Download Primary motor cortex neurons classified in a postural task predict

Articles in PresS. J Neurophysiol (February 3, 2016). doi:10.1152/jn.00971.2015
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Title: Primary motor cortex neurons classified in a postural task predict muscle activation patterns in a
reaching task
Running Head: M1 prediction of movement from postural classification
ETHAN A HEMING1, TIMOTHY P. LILLICRAP2, MOHSEN OMRANI1, TROY M HERTER3, J.
ANDREW PRUSZYNSKI4,5,6, STEPHEN H. SCOTT1,7,8
1. Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada
2. Google DeepMind, London, Kings Cross, UK
3. Department of Exercise Science, University of South Carolina, Columbia, SC, USA
4. Department of Physiology and Pharmacology, Western University, London, ON, Canada
5. Robarts Research Institute, Western University, London, ON, Canada
6. Brain and Mind Institute, Western University, London, ON, Canada
7. Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada
8. Department of Medicine, Queen’s University, Kingston, ON, Canada
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Corresponding Authors:
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Ethan Heming: [email protected]
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Stephen Scott: [email protected]
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Copyright © 2016 by the American Physiological Society.
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Abstract:
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Primary motor cortex (M1) activity correlates with many motor variables making it difficult to
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demonstrate how it participates in motor control. We developed a two-stage process to separate the
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process of classifying the motor field of M1 neurons from the process of predicting the spatiotemporal
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patterns of its motor field during reaching. We tested our approach with a neural network model that
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controlled a two-joint arm to show the statistical relationship between network connectivity and neural
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activity across different motor tasks. In rhesus monkeys, M1 neurons classified by this method showed
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preferred reaching directions similar to their associated muscle groups. Importantly, the neural
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population signals predicted the spatiotemporal dynamics of their associated muscle groups, although a
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sub-group of atypical neurons reversed their directional preference suggesting a selective role in
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antagonist control. These results highlight that M1 provides important details on the spatiotemporal
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patterns of muscle activity during motor skills such as reaching.
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Keywords:
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primary motor cortex
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motor fields
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neural network model
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electromyography
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INTRODUCTION
The motor system precisely controls the activity of muscles to generate smooth and accurate
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motor actions. For example, goal-directed reaching involves agonist muscle activity to propel the hand
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toward the goal and antagonistic muscle activity to decelerate and stop at the goal (Marsden et al., 1983;
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Wierzbicka et al., 1986; Flanders et al., 1994). The selection, onset time and magnitude of muscle
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activity during reaching depends on many factors such as target and initial limb position, arm geometry,
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and external loads (Caminiti et al., 1990; Karst and Hasan, 1991; Hong et al., 1994; Scott, 1997).
Primary motor cortex (M1) plays an important role in voluntary motor functions, such as
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reaching, but its specific role remains debated. The classic dichotomy is whether its activity reflects
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muscles or movements (Phillips, 1975). The latter suggests that M1’s contribution is to define
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behavioural goals (e.g. direction and extent of movements) leaving subcortical and spinal structures as
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the primary source for generating spatiotemporal patterns of muscle activity (Georgopoulos, 1995;
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Raphael et al., 2010). Indeed, many studies have shown that M1 activity correlates with many high level
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parameters such as direction of hand motion (Georgopoulos et al., 1982; Toxopeus et al., 2011; Philip et
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al., 2013), hand movement speed (Schwartz, 1993), and target direction and speed (Johnson et al.,
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1999).
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The other end of the spectrum is the view that primary motor cortex directly generates
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spatiotemporal patterns of muscle activity for goal-directed movements (Bennett and Lemon, 1994;
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Scott, 1997, 2003; Cherian et al., 2013). Although the exact patterns of muscle activity for limb
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movement are only specified at the spinal level, as spinal afferent feedback will influence motor output,
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the idea is that basic features such as the selection, timing and magnitude of muscle activity are specified
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by neurons in M1. Indeed, several studies have quantified how M1 activity correlates with the activity of
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hand and wrist muscles (Evarts, 1968; Humphrey, 1972; Bennett and Lemon, 1996; Kakei et al., 1999;
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Oby et al., 2013) or proximal arm muscles (Murphy et al., 1985; Scott, 1997; Todorov, 2000; Sergio and
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Kalaska, 2003; Cherian et al., 2013).
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The lack of strong causal evidence for one of these options indicates that identifying a simple
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correlation is not sufficient to identify the role of M1 in voluntary motor control. There are many
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different patterns of muscle and neural activity, and finding arbitrary correlations is relatively easy
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(Humphrey, 1972). One way to circumvent this problem is to dissociate different movement parameters,
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such as by making movements with different torques or arm configurations (Fromm and Evarts, 1977;
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Scott and Kalaska, 1997; Kakei et al., 1999; Sergio and Kalaska, 2003; Cherian et al., 2013). Inevitably,
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these studies identify that some neuronal activity in M1 can reflect many different features of motor
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actions. However, the presence of some high level information in M1 does not preclude its role in
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specifying spatiotemporal patterns of muscle activity.
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Better support for a role in generating patterns of muscle activity is to separate the process for
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identifying the portion of the motor periphery associated with a given neuron – its motor field – from the
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process of relating its spatiotemporal activity to muscles in that motor field. A similar concept is the
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sensory field of a neuron, which is portion of the periphery to which applying stimuli elicits firing.
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Identifying a sensory field is relatively straightforward as it requires one to simply apply or present a
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controlled stimuli. It is less clear how to define motor fields.
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The synaptic connectivity of neurons that project to proximal limb motor neuron pools can be
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roughly identified by spike-triggered averaging (Fetz and Cheney, 1978; Cheney and Fetz, 1980, 1985;
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Kasser and Cheney, 1985; McKiernan et al., 1998). However the variability in STA timing, and the
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inability to record from all muscles at once, makes it very difficult to describe a complete connectivity
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mapping from STA connectivity. If the scope of investigation is limited to monosynaptically connected
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corticomotorneurons (CM cells), the proximal limb has reduced CM cell representation compared to the
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distal limb (Buys et al., 1986; Palmer and Ashby, 1992) and CM cell distribution is limited to the caudal
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(new) section of M1 (Rathelot and Strick, 2009). Intra-cortical micro-stimulation can be used to identify
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muscle connectivity by repeated stimulation and stimulus-triggered-averaging (Cheney and Fetz, 1985;
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Buys et al., 1986), however this technique does not guarantee single-neuron activation. Cheney and Fetz
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(1985) and Lemon et al. (1987) show increased muscle activity for increased stimulation current and
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conclude that stimulation likely activates multiple neurons in proximity.
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The present study used two different behavioural tasks to separate the process for identifying a
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neuron’s motor field from the process of comparing spatiotemporal patterns of activity between neurons
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and muscles. First, the motor field of M1 neurons and limb muscles were identified based on their load
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preference using a posture task consisting of combinations of shoulder and elbow flexion or extension
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torques (Figure 1A). A neural network model that we used to drive our predictions (Lillicrap and Scott,
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2013) highlighted that the torque preference of the model’s ‘cortical’ units correlated with the torque
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preference of their synaptically associated muscle groups. The model predicted that neurons with similar
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motor fields – defined by the posture task – would show correlated directional preferences in a reaching
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task. We tested this prediction by examining the directional preferences of monkey M1 neurons during
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center-out reaching to a range of peripheral targets (Figure 1B). Consistent with our model, we found
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that the activity of each neuronal population predicted the spatiotemporal patterns of their respective
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muscle groups. Interestingly, a small subset of the neurons in each group possessed directional
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preferences during reaching that were opposite to that observed for its associated muscle group. These
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atypical neurons may be indicative of selective control when the muscle acts as an antagonist during
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motor skills.
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Experimental Procedures
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Subjects and apparatus
Five male rhesus monkeys (Macaca mulatta, 6–12 kg, monkeys A-E) were trained to perform
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reaching and posture tasks while attached to a robotic upper-limb exoskeleton (KINARM; BKIN
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Technologies, Kingston, Ontario, Canada). This exoskeleton maintained the arm in a horizontal plane at
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shoulder height permitting motion at the shoulder and elbow, and allowing mechanical torques to be
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applied at either joint (Scott, 1999). Targets and a dot representing hand feedback location were
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presented in the plane of the arm via reflection in semi-transparent glass. These experiments were
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conducted in accordance with the Queen's University Animal Care Committee.
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Behavioural Tasks
Posture task. This task has been previously described (Cabel et al., 2001; Herter et al., 2007). In
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each trial, a constant torque was applied to the elbow and/or shoulder. The monkey then stabilized the
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hand within a central 0.8cm-wide stationary target for at least 3 seconds. Nine constant torques were
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used, consisting of elbow flexion (EF) or extension (EE), shoulder flexion (SF) or extension (SE), four
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multi-joint torques (SF+EF, SF+EE, SE+EF, SE+EE), and an unloaded condition (Figure 1A). Torques
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of magnitude 0.12Nm were used for monkeys A-C,E and 0.32 Nm for Monkey D. Five blocks were
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presented, each containing the 9 load conditions in random order, for a total of 45 trials.
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Reaching task. This task has been previously described (Kurtzer et al., 2006b). Monkeys began
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each trial by maintaining their index finger (white dot) within a central start target (8mm radius). This
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start target was positioned such that the shoulder and elbow were at approximately 30⁰ and 90⁰,
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respectively. After a random time period (1.5-2.0s), a peripheral target (12mm radius) then illuminated
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6cm from the central target. The monkey then moved between the start and peripheral target in 220 to
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350ms, generating total reach times of ~500 to 600ms when including intra-target acceleration and
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deceleration. Eight such peripheral targets were located around the start target. For monkeys A and B,
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these were distributed such that they were roughly distributed uniformly in torque space in two
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arrangements (Figure 1B i,ii). For Monkeys C-E, the targets were uniformly distributed in Cartesian
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space in two arrangements (Figure 1B iii,iv). In monkey D, some trials were also performed with 3cm
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reaches (target pattern not shown). Five blocks were presented, each containing the 8 reach directions in
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random order, for a total of 40 trials.
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Data collection
Neuronal recordings were obtained from the elbow/shoulder region of primary motor cortex
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(M1), contralateral to the arm used to perform the behavioural tasks, using standard recording
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techniques (Scott and Kalaska, 1997; Scott et al., 2001). Activity of these neurons was recorded during
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both the reaching and posture tasks.
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We examined the activity of elbow and shoulder flexor and extensor muscles using
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electromyography during the posture and reaching tasks. In most cases, we recorded through a pair of
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percutaneous Teflon-coated 50-μm stainless steel wires using standard recording techniques (Scott and
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Kalaska, 1997). In monkeys A and C, we recorded some muscle activity from chronically implanted
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bipolar multi-strand electrodes (Scott and Kalaska, 1997; Kurtzer et al., 2006b). Chronic electrode
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recordings were only selected if they occurred more than a week apart, to minimize redundant data.
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Recordings were taken from biceps (12 percutaneous, 13 chronic), brachioradialis (7, 11), brachialis (6,
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6), long head triceps (5, 19), posterior deltoid (8, 10), lateral triceps (8, 3), middle triceps (2, 0), anterior
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deltoid (3, 11), and pectoralis major (6, 6). To ensure that the percutaneous recordings were not being
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skewed by the chronic recordings, we compared the mean preferred torque direction (PTD, described in
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Data analysis) between percutaneous and chronic recordings across muscles, and found no difference
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(paired t-test, t=0.02, p=0.98), and while the average variance in PTD was lower for chronic recordings
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(3.4º) than percutaneous (7.0º).
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Joint kinematic data and EMG were recorded at 1 kHz (Monkeys A–C) or 4 kHz (Monkeys D,E).
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Neuronal firing was binned into 5ms bins, and EMG and kinematic data were down-sampled to 200Hz
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for data storage.
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Data analysis
Preferred torque direction. Combinations of shoulder and elbow torques were described in
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torque space where shoulder torque was represented along the x-axis and elbow torque was represented
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along the y-axis. Positive torque, in each axis, was defined as flexor torque to oppose joint-extending
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applied torques, so that shoulder flexor torque was at 0⁰, elbow extensor at 90⁰, shoulder extensor at
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180⁰, and elbow extensor at 270⁰. A plane was fit to EMG activity or cell firing rate associated with the
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elbow/shoulder torque in this space.. We used only the EMG activity or cell firing in the last two
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seconds of hold time to ensure that recordings were from a period of stationary posture. If the plane had
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a statistically significant slope, the angle of maximal slope was defined as the torque which elicited
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either a muscle’s maximal activation or neuron’s maximal firing rate. This preferred torque direction
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(PTD) was measured counter-clockwise from shoulder flexion.
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Preferred reaching direction. A given reach movement was assigned a reach direction in
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Cartesian space, increasing counter-clockwise from the positive x-axis. It was calculated by the angle
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from the origin to the hand position at maximal tangential velocity. EMG activity and neuronal firing
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rate were integrated around (-50ms to 150ms) movement onset, which was defined as the time when the
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hand first attained 5% of maximum hand speed. This activity was fit to a plane based on the angle of
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each reach and the activity during each reach. If the plane had significant slope, the preferred reaching
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direction (PRD) was defined as the angle which had maximal slope on the plane. It described the
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maximum spatial modulation of either a muscle’s activation or neuron’s firing rate.
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Spatiotemporal dynamics of reaching. Activities in each trial were aligned temporally on the
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calculated reach onset time. Baseline activity was removed by calculating the mean activity of each
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neuron or muscle recording during the initial hold period over all trials and subtracted from all trials for
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that recording. Activity for each muscle or neuron was then divided by the maximum activity across
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reaching directions for that recording. Data were smoothed using a Gaussian kernel with a 5ms standard
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deviation.
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Modelling
To predict the responses of a neural system with defined motor fields, we implemented a static
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neural network model similar to that developed by Lillicrap & Scott (2013). . The model consisted of a
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vector of ‘cortical’ units, z, which controlled 6 muscle groups, u, controlling a two-joint arm in the
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horizontal plane via linear output weights, w (Figure 2A). Muscle activity was kept positive via the
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standard sigmoid - that is, u = σ(w · z). Modelled muscle activity generated torques and hand velocities
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via a function that approximated the biomechanics of a 2-joint revolute arm constrained to move in the
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horizontal plane. This function was computed by linearizing a dynamic model that included limb
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geometry, intersegmental dynamics, and mono- and bi-articular muscles with force generation
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dependent on length and velocity. Muscle tension forces, t, are obtained by element-wise multiplication
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of muscle activity with linearized F-L/V scaling factors appropriate for the movement direction, i.e., t =
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H · u. Joint torques are computed via: τ = M t. And hand velocity is determined by the linear
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transformation, y = G F τ, where F and G are local linear approximations to limb dynamics and the
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geometric mapping between joint and hand velocity, respectively. This static model was derived as a
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simplified version of a dynamic model which executed reaching movements over a sequence of time
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steps and in which the network model was connected in closed loop with the arm. One of the findings
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of this previous work was that a static model based on a linearization of the dynamic version captured
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the most salient features of the population neural activity (Lillicrap & Scott 2013). The static version
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also has the benefit of being easier to optimize, analyze, and understand. Parameters for the limb
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biomechanics were derived from published work on monkey limb and muscle characteristics (Cheng
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and Scott, 2000; Singh et al., 2002; Graham and Scott, 2003).
We optimized z to solve analogues of the posture and reach tasks while keeping the square of the
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neural and muscle activities small. For the reaching task, the model captures movement initiation. In
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this case z* is found by minimizing the difference between target and actual hand velocity for a given
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movement,
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−
=
− , while keeping unit and muscle activity small; that is,
∗
=
=
+ ‖ ‖ + ‖ ‖. For the posture task, the model captures the steady state condition
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during which the joint torques are countered and the arm is at zero velocity. In this case z* is found by
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minimizing the difference between applied and actual joint torques,
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and muscle activity small; that is,
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and β are set to 1e-6. Importantly, for a given simulation, the elements of the matrix w were drawn
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randomly from a normal distribution (σ = 0.05) and were unaltered during optimization. This meant that
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a given unit maintained the same relative contribution to each muscle at the periphery across tasks. Unit
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activity was optimized to generate 16 target torques (posture task) and 16 target velocities (reach task) -
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both equally distributed about the unit circle. In practice, we used a preconditioned conjugate gradient
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descent algorithm with back-tracking line searches to find an optimal vector of activity for a given trial.
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In line with the analysis of biological data, the activity of z and u across all 16 torques and reach
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directions were plane fit to determine preferred reach and torque directions.
∗
=
=
10
−
=
− , while keeping unit
+ ‖ ‖ + ‖ ‖. In both cases, α
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RESULTS
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Neural network model: Relation of torque preference and motor field
We used a static neural network model similar to that developed by Lillicrap & Scott (2013) to
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examine the relationship between neural connectivity, torque preferences during posture, and directional
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tuning during reaching. The activation of model cortical units, z, were optimized to generate
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combinations of elbow and shoulder torque for the posture task and different hand velocities for the
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reaching task. Though we used a PCG algorithm for the results reported here, the same essential results
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can be obtained with virtually any gradient based optimization routine. In particular, we find the same
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results using L-BFGS and stochastic gradient descent (SGD), though SGD takes significantly longer to
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converge. Given that the optimization we perform is non-linear and high dimensional, we are not able
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to find a global minimum. Our results are thus based on local minima - but they are robust minima in the
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following sense: we repeated the simulation 10 times from random initializations of the synaptic weight
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matrix and found the same characteristic pattern of PTD/PRD distributions in each case. Thus, there
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appears to be a large family of such minima - all of which produce similar behavioral performance and
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PTD/PRD distributions.
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We compared the torque preference of units in the network to its connectivity to evaluate how
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well its torque preference estimated its motor field. To quantify torque preference, we fit a plane to the
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activation of each unit across torque combinations in the two-dimensional elbow/shoulder torque space.
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The preferred torque direction (PTD) of a unit was the angle in torque space to which the unit was
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maximally active. Across simulations, we found that 99.4% of units had a significant (p<0.01) PTD in
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the posture task. The distribution of the significant PTDs for one training session can be seen in Figure
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3A. The resulting distribution was bimodal (r=0.31, p<0.001), aligned in much the same manner as the
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previously reported distribution of M1 neurons (Herter et al., 2007; Pruszynski et al., 2014), with the
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majority of units related to whole-limb flexion or extension.
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In such a straightforward model, one might assume that a given unit would always show an
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identical relationship between its torque preference and its anatomical connectivity given that the unit
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can only produce torque in a given direction when activated alone. We calculated the motor field
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preferred torque (MFPTD) direction of a given unit, zi, by multiplying its output weights (motor field),
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wij, with the vector of preferred torque direction of each output unit in the posture task, uj, and then
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vector summating. It is important to note that the preferred torque direction of each output unit is not the
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simple direction of force production for that unit. Due to the redundant force profiles of the biarticular
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muscles and the dynamics of the limb the preferred direction of an output unit rotates away from its
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simple force production direction (Lillicrap and Scott, 2013). We found a circular correlation (r=0.86,
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p<0.001) between the unit’s MFPTD and its preferred torque direction (PTD) in the posture task (Figure
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2B), however the relationship was not perfect. To see how different connectivity patterns might be
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influencing this relationship, we looked at the degree to which a unit co-activated opposite muscle
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groups together (co-contraction). We normalized each set of weights from a unit to its muscle groups,
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multiplied these by their respective MFPTD vectors, and vector averaged them. The length of the
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resulting vector for each unit was used as an indicator of co-contraction, with a short vector indicating
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more co-contraction. The average difference between PTD and MFPTD was 22⁰. Units with low co-
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contraction (MFPTD vector length > 0.4, 9.1% of units) had an average difference of 7⁰ (r=0.99),
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whereas units with high co-contraction (MFPTD vector length < 0.1, 11.7% of units) had an average
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difference of 62⁰ (r=0.40). This suggests that the statistical dispersion in the relationship between torque
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preference and anatomical connectivity is caused by those units with stronger synaptic connections to
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antagonist muscles.
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Neural Network Model: Relation of torque and reaching direction
We used our model to predict the relationship between preferred torque direction and reaching
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activity for units in the neural network model. Reaching activity was described by a preferred reaching
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direction (PRD) – the direction in Cartesian space toward which a reach movement would elicit
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maximal unit activity. Across all simulations, we found that 98.7% of units had a significant PRD (plane
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fit p<0.01). The distribution of significant PRDs for one training session of units can be seen in Figure
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3B. The bimodality (r=0.52, p<0.001) towards the top-left and bottom-right that emerges is stable across
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simulations. The distribution was consistent for static and dynamic networks (Lillicrap and Scott, 2013)
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and previous observations on M1 neurons (Scott et al., 2001).
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Novel in this study, we compared PTD and PRD for each model unit which had both a
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significant PTD in the posture task and a significant PRD in the reaching task (98.1%). As can be seen
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in Figure 4A, there is a consistent relationship between the PTDs and PRDs, where two clusters emerge
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due to the interaction of the bimodalities noted in each distribution. Figure 4B displays a histogram of
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the difference of PTD and PRD angles for all units, with the presence of a mean systematic shift of 152⁰
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(circular correlation, r=0.84, p<0.001). This angle roughly corresponds to the shift in coordinate frames
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between torque and hand space, used to define unit responses in the posture and reaching tasks,
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respectively. This rotational shift was stable across all ten simulation sets (150⁰-154⁰, circular SD=1.2⁰),
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indicating that the result is robust. There was some dispersion in the relationship between a unit’s PTD
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and PRD (circular SD=27⁰). Even though individual units had different PRD and PTD tuning across
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simulations, both the population distribution, and the relation between a given unit’s PTD and PRD
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remained statistically the same.
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Non-Human Primate Recordings: Muscle recordings
We recorded and analysed the EMG activity of 9 muscles spanning the shoulder and/or elbow
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(151 suitable recordings) in 5 macaque monkeys in posture and reaching tasks. In the posture task, a
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monkey’s arm was maintained in the horizontal plane while combinations of flexor and/or extensor step
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torques were applied to the shoulder and/or elbow (Figure 1A). Recordings were made while the
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monkey held their hand stationary at a central position and countered these torques. Figure 3C shows an
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example of pectoralis major activity during the posture task. As previously reported (Kurtzer et al.,
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2006a), this shoulder flexor, modulated its activity for torques applied at the elbow in addition to loads
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at the shoulder and, as a result, the preferred torque direction (PTD) for this muscle did not lie at
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shoulder flexion (i.e. 0⁰) line, as one would expect if the muscle activity reflected its anatomical action.
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Shifts across all muscles (Figure 3E) showed stereotypical clustering of preferred torque directions - one
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in the elbow-extension/shoulder-flexion quadrant and the other in the elbow-flexion/shoulder-extension
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quadrant (Kurtzer et al., 2006a).
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In the reaching task, NHPs made fast center-out reaches to peripheral targets arranged around the
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starting position (Figure 1B). Figure 3D shows an example of pectoralis major muscle activity during
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reaching. Maximal activity in the example was observed towards the top left target reflecting the need
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for a large shoulder flexor torque to accelerate the arm towards that target (Graham et al., 2003). In the
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bottom right direction, an activity peak can be seen about 200 ms later related to an antagonist burst to
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decelerate the arm. EMG activity was integrated over a 200 ms period starting 150 ms before movement
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onset (grey bar), to capture the activity associated with initial acceleration. Across all muscles, PRDs
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(128 significant muscle samples) were skewed towards one of two directions (Figure 3F), one centred
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around 110⁰ and one around 280⁰ (Kurtzer et al., 2006a).
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As with the novel model analysis, we compared PTD and PRD for each muscle which had both a
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significant PTD in the posture task and a significant PRD in the reaching task (N=122). A strong
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consistent relationship between the PTDs and PRDs can be seen in Figure 4C where two clusters
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emerge: one (bottom left cluster) that includes the shoulder extensors, the elbow flexors and all bi-
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articulars and the other (top right) that includes the elbow extensors and shoulder flexors. Figure 4D
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displays a histogram of the difference of PTD and PRD angles for all muscles. There is a systematic
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shift of 152⁰ (circular r=0.87, p<0.001), very similar to that obtained for the network model.
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Monkey M1 recordings
We recorded and analysed the activity of 540 M1 neurons in 5 monkeys in the posture and
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reaching tasks, with the same epochs as for muscle activity. Of these, 373 showed a significant PTD in
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the posture task (example neuron in Figure 3G) and 424 M1 neurons that showed a significant PRD
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during the reaching task (example neuron in Figure 3H). As seen in Figure 3I and 3J, the distributions of
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PTDs and PRDs for all M1 neurons showed stereotypical bimodalities as have been previously reported
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(Scott et al., 2001; Kurtzer et al., 2006a).
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As with muscles, we compared PTD and PRD for each neuron which had both a significant PTD
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and PRD (N=314). Figure 4E shows two distinct clusters when comparing the PTD and PRD of M1
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neurons: one cluster with a PRD at 301⁰ and a PTD at 140⁰ (bottom left cluster), and another cluster
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with a PRD at 123⁰ and a PTD at 325⁰. The relation between the two preferred directions of M1 neurons
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is clearly shown in Figure 4F as a 151⁰ shift (circular r=0.43, p<0.001) between PRD and PTD. Cluster
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locations were maxima of cell preferred directions convolved with a 2D windowed Gaussian distribution
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(σ = 10⁰). This shift and clustering is very similar to both the units of the network model, and the muscle
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recordings. Unlike the muscles and network model, a notable portion of M1 neurons show PTD-PRD
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differences not located near the diagonal of the plot. The result is a relatively low PTD-PRD correlation
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(r=0.43 cells, 0.84 model, 0.87 muscles) and large variance in their shift values (circular SD=61⁰ cells,
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27⁰ model, 24⁰ muscles). We estimated whether these variances were significantly different by sampled
350
the PTD-PRD difference values with replacement to create distributions of variances. The distribution of
351
PTD-PRD variance for neurons had no overlap with either the muscle or model distributions. The
352
muscle and model distributions of variance shared a 30% overlap.
353
354
355
Prediction of patterns of muscle activity from M1 population signals
Our first step to predict muscle activity during reaching was to classify the M1 neurons based on
356
their similarity to muscle groups during a postural load task. Figure 5 highlights the distribution of PTDs
357
of muscle for shoulder and elbow muscles. We defined four ranges in torque space representing flexors
358
and extensors at each joint: elbow flexors (90⁰ to 135⁰), shoulder extensors (135⁰ to 180⁰), elbow
359
extensors (270⁰ to 315⁰), and shoulder flexors (315⁰ to 360⁰). The four torque groups captured 75% of
360
motor cortical neurons: 55 neurons were classified as elbow flexor neurons, 71 as shoulder extensor
361
neurons, 54 as elbow extensor neurons, and 68 shoulder flexor neurons. When applied to our network
362
model, these groupings captured 60.8% of units, with 14.5% to 15.6% of units in each group.
363
Figure 6 displays the distribution of PRDs for each muscle group, the associated M1 neurons,
364
and associated network units. Note that these distributions represent vertical slices, 45⁰ thick, of the
365
PRD-PTD relationship shown in Figure 4E. The preferred reaching direction distributions for each
366
group of muscles were unimodal, with shoulder flexors preferring 134⁰ (r=0.85, p<0.001), elbow flexors
16
367
preferring 261⁰ (r=0.83, p<0.001), shoulder extensors preferring 317⁰ (r=0.99, p<0.001), and elbow
368
extensors preferring 98⁰ (r=0.63, p=0.03). Dispersion was relatively small for each muscle group
369
illustrating that muscle within a group had similar reaching preferences.
370
Each group of M1 neurons showed a significant unimodal distribution of PRDs, nearly identical
371
to that of their comparative muscle group (Figure 6). Shoulder flexor neurons showed a preferred
372
reaching direction of 128⁰ (r=0.66, p<0.001), elbow flexor neurons showed a preferred reaching
373
direction of 293⁰ (r=0.32, p<0.001), shoulder extensor neurons showed a preferred reaching direction of
374
313⁰ (r=0.48, p<0.001), and elbow extensor neurons showed a preferred reaching direction of 95⁰
375
(r=0.35, p=0.002). M1 neurons associated with each muscle group showed a much greater range of
376
preferred reach directions. Watson-Williams tests between muscles and cells showed no difference in
377
means, except a small (33.8 degrees), but significant difference for elbow flexors (SF: F1,72=0.02
378
p=0.65, EF: F1,102=5.12 p=0.03, SE: F1,101=0.13 p=0.71, SF: F1,55=0.01 p=0.92).
379
The torque groupings were also used to classify neural network units in a similar fashion (Figure
380
6). Shoulder flexor units showed a preferred reaching direction of 119⁰ (r=0.92, p<0.001), elbow flexor
381
units showed a preferred reaching direction of 269⁰ (r=0.88, p<0.001), shoulder extensor units showed a
382
preferred reaching direction of 298⁰ (r=0.91, p<0.001), and elbow extensor units showed a preferred
383
reaching direction of 89⁰ (r=0.89, p<0.001). As the neural network units followed the same pattern as
384
both muscles and M1 neurons in distribution, it was not surprising that each torque-based group also had
385
close overlap with the PRD of both M1 neurons and muscles.
386
387
388
389
Muscle-like spatiotemporal dynamics
While predicting the agonist burst activity of the spatiotemporal activity was a first step, we
further examined whether M1 neurons could predict the full temporal pattern of shoulder and elbow
17
390
muscle activity across directions during reaching. We evaluated the population activity of each group of
391
M1 neurons over time and compared this to the activity of each group of muscles.
392
Illustrations of normalized population activity over time and across reach direction for each
393
group of M1 neurons can be seen in Figure 7A. Clear maximums of activity emerge prior to reach onset
394
in a primary direction for each group. Given that PRD was determined by the epoch of time around
395
reach onset, it is not a surprise that these maximums align with the PRD of each torque-classified group.
396
Qualitatively, the overall patterns of activity are similar between M1 and their corresponding muscle
397
groups. Given that each group seemed to have a similar profile of activity across time but for different
398
reach directions, we computed 2-dimensional correlations between each M1 group and all muscle
399
groups (Figure 7B) and found high degrees of correlation (shoulder flexor r=0.82, elbow flexor r=0.74,
400
shoulder extensor r=0.74, elbow extensor r=0.75). Each muscle group’s spatiotemporal activity was
401
predicted best by its respective M1 neuronal group, with latencies of maximal correlation that show M1
402
activity precedes muscle activity (shoulder flexor -30ms, elbow flexor -70ms, shoulder extensor -30ms,
403
elbow extensor -65ms). Figure 7C displays the aggregate of all four groups highlighting how specific
404
shifts in the timing and magnitude of agonist muscle activity before movement is preceded (average
405
latency -35ms) by similar spatiotemporal patterns in M1 (Scott, 1997).
406
We also investigated the spatiotemporal output of the subset of M1 neurons that appeared to have
407
an atypical, opposing PRD-PTD relationship. We decomposed the aggregate M1 activity by selecting
408
neurons that were within 30⁰ of the expected relationship (around the 151⁰ PRD-PTD), and those that
409
were opposite to these. As can be seen in Figure 8A and 8B respectively, both sets of neurons seemed to
410
have bursts of activity; however, the atypical M1 neurons had later temporal characteristics for their
411
burst (Figure 8C), which occurred in the opposite reaching direction. A cross-correlation of the
412
maximum aggregate activity showed that the atypical activity burst occurred 50ms later than the typical
18
413
agonist burst. Interestingly, the atypical neurons’ agonist-direction activity (solid red line) appeared to
414
be larger with a longer duration than the regular population’s antagonist-direction activity (dashed black
415
line), lasting well after the reaching movement was completed. Thus across the population, the preferred
416
direction of these atypical cells was aligned with its motor field after movement when maintaining the
417
hand at the peripheral target.
418
19
419
420
421
DISCUSSION
Several studies have shown correlations between M1 activity and EMG in monkeys (Evarts,
422
1968; Humphrey, 1972; Fetz et al., 1989; Bennett and Lemon, 1996) and cats (Drew, 1993; Drew et al.,
423
2002). However, given the diversity of EMG activity across muscles during a motor action, one can
424
likely find that a neuron’s activity will correlate with at least one of them. Here we show these
425
correlations remain when classification and prediction are separated. We classified M1 neurons into one
426
of four distinct motor fields (elbow flexor, elbow extensor, shoulder flexor, and shoulder extensor) using
427
a static load task. We then generated a population signal from each group and predicted the
428
spatiotemporal activity of the corresponding muscle during a dynamic reaching task. The preferred
429
direction of most neurons matched the directional preference of muscles with the same motor field.
430
Interestingly, a small proportion of neurons had preferences in the opposite direction.
431
A receptive field of a sensory neuron can be defined as the part of the body to which a stimulus
432
elicits activity by this neuron. These can be easily defined by repeated application of stimuli and finding
433
the parts of the body to which firing of a neuron correlates. It is implicitly assumed from this that a
434
neuron that fires in relation to a stimulus is caused by that stimulus. A related concept is a motor field,
435
which can be defined as the portion of the body associated with a given motor-related neuron. While the
436
target innervation, or muscle field, of some neurons, such as corticomotoneurons, can be identified by
437
spike-triggered averaging of EMG (Cheney and Fetz, 1980, 1985), this does not necessarily define the
438
portions of the body to which the neuron fires. Even when limiting to CM cells which have
439
monosynaptic connections to motoneuron pools, Griffin et al. (2015) showed how directional tuning, or
440
motor field, of some CM cells does not necessarily align with the muscle field, or connectivity. As well,
441
CM cells represent only a small fraction of corticospinal neurons that predominantly target the distal
20
442
musculature in primates (Buys et al., 1986; Palmer and Ashby, 1992), and are localized mainly in the
443
caudal portion of M1 or “new M1” (Rathelot and Strick, 2009). More directly, a motor field can be
444
defined based on whether a neuron is active when the animal performs a motor action with a given body
445
part, as we have done in the present study. However, unlike sensory fields, it is not necessarily true that
446
there is a causal link between the neuron’s activity (motor field) and motor output of that body part.
447
We examined this relationship using a relatively complex artificial neural network with 1000
448
neural units to observe the relationship between a unit’s connectivity and activity relative to the motor
449
periphery. The model displayed a strong similarity between the unit’s torque preference (PTD) and the
450
activity preference of its motor field (MFPTD) during the postural load task. However, the directional
451
preferences were not identical as there was a mean difference of 22 degrees. Thus, in general, the
452
activity of the unit during a task was a good indicator of its synaptic connectivity to the motor periphery.
453
This was more accurate for the units that had strong unidirectional connectivity without strong synaptic
454
connections to antagonist muscles.
455
The presence of PTD-MFPTD variability in the artificial neural network parallels previous
456
experimental work demonstrating a statistical relationship between the muscle field of CM cells (defined
457
by STA, Fetz and Cheney, 1978) and the associated patterns of activity of forearm and hand muscles
458
during a precision grip task (Bennett and Lemon, 1994). In that study, the observed correlations were
459
relatively modest, the highest of which was 0.69. Some of this variability may be explained by task-
460
dependant variation in the connectivity using STA (Buys et al., 1986). However, our network model
461
showed that a more complex pattern of synaptic connectivity may contribute to a lower overall
462
correlation value. Ultimately, our model suggests that a perfect correlation will not exist between muscle
463
fields and motor fields as the neuronal activity reflects not only the activity of target muscles, but also
464
the activity of the rest of the network (Lillicrap and Scott, 2013).
21
465
Our model also demonstrated that the preferred pattern of activity relative to the motor periphery
466
remains relatively constant across motor behaviours. In other words, if a unit was maximally active
467
when the elbow flexors were maximally active for postural loads, it will tend to be active when the
468
elbow flexors are maximally active during reaching. A fixed motor field is consistent with the
469
observation that neural responses can be predicted when loads are combined at the shoulder and elbow
470
(Gribble and Scott, 2002) and are similar for transient and sustained loads (Herter et al., 2009;
471
Pruszynski et al., 2014). It is also consistent with several studies that highlight how the reaching
472
directional preference of neurons and muscles both tend to rotate similarly with applied curl fields
473
(Cherian et al., 2013), changes in arm geometry (Scott and Kalaska, 1997) or start position (Caminiti et
474
al., 1991; Sergio and Kalaska, 1997). Again, there is some dispersion in the directional preference across
475
tasks or conditions, likely reflecting how unit activity is influenced by factors beyond its motor field.
476
Similar methodology to the present study was used by Sergio et al. (2005) to examine M1 activity
477
between isometric force production and a dynamic reaching paradigm. Their results show similar
478
patterns of correlation between isometric force and reaching preferred direction which support the
479
notion of M1 motor fields that persist across tasks. Here we show this specific correlation between the
480
activity of M1 neurons and its motor field.
481
It is important to recognize that a fixed motor field does not mean that a neuron is always active
482
when its associated muscles are active during voluntary motor actions. It has been shown that neurons
483
strongly active during precision grip were much less active for power grip that required more muscle
484
force (Muir and Lemon, 1983). As well, load representations can change between posture and
485
movement, but their directional preference remains relatively constant (Kurtzer et al., 2005) and can be
486
only active when the muscle is used for a specific phase of movement (Griffin et al., 2015). Even when
487
neural activity reflects features such as the direction of the spatial goal, these neurons likely remain
22
488
associated with a specific body part such as the wrist (Kakei et al., 1999) or elbow/shoulder (Sergio and
489
Kalaska, 2003).
490
While the essential PRD and PTD patterns are quite similar between network model and real
491
neurons, the variability in the difference between the PRDs and PTDs is notably greater in the neurons
492
than in the model. There are many possible sources for this difference in observed variability. First,
493
there is noise in the empirical estimates of the PRDs and PTDs, whereas in the model these quantities
494
can be measured perfectly. As well, the only source of noise in the model, given a fixed and
495
deterministic optimization procedure, is the random initialization of the weights, w. Second, our model
496
assumes that all of the model cortical units directly contributed to driving muscle activity by way of
497
monosynaptic connections. In reality, only a subset of M1 neurons project to the spinal cord, and most
498
of these synapse predominantly on spinal interneurons and can be influenced by spinal processing. As
499
well, we may expect that neurons that do not project to the spinal cord – which may connect to other
500
brain areas (Turner and DeLong, 2000), or recurrently within M1 – to show activity dissociated from the
501
muscles. As well, motor cortical neurons reflect aspects of motor function beyond patterns of motor
502
output, such as correlates of movement kinematics (Kalaska et al., 1989) and behavioural goals (Kakei
503
et al., 1999). Neural activity is also context (Hepp-Reymond et al., 1999), phase (Griffin et al., 2015)
504
and task-dependent (Buys et al., 1986; Kurtzer et al., 2005). These complexities will necessarily
505
influence the relationship between motor cortical activity across postural and movement tasks and likely
506
increase variability in the difference between their PRD and PTD.
507
At the same time, given the complexity of neural processing in M1 it is surprising its activity can
508
predict the patterns of muscle activity. The patterns are not exact however particularly related to activity
509
patterns when the muscles are antagonists. The general similarity between M1 and muscle activity
510
suggests that spinal processing is not dramatically altering descending commands (Lillicrap and Scott,
23
511
2013). While adding an additional network layer to our model to simulate the influence of a spinal cord
512
or other systems may have given us a higher network variability, adding complexity was not the aim of
513
this study. We were not interested in exactly modeling the descending stream from primary motor
514
cortex, but rather to producing testable hypotheses that stem from a system with an unchanging
515
relationship with the periphery.
516
Our analysis focussed on exploring the relationship between M1 neurons and flexor and extensor
517
muscle groups at the shoulder and elbow. However, this does not mean that M1 neurons are associated
518
with all muscles within a given group, nor that they are only associated with a single muscle group.
519
Most certainly M1 neurons synapse onto many motor neurons (directly or indirectly) including those in
520
different muscle groups and muscles that span multiple joints as demonstrated with STA (Cheney and
521
Fetz, 1985; McKiernan et al., 1998; Smith and Fetz, 2009). We find several neurons with load
522
preferences related to combined flexor or extensor loads at the shoulder and elbow, a pattern of activity
523
not observed for any muscles (Kurtzer et al., 2006b). These neurons likely have muscle fields that span
524
both joints. Even those neurons with load preferences associated with a single muscle group, likely have
525
a motor field beyond that group.
526
The idea of a motor field is closely related to the idea of muscle synergies. While the basic
527
definition of a muscle synergy is simply the cooperative action of two or more muscles, some theorize
528
that the motor system simplifies control by only varying a small number of muscle synergies (d’Avella
529
et al., 2006). One prediction from this theory is that there would be a fixed number of motor fields
530
represented in a region like M1. In our postural load task, these synergies should appear as clusters of
531
preferred loads in our M1 sample. In fact, the distribution of load preferences do appear to be clustered
532
into two groups one related to whole-limb flexion and one related to whole-limb extension (Kurtzer et
533
al., 2006a; Herter et al., 2009). However, this bias appears to be due to the presence of bi-articular
24
534
muscles (Lillicrap and Scott, 2013) – in a model with no bi-articular muscles, the distribution of load
535
preferences is relatively uniform with no obvious clustering. The units spanned all possible
536
combinations of control, rather than showing a reduced set of controls. The spanned distribution was
537
mirrored by the distributions of load and reach preferences observed for neurons in M1. As well, studies
538
using STA also highlight diverse connectivity in their cell samples (Smith and Fetz, 2009; Alstermark
539
and Isa, 2012). Thus, there are likely many more motor fields (and muscle synergies) represented in M1
540
neurons than muscles in the body.
541
It was surprising to find atypical cells where the directional preference of the neuron during
542
reaching was opposite to that of its muscle group identified from the postural load task. This sub-
543
population of neurons could not be identified in Scott (1997). In that study, neurons were first divided
544
into elbow and shoulder groupings by their response to passive movement of the arm. Separation into
545
flexor or extensor sub-groups was defined by their preferred direction during reaching, and thus, they
546
would have simply been classified with the antagonist muscle group at that joint.
547
The onset time of this atypical population of neurons was delayed as compared to neurons with
548
directional preferences aligned with the directional preference of their motor field. One possible
549
explanation is that these neurons are preferentially involved in controlling muscle activity when it is an
550
antagonist to decelerate the limb. Drew and colleagues (Drew, 1993; Drew et al., 2002) found that some
551
M1 neurons in cats during locomotion were only active when a specific modification of the muscle
552
activity was required, suggesting that the activity of some M1 neurons could be task-dependent. Task-
553
dependent processing in M1 has also been observed in previous reaching and posture comparisons
554
(Kurtzer et al., 2005). While the latency between atypical and typical activity was much shorter than the
555
latency between the muscular agonist and antagonist, a recent study by Griffin et al. (2015) also showed
556
that cells which were functionally tuned for antagonist activity show activity only shortly after those
25
557
tuned for agonist activity. The antagonist activity was relatively weak in the present study due to the
558
speed of reaching and contribution of friction from the exoskeleton assisting the deceleration of the
559
limb. The potential contribution of these atypical neurons for controlling antagonist muscle activity
560
could be examined by modifying the speed of the movement or inertia of the limb.
561
562
26
563
564
565
566
Acknowledgements
567
568
We would like to acknowledge Kim Moore, Simone Appaqaq, Justin Peterson, and Helen Bretzke for
their laboratory and technical assistance.
569
570
Grants
571
572
573
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This work was supported by the Canadian Institutes of Health Research (CIHR). E. Heming received a
Doctoral Award from NSERC. M. Omrani received a Vanier Doctoral Award from CIHR. J. A.
Pruszynski received salary awards from CIHR and the Human Frontier Science Program. S. H. Scott is
supported by a GSK-CIHR Chair in Neuroscience.
575
576
Disclosures
577
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S. H. Scott is associated with BKIN Technologies, which commercializes the KINARM device used in
this study.
27
579
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Figure 1. Experimental posture and reaching tasks. A. In the posture task, the monkey was required to
715
maintain its hand at a central target while flexion and/or extension torques were applied at the elbow and
716
shoulder. The right panel illustrates the eight combinations of torques and an unloaded condition. B. In
717
the reaching task, relatively fast reaches were made from a central origin to eight peripheral targets. The
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right panel displays the four different combinations of targets used in the datasets.
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Figure 2. Predictions from the static neural network model. A. A representative schematic of one of the
721
models. Two network units, z, are shown connected to muscle outputs, u, via random weightings shown
722
by the thickness of the lines. The output variables, u, drove mono- and bi-articular muscles on a two-
723
joint arm, as shown. The values of the units in z were optimized to solve analogues of the reaching and
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posture tasks. B. The relationship between each unit’s preferred torque direction in the posture task and
725
the calculated preferred torque direction of its motor field (defined by synaptic connectivity) in one
726
network of 1000 units. The size of the circle for each unit corresponds to its strength of tuning. Note that
727
the muscles, plotted here for reference, must lie on the diagonal as they correspond with their own motor
728
field.
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Figure 3. Preferred torque and reaching directions. A. Polar histogram of each network units’ preferred
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torque direction (PTD) in one network. Angles are in torque space (shoulder torque x-axis, elbow torque
732
y-axis). B. Polar histogram of each network units’ preferred reaching direction (PRD) in one network. C.
733
Exemplar activity of pectoralis major in eight shoulder/elbow loaded conditions (unloaded condition not
734
shown) during the last three seconds of in the posture task. The arrow in the centre denotes the muscle’s
735
preferred torque direction (351 degrees). D. Exemplar activity of pectoralis major around reach onset
736
while reaching to the eight targets (set iii in Fig. 1B). The arrow in the centre denotes the muscle’s PRD
33
737
(140 degrees). E-F. Averages of the PTD and PRD of all muscle samples are shown with 95%
738
confidence intervals as shaded areas around each line. G. Activity of an exemplar M1 neuron in the
739
eight shoulder/elbow loaded conditions (unloaded condition not shown) during the last three seconds of
740
in the posture task. The arrow in the centre denotes the neuron’s preferred torque direction (155
741
degrees). H. Activity of an exemplar M1 neuron around reach onset while reaching to the eight targets
742
(Set iii in Fig. 1B). The arrow in the centre denotes the neuron’s PRD (119 degrees). I-J. Polar
743
histograms of all significant PTDs and PRDs across the population of recorded M1 neurons.
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Figure 4. E. Relationship between PTD and PRD (directional preferences must be significant in both
746
tasks) for network units, muscles, and M1 neurons. A. Scatter relationship for network units. B.
747
Histogram of the difference between PTD and PRD for network units, inset shows as polar plot. C.
748
Scatter relationship for muscle recordings. D. Histogram of the difference between PTD and PRD for
749
muscle recordings. E. Scatter relationship for M1 neuron recordings. F. Histogram of the difference
750
between PTD and PRD for M1 neuron recordings.
751
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Figure 5. Distribution of load preferences for M1 neurons and individual muscles. The number of
753
recordings for each muscle is indicated in brackets beside the muscle name. The four assigned motor
754
fields are shown as vertical shaded stripes with their titles marked at the top.
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Figure 6. Distribution of preferred reaching direction for each muscle group, and assigned M1 neurons
757
and network units. Each distribution is normalized as a percentage of the total number of significant
758
PRDs. The x-axis starts at rightward reach (0⁰) and increases counter clockwise.
759
34
760
Figure 7. Spatiotemporal dynamics of muscle groups and associated M1 neuron populations during
761
reaching. A. Colormaps of the aggregate activity over time (x-axis) and across reach direction (y-axis)
762
of each muscle group and associated population of M1 neurons. Data are smoothed in the y-axis for
763
display purposes. B. Two-dimensional correlation coefficients between the spatiotemporal patterns of
764
muscle and neuron groups. Each graph is one muscle group and the bars show the coefficients between
765
that muscle’s spatiotemporal pattern and the pattern of the four populations of M1 neurons. C.
766
Aggregate activity of all neurons and all muscles were generated by rotating the y-axis of each group so
767
that their directions of maximal agonist activity were aligned with the Agonist direction, and then
768
averaged.
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Figure 8. Aggregate spatiotemporal colormaps of M1 neurons that have similar and opposing PRDs
771
compared to their associated muscle group. A. Colormap of population activity of all neurons whose
772
PRD was within 30⁰ of its assigned muscle group (typical neurons). Aggregate of neural activity was
773
rotated as in Figure 7. B Colormap of population activity of all neurons whose PRD was greater than
774
150⁰ away from that of its assigned muscle group (atypical neurons). C. Spatiotemporal pattern of
775
activation for typical (black lines) and atypical (red lines) neurons when the muscle group acts as an
776
agonist (solid line) and an antagonist (dashed line).
777
778
35
A.
Postural contraction
against loads
B.
Monkey reaches to
peripheral targets
i
ii
iii
iv
A.
w
z
1
u
Elb Flex
2
Elb Ext
3
Sho Flex
4
5
Sho Ext
6
Bi Flex
Bi Ext
N
B.
Motor Field PTD (°)
360
0
0
Posture PTD (°)
360
A. Model Posture PTD C.
EMG
in Posture Task
E. EMG Posture PTD
G.
M1 cell
in Posture Task
I.
N=373
Model Reach PRD
D.
EMG
in Reaching Task
F.
EMG Reach PRD
N=125
1au
B.
1s
20 sp/s
BR
Bi
Br
DA
DP
PM
Tlat
Tlong
Tmid
1s
H.
M1 cell
in Reaching Task
J.
M1 Reach PRD
N=424
20 sp/s
1au
N=133
1s
M1 Posture PTD
1s
B.
A.
Neurons (%)
Reach PRD (°)
20
0
0
Posture PTD (°)
10
0
360
C.
0
D.
Recordings (%)
0
90
180
270
360
270
360
270
360
Difference (°)
PRD-PTD
30
179
Reach PRD (°)
152°
15
5
180
25
N=122
152°
20
15
10
5
180
0
Posture PTD (°)
0
360
E.
F.
179
0
12
10
Neurons (%)
Reach PRD (°)
PRD-PTD
25
179
0
180
90
180
Difference (°)
PRD-PTD
N=314
151°
8
6
4
2
0
Posture PTD (°)
360
0
0
90
180
Difference (°)
Elbow Shoulder
flexor extensor
Elbow Shoulder
extensor flexor
Model units
M1 neurons (373)
Biceps (25)
Brachioradialis (18)
Brachialis (12)
Long head triceps (24)
Posterior deltoid (18)
Lateral triceps (8)
Middle triceps (2)
Anterior deltoid (14)
Pectoralis major (12)
0
45
90
135
180
225
270
315
360
A.
shoulder flexor
B.
elbow flexor
D.
elbow extensor
30%
Muscles
M1 neurons
Network
20%
10%
0%
C.
shoulder extensor
30%
20%
10%
0%
0°
360° 0°
360°
Preferred reaching direction Preferred reaching direction
EMG behavior
B.
C.
1
Aggregate M1 prediction
Antagonist
Correlation
Shoulder
flexor
related
M1 prediction
Reach direction
A.
0
Agonist
Reach direction
Elbow
flexor
related
Correlation
1
0
Aggregate EMG behavior
Antagonist
Correlation
Shoulder
extensor
related
Reach direction
1
0
Agonist
Reach direction
Correlation
1
Elbow
extensor
related
0
-1
-1
1
Time around reach onset (s)
1
SF EF SE EE
neuron group
-1
0
Time around reach onset (s)
1
Agonist
Antagonist
A.
Agonist
Antagonist
B.
-1000
100
Percent max activity
C.
1000
0
Time around reach onset (ms)
80
60
40
20
0
-500
0
500
Time around reach onset (ms)