Download Relationship between muscle output and functional MRI

Exp Brain Res (2001) 140:290–300
DOI 10.1007/s002210100815
R E S E A R C H A RT I C L E
Te H. Dai · Jing Z. Liu · Vinod Sahgal
Robert W. Brown · Guang H. Yue
Relationship between muscle output and functional
MRI-measured brain activation
Received: 17 October 2000 / Accepted: 25 May 2001 / Published online: 31 July 2001
© Springer-Verlag 2001
Abstract The relationship between functional MRI
(fMRI)-measured brain signal and muscle force and or
electromyogram (EMG) is critical in interpreting fMRI
data and understanding the control mechanisms of
voluntary motor actions. We designed a system that
could record joint force and surface EMG online with
fMRI data. High-quality force and EMG data were
obtained while maintaining the quality of the fMRI brain
images. Using this system, we determined the relationship
between fMRI-measured brain activation and handgrip
force and between fMRI-measured brain signal and
EMG of extrinsic finger muscles. Ten volunteers participated in the experiments (only seven subjects’ data were
analyzed due to excessive noise in the fMRI data of three
subjects). The participants exerted 20%, 35%, 50%,
65%, and 80% of the maximal force. During each
contraction period, handgrip force, surface EMG of the
finger flexor and extensor muscles, and fMRI brain
images were acquired. The degree of muscle activation
(force and EMG) was directly proportional to the amplitude of the brain signal determined by fMRI in the entire
brain and in a number of motor function-related cortical
fields, including primary motor, sensory regions, supplementary motor area, premotor, prefrontal, parietal and
cingulate cortices, and cerebellum. All the examined
brain areas demonstrated a similar relationship between
the fMRI signal and force. A stronger fMRI signal during
higher force indicates that more cortical output neurons
and/or interneurons may participate in generating
T.H. Dai · J.Z. Liu · G.H. Yue (✉)
Department of Biomedical Engineering/ND20,
The Lerner Research Institute, The Cleveland Clinic Foundation,
9500 Euclid Avenue, Cleveland, OH 44195, USA
e-mail: [email protected]
Tel.: +1-216-4459336, Fax: +1-216-4449198
V. Sahgal · G.H. Yue
Department of Physical Medicine and Rehabilitation,
The Cleveland Clinic Foundation, Cleveland, OH 44195, USA
T.H. Dai · J.Z. Liu · R.W. Brown
Department of Physics, Case Western Reserve University,
Cleveland, OH 44106, USA
descending commands and/or processing additional
sensory information. The similarity in the relationship
between muscle output and fMRI signal in the cortical
regions suggests that correlated or networked activation
among a number of cortical fields may be necessary for
controlling precise static force of finger muscles.
Keywords Finger flexor muscles · Functional magnetic
resonance imaging · Handgrip force · Surface EMG ·
Voluntary contraction
Introduction
Because of its noninvasive nature and good spatial resolution, functional magnetic resonance imaging (fMRI)
has been increasingly used in studying human brain
function since its emergence in 1992 (Bandettini et al.
1992; Kwong et al. 1992; Ogawa et al. 1992). In the
field of human motor control, recent fMRI data of brain
activation have confirmed and extended the findings of
previous electrophysiological studies in animals (Evarts
1968; Hepp-Reymond et al. 1978) and in patients who
undergo neurological surgery (Penfield and Rasmussen
1950). For example, a single fMRI experiment involving
a simple voluntary motor action can result in activation
of the primary sensorimotor cortex and of many other
cortical regions (Bandettini et al. 1992; Boecker et al.
1994; Ehrsson et al. 2000; Kim et al. 1993a, 1993b; Liu
et al. 1999; Rao et al. 1993, 1996; Sanes et al. 1995;
Tyszka et al. 1994; Yue et al. 2000). These fMRI data not
only suggest an important role of the primary sensorimotor
cortex in controlling motor actions, but also indicate that
the participation of multiple cortical areas may be essential
for planning and executing a voluntary motor action
(König and Engel 1995).
Despite the increased use of fMRI in human motor
control investigation, only a small number of studies
have addressed the relation between muscle output
(force) and fMRI-measured brain activity, and the results
are contradictory (Dettmers et al. 1996; Ludman et al.
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1996; Thickbroom et al. 1998). Furthermore, electromyograms (EMG) have not been recorded concurrently
with force and fMRI in any of these studies. Without
EMG data, the muscle activation level cannot be
assessed with high confidence based on force information
alone, as joint force is determined by both the agonist
and the antagonist muscles. Thus, a low level of force
can mean a high level of muscle activation if both the
agonist and antagonist muscles are coactivated. The
primary difficulty with recording force and EMG signals
during an fMRI experiment is that the presence of electrical equipment and signals can disturb the homogeneity
of the magnetic field of the imaging scanner and introduce
noise to the brain images. Similarly, high-voltage signals
generated by running the imaging acquisition sequence
can impair signals recorded from muscle. Recently, we
designed and built a system that can record joint force
and muscle surface EMG online with fMRI data without
compromising data quality of any type (force, EMG, or
fMRI; Liu et al. 2000).
Based on single-cell recordings in the primary motor
cortex (MI) in monkeys, a direct relationship between
the discharge rate and exerted force has been documented
(Evarts 1968; Hepp-Reymond et al. 1978, 1999). A
positron emission tomography (PET) study (Dettmers et
al. 1995) has reported that there is a direct relationship
between the index finger flexion force and increases in
cerebral blood flow in four motor-related brain regions:
contralateral sensorimotor cortex, supplementary motor
area (SMA), cingulate cortex, and cerebellum. One
study (Siemionow et al. 2000) has demonstrated that
surface electroencephalogram (EEG)-derived motor
activity-related cortical potential (MRCP) measured by
electrodes located on the scalp overlying the contralateral
sensorimotor cortex and SMA is linearly related to
isometric joint force and muscle EMG. These findings
suggest that cortical activity measured by single-cell
discharge, cerebral blood flow, and EEG-derived MRCP
is directly related to muscle output. Based on these
results and the conclusion that fMRI signal reflects
primarily the synaptic activities of cortical neurons
(Jueptner and Weiller 1995), we hypothesized that the
magnitude of fMRI-measured brain activity would be
proportional to muscle force and EMG. Thus, the
purpose of this study was to determine the relationship
between fMRI-measured human cortical activation and
handgrip force and surface EMG of finger flexor
muscles.
Methods
Measurement systems
The measurement systems included a 1.5-T Siemens Vision
scanner and a force/EMG recording system specially built for use
in an MRI environment (Liu et al. 2000). In addition, a visualfeedback system was used to provide force information to the
subject in the scanner so that he/she could exert a force that
matched the target. The major challenge for the measurement
systems was that the operation of the force/EMG data acquisition
equipment and/or the force/EMG signals should not affect the
quality of the MRI data; conversely, running of the MRI scanner
should not generate significant noise that would impair the signals
of force and EMG. To minimize the risks of interference among
these systems, the equipment inside the MRI room is either metalfree or well shielded. We used nonmetallic material (polycarbonate)
to build the handgrip device and the connecting tube (nylon). The
EMG electrodes were silver-silver chloride (8-mm recording
diameter) with double-shielded wires. Devices that were considered
to have potential risks of introducing noise were positioned
outside of the MRI room. These included the pressure transducer
(part of the transducer is made of stainless steel), EMG and force
amplifiers, the associated power supply, and the data acquisition
unit (laptop computer and its docking station). The electrode wires
were formed into a flat cable running from the subject in the scanner
to the amplifier outside the MRI room, through the thin gap
between the lower edge of the shielding door and the floor along
with the nylon tube connecting the handgrip device and the pressure
transducer, with the door still tightly shut. Thus, the shielding of
the MRI room was not compromised by these connections. An
additional layer of shielding was built in the flat cable that covered
the electrode wires.
Force data acquisition
Handgrip force was measured by a pressure transducer (EPX-N1
250 PSIG; Entran Devices, Fairfield, N.J.). The metal-free handgrip
device was held in the subject’s right hand in the MRI room and
was connected to the pressure transducer located outside the MRI
room through a nylon tube filled with distilled water. When force
was applied to the handgrip device, it pushed the piston built in
the device, and the piston pressed the water against the transducer.
The output of the transducer was connected to an amplifier whose
output was directed to a data acquisition system (Spike 2;
Cambridge Electronic Design, Cambridge, UK) and recorded
online on the hard disk of a laptop computer. The transducer output
is highly linear (r=0.9998; Liu et al. 2000). The force signal was
digitized at 200 samples/s.
EMG data acquisition
Surface EMG was measured from the flexor digitorum profundus
(FDP), flexor digitorum superficialis (FDS), and extensor
digitorum (ED) muscles. Bipolar electrodes (8-mm recording
diameter; In Vivo Metric, Healdsburg, Calif.) were attached to the
skin overlying the belly of each muscle. The electrodes were
connected with the custom-built differential amplifiers (Liu et al.
2000) located outside the MRI room. The output of the EMG
amplifiers (frequency bandwidth, 10–2,000 Hz) was connected to
the Spike 2 data acquisition system and saved on the hard disk of
the laptop computer. The EMG signal was digitized at a rate of
2,000 samples/s.
Subjects
fMRI data acquisition
Ten right-handed volunteers (eight men and two women, aged
31.3±6.5 years) participated in the study. All subjects were
healthy and had no known neuromuscular disorders at the time of
the study. The experimental procedures were approved by the
Institutional Review Board at the Cleveland Clinic Foundation.
All subjects gave informed consent prior to participation in the
study.
fMRI images were collected on a Siemens 1.5-T Vision system
using a circular polarized head coil and an interleaved multislice
gradient echo EPI pulse sequence (TR/TE=115/22 ms). The
subject was positioned in the MRI chamber (supine) and was told
to remain as still as possible. The subject’s head was stabilized by
padded restraints and by taping the forehead to the frame of the
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head coil. Both T1-weighted (anatomic) images and functional
images were collected in the transverse plane. At each force level,
fMRI images were collected during a rest (baseline) period before
the handgrip contraction and during the contraction period. Brain
activation was detected by comparing the signal intensity of the
active images with that of rest images based on the change of local
blood oxygenation level (DeYoe et al. 1994; Kwong et al. 1992;
Ogawa et al. 1990, 1992, 1993). In each rest or active period, 20
brain slices (from the top of the head) were imaged (6-mm slice
thickness). We refer to these 20 slices that covered the entire brain
as one scan. Eleven scans (220 images) were collected in each
period. The field of view for an image was 256 mm × 256 mm,
and the matrix size was 128×128, thus, yielding a spatial resolution
of 2 mm × 2 mm.
Visual feedback system
This study required subjects to repeatedly exert a predetermined
handgrip force; thus, it was essential for the subject in the MRI
scanner to know exactly how much force they were producing.
This task was accomplished by a visual feedback system. This
system included a Silent Vision system (SV-2200; Avotec, Jensen
Beach, Fla.), a video camera, and an oscilloscope. During the
experiments, a target (e.g., 20% maximal handgrip force) was
placed on the oscilloscope. The video camera was pointed to the
oscilloscope screen to transmit the image to the video interface/
monitor unit located outside the MRI room. The video interface/
monitor unit was connected to a color LCD projector by a long
fiber-optic cable. The LCD projector was located near the scanner
inside the MRI room. The output of the color LCD projector was
directed to a pair of adjustable biocular glasses via a fiber-optic
guide. The biocular glasses were fixed to the top-opening of the
head coil, directly above the subject’s eyes. Through the adjustable
glasses, the subject could clearly see the target and match the
target with the exerted force. The fiber-optic cable and other
cables of the visual feedback system were permanently installed
into the MRI room. The cables ran to the ceiling together with the
MRI system cables and went through the penetration panel to the
operation station outside the MRI room.
Experiment procedures
One pair of electrodes was attached to each of the three muscles
(FDP, FDS, and ED). The FDP and FDS are the agonist of handgrip,
and the ED is the antagonist. The muscles were identified by
palpating the skin when the subject moved the fingers. We located
the FDP from the posterior surface of the forearm near the elbow
joint (near the medial side of the ulna. The FDS was located from
the anterior surface of the forearm between the flexor carpi radialis
and flexor carpi ulnaris. The skin overlying the identified muscles
was cleaned by alcohol pads prior to electrode placement. A
common reference electrode for the three recording pairs was
fixed on the skin overlying the lateral epicondyle near the elbow
joint. Correct electrode placement was confirmed by asking the
subject to make appropriate finger movements while watching the
EMG activity. Movements of the distal phalangial joint are primarily
contributed by the FDP, whereas those of the intermediate
phanlangial joint are accomplished mainly by the FDS. Although
the two muscles were accessed from the two different locations on
the forearm, the signals recorded from each muscle might have
been contributed by both flexors and other forearm muscles.
Finger extension is a result of activation of the ED muscle.
Before the subject was positioned in the MRI chamber, the
force and EMG signals were tested by asking them to squeeze the
handgrip device. All data channels were displayed on the monitor
of the laptop computer. The placement of the EMG electrodes was
adjusted at this time if it was not at the correct location. While
standing upright with the right arm in a vertical position on the
right side of the body, the subject performed a maximal voluntary
contraction (MVC) of the handgrip. Based on the MVC force,
Fig. 1 Examples of force and EMG recordings during fMRI data
acquisition. The upper panel shows the force exerted by a subject
at 80% MVC. The middle panel displays the EMG signals of the
entire trial recorded from the flexor digitorum profundus (FDP)
muscle; note that high-voltage noises associated with the pulse
sequence were recorded during most of the trial except the first
several seconds of the recording. The lower panel exhibits the
EMG signal within the 200-ms gap between the two adjacent
scans of image acquisition. The EMG signal in the 200-ms period
was not significantly affected by the fMRI pulse sequence
20%, 35%, 50%, 65%, and 80% handgrip force was calculated.
Each level was displayed on the oscilloscope as the target, and the
subject was asked to match the target with the exerted force. One
trial was performed at each level, and the time during which the
force was on the target was about 5 s for each contraction. At least
a 30-s rest was provided between contractions. Five subjects
performed the five levels in ascending order and the other five
subjects in descending order. The purpose of recording the EMG
data at the five levels of force outside the MRI room was to compare
the EMG signal recorded without the influence of operating the
MRI system to that obtained inside the MRI bore when the
scanner was running. If the EMG signal recorded from inside the
scanner was contaminated by the magnetic field and image acquisition pulse sequences, then the signal should be significantly
different from that recorded outside.
The subject was then positioned (supine) within the MRI
chamber. The biocular glasses of the feedback system were adjusted
to a position at which the subject could see the oscilloscope screen
clearly with both eyes and the cursor lines were well focused. The
right arm rested comfortably on a pile of hospital sheets placed on
the sliding board of the scanner. The handgrip device was held in
the right hand, but the subject was told not to apply any force until
they were told to do so. One trial of handgrip MVC was tested after
the subject was placed at the center of the scanner. Based on this
MVC force, the five target levels (20%, 35%, 50%, 65%, and 80%)
in the scanner were calculated. Any communication between the
experimenter and the subject was through intercom.
Functional images were acquired in the transverse plane across
the brain while the subject performed a handgrip contraction
(active images) and while they rested (rest images) with the eyes
looking at the oscilloscope screen on which two cursors were
shown. The lower cursor was the force signal (zero or baseline
force at rest) and the upper one indicated the target. Twenty
contiguous brain slices (one scan) were imaged. T1-weighted
anatomical images of the same number of slices at the same
positions were collected before acquiring the functional images.
Eleven scans were acquired during each fMRI (active or rest)
period. The duration of each period was 22 s. During the active
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Fig. 2 Examples of handgrip
force and EMG data at five
intensity levels (percentage
MVC) from a representative
subject. The EMG signals
shown here were those recorded
before the image acquisition
began (FDP flexor digitorum
profundus, FDS flexor digitorum superficialis)
period, the subject was given a “go” command, and they squeezed
the handgrip device to match the force cursor with the target.
About 5 s after the two cursors were matched, the fMRI sequence
began. Thus, the actual duration of each contraction was ~29 s.
Subjects were told not to move the arm and wrist during the entire
data collection period (~20 min), since alteration of muscle length
would affect force and EMG output. Five subjects performed the
five levels of contractions in descending order and the other five
subjects, ascending order. After collecting 11 scans of images at
each force level, the system needed about 3 min to save the data
before image collections at the next force level. This 3-min period
served as a rest for the subject after completing each contraction.
Not all EMG data were noise free. As indicated in Fig. 1,
“clean” EMG signals are shown only during the time period
before the fMRI data collection and during the sequence gaps
between adjacent fMRI scans. Each gap was 200 ms. Because of
substantial noise created by the pulse sequence, the EMG signal
was not readable during each scan period (Fig. 1). However, the
available “clean” EMG before or between the fMRI scans was
adequate to reflect the level of muscle activation for the whole
contraction (Figs. 2, 3; see also Liu et al. 2000).
Force/EMG data analysis
Fig. 3A–C Relationship between EMG and force shown by group
data. A The EMG signals used for the analysis were recorded from
outside the MRI room. B The EMG recordings were obtained
prior to the fMRI data acquisition. C The EMG data used were
those recorded between the fMRI scans. There was no significant
difference in the relationship between the two variables regardless
of which set of EMG data was used (ED extensor digitorum)
The force signal was converted from voltage to newtons according
to the calibration equation developed for the pressure transducer
(Liu et al. 2000). At each level, the force was averaged across the
entire contraction, from the time when it reached the target and
was kept steady to the time when the force began to decline from
the target after the subject was told to relax.
For the EMG data recorded inside the scanner, the signal was
analyzed during the first 5 s of each contraction, during which no
fMRI pulse sequence was executed. In addition, the EMG was also
analyzed during each 200-ms period between each two consecutive
scans. The EMG signal was first rectified and then averaged over the
beginning 5-s period and each of the 200-ms periods using the Spike
2 data-analysis software. A grand average was then calculated over
the ten 200-ms periods. These values of average EMG were then
normalized to the average MVC EMG performed inside the scanner.
For the EMG data obtained outside the MRI room, the signal was
rectified and averaged over the period (~5 s) during which the force
was on the target and stable. The averaged EMG was normalized to
the averaged MVC EMG acquired outside the MRI room.
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fMRI data analysis
fMRI data analysis was performed by using the MEDx 3.2 software
package (Sensor Systems, Sterling, Va.) specially designed for
functional imaging analysis. During postexperiment image
processing, image motion detection and correction were performed
before the statistical comparisons. The first set of 20 rest images
preceding the images during each force-level contraction was used
as the reference images. Detected displacement on the x (ear-toear), y (backhead-to-forehead), and z (head-to-feet) axes was
1.41±1.28 mm, 2.47±0.77 mm, and 0.35±0.20 mm, respectively.
Images obtained during force performance were realigned with the
reference images (motion correction). After motion correction, the
displacement between the reference and experimental images was
reduced, on average, to less than 0.5 mm on all axes, which was
substantially smaller than the pixel size (2 mm). When motion
correction was performed, we considered not only the x, y, z
translation, but also the rotation. Detected displacement on x-, y-,
and z-axes was the total result of both the translation motion
correction and the rotational motion correction.
Images acquired during the force production period were
compared with the same (location) images acquired during the rest
period on a pixel-to-pixel basis with Student t-tests. (The first of the
11 image scans in each period was excluded from the data analysis
to allow a same T1 weighting for all the images.) Pixels with a
z-score of 2.5 or more (corresponding P-value ≤0.006) were included
in the functional map. The statistical functional map (z map) was
overlaid onto a T1-weighted anatomical image to determine locations of activation. For measurements of activation in the individual
cortical fields, activated pixels were circled by hand at each location
and the number calculated. Up to this stage, we found that fMRI
data of three of the ten subjects could not be used due to substantial
noise, probably caused by head motion during the experiment. In
these three subjects, no activation pattern could be recognized. In
one subject, an extremely large number of activated pixels were
almost evenly spread in every image. In the other two, activated
pixels were concentrated around the edge of the brain to form a
“circular color band,” a typical noise pattern caused by head
movement. Thus, the data reported in the Results section are based
on the results of seven subjects who had “good” fMRI data.
The MI is defined as the region between the precentral sulcus
and central sulcus. The primary sensory cortex (SI) was identified
from the central sulcus to the postcentral sulcus. The SMA was
classified as the medial wall area above the cingulate sulcus, posterior
to the anterior paracentral sulcus, and anterior to the precentral
sulcus. The premotor cortex (PM) was an area lateral to the SMA
and anterior to the precentral sulcus or primary motor cortex. The
cingulate gyrus (CG) was identified between the corpus callosum
and cingulate sulcus. Prefrontal cortex (PFC) activity was measured
from the superior, middle, inferior, and orbital frontal gyri anterior
to the premotor cortex, frontal eye area, and Broca’s area. Activation
in the parietal lobe (PL) was measured in the area between the postcentral sulcus and occipital lobe, not including any areas in the
temporal lobe. We measured cerebellum (CBL) activation globally
without further quantifying the activity in single hemispheres or
individual cerebellar areas. The identification of the brain areas was
assisted by consulting a standard brain atlas, high-resolution MRI
brain atlas, and experienced neurologists (see Acknowledgements).
Activation in the entire brain and in each of the identified
cortical fields was quantified by the number of pixels that passed
the z-score threshold and by the average z-score (average intensity).
The average intensity was defined as the total z-score divided by
the total number of activated pixels in the entire brain or in each of
the cortical fields defined above. Our earlier analyses have shown
that changes in the z-score-indicated average intensity did not
differ substantially from the changes in the average intensity
calculated from the original image signals (unpublished results).
Statistical analysis
The signal difference between baseline and performance conditions
was determined by pixel-to-pixel comparisons using Student
t-tests with Bonferroni adjustment for the significance level. A
significant increase in signal was determined by a z-threshold
(unadjusted z-score, 2.5 or more) in each comparison (corresponding P-value for z≥2.5 is 0.006). Linear regression analyses
were performed between EMG and force, fMRI and force, and
fMRI and EMG to determine the relationship between the two
variables. All data in the text are presented as mean ± standard
deviation (SD).
Results
Force and EMG
In Fig. 2, data of handgrip force (left column) at five
different levels (20%, 35%, 50%, 65%, and 80%) from a
subject are displayed with the corresponding surface
EMG signals from the FDP (middle column) and FDS
(right column). These data show that higher force is
associated with a greater EMG signal and that the quality
of force and EMG signals was not affected by the
presence of the strong magnetic field.
The group data of the force and EMG are shown in
Fig. 3. EMG data recorded outside the MRI room are
shown in Fig. 3A. EMG signals analyzed from the first
5 s of each contraction inside the scanner are presented
in Fig. 3B, and those averaged from the 200-ms periods
between the fMRI scans are displayed in Fig. 3C. The
EMG values between the three data sets (outside, inside
before the scans, and inside between the scans) were
similar, indicating that: (1) the signals were not significantly affected by the magnetic field; and (2) the signals
recorded during a brief interruption (200 ms) between
two fMRI scans at each force level were adequate to
represent EMG activity of the contraction.
For the EMG recorded outside the MRI room, the
values of the normalized EMG signal corresponding to
the five force levels were 16%, 33%, 44%, 64%, and
78% MVC for the FDS muscle. The same five values
were 19%, 30%, 50%, 58%, and 79% for the FDP
muscle (Fig. 3A). For the first 5-s EMG recorded inside
the scanner, the five values were 14%, 33%, 41%, 62%,
and 73% MVC for the FDS muscle. For the FDP muscle,
the five EMG values were 14%, 30%, 51%, 65%, and
78%, respectively (Fig. 3B). For the normalized average
EMG recorded during the 200-ms periods between the
fMRI scans, the values at the five force levels for the
FDP muscle were 12%, 24%, 39%, 58%, and 74%; and
those for the FDS were 13%, 28%, 41%, 66%, and 81%
(Fig. 3C). Linear regression analysis resulted in correlation
values of 0.98, 0.99, and 0.94 between force and EMG
for the FDS, FDP, and ED muscles, respectively, for the
EMG data collected outside (Fig. 3A). The three correlation values were 0.98, 0.99, and 0.99, respectively, for
the EMG data of the first 5 s in the scanner (Fig. 3B).
The three correlation values for the EMG data of the
200-ms periods between the scans were 0.99, 0.99, 0.98,
respectively (Fig. 3C). Note that values from the ED
muscle, the antagonist, also correlated strongly with
handgrip force.
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Fig. 4A, B Functional images (at the transverse plane) taken at
the same brain locations at the five force levels. A The cortical
level, clearly showing activation of the primary motor, sensory,
and premotor cortices. Primary motor and sensory cortices are
separated by the central sulcus, with the motor cortex located
above the central sulcus. The premotor cortex is located above the
precentral sulcus. B Activities of the three association cortices:
prefrontal (PFC), cingulate (CG), and parietal (PL). It is apparent
that at these particular cortical locations both the number of
activated pixels and average intensity increased with handgrip
force. A and B were taken from two different subjects. The z-score
threshold for Fig. 6 was set at 3.0 for greater image clarity.
Because the image is viewed from feet to head orientation, the
right side of the image represents the left side of the brain
Correlation between fMRI and force
Cortical activation was quantified by calculating the
number of pixels passed the statistical threshold (z≥2.5)
and average activation intensity (summed z-score per
number of activated pixels) in the entire brain and
individual cortical regions. Examples are given in Fig. 4,
showing brain activation pattern during five levels of
handgrip contraction; each image was collected from one
of the five force levels. The five images in Fig. 4A show
activities of the MI, SI, and PM in the hemisphere
contralateral to the performing hand (right-side image
means left-side brain). The five images in Fig. 4B illustrate
activation patterns of the PFC, CG, and PL. Clearly, as
the force increased from low to high, both the activated
pixel number and average intensity increased.
Figure 5A shows the relationship between force and
number of activated pixels (filled diamonds) and
between force and average intensity (filled circles),
calculated based on the data of the entire brain. The correlation (r) value for the force and pixel number was 0.97,
and that for the force and average intensity was 0.95.
The correlation data between force and activated pixel
number and average intensity in each individual cortical
region is presented in Fig. 6. PFC (r=0.96), SMA
(r=0.93), CG (r=0.95), and CBL (r=0.94) were measured
Fig. 5A, B Relationship between number of activated pixels and
force (filled diamonds) and between average intensity and force
(filled circles). The two fMRI measurements (number of pixels
and average intensity) were made from the data of the entire brain
bilaterally; and MI (r=0.92), SI (r=0.89), PM (r=0.99),
and PL (r=0.90) were measured contralaterally in the left
hemisphere. The correlation values between force and
average intensity in the PFC, SMA, CG, and CBL were
0.98, 0.97, 0.95, and 0.98, respectively. Those for the
MI, SI, PM, and PL were 0.96, 0.98, 0.77, and 0.92,
respectively. The actual values of the pixel number and
average intensity at each force level are listed in Table 1.
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activated pixels (filled diamonds) and between EMG and
average intensity (filled circles), calculated based on the
data of the entire brain. The r-value for the EMG and
pixel number was 0.97, and that for the EMG and average
intensity was 0.96.
The correlation data between EMG and activated pixel
number and average intensity in each individual cortical
region were also determined. PFC (r=0.98), SMA
(r=0.97), CG (r=0.96), and CBL (r=0.98) were measured
bilaterally; and MI (r=0.94), SI (r=0.99), PM (r=0.78),
and PL (r=0.93) were measured contralaterally in the left
hemisphere. The correlation values between EMG and
average intensity in the PFC, SMA, CG, and CBL were
0.99, 0.99, 0.97, and 0.97, respectively. Those for the
MI, SI, PM, and PL were 0.95, 0.93, 0.98, and 0.93,
respectively.
Activation of the ipsilateral cortical fields
Fig. 6A–D Relationship between number of activated pixels and
force (filled diamonds) and between average intensity and force
(filled circles). The pixel number and average intensity were
measured from individual cortical areas: left side of primary motor
cortex (MI_L), left side of primary sensory cortex (SI_L), left side
of premotor cortex (PM_L), left side of parietal lobe (PL_L),
cingulate gyrus (CG), supplementary motor area (SMA), prefrontal
cortex (PFC), and cerebellum (CBL). The measurements were
made bilaterally in the CG, SMA, PFC, and CBL
Correlation between fMRI and EMG
The number of pixels passing the statistical threshold and
average intensity were also quantified in the MI, SI, PM,
and PL of the ipsilateral (right) hemisphere. These values
are presented in Table 1. The ipsilateral activation in
these cortical areas seems quite strong as compared to the
fMRI data of the same regions of the contralateral side.
The high ipsilateral activity may be a result of precision
control of force during the contractions (subjects were
required to precisely match the target force). It has been
reported that power grip contractions requiring little
precision control were associated predominantly with
contralateral primary sensorimotor area activation, whereas
the precision grip involved extensive activation in a number
of motor areas in both hemispheres (Ehrsson et al. 2000).
It is particularly interesting that the number of activated
pixels in the ipsilateral PL (PLR) was consistently greater
than that in the contralateral PL (PLL; Table 1).
Because the three sets of the EMG data were similar
(Fig. 3), we used only the EMG data in Fig. 3B to perform
correlation analyses with the fMRI data. Figure 5B
shows the relationship between EMG and number of
Discussion
Table 1 Number of “activated” pixels and average intensity
(z-score) of these pixels measured in the entire brain (Global) and
individual cortical regions. (PFC prefrontal cortex, CG cingulate
gyrus, SMA supplementary motor area, CBL cerebellum, L and R
left and right hemisphere, PM premotor cortex, MI primary motor
cortex, SI primary sensory cortex, PL parietal lobe)
Force(%) Global
Pixels (n) 20
35
50
65
80
Average 20
intensity 35
50
65
80
PFC
CG
SMA
CBL
The purpose of this study was to determine the relationship
between muscle output and fMRI-measured brain activity
PML
PMR
MIL
MIR
1,655.00
334.00 40.00 25.00 90.00 39.00 27.00 29.00 29.00
3,683.00
478.00 116.00 86.00 216.00 77.00 67.00 87.00 78.00
6,626.00
808.00 240.00 176.00 427.00 116.00 128.00 148.00 165.00
7,503.00 1,041.00 257.00 232.00 461.00 153.00 196.00 197.00 175.00
8,429.00 1,220.00 321.00 237.00 541.00 198.00 184.00 192.00 159.00
3.01
3.27
3.38
3.45
3.60
3.03
3.18
3.29
3.41
3.62
2.82
2.97
3.10
3.12
3.31
2.88
2.99
3.12
3.16
3.24
2.99
3.23
3.33
3.48
3.63
2.91
3.17
3.24
3.17
3.41
2.90
3.29
3.38
3.44
3.50
3.01
3.25
3.36
3.43
3.53
2.84
3.19
3.42
3.33
3.44
SIL
SIR
PLL
PLR
35.00 29.00 50.00 99.00
104.00 84.00 170.00 222.00
165.00 154.00 258.00 354.00
206.00 161.00 278.00 335.00
199.00 161.00 315.00 320.00
2.92
3.13
3.26
3.37
3.54
2.91
3.20
3.35
3.35
3.50
2.92
3.17
3.27
3.30
3.51
2.90
3.16
3.34
3.37
3.51
297
in healthy human subjects. The major findings were that:
(1) high-quality fMRI data, handgrip force, and surface
EMG of the finger flexor and extensor muscles could be
acquired at the same time; (2) there was a direct relationship between handgrip force and fMRI signal and
between muscle EMG and fMRI signal in a number of
motor function-related cortical fields; and (3) these cortical
fields acted in a correlated way in controlling magnitude
of the static force.
Recording force and EMG information
in an MRI environment
A major feature of this study was that we were able to
concurrently record fMRI, joint force, and muscle EMG
data without compromising data quality. Precise recordings
of these data are critical in assessing the relation between
central nervous system activity and muscle output.
Comparisons between fMRI images acquired with and
without operation of the force/EMG recording system
indicated that the quality of the fMRI data was the same
between the two conditions (Liu et al. 2000). Similarly,
there was no difference in the force and EMG signals
recorded inside the operating MRI scanner and those
signals recorded outside the MRI room (Fig. 3). These
data suggest that the force/EMG recording device is a
well-shielded system that may be used in a wide range of
studies for investigating neural control mechanisms of
voluntary motor actions. In addition, the visual feedback
system incorporated in the data acquisition system
enabled the subjects to precisely control the exerted
force, thus improving the data quality.
The fMRI signal
The fMRI signal is not a direct measure of synaptic
activities or action potentials of cortical neurons; instead,
it results from the so-called blood-oxygen-level-dependent
(BOLD) effect (reviews: DeYoe et al. 1994; Kim and
Ugurbil 1997; Ogawa et al. 1998). An increase in neural
activity in a cortical region increases local blood flow.
On the other hand, the consumption of oxygen in the
region does not increase or increases only slightly (Fox
and Raichle 1986; Fox et al. 1988). Consequently, the
relative content of venous deoxyhemoglobin in the
affected brain area is reduced. Because deoxyhemoglobin
is paramagnetic, it produces microscopic magnetic
inhomogeneities that increase the dephasing of spinning
hydrogen protons. A decrease in the quantity of deoxyhemoglobin reduces the rate of dephasing and causes the
magnetic resonance signal to decay at a slower rate. As a
result, the signal is increased in the area where the
uncoupling of the changes in blood flow and oxygen
consumption occurs.
Recent reports have shown that human fMRI signal
was directly proportional to the firing rate of single
neurons recorded in the same cortical region of monkey
(Heeger et al. 2000; Rees et al. 2000; however, see
Disbrow et al. 2000). A study measured human surface
EEG-derived MRCP from the electrodes overlying the
contralateral sensorimotor cortex and the SMA during
five levels of elbow flexion contractions. The magnitude of
the MRCP from both electrode locations was directly proportional to the elbow flexion force (r=0.95) and surface
EMG (r=0.85) of the elbow flexor muscles (Siemionow
et al. 2000). These results suggest that the BOLD-based
fMRI signal probably reflects synaptic activities of neural
cells in the brain (Jueptner and Weiller 1995). A major
limitation of fMRI measurement is that it cannot distinguish the signal of the output neurons from that of other
neurons (e.g., interneurons). The fMRI measurement is
an integrated signal probably contributed by synaptic
activities occurring on all types of cortical neurons. In
addition, because the BOLD effect-induced signal
change lags the synaptic activity, it cannot capture the
dynamic temporal activation pattern among the cortical
fields that show fMRI signal changes.
Relationship between muscle output and fMRI signal
Primary sensorimotor cortex
An interesting observation of this study was that in the
primary motor and sensory cortices, the signal intensity
increased linearly across the entire range of force levels
tested. The number of activated pixels, however, only
showed a linear increase from 20% to 65% force level.
From 65% to 80% level, the number of activated pixels
did not increase (Fig. 6A). Perhaps an increase in the
number of pixels represented primarily an increase in the
number of neurons participating in controlling force, and
an increase in fMRI signal intensity represented mainly
an increase in the discharge rate of these neurons. If
these assumptions are true, then the results indicate that
the nervous system cannot recruit additional neurons in
the primary motor and sensory cortices at a force higher
than 65% MVC level, but the discharge rate of the neurons
continues to increase. It is worth noting that as the rate
of increase in the number of pixels flattened from the
65% to 80% level (Fig. 6A), the rate of increase in the
signal intensity from 65% to 80% force became higher
(compare the rate from 65% to 80% to that from 50% to
65%). Perhaps the higher increase rate in signal intensity
(neuron discharge rate) was intended to compensate for
the inability to recruit additional neurons (pixels).
Electrophysiological results have long indicated that
the discharge rate of output neurons in the primary motor
cortex of primates is directly related to force exerted by
upper limbs (Cheney and Fetz 1980; Evarts 1968; Evarts
et al. 1983; Hepp-Reymond et al. 1978, 1999; Werner et
al. 1991). These results support our findings of increases
in fMRI signal intensity at higher force levels, although
the range of force that could be studied in primates was
usually small and the upper level (amplitude) of force
that could be examined in the animals was usually low.
298
Thus, we do not know whether the discharge rate of
motor cortex cells in primates continues to increase at
very high force levels (e.g., more than 65%). It is also
difficult for single-cell studies to determine whether the
number of participating cells increases with force.
that the CBL is important in controlling posture and
balance, and it participates in motor learning (review: Ghez
and Thach 2000). The finding of a linear involvement of
the CBL in controlling human muscle force stresses the
importance of the CBL in sensorimotor integration.
SMA and premotor cortex
Association cortices and correlated activation
We found that both the SMA and PM are proportionally
activated with handgrip force. These two regions consist
of Brodmann’s area 6, but each contributes to a different
aspect of motor planning (Krakauer and Ghez 2000). The
SMA has been shown to be important in programming
sequential finger movements (Roland et al. 1980),
storing information necessary for the orderly performance
of multiple movements of the arm (Tanji and Shima
1994), and bimanual coordination (Brinkman 1981,
1984). The SMA is also associated with controlling
muscle force. Smith (1979) has reported that the discharge
rate of the SMA neurons is proportional to the finger
grip force in monkeys. Dettmers et al. (1995) have found
a positive relationship between dynamic force of human
index flexion and cerebral blood flow in the SMA. Our
fMRI data provide further evidence that the SMA is
involved in scaling static force of finger flexor muscles.
Similar to the MI and SI, the SMA showed a linear
relationship between force and fMRI signal intensity.
The number of activated pixels increased steeply from
20% to 65% force but leveled off from 65% to 80%
force (Fig. 6). This similarity in the force and fMRI
signal relation between the primary sensorimotor cortex
and SMA suggests that a population of SMA neurons
may share a similar function.
The PM is considered to contribute to the selection of
motor actions on the basis of visual cues (Halsband and
Passingham 1985; Passingham 1986), and the activity of
PM neurons is linked to motor set, i.e., intention to make
a movement in response to the cue (Weinrich and Wise
1982; Wise et al. 1983). However, the discharge rate of
PM neurons was also found to be related to movement
velocity and acceleration (Kubota and Hamada 1978;
Weinrich et al. 1984), and amplitude and direction
(Kurata 1993). More recent studies have reported direct
relationships between PM neuron firing rate and static
force of finger pinch in monkeys (Hepp-Reymond et al.
1994, 1999). Our study further supports the conclusion
that the PM neurons are involved in controlling static
force of the finger muscles.
Three association cortices (PFC, CG, and PL) demonstrated
a direct relationship between their fMRI signals and
force. These association areas are all associated with
sensorimotor integration. For example, the PFC is
involved in the selection of upper limb movements (Frith
et al. 1991), and in the CG a proportional relationship
between cerebral blood flow and human finger flexion
force has been reported (Dettmers et al. 1995). In the
parietal association cortex (areas 5 and 7), neurons are
involved in passive and active joint movements (area 5)
and discharge in high rates (area 7) when the monkey
purposefully projects his arm or manipulates with his
hand (Mountcastle et al. 1975). Our results show, for the
first time, that the activation level of prefrontal and
parietal cortices is linearly related to muscle force.
It is surprising to see that the slopes of the curves of
the fMRI signal in the association cortices were similar
to or even steeper than the slope for the primary motor
cortex (Fig. 6). In particular, the number of activated
pixels in the PFC (more than 1,200 at 80% force) was
substantially larger than that in the MI and other cortical
regions (~300 at 80% force) except the CBL. In a simple
hierarchical control model, one would expect that the
association cortices only give abstractive commands,
which should not need a tremendous level of activation
and linearity in force and activation relation. The
secondary (SMA, PM) and primary (MI, SI) sensorimotor
areas are expected to formulate and carry out details of
the planning and execution for the task, which should
demand a greater level of activation and activation
linearity. The novel finding of linear involvement of
multiassociation cortices in a relatively simple static
force task deserves further investigation.
A prominent feature of this study was that many
cortical regions (MI, SI, SMA, PM, PL, CG, PFC, and
CBL) showed a similar proportional relationship between
muscle output and fMRI signal. This type of correlated
or networked activation among various cortical fields
raises an intriguing question of whether these brain
regions activate in a parallel or a hierarchical manner.
It seems unlikely that they were activated in a strictly
hierarchical order, since it is not efficient to make six
relays of signal transduction from a high-order association
cortex to a primary functional area such as motor cortex.
Increasing evidence has suggested that the central
nervous system acts in a correlated or networked manner
and the brain stores and processes information only
when millions of neurons work together, perhaps with
their electrical potentials correlated or synchronized in
patterns at various frequencies (König and Engel 1995).
Cerebellum
The fMRI signals recorded from the entire CBL showed
a linear correlation with force. Dettmers et al. (1995)
have reported a significant correlation between cerebral
blood flow in cerebellar vermis and finger flexion force;
due to technical limitations, these authors could not
image the entire CBL. Numerous studies have shown
299
Multiple cortical-region activation during simple motor
actions such as finger tapping has been reported in many
neuroimaging studies (Colebatch et al. 1991; Ehrsson et
al. 2000; Roland et al. 1980; Stephan et al. 1995; Yue et
al. 2000). Many motor function-related brain regions are
activated in a similar pattern (Dettmers et al. 1995). In
animal experiments, correlated firing of cells between
somatosensory and motor cortex (Murthy and Fetz
1992), between thalamus and sensory cortex (Johnson
and Alloway 1994), and among intra- and interhemispheric
regions (Nowak et al. 1995) has been observed.
Synchronous or correlated activity among cortical
regions has been suggested to have an important function
in sensory-motor integration and memory (König and
Engel 1995). Our data argue that generating precision
static force of human upper limb muscles requires
correlated activities of primary, secondary, and association
cortical regions.
Conclusions
High-quality joint force, surface EMG, and fMRI data
can be measured at the same time. The degree of muscle
activation (force and EMG) is directly proportional to
the amplitude of the brain signal determined by fMRI in
the entire brain and individual primary, secondary, and
association motor function-related cortical fields. The
stronger fMRI signals recorded during exertion of
higher force levels probably indicate that more cortical
output neurons and interneurons (indicated by a larger
number of activated pixels) participate in generating
descending commands and processing additional
sensory information. The activity (discharge rate) of
individual neurons may also be higher (indicated by
higher signal intensity) during stronger contractions.
The similarity in the relationship between muscle output
and fMRI signal in various cortical regions suggests
that they participate in controlling finger force in a
correlated manner.
Acknowledgements This work was supported by NIH grants
R01-NS35130, R01-NS37400, and R01-HD36725 to G.H. Yue,
and by departmental research funds of Physical Medicine and
Rehabilitation at the Cleveland Clinic Foundation. The Silent
Vision (visual feedback) system was purchased by an Infrastructure
Supplement Award, R01-NS37400. The authors thank Drs. I.M.
Najm and E.P. Pioro, Department of Neurology, the Cleveland
Clinic Foundation, for their assistance in outlining functional brain
regions on the magnetic resonance images. Finally, we thank the
anonymous reviewers whose comments improved the manuscript.
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