics we conducted a second
experiment with a combined elbow and shoulder torque to induce substantial elbow
motion with almost no shoulder motion (Fig. 5.2a,b). This perturbation neither stretched
nor slackened the shoulder muscle so its local sensors (detecting muscle length, velocity
and tension (Gandevia and Burke 1992) were unaffected. Nonetheless, the shoulder’s
long-latency reflex should be evoked if it possessed an internal model that decoded pure
elbow motion into the underlying shoulder torque perturbation.
Elbow-flexion motion was induced by a combination of elbow-flexion and
shoulder-flexion torque. This perturbation evoked a significant increase in activity for the
R2 (t(7) = 4.3, P < 0.005), R3 (t(7) = 6.2; P < 0.001), and voluntary periods (t(7) = 4.4, P <
0.001). In contrast, elbow-extension motion (induced by a combination of elbowextension and shoulder-extension torque) resulted in significant inhibition in the R2 (t(7) =
-3.8, P < 0.01), R3 (t(7) = -8.4, P < 0.01), and voluntary period (t(7) = -3.9, P < 0.01).
The reciprocal pattern of reflex activation rules out a non-specific co-contraction and
provides further evidence of a coordinated read-out of multi-joint motion via an internal
model. Also note that this degree of motor intelligence was only expressed for the longlatency reflexes and voluntary period. The two perturbations failed to influence activity in
the R1 period (Elbow flexion t(7) = -1.8, P = 0.12; Elbow extension t(7) = 0.5, P = 0.65)
consistent with the negligible shoulder motion. Identical results were observed for
pectoralis major in the complementary conditions.
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C
te
75o
te
ts
o
EMG (au)
A
6
1
-50
B
0
50
100
Time (ms)
150
D
shoulder
elbow
7
4
EMG (au)
D Joint Angle (deg)
8
R1 R2 R3
3
ts
45
9
0
R1 R2 R3
5
3
-4
1
-8
-25
0
50
Time (ms)
-50
100
0
50
100
Time (ms)
150
Figure 5.2. Reflex activity following multi-joint torque perturbations to induce large elbow
motion and negligible shoulder motion: Experiment 2. All panels are in the same format as
Fig 1. A. Depiction of a subject's limb configuration following flexion torque simultaneously
applied at both joints which resulted in elbow flexion (red) and almost no shoulder motion.
Extension torque at both joints resulted in elbow extension (blue) and almost no shoulder
motion. B. The induced joint motion is shown over time for same subject where the elbow
motion is substantial and the shoulder motion is negligible. C. Evoked muscle activity from
the representative subject's posterior deltoid. D. Group data for the same muscle.
178
Taken together, the two experiments indicate that short-latency reflexes (R1) of
the shoulder muscles reflect the local shoulder motion whereas their long-latency
reflexes (R2/3) are strongly influenced by both shoulder and elbow motion consistent
with an internal model of limb dynamics. To determine the efficacy of this putative
internal model we analyzed the data from both experiments using a simple linear
regression of observed reflex activity versus the imposed shoulder and/or elbow torques
(four loads total, see METHODS – Data analysis). The orientation of this plane-fit
expresses the sensitivity of muscle activity to the perturbing shoulder-elbow torque, or a
preferred torque direction (PTD), which we compared to the predicted sensitivity of an
ideal internal model (PTD aligned to shoulder torque-only) and no internal model (PTD
aligned to the torque combination that induces the greatest shoulder motion).
The measured PTDs were consistent with our earlier analyses (Fig. 5.3A) and
displayed a transition from no internal model in the earliest period towards the ideal
internal model in the later periods. Short-latency PTDs were indistinguishable from the
prediction of no internal model (R1 t(9) = -1.4, P > 0.2), whereas long-latency PTDs were
significantly different (R2/3 t(19) = 7.5, P < 0.001). This shift in PTD in the long-latency
period involved a substantial approach towards the ideal internal model (61% on
average) rather than slight systematic bias. And while the PTDs undershot the ideal
prediction (R2/3 t(19) = -4.8, P < 0.001) similar undershoots were observed for voluntary
reactions and even during postural maintenance (83% on average) when the nervous
system expresses its steady-state response; in fact, the preference of single-joint
muscles to moderately biased multi-joint torque likely reflects the optimal pattern of
coordination for redundant, multi-functional muscle systems (van Zuylen et al. 1988;
179
PTD (deg)
A
B
Shoulder Extensor
360
t
313
q
R1
R2
R3
Shoulder Flexor
180
t
133
q
Vol Posture
R1
Periods of Activity
R2
R3
Vol Posture
Periods of Activity
Figure 5.3. Reflex tuning to the applied shoulder-elbow torque. A. PTDs of the posterior
deltoid. Small circles show PTDs of individual subjects as determined by planar regression
for each reflex period and during voluntary reaction and postural maintenance. Diamonds
indicate the mean PTD averaged across subjects. Note that the postural data is from a
separate group of subjects (see Methods). The two horizontal lines are the predicted PTDs if
reflexes only reflect shoulder motion (q) or shoulder torque (t) (see Methods). A PTD of 360º
is directed to pure shoulder flexion torque, <360º is directed towards a combination of
shoulder flexion-elbow extension torque, and >360º is directed towards a combination of
shoulder flexion-elbow flexion torque; 313º is the predicted PTD for shoulder extension
motion. B. Same format for the pectoralis major (a shoulder flexor muscle). A PTD of 180º
is directed towards pure shoulder extension torque whereas <180º and >180º is directed
towards shoulder extension-elbow flexion torque and shoulder extension-elbow extension
torque; 133º is the predicted PTD for shoulder flexion motion.
180
Kurzter et al., 2006). Analysis of the shoulder flexors (Fig. 5.3B) revealed the
same pattern.
Motivated by these positive results we conducted a third experiment that tested
whether the internal model for long-latency reflexes accounts for the influence of limb
configuration on the mapping between torque and motion (Mussa-Ivaldi et al. 1985). For
example, the interaction torques between the shoulder and elbow increase when the
elbow is more extended. This means that the same joint motion will have different
underlying torque for an extended and flexed elbow posture. We used a flexed and
extended elbow posture such that ~85% more shoulder torque was required to induce
the same level of joint motion (Fig. 5.4a,b, see Methods). If the nervous system
represents this level of dynamic complexity then the shoulder’s long-latency reflexes will
respond to same joint motion with greater activity at the extended posture as the same
motion was caused by a larger shoulder torque.
In fact, we found that the shoulder extensor did incorporate knowledge of the
limb’s configuration-dependent resistance to motion. Larger evoked activity was
observed in the R3 (t(9) = 4.47; P < 0.001, one-tailed) and voluntary periods (t(9) = 5.2, P
< 0.001, one-tailed) for the more extended posture. In contrast, the earlier R1 (t(9) = 2.58, P = 0.99, one-tailed) or R2 periods (t(9) = -1.1, P = 0.85, one-tailed) did not differ
between limb configurations. Similar results were also observed for pectoralis major in
the complementary conditions.
5.4 – DISCUSSION
The motivation for using internal models in voluntary control has long been
recognized and extensively explored (Kawato 1999). Less appreciated is that the same
181
A
C
R1 R2 R3
te
45o
120o
ts
te ts
o
45
EMG (au)
12
8
4
1
-50
B
D
shoulder
elbow
2
EMG (au)
D Joint Angle (deg)
4
0
0
7
50
100
Time (ms)
150
R1 R2 R3
5
3
-2
1
-4
-25
0
50
Time (ms)
-50
100
0
50
100
Time (ms)
150
Figure 5.4. Reflex activity for the same joint motion at two different limb configurations:
Experiment 3. All panels are in the same format as Fig 1. A. Depiction of the limb
configuration for the far target (shoulder angle = 45º; elbow angle = 45º) and near target
(shoulder angle = 45º; elbow angle = 120º). Different combinations of flexion torques were
applied at both joints to induce a similar amount of elbow flexion motion at the far target (red)
and near target (blue). Data taken from a representative subject at 50 ms post-perturbation
(scaled by 15X for clarity). B. The joint displacement of the representative subject reveals
that elbow motion is similar across conditions and shoulder motion is minimal. C. Evoked
muscle activity from a subject's posterior deltoid. D. Group data for the same muscle, same
format.
182
difficulties are present when countering an external perturbation: how can we quickly
and accurately stabilize our limb with motor noise, delayed sensory feedback, and a
complex musculoskeletal apparatus? While insightful, the few previous studies on this
topic (Lacquaniti and Soechting 1984, 1986a,b; Soechting and Lacquaniti 1988)
possessed several experimental and technological limitations such as imposing trains of
force pulses at the hand which led to highly variable joint motions, inferring the joint
torques from the resultant unconstrained kinematics, and a non-specific sampling of test
conditions. In contrast, we applied known loads directly to the shoulder and elbow joints
while the limb was constrained to a single plane. This led to better control of the imposed
motion, background motion, and muscle activity, all factors known to affect reflex
processing (Matthews 1986; Rothwell et al. 1986; Smeets and Erkelens 1991).
Moreover, our carefully matched comparisons allowed an unambiguous and model-free
test of reflex processing which clearly indicates that long-latency reflexes of the human
upper limb include an internal model of limb dynamics.
Importantly, we found that not all reflexes were equal. Short-latency reflexes (R1)
of the shoulder muscles reflected shoulder motion whereas its long-latency reflexes
(R2/3) reflected the underlying torque perturbation. Short latency reflexes are known to
reflect processing confined to the spinal cord (Pierrot-Deseilligny and Burke 2005). In
contrast, the broad window of 45-100 ms post-perturbation is termed a long-latency
reflex since it is too early to result from voluntary commands but occurs after the shortlatency reflex (Hammond 1956; Marsden et al. 1983; Matthews 1991). Moreover, longlatency reflexes possess a greater degree of task-dependency (Hammond 1956; Jaeger
et al. 1982; Crago et al. 1976; Kimura et al. 2006) and inter-muscular coordination
(Gielen et al. 1988; Koshland et al. 1991) than the short-latency reflex.
183
Previous authors have suggested that long-latency reflexes are coordinated
approximations of voluntary responses (Gielen et al. 1988; Hasan 2005). We believe
that such similar functions of the long-latency reflexes and voluntary responses are a
direct consequence of a shared neural substrate. In particular, over 30 years of evidence
suggests long-latency reflexes are predominately supported by primary motor cortex
(MI) (Evarts and Tanji 1976; Cheney and Fetz 1984; Suminski et al. 2007), a structure
known for supporting voluntary control (Porter and Lemon 1993). It then becomes
understandable, even predicted, that long-latency reflexes should possess many of the
neural resources available to M1 during voluntary control (Scott 2004) including its
diverse somatosensory inputs, diverse effects at the spinal cord, context-dependent
responses, and a rich intrinsic connectivity that is highly modifiable over both brief and
long time-scales. In addition, overlapping substrates and strategies for reflexive and
voluntary motor control is consistent with modern concepts of feedback control where
flexible feedback gains and internal models work together to balance the multiple
competing requirements defined by each task (Todorov and Jordan 2002). This view
motivates leveraging the vast literature on the properties of internal models for voluntary
control to unravel the organization of long-latency reflexes including their function in
motor learning (Wang et al. 2001) and dysfunction in motor pathology (Scott and
Norman 2003).
5.5 – METHODS
Subjects
Eighteen subjects (11 males and 7 females, median age = 25 yrs) participated in
one of several sessions that lasted 60-90 minutes each. One session comprised the first
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and second experiment; ten subjects participated in Experiment 1 whereas 8 subjects
participated in Experiment 2. Ten subjects participated in Experiment 3. Lastly, ten
subjects participated in an associated experiment on postural maintenance. The
procedures were approved by the ethics committee of Queen’s University and subjects
were paid for their time.
Apparatus and task
As described in previous studies (Singh and Scott 2003; Scott 1999) we utilized a
robotic exoskeleton (KINARM, BKIN Technology Ltd, Kingston, ON) that permits
flexion/extension movements of the shoulder and elbow in the horizontal plane and can
selectively apply torques to each joint. The device is also coupled to a virtual-reality
system for displaying the target and hand-aligned cursor; a cloth bib and metal partition
obscured any direct vision of the arm.
All trials for the reflex experiments proceeded in a similar order:
1. A background shoulder flexion load was slowly ramped from zero to 2 Nm to
elicit steady-state activity of the shoulder extensor muscle.
2. Subjects stabilized their hand-aligned cursor (0.4 cm radius) within the center of
a small target area (2 cm radius) for a random interval (500-3500 ms). Subjects
were instructed not to anticipate the time or direction of the impending
perturbation. During this period the cursor was extinguished so that subsequent
corrective responses were guided entirely by proprioception.
3. The perturbation load was then rapidly applied (sigmoid-interpolation over a 10
ms window to lessen the high-frequency ring). The direction of this perturbation
was randomly varied between trials.
185
4. The new load level was maintained for a fixed interval (1000 ms) while subjects
needed to return to the target within 500 ms and remain within it. The circle was
filled green or red if their performance was accurate or inaccurate, respectively.
5. Finally, the load slowly ramped back to zero (500 ms) and remained zero for a
brief inter-trial period (1000ms).
6. Thirty repeats were collected for each perturbation condition, see below.
Experiment 1: This experiment applied a single-joint perturbation (2Nm shoulderflexor torque or 2Nm elbow-extensor torque) to create multi-joint motion. Importantly, the
two perturbations induced similar amounts of shoulder flexion but different amounts of
elbow extension (see Fig. 5.1a,b).
Experiment 2: This experiment applied a multi-joint perturbation (2Nm shoulderflexor/2Nm elbow-flexor torque or 2Nm shoulder-extensor/2Nm elbow-extensor torque)
to create single-joint motion. The two perturbations induced substantial elbow motion
but negligible shoulder motion (see Fig. 5.2a,b). Both Experiments 1 and 2 utilized a
target whose origin was shoulder angle = 45º and elbow angle = 75º; shoulder angle is
relative to the frontal plane whereas elbow angle is relative to the forearm and upper
arm, 0º is full extension.
Experiment 3: This experiment applied multi-joint perturbations to induce single-joint
motion of the elbow while the limb was either near the body (shoulder angle = 45º elbow
angle = 120º) or far from the body (shoulder angle = 45º elbow angle = 45º) (Fig. 5.4a).
To induce similar motion at these two arm postures we imposed two different
combinations of joint torque which were tailored for each subject based on an estimation
of their limb’s inertia: 1) we imposed eight torque pulses of equal magnitude (2 Nm) and
186
equally distributed in shoulder-elbow torque space; 2) we determined the covariance
matrix between joint torque and joint motion at 50ms; 3) we used the inverse of this
covariance matrix to select the joint torques needed to displace the elbow by 1º within
50ms.
Note that the procedures in Experiments 1-3 were used to study posterior deltoid
(a shoulder extensor muscle). Complementary procedures were also used to study
pectoralis major (a shoulder flexor muscle).
Associated Experiment: The relatively short perturbation durations of Experiment
1 and 2 precluded long periods of stabilization for examining steady-state postural
responses. Instead, we used the information from a separate study on postural
maintenance  similar to a previous study with non-human primates (Kurtzer et al. 2006)
 where subjects adopted a stable posture (shoulder angle = 45º and elbow angle = 75º)
against 8 combinations of shoulder-elbow loads: shoulder flexion, shoulder extension,
elbow flexion, elbow extension, shoulder flexion-elbow flexion, shoulder extension-elbow
extension, shoulder extension-elbow flexion, and shoulder flexion-elbow flexion. Three
magnitudes (± 1, 2, and 3 Nm) of each load combination were employed for a total of 24
conditions, plus a no-load condition. Three trials of 3 seconds each were collected for
each condition.
Muscle recording
We recorded surface EMG from two single-joint shoulder muscles of each
subject: posterior deltoid and pectoralis major. The skin surfaces were first lightly
abrased with alcohol. A two-bar electrode (DE-2.1 Delsys Inc., Boston, MA) was then
187
coated with electrode gel and affixed to the muscle belly while the ground electrode was
placed on the subject’s ankle.
Data analysis
Angular position of the shoulder and elbow was lowpass filtered (25 Hz, 2-pass,
6th order Butterworth). Processing of the EMG signals included an amplification (gain =
10 K), bandpass filter (20-450 Hz), digital sampling at 1000 Hz (PCI 6071E, National
Instruments, Austin, TX), and normalization by each muscle’s mean activity prior to
perturbation (-100 to 0 ms).
We considered several periods of reflex activity based on earlier reports (Lee et
al. 1983; Marsden et al. 1983) and our pilot studies: R1 = 20-45 ms; R2 = 45-75 ms; R3
= 75-100 ms. The voluntary response was considered to occur at 120-180 ms postperturbation.
We utilized several analyses to examine the patterns of reflex action. T-tests
determined changes from baseline and/or changes between conditions (P < 0.01). We
also examined the coordinated activity across the first two experiments using a planar
regression. Accordingly, the activity in a particular reflex period is regressed against the
shoulder-elbow torques that were imposed, two from each experiment; the shoulder and
elbow slope coefficients from the resulting plane-fit describe the relative sensitivity to
shoulder and elbow joint torque (Kurtzer et al. 2006). This so-called preferred torque
direction (PTD) was measured counter-clockwise such that preferred shoulder flexion,
elbow flexion, shoulder extension, and elbow extension torques occur 0/360, 90, 180,
and 270º, respectively.
188
The measured PTDs of the reflex, voluntary, and postural periods were judged
against two contrasting predictions: pure shoulder torque or pure shoulder motion. As
described above a PTD for pure shoulder torque would be at 0/360º or 180º for encoding
shoulder flexion or extension torque, respectively. The prediction of pure shoulder
motion is that combination of joint torques that result in the greatest amount of measured
shoulder motion, i.e. a plane-fit of the shoulder motion to the applied shoulder and elbow
torques. The resulting PTDs of 313º and 135º are quite close to the ideal case (315º
and 135º) where single-joint perturbations (matched shoulder and elbow) induce the
exact same shoulder motion and multi-joint perturbations induce exactly no shoulder
motion. Comparisons of the measured and predicted PTDs were conducted with t-tests.
189
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control. Nat Rev Neurosci 2004, 5: 532-546.
Shadmehr R., Mussa-Ivaldi F.A. (1994). Adaptive representation of dynamics during
learning of a motor task. J Neurosci 14: 3208-3224.
Singh K., Scott S.H. (2003). A motor learning strategy reflects neural circuitry for limb
control. Nat Neurosci, 6: 399-403.
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reflexes on pre-load activity in the human arm. J Physiol, 440: 455-465.
Soechting J.F., Lacquaniti F. (1988). Quantitative evaluation of the electromyographic
responses to multidirectional load perturbations of the human arm. J Neurophysiol,
59: 1296-1313.
Suminski A.J., Rao S.M., Mosier K.M., Scheidt R.A. (2007). Neural and
electromyographic correlates of wrist posture control. J Neurophysiol, 97: 1527-1545.
Todorov E., Jordan M.I. (2002). Optimal feedback control as a theory of motor
coordination. Nat Neurosci, 5: 1226-1235.
Wolpert D.M., Flanagan J.R. (2001). Motor prediction. Curr Biol, 11: R729-R732.
Wang T., Dordevic G.S., Shadmehr R. (2001). Learning the dynamics of reaching
movements results in the modification of arm impedance and long-latency
perturbation responses. Biol Cybern, 85: 437-448.
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inhomogeneous activation of human arm muscles during isometric torques. J
Neurophysiol, 60: 1523-1548.
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Chapter 6
Primary motor cortex underlies multi-joint integration for fast
feedback control
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6.1 – ABSTRACT
A basic difficulty for the nervous system is solving global problems based on
locally ambiguous sensory information, such as the classical ‘aperture’ problem in the
visual system (Allman et al., 1985; Bradley and Goyal, 2008; Rust et al., 2006; Pack et
al., 2003). This local-to-global problem is also fundamental to sensorimotor control of the
arm since the complex mapping between torque and motion allows a particular amount
of shoulder motion to arise from an infinite combination of shoulder and elbow torques
(Craig, 2005; Graham et al., 2003; Hollerbach and Flash, 1982). We have recently
demonstrated that feedback responses of shoulder muscles resolve this ambiguity in
~60ms by integrating motion information from both the shoulder and elbow joints into a
pattern of motor commands which appropriately counter the underlying shoulder
torque(Kurtzer et al., 2009; Kurtzer et al., 2008). Here we show that a transcortical
pathway through primary motor cortex (M1) contributes to this multi-joint integration
during fast feedback control. We demonstrate that single M1 neurons in monkeys
integrate shoulder and elbow information within ~50ms of perturbation onset, early
enough to contribute to the sophisticated feedback responses observed in monkey
shoulder muscles. Moreover, we reveal a causal link between M1 processing and multijoint integration in humans by evoking supra-linear responses in shoulder muscles when
transcranial magnetic stimulation is combined with pure elbow displacement. Our results
are the first to show that M1 underlies multi-joint integration in fast feedback control,
demonstrating that the transcortical feedback pathway possesses a level of
sophistication previously reserved for voluntary control and providing neurophysiological
support for influential theories positing that voluntary movement is generated by the
195
intelligent manipulation of sensory feedback (Todorov and Jordan, 2002; Scott, 2004;
Shadmehr and Krakauer, 2008).
6.2 – MAIN TEXT
The present monkey and human studies test whether primary motor cortex (M1)
provides a neural substrate for rapidly integrating shoulder and elbow motion information
for fast feedback control. M1 is a prime candidate to mediate this sophisticated capability
for the following reasons: 1) M1 forms part of a fast transcortical feedback pathway,
giving it rapid access to the required afferent information (Evarts and Tanji, 1976;
Graham et al., 2003; Cheney and Fetz, 1984); 2) M1 is a key node for voluntary control,
which appropriately incorporates shoulder and elbow information when generating motor
commands (Porter and Lemon, 1993; Scott, 2003); and 3) influential theories posit that
voluntary movement is generated by the sophisticated manipulation of sensory feedback
(Todorov and Jordan, 2002), suggesting substantial functional and anatomical overlap
between voluntary and feedback control (Shadmehr and Krakauer, 2008; Scott, 2004).
Our first study investigated if individual neurons in M1 exhibit a pattern of activity
consistent with multi-joint motion integration during fast feedback control. Two rhesus
monkeys were trained to counter unpredictable step-torque perturbations applied at the
shoulder and/or elbow which displaced their hand from a central target. They were
trained to return to the target within 500ms and then remain within it for an additional
three seconds, allowing us to analyze both fast feedback responses (<100ms postperturbation) and steady-state motor outputs (last 2s of stabilization) in the same trial.
Because our paradigm (Fig 1a-f) was specifically designed to cause ambiguous motion
patterns at the shoulder, we were principally interested in analyzing the fast feedback
196
responses of shoulder-like neurons, whose steady-state motor outputs were correlated
with the generation of shoulder torque. As in our previous studies, we found that the
population of neurons was biased toward whole-arm flexion and extension (Rayleigh
Test for Bimodality, p < 0.05, Fig. 6.1g) making shoulder-like neurons relatively rare
(Cabel et al., 2001; Herter et al., 2009). In total, 25 of 356 M1 neurons were categorized
as shoulder-like because they exhibited significant directional tuning to steady-state
loads (plane-fit, p < 0.05) and a preferred torque direction within 15° of either shoulder
flexion of extension torque (yellow area in Fig. 6.1g).
The key question is how quickly shoulder-like neurons become selectively tuned
to shoulder torque following an unexpected torque perturbation. It is important to
emphasize that this question is not a mere restatement of our pre-selection of neurons
that are maximally sensitive to shoulder torque in the steady-state, a problem that can
be solved using only local shoulder information. In contrast, the only way that fast
feedback responses can appropriately resolve the underlying torque is by integrating
information from both the shoulder and elbow because the limb’s intersegmental
dynamics create an ambiguous relationship between local joint motion and global
torque.
The need to resolve ambiguous motion information is exemplified in our first
experiment where we applied either pure shoulder torque or pure elbow torque
perturbations (Fig. 6.1a,b). These perturbations caused substantially different amounts
of elbow motion but nearly identical shoulder motion (ratio of change in shoulder motion
and change in elbow motion <5%, Fig 1c). If shoulder-like neurons integrate both
shoulder and elbow motion information, they will respond more robustly for the shoulder
torque perturbation than the elbow torque perturbation. Fig. 6.2a presents an exemplar
197
Experiment 1
a
Experiment 2
d
te
ts
Shoulder
Extension
ts
b
e
te
ts
DJoint Motion (deg)
f
4
Shoulder
Elbow
0
-4
n =13
25
50
Time (ms)
75
n =12
2
Patch (20 of 233, 9%)
Xander (5 of 63, 8%)
0
0
Shoulder
Flexion
Elbow
Extension
te
c
Applied Elbow
Flexion
g
-2
0
25
50
75
Figure 6.1. Experimental Methods and Neural Population. a,b, Schematic of a subject's limb
configuration before (unfilled) and after (filled) a torque perturbation was applied at either the
shoulder joint (a) or elbow joint (b). c, Temporal profile of the resultant change in joint
displacement resulting from the two perturbation conditions (shoulder torque = red; elbow
torque = blue). Critically, the two perturbations yielded similar shoulder displacement (solid
lines) but substantially different elbow displacement (dashed lines). d and e, Limb
configuration before and after a multi-joint flexion (c) or multi-joint extension (d) torque
perturbation. e, The multi-joint flexion perturbation (red) yielded pure elbow flexion and the
multi-joint extension motion (blue) yielded pure elbow extension motion. Critically, neither
multi-joint torque caused substantial shoulder motion (see solid red and blue lines). g, Polar
histogram depicting the distribution of preferred directions of neurons in primary motor cortex
to multi-joint postural loads. Each dot represents the preferred tuning in of a single-neuron
from one of two monkeys (black = Monkey X; grey = Monkey P) relative to the applied torque
as calculated by planar linear regression. Note that the neurons are not uniformly distributed,
with a substantial over-representation of whole-arm flexion and extension loads (2nd and 4th
quadrants). Our experiments focused on a specific subpopulation of 'shoulder-like' neurons
centered on pure shoulder flexion and extension (yellow wedge, range -15° to +15° and 165°
to 195°).
198
Experiment 1
Firing Rate (Hz)
a
40
x091007a
e
60
Firing Rate (Hz)
Experiment 2
80
0
b
d
50
40
30
20
10
f
c
30
DFiring Rate (Hz)
20
Excitatory
10
20
10
0
Inhibitory
0
-10
0
100
Time (ms)
200
0
100
Time (ms)
200
Figure 6.2. Activity of neurons in primary motor cortex. a, Responses of an exemplar
shoulder-like neuron to either an elbow (blue) or shoulder (red) torque perturbation that yielded
the same shoulder motion. All data is aligned on perturbation onset (vertical line). Each row of
tick marks represents a single trial of action potentials and the trace below is the average
response across trials. b, Same format as a but representing the average response across
shoulder-like neurons (n = 25). c, same format as a but representing the average difference for
excitatory (thick line) and inhibitory (thin line) conditions. d,e,f, Same format as a, b and c,
respectively, but for Experiment 2 where combined shoulder and elbow torque perturbations
yielded no shoulder motion.
199
neuron which was maximally active during steady-state compensation of shoulderextension torque. Critically, this neuron expressed the appropriate pattern of activity
~60ms after perturbation onset by responding more vigorously to shoulder-extension
torque than elbow-flexion torque (t-test, p < 0.05). Comparable patterns of activity were
evident for the population of shoulder-like neurons (Fig. 6.2b,c) as they quickly
expressed larger responses for shoulder torque perturbations than elbow torque
perturbations (paired t-test, t24 = 2.7, p < 0.01; 19 of 25 neurons show the expected
trend).
Our hypothesis makes the additional prediction that differential amounts of
inhibition should occur for torque perturbations opposite the neuron's steady-state
preference. For example, a neuron which is maximally active during steady-state
compensation of shoulder flexion should be more strongly inhibited by shoulderextension torque than elbow-flexion torque. This additional prediction was verified across
the population (paired t-test, t24 = 2.1, p = 0.03; 19 of 25 neurons show the expected
trend) demonstrating that individual neurons possess a lawful pattern of multii-joint
integration for perturbations that require both excitatory and inhibitory responses (Fig
2c).
Another situation where the nervous system needs to resolve locally-ambiguous
information is exemplified in our second experiment where we caused substantial elbow
motion and no shoulder motion (Fig. 6.1f, ratio of shoulder to elbow motion << 1%) by
simultaneously applying torque perturbations at both the shoulder and elbow (Fig.
6.1d,e). If fast feedback responses of shoulder-like neurons appropriately integrate
shoulder and elbow motion, then they must respond to this perturbation even though the
shoulder joint is not displaced and no local shoulder sensors can signal the event.
200
Consistent with our prediction, the exemplar neuron (Fig. 6.2d) increased its activity
within ~60ms during pure elbow extension motion which is appropriate to counter the
underlying shoulder extensor torque and similar to its response in Experiment 1. The
same pattern of responsiveness to pure elbow motion was observed across the
population of neurons (paired t-test, t24 = 4.4, p < 10-3; 19 of 25 neurons show the
expected trend; Fig. 6.2d,e).
The above analysis established that feedback responses within an early temporal
epoch (50-100ms) appropriately integrate shoulder and elbow motion to resolve the
underlying torque perturbation (Fig. 6.3a). This binned response of shoulder-like neurons
paralleled the binned response of monkey shoulder muscles (Fig. 6.3b, Fig. 6.4)
suggesting that M1 contributes to the observed muscular response. To provide further
evidence of a functional link, we calculated the temporal evolution of multi-joint
integration for both neurons and muscles using an ROC analysis (Green and Swets,
1966; Pruszynski et al., 2008). We found that multi-joint integration occurred in M1
neurons approximately 10-20ms before it occurred in muscles (Fig. 6.3c), a temporal
lead consistent with the known conduction delay between M1 and the monkey upperlimb (Porter and Lemon, 1993; Cheney and Fetz, 1984).
Feedback responses in M1 did not immediately account for the limb’s mechanical
properties since the population of neurons responded to perturbations ~30ms prior to
expressing any sensitivity to multi-joint motion (Fig. 6.2b,d). This finding is strikingly
similar to a subpopulation of neurons in primary visual cortex (V1) which initially respond
to objects placed anywhere in their receptive field and become sensitive to motion
direction (i.e. solve the ‘aperture’ problem) only after 20-30ms (Pack et al., 2003), a
delay attributed to interactions between that V1 neuron and other V1 neurons with
201
50
b
Neurons
*
†
40
30
*
ns
20
10
0
Exp. 1
2
Muscle Activity (au)
Firing Rate (Hz)
a
Discrimination Probability
0.75
0.7
Muscles
1.5
1
†
*
0.5
ns
0
Exp. 2
c
*
Exp. 1
Exp. 2
Neurons
Muscles
DT = 18.7ms
0.65
DT
0.6
0.55
0.5
25
50
75
100
125
Time (ms)
Figure 6.3. Population analysis of monkey neurons and muscles. a, Average response of
shoulder-like neurons in an early temporal window (50-100ms) for both Experiment 1 and 2.
For experiment 1, the red and blue bars represent responses to shoulder and elbow torque
perturbations, respectively. For experiment 2, the red and blue bars represent the activity for
pure elbow motion caused by an excitatory (i.e. multi-joint flexion for shoulder-flexor-like
neurons and multi-joint extension for shoulder-extension-like neurons) or inhibitory (i.e. multijoint extension for shoulder-flexor-like neurons and multi-joint flexion for shoulder-extensionlike neurons) torque perturbations, respectively. Error bars indicate SEM, the (*) indicates a
significant difference between applied-torque conditions (paired t-test, p < 0.05) and the (†)
denotes significant difference from baseline (grey line). b, Same format as a but for the
population of muscles. Because of the normalization procedure, muscle baseline activity
occurs at 0au. c, Average ROC curve for the population of neurons and muscles. The
horizontal axis represents time and is aligned on perturbation onset. The vertical axis
represents the ability of an ideal observer to distinguish between perturbation conditions
(averaged across neurons or muscles). These data are collapsed across the two experiments
such that the vertical axis is a proxy of when the neurons and muscles show appropriate
integration of multi-joint motion. On average, the neurons lead the muscles by ~18ms.
202
Experiment 1
Muscle Activity (norm)
a
b
Experiment 2
4
3
2
1
0
0
100
Time (ms)
200
Figure 6.4. Activity of monkey shoulder muscles. a, Average response of shoulder muscles
(both flexors and extensors) to either an elbow (blue) or shoulder (red) torque perturbation that
yielded the same shoulder motion but different elbow motion (Exp. 1). All data is aligned on
perturbation onset (vertical line). Prior to averaging, muscle activity was normalized to a
condition where the muscle-of-interest was recruited to counter a 0.28Nm background load. b,
Same format as a but for Experiment 2 where combined shoulder and elbow torque
perturbations yielded no shoulder motion.
203
surrounding receptive fields(Knierim and Vanessen, 1992; Lamme et al., 1998). The
temporal evolution of multi-joint integration that we report may also reflect processing
intrinsic to M1 or it may be caused by delayed contributions from interconnected neural
structures such as somatosensory cortex and cerebellum, an important issue that
warrants further investigation.
Although the activity of single neurons provides strong evidence that M1 is
functionally linked to multi-joint integration for fast feedback control, the data is ultimately
correlational and cannot establish whether M1 causes the co-varying pattern of shoulder
muscle activity. This issue was addressed by directly influencing the processing of
human M1 while subjects generated feedback corrections similar to the monkey study
(Fig. 6.1). Previous researchers have demonstrated that applying single pulses of
magnetic stimulation (TMS) in conjunction with torque perturbations can evoke a much
larger response in the stretched muscle than the sum of their individual effects (Day et
al., 1991; Palmer and Ashby, 1992; Lewis et al., 2004). Such supra-linear interactions
only occur when TMS-evoked responses are timed to occur >50ms following the
perturbation, providing clear evidence that feedback activity at this time reflects
processing in M1. We established the validity of this technique for shoulder muscles as
we observed a supra-linear interaction when we delivered TMS ~65ms after the shoulder
muscle was stretched (t-test, p < 0.001) but not 25ms after the muscle was stretched (ttest, p > 0.5) when only spinal processes could contribute (Fig. 6.5, left column).
The critical question is whether M1 causally contributes to multi-joint integration
for fast feedback control. We tested this hypothesis by applying TMS in conjunction with
the torque perturbation that causes pure elbow displacement (Experiment 2). Any supralinearity of the shoulder muscle response in this condition must reflect afferent
204
a
25
m
s
25
m
s
b
65
m
s
65
m
EMG (au)
s
c
0
50
Time (ms)
Normalized Increase
d
3
*
100
*
0
†
2
†
*
50
Time (ms)
100
*
†
†
2
1
1
0
Extensors
Flexors
0
Extensors
Flexors
Figure 6.5. Activity in human shoulder muscles evoked by torque perturbations and cortical
stimulation. a, Two exemplar shoulder extensor muscles (black traces) during shoulder
displacement as in Experiment 1 (left) and pure elbow displacement as in Experiment 2 (right).
b, Muscle activity when TMS evoked a response during the short-latency epoch (verticals
arrow indicate onset of TMS stimulation) and the predicted muscle activity from summing the
perturbation and TMS responses (red traces). c. Observed and predicted muscle activity when
TMS evoked a response during the long-latency epoch. d. Group muscle response when TMS
was paired with the perturbation normalized by sum of the separate effects (Enorm =
ETMS,pert/(ETMS + Epert). Normalized activity during the short-latency and long-latency
epochs are depicted by the white and black bars, respectively, for both the shoulder extensor
and shoulder flexor muscles. Values of 1 indicate pure linear summation and no interaction
whereas values above 1 indicate a supralinear effect and positive interaction of the two stimuli.
205
information from muscles spanning the elbow joint onto cortical circuits controlling
shoulder muscles since shoulder muscle afferents (both force and motion) are not
physically affected by pure elbow motion. The predicted supra-linear interaction was
observed for both shoulder flexors and extensors when TMS was delivered at 65ms (ttest, p < 0.005) but not when TMS was delivered at 25ms when perturbation responses
could be only be caused by spinal processing (t-test, p > 0.5; Fig. 6.5, right column).
Taken together, these results provide strong evidence that M1 causally underlies multijoint integration for fast feedback control.
Previous studies have demonstrated that fast feedback responses in M1 are
scaled by task-constraints such as movement amplitude (Evarts and Fromm, 1977;
Fromm and Evarts, 1977), surface texture (Picard and Smith, 1992) and intended vigor
(Evarts and Tanji, 1976; Evarts, 1973). Our results clearly illustrate that feedback
responses in M1 also integrate locally-ambiguous motion information into a global
response that accounts for the limb’s mechanical properties, a more complex capability
that is central to successfully guiding whole-arm movements (Scott, 2003). It is well
established that the voluntary motor system accounts for the mechanical properties of
the limb and that this capability is expressed in the activity of M1 neurons (Scott et al.,
2001). We have previously argued that the functional similarity of voluntary and
feedback control is not an accident and likely arises because of a common neural
implementation centered on M1 (Scott, 2004). This expectation is consistent with recent
theories of sensorimotor control which posit that voluntary behaviour is generated by the
sophisticated manipulation of sensory information (Todorov and Jordan, 2002;
Shadmehr and Krakauer, 2008). If our suggestion is true then feedback processing in
M1 should possess all the capabilities of voluntary processing in M1 (Shadmehr and
206
Wise, 2005) and, likewise, studying feedback processing may provide a powerful
window into voluntary control.
6.3 – METHODS SUMMARY
All studies were approved by the Queen’s University Research Ethics Board.
Human subjects completed the experiments as previously described (Kurtzer et al.,
2008) and monkeys performed essentially the same paradigm in a miniaturized version
of the same apparatus with ~10x smaller loads (KINARM, BKIN Technologies, Kingston,
ON) (Scott, 1999; Herter et al., 2009). Unlike humans, monkeys did not counter a preperturbation background load and they were exposed to eight randomly-interleaved
step-torque perturbations, four of which formed Experiment 1 and two of which formed
Experiment 2. All eight of these conditions were used to calculate the steady-state tuning
of each neuron by performing a planar regression on the neural activity when the
monkey had re-stabilized their hand at the central target (Cabel et al., 2001; Herter et al.,
2009).
Neural recordings were performed with single electrodes and processed
according to standard techniques (Cabel et al., 2001). Monkey muscle activity was
acquired from mono-articular shoulder muscles (Anterior Deltoid, Middle Deltoid,
Posterior Deltoid and Pectoralis Major) using fine-wire electrodes. Human experiments
used surface electrodes (Bortec AMT-8, Bortec Biomedical, Calgary, AB) and focused
on Posterior Deltoid and Pectoralis Major.
Single pulses of transcranial magnetic stimulation (MES-10, Cadwell, Kennewick,
WA) were applied over the left primary motor cortex with a posterior orientation of 3045°. Placement and orientation of the double coil was chosen to evoke the largest
207
response from the muscle of interest, ~4.5cm lateral from vertex (Brouwer and HopkinsRosseel, 1997). Stimulation magnitude was selected to deliver the smallest-possible
consistent response (evoked response on seven consecutive stimulations, range 4050% of the stimulators maximum output) when the muscle of interest actively countered
a 3Nm background load. TMS only, perturbation only and combined TMS and
perturbation trials were randomly interleaved and the application of TMS was timed such
that its effect was present on the muscle of interest either ~25ms or ~65ms after
perturbation onset.
208
6.4 – REFERENCES
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Bradley DC, Goyal MS (2008) Velocity computation in the primate visual system.
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Brouwer B, Hopkins-Rosseel DH (1997) Motor cortical mapping of proximal upper
extremity muscles following spinal cord injury. Spinal Cord 35: 205-212.
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mechanical loads applied to the shoulder and elbow during a postural task. Journal of
Neurophysiology 86: 2102-2108.
Cheney PD, Fetz EE (1984) Corticomotoneuronal Cells Contribute to Long-Latency
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Green DM, Swets JA (1966) Signal Detection Theory and Psychophysics. New York:
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Lamme VA, Zipser K, Spekreijse H (1998) Figure-ground activity in primary visual
cortex is suppressed by anesthesia. Proc Natl Acad Sci U S A 95: 3263-3268.
Lewis GN, Polych MA, Byblow WD (2004) Proposed cortical and sub-cortical
contributions to the long-latency stretch reflex in the forearm. Experimental Brain
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problem: Two-dimensional motion signals in macaque V1. Neuron 39: 671-680.
Palmer E, Ashby P (1992) Evidence That A Long Latency Stretch Reflex in Humans Is
Transcortical. Journal of Physiology-London 449: 429-440.
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Picard N, Smith AM (1992) Primary Motor Cortical Responses to Perturbations of
Prehension in the Monkey. Journal of Neurophysiology 68: 1882-1894.
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Pruszynski JA, Kurtzer I, Scott SH (2008) Rapid motor responses are appropriately
tuned to the metrics of a visuospatial task. J Neurophysiol 100: 224-238.
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motion of visual patterns. Nature Neuroscience 9: 1421-1431.
Scott SH (1999) Apparatus for measuring and perturbing shoulder and elbow joint
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Chapter 7
General Discussion
212
7.1 – SUMMARY OF FINDINGS
Recent theories of motor control posit that voluntary movement involves the
sophisticated manipulation of sensory feedback (Todorov and Jordan, 2002). The
experiments presented in this dissertation were motivated by this theory and by the
related suggestion that long-latency activity is a natural candidate to mediate such an
intelligent feedback signal (Scott, 2004; Shadmehr and Krakauer, 2008). With the goal of
exploring this suggestion, we performed a series of experiments to elaborate the
functional attributes of the long-latency response and determine the neural substrates
that underlie its sophistication. Many previous experiments had already shown that the
long-latency response can generate a variety of sophisticated responses, but we were
working under the hypothesis that long-latency activity should possess a level of
sophistication comparable to voluntary control. Therefore, our experiments went further
than previous studies by trying to establish that long-latency responses can demonstrate
the types of sophistication commonly reserved for voluntary control. What follows is a
brief summary of the studies and their principle findings.
1. In our first study (Chapter 2), we modified the dominant paradigm used to study
voluntary reaching and showed that the long-latency response, like voluntary
control, is continuously sensitive to the direction and distance of a required
correction in both one and two dimensions. Since the position of the visual target
instructed subjects how they should respond to the perturbation, we could show
that the long-latency response was continuously tuned to subject intent. This
result extends previous results using verbal instructions which could only
establish categorical modulation associated with those verbal instructions which
213
have reliable interpretations (Colebatch et al., 1979; Crago et al., 1976;
Hagbarth, 1967; Hammond, 1956; Rothwell et al., 1980).
2. In our second study (Chapter 3), we tested whether the long-latency response
accounts for the size-recruitment principle of the motoneuron pool which causes
the same motor command to be scaled according to the background activation
level of a muscle. Such automatic gain-scaling is inappropriate if the mapping
between muscle activity and force is relatively linear. Under the assumption of
linearity, a unit increase in load should be countered by a unit increase in motor
output regardless of the background load. Our results indicate that the longlatency response is less sensitive to background activation than the spinal shortlatency reflex and that the later portion of the long-latency response, like
voluntary control, fully accounts for the state of the motoneuron pool.
3. In our third study (Chapter 4), we explored the functional contributions to longlatency activity. Our results indicate that the long-latency response reflects the
temporal overlap of at least two functionally-independent processes, one of
which is sensitive to spatial task-constraints, as in our first study, and the other
which is sensitive to background load, as in our second study. It is tempting to
speculate that these processes reflect cortical and spinal contributions to the
long-latency response, respectively. Importantly, these results provide an
experimental paradigm to functionally dissect long-latency activity. We used this
dissection in the final experiment of this chapter to show that the task-dependent
214
component of the long-latency response is not a simple triggered reaction or
startle response as often suggested (Crago et al., 1976; Shemmell et al., 2010).
4. In our fourth study (Chapter 5), we investigated whether the long-latency
response accounts for the intersegmental interactions present in the upper-limb.
Our results indicate that the long-latency response in shoulder muscles
appropriately integrates motion information from both the shoulder and elbow to
counter the underlying torques. This is in contrast to the short-latency stretch
response which responds only to local joint motion. This impressive ability of the
long-latency response is consistent with the known capabilities of voluntary
control during self-initiated reaching (Graham et al., 2003; Hollerbach and Flash,
1982).
5. In our final study (Chapter 6), we used the same paradigm as in Chapter 5 to
reveal that monkey shoulder muscles also quickly respond to appropriately
counter the underlying torques. More importantly, we demonstrated that single
neurons in monkey primary motor cortex also exhibit this sophisticated pattern of
activation and that they do this fast enough to underlie the observed muscle
activity. This correlational finding was further strengthened using transcranial
magnetic stimulation in human subjects which showed that primary motor cortex
is causally involved in resolving the underlying torque perturbation by mapping
elbow afferent information onto shoulder motor outputs.
215
7.2 – FUTURE DIRECTIONS
Exploring the sophistication of the long-latency stretch response
This dissertation demonstrates that the long-latency response is sensitive to a
range of experimental factors that also affect voluntary control. It would be fruitful for
future experiments to push these results to their natural limit and explore the functional
attributes of the long-latency response under the hypothesis that it should demonstrate
all the sophistication of voluntary control. For example, the results of Chapter 2
demonstrate that long-latency activity is sensitive to the location of the goal target in both
one and two dimensions. The visuospatial paradigm presented in that chapter could be
leveraged to explore whether long-latency activity signals decision-level variables about
where to move. Consider the situation where the subject initially places their hand at a
small central area and is instructed to respond to a mechanical perturbation by moving
to either of two presented targets. Critically, the experiment involves a range of
unpredictable perturbation magnitudes such that the smallest perturbation displaces the
hand towards the close target and the largest perturbation displaces the hand past the
far target. If the long-latency response reflects decision-level signals, then it should
reliably respond to the close target for small perturbations and switch at some
perturbation magnitude to responses appropriate for acquiring the far target. On the
other hand, the long-latency response may simply alter its sensitivity for the average
behaviour and allow later motor responses to guide the hand towards the target.
Another level of sophistication that is critical to voluntary control but largely
unexplored for long-latency activity is the ability to adapt to novel force environments.
When a subject is making self-initiated reaches from one target to another, they do so
with a relatively straight hand path (Hollerbach and Flash, 1982). If a mechanical load is
216
then suddenly introduced, the subject will be initially pushed from their straight
trajectories but after a few trials will re-establish relatively straight reaches (Lackner and
Dizio, 1994; Shadmehr and Mussa-Ivaldi, 1994). When the load is then suddenly turned
off, the subject demonstrates an after-effect whereby they deviate in the direction
opposite to the direction the force field had pushed them. This after-effect is seen as a
signature of motor learning because it demonstrates that the nervous system had
learned to alter the pattern of motor commands to counter the underlying force-field.
Because motor learning is often seen as the principle example of sophistication during
voluntary control, establishing that the long-latency response is also modified during
learning would dramatically reduce the functional differences between these two levels
of control. There are two different issues to explore here. First, it would be useful to
establish whether exposure to mechanical perturbations in different force environments
yields adaptation of long-latency activity. And second, it would be interesting to test
whether adaptation of the voluntary motor system causes concomitant adaptation of the
long-latency response.
Why is the short-latency reflex not modified by subject intent?
In Chapter 2, we confirmed that the short-latency reflex is not modulated by
subject intent (Colebatch et al., 1979; Crago et al., 1976; Hammond, 1956; Rothwell et
al., 1980). Such inflexibility is extremely surprising because subjects have plenty of time
to prepare a response and there exist mechanisms to modulate their spinal reflexes in
advance of the perturbation (Zehr and Stein, 1999). Furthermore, recent work has
demonstrated that spinal interneurons exhibit task-specific preparatory activity in
advance of a single-joint reaching task (Prut and Fetz, 1999). The implication is that
217
cortical networks, which mediate voluntary intentions, have access to the spinal cord and
should be able to tune that circuitry to an upcoming perturbation without changing the
pre-perturbation muscles activity.
So why do these spinal interneurons not modify the sensitivity of the shortlatency reflex as in our task? One rational possibility is that the preparatory activity
observed in spinal interneurons was not preparatory at all because the muscles were
silent in the preparatory period. This silence could allow for systematic sub-threshold
changes in muscle activity associated with the cortical preparation which would not be
visible in the recorded muscle activity (Capaday and Stein, 1987). Therefore, while it
remains possible that spinal interneurons reflect a preparatory process that simply does
not regulate the short-latency reflex, perhaps because it targets a specific subset of
interneurons, another legitimate possibility is that the preparatory activity (i.e. changes in
neural activity without changes in muscle activity) would be eliminated if the muscles
were brought above threshold and their activation level was actively controlled.
The above suggestion illustrates a more general problem about preparatory
activity in the limb motor system. If neurons so close to the periphery, either in the spinal
cord or primary motor cortex, are activated during the preparation of a movement, what
prevents the effector from moving? In the oculomotor system there exists an active
mechanism which gates all preparatory activity from accessing the effector and causing
movement (Leigh and Zee, 2006; Sparks, 2002). There are no reports of equivalent
omni-pause neurons in the limb motor system and results from gaze shifts imply that
these neurons do not exist (Corneil et al., 2008). Therefore, it may be that the
mechanisms which generate preparatory activity rely on the non-linearity of the
motoneuron pool to prevent movement. Alternatively, the nervous system may
218
coordinate the preparatory activity in higher cortical such that the net result is zerochange at the muscular level. This is an important issue that warrants further
investigation and may reveal that preparatory activity is much more motor than generally
thought.
Which component of long-latency activity contributes the sophistication?
An important result in this dissertation is that the long-latency reflex is composed
of two functionally-distinct signals (Chapter 4). Although there was substantial evidence
that multiple neural generators contribute to the long-latency epoch (Gomi and Osu,
1998; Lourenco et al., 2006; Kimura et al., 2006;Kurtzer et al., 2010; Lewis et al., 2004;
Matthews and Miles, 1988; Shemmell et al., 2009), no previous study was able to
establish whether these components had unique functional characteristics.
Future research could leverage our paradigm for functionally dissecting the longlatency response into its constituent components. For example, the final experiment in
Chapter 4 showed that isolated task-dependent activity is sensitive to perturbation
magnitude, ruling out a prominent suggestion that the task-dependent nature of longlatency activity reflects a simple triggered reaction or startle response (Crago et al.,
1976). It would be fruitful to determine whether other types of sophistication observed in
the long-latency epoch can be attributed to the task-dependent component of longlatency activity or whether some of this sophistication is also present for the loaddependent component. Interestingly, our initial results suggest that the ability of longlatency activity to integrate multi-joint motion information, as in Chapter 5 and 6, is
present for both the load-dependent and task-dependent components of the long-latency
response. This preliminary result emphasizes that the putatively spinal process which
219
generates load-dependent activity may also possess certain types of sophistication. This
result also points out the findings reported in Chapter 4 do not rule out the possibility that
the two components we identified can be further subdivided. They do, however, predict
that all the potential sub-components of load-dependent activity will not be sensitive to
task-constraints and that all potential sub-components of task-dependent activity will not
be sensitive to pre-perturbation muscle activity.
Another line of research would be to link the functional components to their
underlying neural circuitry. It is natural to speculate that the task-dependent component
reflects a transcortical pathway and that the load-dependent component reflects a spinal
pathway, but this does not need not be the case. For example, there are likely multiple
nested loops that make up the transcortical pathway such that both the load-dependent
and task-dependent components could traverse a cortical pathway. Alternatively, there
may be multiple spinal and cortical circuits, each of which contributes to both functional
components. However it works out, linking the functional components to their underlying
neural circuits would dramatically further our understanding of the functional and
structural organization of the motor system.
Further explorations of the neural circuitry
The results of this dissertation prompt a detailed examination of the neural circuit
which underlies sophisticated feedback control mechanisms. We have argued that
primary motor cortex is a critical node in such a control scheme because it includes
substantial somatosensory inputs, provides the largest contribution to the descending
corticospinal tract, possesses intrinsic connectivity that is highly modifiable and is richly
interconnected with other cortical and subcortical structures that are central to
220
successful motor control (Porter and Lemon, 1993). But the motor system is not just one
big feedback loop and tracking feedback control in these various circuits is critical to
further our understanding of how the nervous system instantiates sophisticated feedback
control (Scott, 2004; Shadmehr and Krakauer, 2008).
In one line of experiments, not presented in the dissertation (see Appendix 2), we
have investigated the neural basis of how the long-latency stretch response is
modulated by subject intent (Pruszynski et al., 2009). Since our task to get at this issue
uses spatial targets rather than verbal instructions and because we have the same
experimental device in the monkey facility as the human lab, it is easy to transfer the
experiments presented in Chapter 2 to the monkey model. Briefly, two monkeys were
trained to maintain their hand in a small central area and respond to shoulder/elbow
step-torque perturbations by quickly placing their hand into visually defined targets.
Critically, the targets were placed so that the same perturbation displaced the hand into
one target (i.e. “do not intervene”), a short distance from a second target and out of a
third target (i.e. “resist”).
Our initial results are based relatively large datasets in primary motor cortex and
dorsal premotor cortex (>100 neurons) and a smaller number of neurons in primary
somatosensory cortex (~20 neurons). We found that the majority of neurons in all the
areas were significantly modulated by the mechanical perturbations within 100ms of
perturbation onset. Across the population, our results indicate the presence of a
gradation of perturbation responses whereby more caudal neurons tend to have earlier
responses (primary somatosensory: ~15ms; primary motor: ~20ms; dorsal premotor:
~25ms) of greater magnitude (regression slope: -2Hz per caudal-rostral millimieter). We
also noted that neurons in both dorsal premotor (47%) and primary motor cortex (26%)
221
were significantly modulated by spatial target position (i.e. subject intent) but such
modulation was essentially absent in primary somatosensory cortex (0%). Those
neurons that did show target-dependent activity tended to demonstrate a simple
mapping to spatial target position whereby response magnitude grew with target
distance (IN < CENTER < OUT). Taken together, these results highlight the presence of
rapid sensory feedback throughout sensorimotor cortex and suggest that such feedback
is actively manipulated according to spatial task-demands.
One very intriguing aspect of these results is that primary somatosensory cortex,
the principle input of somatosensory information to cortex, is not modulated by spatial
target position. Since this modulation is present in primary motor cortex, the primary
motor output from cortex, our results imply that the sensitivity to target positions arises at
a cortical level. Another important aspect of our results is that dorsal premotor cortex
shows substantial perturbation related activity and that it actually demonstrates the
greatest sensitivity to target position. In fact, close inspection of the temporal profile of
target-dependent activation suggests that dorsal premotor cortex may signal target
position earlier than primary motor cortex. This may reflect the nature of the task which
requires the monkey to plan a response and delay its implementation until the
mechanical perturbation is delivered; the mechanical perturbation is effectively the ‘go’
cue to initiate a planned reach. Our results are similar to what happens when a monkey
makes a delayed reach to a peripheral target (Cisek and Kalaska, 2005) and so they
question the general assumption that verbal instructions involve the manipulation of
feedback gains in the simplest sense. Rather, they support the concept of multiple
overlapping processes in the long-latency window, one of which (perhaps the one that
involves dorsal premotor cortex) appears to have many of the qualities of self-initiated
222
reaching. In the context of optimal feedback control, it may be that responding according
to subject intent actually prompts the system to instantiate a new cost function at the
time of perturbation rather than manipulate the existing cost function. The implication is
that this project may be studying the process of task selection rather than online
feedback control. Future experiments can address this question by comparing these
results to tasks where manipulation of long-latency responses happens implicitly based
on behavioural context rather than explicitly with subject intent.
The results of our final study provide another appealing avenue to study the
neural basis of sophisticated feedback control. Although our principle result was that
primary motor cortex contributes to multi-joint integration during fast feedback control, it
is important to emphasize that the initial response of single neurons was not sensitive to
multi-joint motion and that this sensitivity only appeared 30ms after the initial response.
The temporal delay between their initial responses and their tuning to multi-joint motion
may reflect intrinsic processing in primary motor cortex or it may be caused by delayed
contributions from other structures. One rational place to look is in primary
somatosensory cortex, which we would expect is not sensitive to multi-joint mechanics
as projections from this area to primary motor cortex provide the bulk of the initial
response in primary motor cortex. A more likely candidate to mediate the later
sophisticated response is the cerebellum, which has been shown to contribute to the
transcortical feedback pathway through primary motor cortex (Hore and Vilis, 1984;
Strick, 1983). As such, it would be fruitful to record from the cerebellum (actually, its
principle output nuclei, dentate and interpositus) and determine whether the activity of
these neurons show the expected pattern of activation prior to primary motor cortex.
More exciting experiments would causally interfere with cerebellar processing to see if
223
the signal present in primary motor cortex or in the muscle itself is abolished. This could
be done via cooling probes lowered directly into the nuclei (Hore and Vilis, 1984) or
cooling loops resting on the cerebellar cortex (Lomber, 1999; Lomber et al., 1999),
although it is questionable whether the latter approach could deactivate the entire
cerebellum and it certainly could not be selective with respect to the output nuclei.
7.3 – CONCLUSIONS
The volitional motor system possesses an incredible capacity to control many
parameters of movement including direction, distance, speed, accuracy, and load. The
results of this thesis demonstrate that long-latency responses of multiple upper-limb
muscles (monoarticular and biarticular muscles of the shoulder and elbow) share some
of the key functional attributes of voluntary control. We suspect that these similarities are
not accidental and can be readily understood by their shared neural substrate. Both
long-latency responses and voluntary control mechanisms engage motor cortex,
somatosensory cortex, cerebellum and a variety of subcortical and spinal structures.
Accordingly, the long-latency response may reflect the earliest volley of activity through
the same (or very similar) neural circuit that is later engaged by voluntary control
mechanisms. This concept of an evolving sensorimotor transformation through the same
neural structures fits well within recent theories of sensorimotor control – based on
optimal feedback control – which posit that voluntary movement involves the
sophisticated manipulation of sensory feedback. We suspect that future work comparing
the functional attributes of the long-latency response and voluntary control will reveal
many more similarities than differences at both the functional and neural level.
224
7.4 – REFERENCES
Capaday C, Stein RB (1987) A Method for Simulating the Reflex Output of A
Motoneuron Pool. J Neurosci Methods 21: 91-104.
Cisek P, Kalaska JF (2005) Neural correlates of reaching decisions in dorsal premotor
cortex: Specification of multiple direction choices and final selection of action.
Neuron 45: 801-814.
Colebatch JG, Gandevia SC, Mccloskey DI, Potter EK (1979) Subject Instruction and
Long Latency Reflex Responses to Muscle Stretch. J Physiol (Lond) 292: 527-534.
Corneil BD, Munoz DP, Chapman BB, Admans T, Cushing SL (2008) Neuromuscular
consequences of reflexive covert orienting. Nature Neuroscience 11: 13-15.
Crago PE, Houk JC, Hasan Z (1976) Regulatory Actions of Human Stretch Reflex. J
Neurophysiol 39: 925-935.
Gomi H, Osu R (1998) Task-dependent viscoelasticity of human multijoint arm and its
spatial characteristics for interaction with environments. Journal of Neuroscience 18:
8965-8978.
Graham KM, Moore KD, Cabel DW, Gribble PL, Cisek P, Scott SH (2003) Kinematics
and kinetics of multijoint reaching in nonhuman primates. Journal of Neurophysiology
89: 2667-2677.
Hagbarth KE (1967) EMG studies of stretch reflexes in man. Electroencephalogr Clin
Neurophysiol 74-79.
Hammond PH (1956) The influence of prior instruction to the subject on an apparently
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Hollerbach JM, Flash T (1982) Dynamic Interactions Between Limb Segments During
Planar Arm Movement. Biological Cybernetics 44: 67-77.
Hore J, Vilis T (1984) Loss of Set in Muscle Responses to Limb Perturbations During
Cerebellar Dysfunction. Journal of Neurophysiology 51: 1137-1148.
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Kimura T, Haggard P, Gomi H (2006) Transcranial magnetic stimulation over
sensorimotor cortex disrupts anticipatory reflex gain modulation for skilled action.
Journal of Neuroscience 26: 9272-9281.
Kurtzer I, Pruszynski JA, Scott SH (2010) Long-Latency and Voluntary Responses to
an Arm Displacement Can Be Rapidly Attenuated By Perturbation Offset. Journal of
Neurophysiology 103: 3195-3204.
Lackner JR, Dizio P (1994) Rapid Adaptation to Coriolis-Force Perturbations of Arm
Trajectory. Journal of Neurophysiology 72: 299-313.
Leigh RJ, Zee DS (2006) The Neurology of Eye Movements. New York: Oxford
University Press.
Lewis GN, Polych MA, Byblow WD (2004) Proposed cortical and sub-cortical
contributions to the long-latency stretch reflex in the forearm. Experimental Brain
Research 156: 72-79.
Lomber SG (1999) The advantages and limitations of permanent or reversible
deactivation techniques in the assessment of neural function. Journal of
Neuroscience Methods 86: 109-117.
Lomber SG, Payne BR, Horel JA (1999) The cryoloop: an adaptable reversible cooling
deactivation method for behavioral or electrophysiological assessment of neural
function. Journal of Neuroscience Methods 86: 179-194.
Lourenco G, Iglesias C, Cavallari P, Pierrot-Deseilligny E, Marchand-Pauvert V
(2006) Mediation of late excitation from human hand muscles via parallel group II
spinal and group I transcortical pathways. J Physiol 572: 585-603.
Matthews PBC, Miles TS (1988) On the Long-Latency Reflex Responses of the Human
Flexor Digitorum Profundus. Journal of Physiology-London 404: 515-534.
Porter R, Lemon RN (1993) Corticospinal function and voluntary movement. New York:
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Pruszynski JA, Omrani M, Scott SH (2009) Modulation of perturbation responses by
spatial goals: primary motor cortex, dorsal premotor cortex and primary
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Rothwell JC, Traub MM, Marsden CD (1980) Influence of Voluntary Intent on the
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Appendix 1
228
A1.1 – SPECIFICATION OF MODEL
To examine how changes in muscle activation contribute to joint stiffness we: (a)
built a simple model of the musculoskeletal system, (b) held the activation of the muscle
constant at different levels, (c) applied perturbations and, (d) observed what effect the
different levels of muscle activation had on the induced kinematics.
The examined system was a single segment with a revolute joint controlled by a
single lumped muscle. The single joint, which was made to emulate the elbow joint in the
related experiments, was simulated as a rigid segment with inertial properties similar to
that of the human forearm and hand taken together (Winter 1979). The dynamics were
solved using Euler integration (time step = 0.005s).
The joint was driven by a single monoarticular flexor muscle with a physiological
cross-sectional area of 23cm2, a moment arm of 0.04m, which approximates the net
action of mono- and bi-articular muscles crossing the elbow joint (Li and Todorov 2004).
The muscle model, which transformed muscle activation into joint torque, was a
simplified version of the Virtual Muscle and was composed of three discernable
components: a force-length curve, a force-velocity curve, and a component which
models the dependence of force on length and activation together (Brown et al. 1999;
Cheng et al. 2000).
Each modeled ‘trial’ was similar to the corresponding experimental work. The
sequence of events for each trial was:
1. A PID servo-control law is used to move the arm to the same position, at the
middle of the workspace.
229
2. Muscle activation was kept constant throughout the trial such that the virtual joint
stabilized at the central position using 1, 2, or 3 Nm of force. These are the
background loads as described in the paper.
3. The PID control was given enough time to stabilize the joint position. Then, its
output was held constant for the remainder of the trial
4. Step perturbations were added in on top of the constant background load. The
perturbation magnitude was -1.25 or -2.5 Nm. Since there were two perturbations
and three background load, there were a total of 6 different trials. Perturbations
were held on for 2 seconds but we only analyzed the 250ms of movement.
230
A1.2 – REFERENCES
Brown IE, Cheng EJ, Loeb GE. Measured and modeled properties of mammalian
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Cheng EJ, Brown IE, Loeb GE. Virtual muscle: a computational approach to
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Li W, Todorov E. Iterative linear quadratic regulator design for nonlinear biological
movement systems. 1st International Conference on Informatics in Control,
Automation and Robotics, 1:222–229, 2004.
231
Appendix 2
232
2cm
C
IN
SE
EF
SF
EE
-0.28
0
+0.28
Applied Shoulder Torque (Nm)
-0.24
0
+0.24
Applied Perturbation
5mm
SE
EE
SF
EF
Resulting Hand Motion
Mechanical perturbations and target locations were
chosen based on initial characterization task where
the monkey's arm was perturbed in one of eight
directions and required to return to a central target.
tbg, tp
OUT
Two monkeys (p and x) were trained to maintain their
hand at a small central target and respond to
shoulder/elbow step-torque perturbations by placing
their hand into a visually-defined target. Results are
based on single units recorded in M1 (np=227;
nx=64), dPM (np=113) and S1 (np=49).
Time (ms)
0 100
EE
EF
Chosen
Condition
SF
Preferred
Perturbation
Direction
SE
EE
EF
o
-3
SF
Axis: 119-299
r = 0.52
Rayleigh Bimodal: p<10
n=134
Preferred Directions:
M1 Cells (Monkey P)
49
24
45
34
64
dPMp 113
M1x
0.52
r
102o 0.62
124o 0.50
o
117o 0.59
Ntot Nsig Axis
M1p 227 134 119
S1p
Preferred Directions Across Areas
0
50 100 150 200 250
Time (ms)
S1p (24/49)
M1p (134/227)
M1x (45/64)
dPMp (34/113)
-2
0
2
4
6
8
10
0
10 20 30
Time (ms)
40
10
20
30
40
50
p
S1
x
M1
Area
p
2,236=10.5,
*F
p<10-3
dPM
p
Response by Area
These results highlight the presence of rapid sensory feedback throughout sensorimotor cortex
and suggest that such feedback is manipulated according to spatial task-demands. We found
a rostro-caudal gradient with respect to perturbation response magnitude (dPM<M1<S1),
onset timing (dPM>M1>S1) and sensitivity to task-demands (dPM>M1>S1). That is, S1
perturbation responses were most robust but least modifiable while dPM perturbation
responses were least robust but most modifiable; M1 responses were intermediate.
Discussion and Conclusion
Here, we consider perturbation responses in each neuron's preferred direction (chosen
condition, see above). Population level analysis reveals that all areas were robustly
modulated by the perturbation and demonstrates the presence of a rostro-caudal increase in
perturbation response magnitude (dPM<M1<S1) and a rostro-caudal decrease in the onset of
perturbation related activity (dPM>M1>S1).
20
40
60
80
Average Response in Preferred Perturbation Condition
Result #2: Magnitude and Timing of Perturbation Response
Isolated neurons were robustly modulated by mechanical perturbations in all areas of interest
(ANOVA, p<0.05; S1: 67%; M1: 68%; dPM: 42%). Of neurons with perturbation responses,
most were significantly (p<0.05) tuned according to a planar regression (S1: 72%; M1: 90%;
dPM: 71%). Across the neural population, all areas exhibited a strong bias towards either
whole-arm flexion loads (SE/EF) or whole arm extension loads (SF/EE)3.
0
50
100
Firing Rate (Hz)
Methods
SE
Activity (Hz)
Result #1: Directional Preference of Perturbation Response
Perturbation Response:
Exemplar Cell in M1
DFiring Rate (Hz)
Introduction
DFiring Rate, OUT-IN (Hz)
20
15
10
5
0
20
15
10
5
0
0
50 100 150 200 250
Time (ms)
n=114
0
5
10
15
20
25
20
40
60
80
0
50ms
1Hz
50 100 150 200 250
Time (ms)
0
n=42
Dorsal Premotor Cortex
Muscles
Sig
Not Sig
S1p
M1p
M1x
dPMp
-50
-50
-25
0
25
50
DPreparatory Activity (DHz)
-25
0
25
50
0
5
10
S1
M1
Area
Kruskal-Wallis, p > 0.1
dPM
0
10
20
30
40
50
S1
M1
Area
dPM
*Kruskal-Wallis, p = 0.01
When neurons with significant perturbation responses are sorted according to spatial
target position, the resulting population level signals suggest that task-dependent
modulation is greatest in dPM, intermediate in M1 and smallest in S1.
25
25
50 100 150 200 250
Time (ms)
20
20
0
40
40
n=18
60
80
60
80
OUT
CENTRE
IN
Primary Motor Cortex
Result #3: Modulation by Spatial Task-Demands
Somatosensory Cortex
1. Pruszynski, JA., Kurtzer I., and Scott, SH. (2008). J Neurophysiol 100: 224-38.
2. Hammond P. (1956) J Physiol 132: 17-8P.
3. Herter TM, Korbel T, Scott SH. (2009). J Neurophysiol 101:150-63.
References
Individual neurons often show a decrease in activity for the OUT target, a result which
does not occur for muscles and that obscures task-dependency in the population
signals shown above. When such neurons are accounted for by comparing absolute
modulation in the perturbation response (middle), a rostro-caudal decrease in taskdependency remains present (dPM>M1>S1) and is particularly clear when calculated
relative to perturbation response magnitude (right).
DPerturbation Response (DHz)
Recent theories posit that movement is generated by
intelligent manipulation of sensory feedback. One
implication is that rapid motor responses to
mechanical perturbations should be tuned to taskdemands. We have recently demonstrated such
tuning in human upper-limb muscles using a spatial
analog1 of the classical "resist / do not intervene"
paradigm2. Here, we use the same task to explore
the neural basis of such tuning in three regions of
sensorimotor cortex: primary motor cortex (M1),
dorsal premotor cortex (dPM) and primary
somatosensory cortex (S1). Our principle interest is
testing whether rapid cortical responses
(<100ms) are modulated for the same mechanical
perturbation but different spatial target locations.
Perturbation Response (DHz)
Firing Rate (Hz)
J. Andrew Pruszynski*, Mohsen Omrani, and Stephen H. Scott
Centre for Neuroscience Studies, Queen's University, Kingston, Canada
Modulation of perturbation responses by spatial task-demands: M1, dPM and S1
Abs. Target Dependency (Hz)
463.6
Applied Elbow Torque (Nm)
Rel. Target Dependency (%)
233