J Neurophysiol 114: 2843–2853, 2015.
First published September 9, 2015; doi:10.1152/jn.00574.2015.
Spatial variability in cortex-muscle coherence investigated with
magnetoencephalography and high-density surface electromyography
Harri Piitulainen,1 Alberto Botter,2 Mathieu Bourguignon,1 Veikko Jousmäki,1 and Riitta Hari1
1
Brain Research Unit, Department of Neuroscience and Biomedical Engineering, and MEG Core and Advanced Magnetic
Imaging (AMI) Centre, Aalto NeuroImaging, Aalto University School of Science, Aalto, Espoo, Finland; and 2Laboratory of
Engineering of Neuromuscular System and Motor Rehabilitation, Dipartimento di Elettronica e Telecomunicazioni,
Politecnico di Torino, Turin, Italy
Submitted 10 June 2015; accepted in final form 4 September 2015
corticomuscular coherence; multichannel EMG; magnetoencephalography; sensorimotor cortex; spatial filter
DURING SUSTAINED ISOMETRIC CONTRACTION, surface electromyogram (sEMG), representing the algebraic sum of motor unit
action potentials (MUAPs), is coherent with magnetoencephalographic (MEG) and electroencephalographic (EEG) signals
recorded from the primary motor cortex (M1) (Brown et al.
1998; Conway et al. 1995; Gross et al. 2000; Halliday et al.
1998; Salenius et al. 1997). This coupling, referred to as
cortex-muscle coherence (CMC), typically occurs at 15– 40
Hz, depending on the exerted force and the muscle (Hari and
Salenius 1999), and it likely reflects oscillatory discharge of
Address for reprint requests and other correspondence: H. Piitulainen, Brain
Research Unit, Dept. of Neuroscience and Biomedical Engineering, Aalto
Univ. School of Science, PO Box 15100, 00076 Aalto, Espoo, Finland (e-mail:
harri.piitulainen@aalto.fi).
www.jn.org
the M1-pyramidal neurons projecting to the spinal ␣-motoneurons and thus modulating the population-level motor unit
discharges (Baker et al. 1997; Conway et al. 1995; Salenius et
al. 1997).
CMC was first demonstrated with MEG recordings that
measure cortical activity with millisecond temporal resolution.
MEG picks up magnetic fields that are generated mainly by
postsynaptic currents in the apical dendrites of cortical pyramidal neurons (Hämäläinen et al. 1993; Hari et al. 2015; Hari
and Salmelin 2012). As the skull does not distort the MEG
signals, pointlike cortical sources can be located with spatial
resolution of a few millimeters (Hämäläinen et al. 1993).
Accordingly, MEG-based CMC has been used in functional
mapping of, e.g., the hand area of M1 (Bourguignon et al.
2013; Mäkelä et al. 2001). Unfortunately, CMC is not detected
in all individuals, missing in ⬃15% (Pohja et al. 2005).
Recent evidence indicates that CMC can be detected more
robustly with high-density surface electromyography (HDsEMG) than with the classical bipolar sEMG recording (van de
Steeg et al. 2014). Indeed, by sampling the spatial distribution
of sEMG with a grid of small, closely spaced electrodes, it is
possible to identify the anatomical features of the muscle, to
reduce the filtering effect associated with the electrode size
(Farina and Merletti 2001; Helal and Bouissou 1992), and to
apply signal transformations (either linear or nonlinear) to the
set of simultaneously recorded sEMG signals. The combination of these features allows a better estimation of the level of
muscle activation (Merletti et al. 2010).
The level of sEMG-sEMG coherence between two synergistic muscles, which stems from the common oscillatory synaptic
inputs to ␣-motoneurons, also varies according to the sEMG
recording site over the hand muscles (Keenan et al. 2011,
2012). Therefore, it is expected that the sEMG recording site
would influence the level of CMC and thus the reliability of
cortical source estimation, important for functional mapping of
the rolandic cortex.
Here we aimed to evaluate the effect of 1) sEMG recording
site (i.e., spatial location of the electrode on the skin over the
muscle) and 2) sEMG derivation (monopolar, bipolar, and
Laplacian) on the strength of CMC and on the cortical source
estimates. The results obtained with the HD-sEMG derivations
were further compared with results obtained with computationally produced “macroscopic” bipolar and monopolar sEMG
derivations, mimicking conventional bipolar or monopolar
sEMG recordings, in terms of electrode size (diameter 9 mm),
interelectrode distance (IED, 21 mm), and electrode placement.
0022-3077/15 Copyright © 2015 the American Physiological Society
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Piitulainen H, Botter A, Bourguignon M, Jousmäki V, Hari R.
Spatial variability in cortex-muscle coherence investigated with magnetoencephalography and high-density surface electromyography. J
Neurophysiol 114: 2843–2853, 2015. First published September 9,
2015; doi:10.1152/jn.00574.2015.—Cortex-muscle coherence (CMC)
reflects coupling between magnetoencephalography (MEG) and surface electromyography (sEMG), being strongest during isometric
contraction but absent, for unknown reasons, in some individuals. We
used a novel nonmagnetic high-density sEMG (HD-sEMG) electrode
grid (36 mm ⫻ 12 mm; 60 electrodes separated by 3 mm) to study
effects of sEMG recording site, electrode derivation, and rectification
on the strength of CMC. Monopolar sEMG from right thenar and
306-channel whole-scalp MEG were recorded from 14 subjects during
4-min isometric thumb abduction. CMC was computed for 60 monopolar, 55 bipolar, and 32 Laplacian HD-sEMG derivations, and two
derivations were computed to mimic “macroscopic” monopolar and
bipolar sEMG (electrode diameter 9 mm; interelectrode distance 21
mm). With unrectified sEMG, 12 subjects showed statistically significant CMC in 91–95% of the HD-sEMG channels, with maximum
coherence at ⬃25 Hz. CMC was about a fifth stronger for monopolar
than bipolar and Laplacian derivations. Monopolar derivations resulted in most uniform CMC distributions across the thenar and in
tightest cortical source clusters in the left rolandic hand area. CMC
was 19 –27% stronger for HD-sEMG than for “macroscopic” monopolar or bipolar derivations. EMG rectification reduced the CMC peak
by a quarter, resulted in a more uniformly distributed CMC across the
thenar, and provided more tightly clustered cortical sources than
unrectifed sEMGs. Moreover, it revealed CMC at ⬃12 Hz. We
conclude that HD-sEMG, especially with monopolar derivation, can
facilitate detection of CMC and that individual muscle anatomy
cannot explain the high interindividual CMC variability.
SPATIAL VARIABILITY IN CORTEX-MUSCLE COHERENCE
Experimental Protocol
It has been suggested that it is possible to obtain more
accurate timings for motor units with rectified than with unrectifed sEMG (Halliday and Farmer 2010), meaning that
rectification could also affect the detection of corticospinal
coupling. In fact, rectification of sEMG prior to coherence
analysis has in some cases hindered CMC detection (McClelland et al. 2012), although this is not always the case (Yao et
al. 2007). In this study we thus also compared our main CMC
results obtained with unrectified sEMG with those obtained
with unrectified sEMG.
MATERIALS AND METHODS
Subjects
We studied 14 healthy subjects (mean age 28.1 yr, range 23– 44 yr;
9 men, 5 women) without any history of neuropsychiatric disease or
movement disorder. According to the Edinburgh handedness inventory (Oldfield 1971), all subjects were right-handed (mean score 93.2,
range 73–100 on a scale from ⫺100 to 100). The study had prior
approval by the ethics committee of Aalto University. The subjects
gave informed consent before participation. Subjects were compensated monetarily for lost working hours and travel expenses.
A
9 mm
D
F
Force
transducer
N
4
2
0
BP1
C
r5
r6
r7
r8
r9
r10
r11
r12
Lateral
MEG
Monopolar
µV
50 µV
0
−50
c1 c2 c3 c4 c5
1
1
-4
1
1
Laplacian
r2
r3
r4
100
0
−100
BP2
100
µV
0
−100
µV
One-channel
Monopolar Bipolar
r1
0
−100
Bipolar
Proximal
Distal
MP
Medial
E
21 mm
B
fT/cm
100
100
0
−100
µV
100
0
−200
0
0.1
0.2
0.3
0.4
0.5
Time [s]
Fig. 1. Experimental setup and representative signals of subject S1. A: nonmagnetic device used to measure the force during isometric thumb abduction. B:
high-density surface electromyography (HD-sEMG) grid and its position on the hand. C: position of the subject during magnetoencephalographic (MEG)
recording. D and E: HD-sEMG derivations (from top view). Each circle corresponds to a single monopolar sEMG electrode. D: clusters of filled black circles
indicate the channel selections for macroscopic bipolar sEMG signals and gray circles for macroscopic monopolar sEMG signals. E: HD-sEMG electrode
derivations. Monopolar signals were detected by sixty electrodes with respect to a remote (wrist) reference. Fifty-five bipolar signals were computed, for each
column (c1– c5), between consecutive electrodes in the proximal-distal direction. Five filled gray circles indicate an example of the Laplacian sEMG computation.
F: examples of the measured signals. Rows from top to bottom: 10-Hz low-pass filtered force signal (horizontal dashed lines indicate the task limits, 5–7% of
maximum voluntary contraction), 1- to 195-Hz MEG signal (from the sensor showing the strongest CMC), monopolar, bipolar, Laplacian, macroscopic bipolar,
and macroscopic monopolar sEMG signals (passband 10 – 400 Hz). The sEMG signals are from the channel showing the peak coherence with MEG signals.
J Neurophysiol • doi:10.1152/jn.00574.2015 • www.jn.org
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Figure 1A illustrates the experimental setup. MEG was recorded
concomitantly with HD-sEMG from thenar muscle while the subject
maintained a steady isometric thumb abduction.
Before MEG recordings, maximum isometric thumb abduction
force was measured with a custom-made device comprising a rigid
load cell (model 1042, dynamic range up to 29.4 N; Vishay Precision
Group, Malvern, PA). The subjects performed two or three maximum
contractions, each lasting for 3– 4 s, with 2-min rest periods inbetween. The trial with the highest force value was used as the
maximum voluntary contraction (MVC) force (mean force over 1 s of
stable contraction).
During MEG recordings, the subjects were sitting with their left
hand on their thigh and their right hand on a table in front of them,
inserted in the device to measure the isometric force. They were asked
to maintain a steady isometric force and to fixate a blue line (representing their force level for the past 1 s) on a gray background in the
middle of a translucent screen 180 cm in front of them. The target
force level (6% of their MVC) was visible as a white continuous line,
and the boundaries of the target force level (5% and 7% of their MVC)
were presented with continuous black lines. When needed, the subject’s vision was optically corrected with nonmagnetic goggles. The
recording lasted for 4 min. The subjects were instructed to avoid eye
Force
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SPATIAL VARIABILITY IN CORTEX-MUSCLE COHERENCE
movements and excessive blinking and to focus on maintaining the
isometric contraction as steady as possible.
Measurements
Data Processing
Preprocessing. Continuous MEG data were first preprocessed offline with the temporal signal-space separation (tSSS) method implemented in MaxFilter 2.2.10 (Elekta Neuromag; Elekta) to suppress
external interferences and to correct for head movements (Taulu and
Simola 2006). A zero-phase filter was used to band-pass filter MEG
signals between 1 and 195 Hz and monopolar sEMG signals between
10 and 400 Hz. Monopolar sEMG signals from electrodes contaminated by artifacts (mean 1.7, range 0 –5 of 60 electrodes) were
discarded.
sEMG derivations. Three HD-sEMG (monopolar, bipolar, Laplacian) and two computationally produced “macroscopic” sEMG derivations (bipolar and monopolar) were tested (see Fig. 1, D and E).
HD-sEMG monopolar derivation consisted of the 60 monopolar
sEMG signals detected with respect to the reference electrode on the
right radial bone. Longitudinal bipolar sEMG signals (55 in total)
were computed from the monopolar derivations as the differences
between adjacent monopolar sEMG channels in the longitudinal
direction of the muscle, i.e., for each of the five electrode columns
separately. Laplacian sEMG signals (32 in total) were derived from
the monopolar sEMG signals by combining five crosswise electrodes,
with the central electrode weighted ⫺4 and the four surrounding
electrodes weighted ⫹1.
Macroscopic bipolar and monopolar sEMG signals were based on
large “virtual” electrodes computationally produced by averaging a
subgroup of the small-sized electrodes of the HD-sEMG grid
(Staudenmann et al. 2006), and thus mimicking well the conventional
sEMG recordings using large surface electrodes (see Fig. 1D). The
macroscopic bipolar signal was derived as differential signal between
two “virtual” electrodes, each consisting of the average of 12 monopolar sEMG channels in “circular” derivation (see Fig. 1D). The
resulting electrode diameter was 9 mm, and the distance between the
centers of the two electrodes was 21 mm. Similarly, the macroscopic
monopolar signal was derived by averaging 12 monopolar sEMG
channels in the middle portion of the HD-sEMG grid.
Cortex-muscle coherence analysis. Continuous data from the recording were split into 1,000-ms epochs with 800-ms epoch overlap
(Bortel and Sovka 2007), leading to frequency resolution of 1 Hz.
Epochs with MEG signals exceeding 6 pT (magnetometers) or 1.4
pT/cm (gradiometers) were rejected to avoid contamination of the
data by eye movements, muscle activity, and artifacts in the MEG
sensors. Power and cross-spectra, as well as cross-correlograms, were
calculated between MEG and unrectified RMS-normalized sEMG
signals with the multitaper approach [5 orthogonal Slepian tapers,
yielding a spectral smoothing of ⫾2 Hz (Thomson 1982)]. Coherence
spectra were then computed according to the formulation of Halliday
et al. (1995). The analysis was repeated for each sEMG channel in
HD-sEMG monopolar, bipolar, Laplacian, and macroscopic bipolar
and monopolar derivations separately. To clarify the effects of rectification of sEMG on CMC, the same analysis was repeated using
rectified sEMG, and the results were compared with those obtained
using unrectified sEMG signals.
Location of innervation zone. The location of the main innervation
zone (i.e., the end-plate area where the individual neuromuscular
junctions are concentrated) was determined individually for each of
the five sEMG electrode columns based on visual inspection of the
bipolar sEMG signals (Barbero et al. 2011; Piitulainen et al. 2009).
The innervation zone corresponded to the bipolar sEMG channel
showing a low amplitude, associated with a reversal in signal polarity
in the adjacent bipolar signals (either the proximal or distal one).
Because of the short 3-mm IED, the innervation zone of different
motor units occupied an area covered by more than one bipolar sEMG
channel. For this reason, innervation zone area was defined for each
column of the electrode grid, as the bipolar sEMG channel directly on
top of the main innervation zone and one channel proximal and one
channel distal to it (i.e., including three consecutive channels).
To study the potential effect of innervation zone on the level of
CMC, the mean CMC level in the sEMG signals over the innervation
zone area was compared with that of sEMG signals outside the
innervation zone (i.e., extrajunctional channels) for each HD-sEMG
derivation separately.
Location of coherent cortical sources. For each sEMG signal in the
HD-sEMG and macroscopic derivations, the cross-correlograms with
all MEG signals were band-pass filtered at 6 – 45 Hz and the timing of
its most prominent peak was determined among a fixed selection of 40
gradiometers that comprised all the most responsive sensors over the
rolandic area of the left hemisphere. Then the source was estimated by
fitting an equivalent current dipole (ECD) at the determined time to
the spatial distribution of the filtered cross-correlograms, using the
same fixed selection of 40 gradiometers and the corresponding 20
magnetometers. ECDs were estimated within a spherical head model
fitted to individual MRIs (10 subjects) or to the standard Montreal
Neurological Institute (MNI) brain (4 subjects) so as to match the
centroparietal brain region. ECDs were then visualized on the coregistered individual MRIs.
Spread of coherent cortical sources. For each subject, the center of
mass of the fitted ECDs was first calculated as the mean of all ECD
coordinates across all sEMG channels, for each HD-sEMG derivation
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MEG. The measurements were carried out at the MEG Core of
Aalto NeuroImaging, Aalto University. MEG signals were recorded in
a magnetically shielded room (Imedco, Hägendorf, Switzerland) with
a 306-channel whole-scalp neuromagnetometer (Elekta Neuromag;
Elekta, Helsinki, Finland). The recording passband was 0.1– 400 Hz,
and the signals were sampled at 2,000 Hz. The subject’s head position
inside the MEG helmet was continuously monitored by five headtracking coils located on the scalp; the locations of the coils with
respect to anatomical fiducials were determined with an electromagnetic tracker (Fastrak; Polhemus, Colchester, VT).
sEMG. Monopolar sEMG was measured from the right thenar
(targeting mainly the abductor pollicis brevis muscle) with a custommade nonmagnetic electrode grid of 60 silver electrodes (contact area
0.8 mm2 in each; LISiN, Politecnico di Torino) arranged in five
columns (from medial to lateral: 10, 12, 13, 13, and 12 electrodes in
each) aligned in the longitudinal direction of the muscle. IED was 3
mm in both longitudinal and transverse directions. Monopolar sEMG
signals were recorded with respect to a reference electrode (Neuroline
720; Ambu, Ballerup, Denmark) placed on the lateral edge of the
distal third of the right radial bone and then band-pass filtered
(10 – 400 Hz) and sampled at 2,000 Hz (Elekta Neuromag; Elekta).
The impedance (at 30 Hz; electrode impedance meter EZM5; Grass,
West Warwick, RI) between the reference electrode and each HDsEMG electrode was below 100 k⍀, and the sEMG signal quality was
always checked visually during real-time monitoring prior to data
acquisition.
Force. The same custom-made device with which we measured the
MVC force was used during the steady 4-min isometric thumb
abduction, but the force was measured with a more sensitive load cell
(model 1004; Vishay Precision Group) with a dynamic range up to 5.9
N. The force signal was low-pass filtered at 400 Hz and sampled at
2,000 Hz, time-locked to MEG and sEMG signals.
MRI. Three-dimensional T1 magnetic resonance images (MRIs)
were acquired with a whole-body General Electric Signa VR 3.0T
MRI scanner (Signa VH/I; General Electric, Milwaukee, WI) or a 3-T
MAGNETOM Skyra whole-body scanner (Siemens Healthcare, Erlangen, Germany) at AMI Centre, Aalto NeuroImaging, Aalto
University.
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SPATIAL VARIABILITY IN CORTEX-MUSCLE COHERENCE
Statistical Analyses
Statistical significance of cortex-muscle coherence. The statistical
significance of the coherence level was assessed with surrogate data
separately for each sEMG signal (Faes et al. 2004). First, 1,000
surrogate coherence spectra were computed between MEG signals
and Fourier transform surrogate sEMG signals. The Fourier transform
surrogate restrains the power spectrum to remain the same as in the
original signal but replaces the phase of the Fourier coefficients by
random numbers in the range [⫺␲; ␲]. Then, the peak coherence
value across the preselected 40 gradiometers in the 7–35 Hz frequency
range was extracted for each surrogate coherence spectrum to compute the cumulative density function of the peak coherence value
occurring because of stochastic matching between sEMG and MEG
signals. The coherence thresholds at P ⬍ 0.05 corrected for multiple
comparisons across MEG channels were then evaluated as the 95th
percentile of the corresponding cumulative density function.
Comparisons between sEMG derivations. A nonparametric Friedman test was used to compare the peak and mean CMC values
between the five sEMG derivations. The same test was used to
compare the spread of the ECD cluster (volume and length of the
ellipsoid) in the left rolandic cortex, the coefficient of variation in the
sEMG amplitude and level of CMC, and the mean CMC value on and
outside the innervation zone area between the three HD-sEMG derivations. In the case of statistically significant differences, Wilcoxon
signed-rank tests were performed between all combinations of the
sEMG derivations and the respective P values were corrected according to Bonferroni. Wilcoxon signed-rank test was used also to compare the mean CMC value on and outside the innervation zone area for
each HD-sEMG derivation separately. The threshold for statistical
significance was set at P ⬍ 0.05 for all comparisons. Friedman and
Wilcoxon tests were performed with IBM SPSS Statistics (version 22,
SPSS, Chicago, IL).
Effect of rectification of sEMGs on cortex-muscle coherence. In the
statistical evaluation of the effects of sEMG rectification, we used a
Wilcoxon signed-rank test (implemented in SPSS) to compare the
peak and mean CMC values, the coefficients of variation of CMC
level across the thenar, and the spread of the ECD clusters (volume
and length of the ellipsoid) in the left rolandic cortex between
coherence analyses using unrectified vs. rectified sEMG. The tests
were run separately for each HD-sEMG derivation, and the threshold
for statistical significance was set at P ⬍ 0.05.
RESULTS
Figure 1F illustrates the force, MEG, and sEMG signals of
a representative subject (S1) during an isometric right thumb
abduction task. All 14 subjects were able to maintain the
isometric force within the task limits throughout the 4-min
measurement, and they did not report perceptible muscle fatigue during the task.
Cortex-Muscle Coherence
In two subjects (1 male, 1 female), the CMC level was
low and statistically significant in ⬍20% of the HD-sEMG
channels. These two subjects were excluded from further
analysis, as their data would have added noise to our
analyses of spatial variability of CMC over the thenar. The
remaining 12 subjects displayed significant CMC in more
than half of the HD-sEMG channels (mean ⫾ SD 95 ⫾ 12%
in monopolar, 94 ⫾ 7% in bipolar, and 92 ⫾ 11% in
Laplacian) and showed significant CMC for macroscopic
monopolar signal, whereas CMC did not reach significance
in 2 subjects for macroscopic bipolar signal. Figure 2 shows
the CMC spectra for the most and least coherent sEMG
channels for each sEMG derivation. Table 1 summarizes the
peak frequency of CMC that always peaked at ⬃25 Hz for
all five sEMG derivations.
Table 2 summarizes the CMC results. Peak CMC differed
significantly between the sEMG derivations [␹2(4) ⫽ 23.5,
P ⬍ 0.001]. The macroscopic bipolar signal showed lower
peak CMC than the HD-sEMG monopolar (Z ⫽ ⫺2.9, P ⫽
0.04) and bipolar (Z ⫽ ⫺2.8, P ⫽ 0.05) derivations. The
macroscopic monopolar signal also showed lower peak CMC
than HD-sEMG monopolar (Z ⫽ ⫺3.1, P ⫽ 0.02) derivation
but did not differ from the other derivations.
Significant differences were detected in mean CMC (across
all sEMG channels) between the HD-sEMG derivations
[␹2(4) ⫽ 18.3, P ⬍ 0.001]; monopolar derivation showed
higher mean CMC (0.056 ⫾ 0.052) than bipolar (0.039 ⫾
0.033, Z ⫽ ⫺2.7, P ⫽ 0.018) and Laplacian (0.040 ⫾ 0.033,
Z ⫽ 3.1, P ⫽ 0.006) derivations. Interindividual CMC variability (i.e., coefficient of variation across subjects’ mean CMC
values) was 93% for monopolar, 84% for bipolar, and 86% for
Laplacian derivation.
Effect of sEMG Recording Site on Cortex-Muscle Coherence
Figure 3 shows normalized sEMG amplitude and CMC
maps superimposed with location of the main innervation
zones. As expected, high sEMG amplitude was observed on the
main innervation zone in the monopolar and Laplacian derivations and low amplitude in the bipolar derivation. As is evident
also from the maps, the coefficient of variations of the sEMG
amplitude [␹2(2) ⫽ 12.7, P ⬍ 0.05] and the CMC [␹2(2) ⫽
13.2, P ⬍ 0.01] differed significantly between the sEMG
derivations. Table 3 summarizes coefficient of variation results
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separately. Then, to characterize the spread of the ECDs, their coordinates relative to their center of mass were subjected to a principal
component analysis to construct an ellipsoid centered on their center
of mass, with the lengths of the axes along principal components equal
to one standard deviation as derived from singular values (Fischer et
al. 2005). The volume and major axis of this ellipsoid provided a
measure of the degree of the ECD spread around the center of mass.
sEMG amplitude and spatial cortex-muscle coherence maps over
the muscle. For each subject and HD-sEMG derivation (monopolar,
bipolar, and Laplacian), maps for sEMG amplitude and CMC over the
thenar were calculated. sEMG amplitude maps were computed as the
RMS of each sEMG signal from the start of the steady phase of
the isometric contraction to the end of the recording (i.e., for the
whole 4-min contraction). CMC maps were computed as the maximum coherence value in each sEMG signal across the 7–35 Hz
frequency range and across the fixed selection of 40 gradiometers over
the rolandic area of the left hemisphere. The individual maps were
normalized by their maximum value and averaged across all subjects
to obtain group-level maps.
To estimate the homogeneity of sEMG amplitude and CMC over
the muscle (i.e., recording sites of the sEMG), coefficients of variations were calculated across all sEMG channels separately for each
subject and HD-sEMG derivation (monopolar, bipolar, and
Laplacian).
Spatial correlation between sEMG amplitude and cortex-muscle
coherence maps. To estimate the possible association between sEMG
signal amplitude and the level of CMC, Pearson’s correlation coefficient was calculated between the sEMG amplitude and CMC maps
separately for each subject and HD-sEMG derivation. We then used a
one-sample t-test to assess whether correlation coefficients differed
from zero for each EMG derivation separately.
Least coherent EMG channel Most coherent EMG channel
SPATIAL VARIABILITY IN CORTEX-MUSCLE COHERENCE
Monopolar
0.2
A
0.16
B
Bipolar
C
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Laplacian
Macroscopic bipolar
0.12
Macroscopic monopolar
G
0.08
H
0.04
0
0.2
0.16
D
E
F
0.12
0.08
0.04
10
20
30
40
50
Frequency(Hz)
Fig. 2. Individual coherence spectra between MEG (the most coherent MEG sensor) and sEMG for all 12 subjects. A–F: the most (A–C) and the least (D–F)
coherent EMG channels are shown for the HD-sEMG derivations. G and H: coherence spectra between MEG and macroscopic bipolar or monopolar sEMG
signal. Gray horizontal lines indicate the threshold for statistical significance (P ⬍ 0.05).
for CMC (i.e., intraindividual variation of CMC across the
sEMG recording sites). More uniform distributions of sEMG
amplitude and CMC were observed for the spatially least
selective monopolar derivation (mean ⫾ SD for coefficient of
variations 0.28 ⫾ 0.11 of EMGRMS) than for bipolar
(0.43 ⫾ 0.10, Z ⫽ ⫺2.9, P ⫽ 0.012 for EMGRMS and Z ⫽
⫺2.7, P ⫽ 0.018 for CMC) and Laplacian (0.49 ⫾ 0.18, Z ⫽
⫺3.2, P ⫽ 0.003 for EMGRMS and Z ⫽ ⫺3.1, P ⫽ 0.006 for
CMC) derivations, whereas the bipolar and Laplacian derivations did not differ significantly (Z ⫽ ⫺1.2, P ⫽ 0.239 for
EMGRMS and Z ⫽ ⫺0.9, P ⫽ 0.388 for CMC).
Spatial Correlation Between sEMG Amplitude and
Cortex-Muscle Coherence Maps
Individual subjects showed high correlation coefficients
(up to r ⫽ 0.88) between sEMG amplitude and CMC maps.
The coefficients were predominantly positive for monopolar
(75%) and bipolar (80%) derivations but even for Laplacian
(50%) derivation. However, the group mean (n ⫽ 12) of the
correlation coefficients did not differ significantly from zero
(monopolar P ⫽ 0.21; bipolar P ⫽ 0.06; Laplacian P ⫽
0.68).
Effect of Innervation Zone on Cortex-Muscle Coherence
Figure 3B shows the mean ⫾ SD location of the subjects’
main innervation zones superimposed on group-level CMC
maps. The spatial relationship between sEMG electrodes
and the innervation zone did not affect the level of cortico-
spinal coupling, as the mean CMC value did not differ
between the innervation zone and extrajunctional areas in
any of the HD-sEMG derivations (Z from ⫺1.1 to ⫺1.6 and
all P ⬎ 0.1).
Location of Coherent Cortical Sources
Figure 4 shows the location of coherent sources and
magnetic field patterns for different sEMG derivations. Two
subjects are shown, one subject (S14) with statistically
significant CMC in 98% of the sEMG channels and one
subject (S12) whose data were excluded from further analyses because of very weak CMC, reaching the significance
level only in 0 –16% of the sEMG channels. In all subjects,
the main cluster was located in the left-hand area of the
rolandic cortex, either in the precentral or postcentral gyrus
or spreading to both.
Spread of Coherent Cortical Sources
The spread of the source clusters (volume and length of the
ellipsoid, including all sEMG channels) differed significantly
between the sEMG derivations [volume: ␹2(2) ⫽ 18, P ⬍
0.001; length: ␹2(2) ⫽ 12, P ⫽ 0.04]. The coherent sources
derived from the monopolar sEMG signals were clustered
within a smaller region (mean ⫾ SD: volume 0.8 ⫾ 1.7 cm3;
length 0.8 ⫾ 0.7 cm) compared with bipolar (volume: 4.0 ⫾
5.3 cm3, Z ⫽ ⫺3.1, P ⫽ 0.006; length: 1.3 ⫾ 0.7 cm, Z ⫽
⫺2.2, P ⫽ 0.048) and Laplacian (volume: 4.9 ⫾ 6.5 cm3, Z ⫽
Table 1. Mean (range) frequency of peak cortex-muscle coherence
Unrectified sEMG
EMG Configuration
Monopolar
Bipolar
Laplacian
Macroscopic bipolar
Macroscopic monopolar
⬃12 Hz
n
Rectified sEMG
⬃25 Hz
n
⬃12 Hz
n
⬃25 Hz
n
24 (20–34)
25 (20–34)
24 (19–34)
26 (20–34)
26 (20–41)
12
12
12
10
12
12 (11–13)
12 (10–13)
12 (10–13)
12 (11–12)
12 (11–15)
4
4
4
4
3
23 (20–28)
25 (21–34)
23 (20–28)
25 (21–31)
24 (20–31)
8
8
8
8
9
Values are mean (range) frequencies of peak cortex-muscle coherence (CMC); n ⫽ no. of subjects with dominant peak CMC at respective frequency. sEMG,
surface electromyogram.
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0
0
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SPATIAL VARIABILITY IN CORTEX-MUSCLE COHERENCE
Table 2. Peak and mean cortex-muscle coherence for unrectified and rectified sEMGs
Peak Coherence (0–1)
Mean Coherence (0–1)
EMG Derivation
Mean
Range
Median
Mean
Range
Median
Monopolar(‡)
Bipolar
Laplacian
Macroscopic bipolar(*)
Macroscopic monopolar(†)
Monopolarr
Bipolarr
Laplacianr
Macroscopic bipolarr(*)
Macroscopic monopolarr(†)
0.068*†§
0.063*
0.064§
0.048
0.060
0.054*†§
0.054*
0.051*§
0.043
0.048
0.014–0.199
0.014–0.192
0.013–0.194
0.005–0.188
0.051–0.193
0.010–0.180
0.010–0.172
0.008–0.155
0.007–0.137
0.006–0.178
0.045
0.042
0.043
0.043
0.039
0.036
0.040
0.038
0.032
0.029
0.056
0.039‡
0.040‡
0.01–0.156
0.01–0.109
0.008–0.111
0.037
0.030
0.028
0.046
0.042
0.037
0.008–0.16
0.009–0.146
0.008–0.119
0.031
0.028
0.028
*P ⬍ 0.05 vs. macroscopic bipolar; †P ⬍ 0.05 vs. macroscopic monopolar; ‡P ⬍ 0.05 vs. monopolar; §P ⬍ 0.05 unrectified vs. rectified (r ⫽ rectified sEMG).
Effect of Rectification of sEMGs on Cortex-Muscle
Coherence
Table 1 presents mean frequency of CMC for unrectifed and
rectified sEMG derivations. Unrectified sEMG signals yielded
significant CMC at ⬃25 Hz. However, with rectified sEMG
nine subjects showed significant CMC also at ⬃12 Hz and up
to four subjects had their dominant peak at ⬃12 Hz.
Table 2 summarizes the CMC results for unrectified and
rectified sEMG derivations. For monopolar and Laplacian
derivations, the peak CMC was about a quarter stronger with
unrectified than rectified sEMG (mean increase 25.6%, Z ⫽
⫺2.3, P ⫽ 0.023 for monopolar and 25.4%, Z ⫽ ⫺2.0, P ⫽
0.041 for Laplacian). No significant effect of rectification was
observed for the bipolar or macroscopic derivations or for the
mean CMC (across all sEMG channels).
For all derivations, the coefficient of variation of CMC
across the thenar was significantly lower for rectified sEMG
(see Table 3; Z ⫽ ⫺2.1, P ⫽ 0.034 for monopolar, Z ⫽ ⫺3.0,
P ⫽ 0.003 for bipolar, and Z ⫽ ⫺2.9, P ⫽ 0.004 for Laplacian).
Rectification strikingly reduced cortical source cluster volume (by 93%, Z ⫽ ⫺2.3, P ⫽ 0.023 for monopolar; by 92%,
Z ⫽ ⫺3.1, P ⫽ 0.002 for bipolar; by 85%, Z ⫽ 3.0, P ⫽ 0.003
for Laplacian) and ellipsoid length (by 63%, Z ⫽ ⫺2.8, P ⫽
0.005 for monopolar; by 59%, ⫺3.1, P ⫽ 0.002 for bipolar; by
54%, Z ⫽ 2.7, P ⫽ 0.006 for Laplacian). After rectification, the
volumes were 0.05 ⫾ 0.12 cm3, 0.30 ⫾ 0.78 cm3, and 0.75 ⫾
1.54 cm3 and the cluster ellipsoid lengths were 0.3 ⫾ 0.2 cm,
0.5 ⫾ 0.6 cm, and 0.7 ⫾ 0.6 cm for monopolar, bipolar, and
Laplacian derivations, respectively.
DISCUSSION
We showed that the level of corticospinal coupling, measured with CMC, is affected by the recording site of sEMG
over the thenar and by the derivation used to detect the sEMG
signals. The monopolar HD-sEMG derivation yielded on average ⬃20% stronger CMC, more uniform CMC distribution
over the thenar, and tighter cortical source clusters than bipolar
and Laplacian HD-sEMG derivations. In subjects showing
robust CMC, the coherent cortical sources were tightly clustered in the right rolandic hand area, whereas more scattered
source clusters were observed in subjects demonstrating weak
CMC. The HD-sEMGs provided on average ⬃20 –30% stronger peak CMC than the respective derivations mimicking
bipolar macroscopic or monopolar sEMG recordings. Finally,
sEMG rectification reduced the peak CMC by a quarter, but it
provided more uniform distribution of CMC values across the
thenar, which might explain the tighter source clusters.
Spatial Variability of CMC Across Muscle Surface
Both bipolar and Laplacian HD-sEMG derivations provide
spatially more selective signals than monopolar HD-sEMG
(Disselhorst-Klug et al. 1997; Farina et al. 2003). The broad
sensitivity pattern of the monopolar derivations largely explains the uniform distribution of the CMC level across the
muscle surface.
The local muscle anatomy (e.g., orientation, size, and depth
of muscle fiber) affects the amplitude distribution of sEMG,
especially in spatially sensitive bipolar sEMG recordings (Mesin et al. 2009a; Farina et al. 2002), and it may in part explain
the greater spatial variability of CMC level. Action potentials
start to propagate from innervation zones that are typically
located in the middle portion of the muscle and prominently
visible in bipolar HD-sEMG (Masuda and Sadoyama 1986,
1988). The strongest monopolar sEMG signals are typically
measured above the innervation zone, whereas bipolar sEMG
signals are reduced in amplitude at the same area (Kleine et al.
2000; Roy et al. 1986). The locations of the innervation zones
vary between individuals (Masuda et al. 1985) and muscles
(Barbero et al. 2012; Rainoldi et al. 2004), and they can vary
also with respect to the sEMG electrodes in a force leveldependent manner due to the relative motion between the
muscle and the skin (Piitulainen et al. 2009).
Somewhat surprisingly, the local sEMG amplitude variations related to the locations of the innervation zones (especially the amplitude drops in bipolar derivation) were not
systematically reflected in the strength of CMC. This result
may reflect our short (3 mm) IED, which is likely less than (or
similar to) the spread of the innervation zones. Therefore, the
effect of the innervation zone can be distributed over a set of
HD-sEMG signals and the drop in bipolar amplitude can be
less than expected.
Signal-to-noise ratio of sEMG was sufficient for CMC
analysis throughout the muscle, including the innervation zone.
However, the amplitudes of both sEMG and CMC showed
clear spatial variability. According to simulations, an increase
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⫺3.1, P ⫽ 0.006; length: 1.4 ⫾ 0.9 cm, Z ⫽ ⫺3.0, P ⫽ 0.009)
sEMG signals.
SPATIAL VARIABILITY IN CORTEX-MUSCLE COHERENCE
A
Monopolar
Bipolar
Laplacian
1
1
2
1
2
90
3
2
EMG-RMS amplitude (%)
3
4
3
80
4
EMG rows
5
4
5
6
5
70
6
7
6
7
8
60
7
8
8
9
9
50
9
10
10
11
10
12
11
12
13
B1
1
2
3
4
1
2
3
4
1
2
3
5
Contribution of End-of-Fiber Effects to Monopolar sEMG
5
1
2
1
2
80
3
2
EMG-MEG coherence (%)
3
4
3
4
EMG rows
5
4
70
5
6
5
6
7
6
7
8
7
60
8
9
8
9
Proximal Distal
10
9
10
11
50
10
11
12
13
11
Lateral
1
12
Medial
2
3
4
conclude that the level of CMC was not affected by sEMG
signal amplitude.
In addition to sEMG amplitude, the shapes of the MUAPs
(and thus sEMG power spectra) vary with the sEMG recording
site, and the change occurs more rapidly in space with spatially
more selective sEMG recordings (Kleine et al. 2007). The
variations in the spatial dimension of the sEMG signal may in
part explain the overall variation in the level of CMC across the
muscle surface, as well as the finding that the level of CMC
computed with monopolar sEMG recordings showed a more
uniform distribution across the muscle surface.
Finally, the individual muscle anatomy (i.e., sEMG recording site) cannot solely explain the high CMC variability across
the subjects. The interindividual CMC variability was 84 –
93%, and, in comparison, intraindividual-CMC variability (i.e.,
across sEMG recording sites) was on average only 19 –35% for
the HD-sEMG derivations.
1
2
3
4
1
2
3
5
5
EMG columns
Fig. 3. Group maps (n ⫽ 12) of muscle activity and its coupling with primary
motor cortex (M1) activity using different HD-sEMG derivations. Maps
represent the means across subjects of the normalized spatial distributions of
EMG-RMS amplitude (A) and CMC strength (B) over the thenar muscles
during 4-min isometric thumb abduction. Mean ⫾ SD innervation zone
location across the subjects is indicated for each longitudinal sEMG electrode
column (black dots and black bars).
in signal amplitude may increase the level of coherence (i.e.,
positive correlation), but only when the signal-to-noise ratio is
low (Muthukumaraswamy and Singh 2011). However, the
signal-to-noise ratio of sEMG is relatively high, including the
coherent ⬃25-Hz band. The spatial correlation coefficient
between sEMG power and CMC was positive in 50 – 80% of
our subjects depending on the HD-sEMG derivation, but it did
not differ significantly from zero at group level. We thus
Two main components can be identified in monopolar
sEMG. The first reflects propagating MUAPs along the muscle
fibers and the second the nonpropagating end-of-fiber effects
arising when the MUAPs meet the muscle-tendon junction
(Stegeman et al. 1997). Importantly, the currents and potentials
associated with these two phenomena differ. The MUAPs
are associated with quadrupolar intracellular currents within
the muscle fibers, with one depolarizing current dipole in the
leading edge of the MUAP and one repolarizing current dipole
at the trailing end of the MUAP. In contrast, the end-of-fiber
effects arise when the MUAP reaches the tendon at the muscletendon junction, and then for a short time (for the duration of
the MUAP) only the trailing-end dipole of the MUAP prevails.
The electric potentials due to nonpropagating end-of-fiber
effects (due to the intracellular current dipoles) spread further
in space than those of the propagating MUAPs (intracellular
current quadrupoles) because electric potential decays with
distance r from the source approximately as 1/r⫺2 for dipolar
and 1/r⫺3 for quadrupolar sources. The resulting monophasic
potentials are positive over the entire muscle, and thus simultaneously arriving MUAPs effectively summate. Instead, the
presence of positive and negative phases in the potential
distributions associated to the propagating MUAPs easily cancel each other because of asynchronous firings of different
motor units and variation in conduction velocities in muscle
fibers of different sizes. Consequently, the monopolar sEMG
signals are less affected by amplitude cancellation (Staudenmann et al. 2010; Tucker and Turker 2005), and they thus
provide a better estimation of muscle force compared with
bipolar and Laplacian derivations (Staudenmann et al. 2006).
When more than single motor units are recruited, the MUAPs
of different sizes and firing frequencies are superimposed and start
Table 3. Coefficient of variation (0 –1) of cortex-muscle coherence across thenar muscle
Unrectified sEMG
Rectified sEMG
EMG Derivation
Mean
Range
Median
Mean
Range
Median
Monopolar (*)
Bipolar
Laplacian
0.19
0.33*
0.35*
0.05–0.44
0.14–0.54
0.20–0.69
0.19
0.31
0.30
0.11†
0.17*†
0.21*†
0.07–0.20
0.09–0.31
0.06–0.40
0.11
0.16
0.19
*P ⬍ 0.05 vs. monopolar; †P ⬍ 0.05 unrectified vs. rectified.
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11
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SPATIAL VARIABILITY IN CORTEX-MUSCLE COHERENCE
Subject 6
Laplacian
Macroscopic
monopolar
Macroscopic
bipolar
Fig. 4. Source locations [equivalent current
dipoles (ECDs)] and the respective magnetic
field patterns based on the cross-correlograms
between MEG and sEMG signals for 2 representative subjects (subject S14 showing strong
CMC and subject S6 showing weak CMC).
The ECDs are superimposed on subjects’ MRIs
in transverse plane, and the magnetic field
patterns are superimposed on the MEG sensor
array. The isocontour lines are separated by 30
fT for S14 and 5 fT for S6, red lines indicating
the flux out of the skull and blue lines flux into
the skull. Green arrows depict the surface projections of the ECDs best explaining the field
pattern. Left side indicates the left hemisphere.
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Bipolar
Monopolar
Subject 14
to form interference patterns where single MUAPs are difficult to
discern and potentials of different phases cancel each other,
thereby affecting the amplitude of the sEMG (Day and Hulliger
2001; Keenan et al. 2005). It may thus become more difficult to
detect from the sEMG the common oscillatory synaptic input to
␣-motoneurons (Farina et al. 2013), which is suggested to be the
coherent signal with the cortical activity (Baker et al. 1997;
Conway et al. 1995; Salenius et al. 1997). The effective summation of the end-of-fiber effects could carry the coherent
information (i.e., timing of the oscillatory inputs) and may
thus in part explain the ⬃20% stronger level of CMC when
using monopolar rather than bipolar HD-sEMG signals. The
degree of amplitude cancellation increases with muscle
contraction intensity, i.e., with the number and discharge
rate of active motor units (Farina et al. 2008; Keenan et al.
2005). Although the intensity of the muscle contraction was
low (⬃6% of MVC), the effects of amplitude cancellation
on the present results cannot be ruled out.
Common-mode signals are effectively suppressed in bipolar
and Laplacian derivations so that these recordings are thus
rather selective to the muscle just under the electrodes, whereas
the monopolar sEMG is more prone to cross talk from the other
nearby muscles. Because thenar contains several muscles that
cannot be selectively activated, cross talk did in our study more
likely affect monopolar than bipolar sEMG (Dimitrov et al.
2003; Mesin et al. 2009b).
J Neurophysiol • doi:10.1152/jn.00574.2015 • www.jn.org
SPATIAL VARIABILITY IN CORTEX-MUSCLE COHERENCE
Finally, our results imply that other factors affecting the
quality of sEMG amplitude estimation, such as the effect of
anatomical properties of the muscle, influence the CMC to a
lesser extent than do the end-of-fiber effects and amplitude
cancellation (see Spatial Variability of CMC Across Muscle
Surface).
HD-sEMG vs. “Macroscopic” sEMG Recordings
2851
merely reflected the signal-to-noise ratio of cross-correlograms
(which covaries with the level of coherence) from which the
sources were estimated. Accordingly, the better spatial selectivity of the sEMG (obtained with bipolar derivations) was
associated with lower CMC level and larger spread of cortical
sources. Nevertheless, it was possible to define the hand area in
the left M1 for all 12 subjects (statistically significant CMC for
⬎58% of the sEMG channels), relying on the location of the
main source cluster.
van de Steeg et al. (2014), using an 8 ⫻ 8 HD-sEMG
electrode grid, found stronger CMC for monopolar and Laplacian sEMG than for bipolar sEMG. They also computed CMC
after applying principal component analysis to remove the
most common components (across all channels of the electrode
grid) from the monopolar sEMG. The obtained CMC levels
were similar to those with “raw” monopolar and Laplacian
signals (no statistics performed) but, again, significantly higher
than with the bipolar sEMG derivation. These results are not
directly comparable with ours, because the monopolar sEMG
was rereferenced to one sEMG channel of the electrode grid on
the same muscle, while we used a remote reference electrode
on the radial bone. However, our findings are in line with their
observations that the use of HD-sEMG may enhance detection
of statistically significant CMC.
We further compared the HD-sEMG derivations with sEMG
derivations mimicking conventional macroscopic bipolar and
monopolar sEMG recordings and observed ⬃20 –30% stronger
CMC than with the respective HD-sEMG approaches. The
benefit of HD-sEMG approaches is their ability to “scan”
nearly the whole muscle surface with the HD-sEMG electrode
grid to identify the optimal sEMG recording site for CMC. We
expected the spatially more selective sEMG derivations (bipolar and Laplacian) to perform better in identifying the optimal
sEMG recording site, but the peak CMC (see Table 2) did not
differ between the HD-sEMG derivations. However, the bipolar and Laplacian derivations showed lower mean CMC and
higher spatial variability in the level of CMC across the
muscle, decreasing the probability to detect significant CMC.
Our results indicate that a more “global” measure of muscle
activity can enhance the detection of the CMC, possibly
because CMC likely reflects the modulation of ␣-motoneuron
drive to a population of motor units rather than to single motor
units (Williams and Baker 2009). In our case, monopolar
sEMG recording appeared optimal for CMC detection. Whenever HD-sEMG grids are not available, our results suggest the
use of a single monopolar sEMG recording with remote reference on an inactive site (e.g., on bone) to measure CMC, taking
in account that the monopolar recordings may be more prone to
interference (e.g., from power lines) in noisy environments.
In 9 of 12 participants, rectification of sEMG signals revealed corticospinal coupling at ⬃12 Hz that was not visible in
CMC computed with unrectified sEMGs. With rectified sEMG,
CMC typically peaks between ⬃15 and 30 Hz during weak
isometric contractions (Brown et al. 1998; Conway et al. 1995;
Gross et al. 2000; Halliday et al. 1998; Salenius et al. 1997),
but peaks at ⬃6 –15 Hz have been detected in some individuals
with MEG (Salenius et al. 1997) or epicortical recordings over
M1 (Raethjen et al. 2002) as well as in epicortical recordings
over the primary somatosensory cortex and the supplementary
motor area (Ohara et al. 2000). It is apparent that the presence
of the 12-Hz CMC only with rectified, and not with unrectified,
EMG will require more rigid examination in the future, using
for example variable contraction levels that result in different
levels of motor unit recruitment and firing frequencies, and
consequently to different levels of cancellation between the
motor unit action potentials in the sEMG. Indeed, recent
evidence suggests that the oscillatory cortical input components may be more strongly represented in rectified or unrectified sEMG signals depending on the degree of amplitude
cancellation of sEMGs (Farina et al. 2013).
Rectification of sEMG has in some studies hindered CMC
detection (McClelland et al. 2012), and van de Steeg et al.
(2014) found a tendency toward reduced peak and mean CMC
after rectification of sEMG. Our results support these observations, as rectification of the sEMG signals reduced the peak of
the 20-Hz CMC (in our case, by a quarter) but, surprisingly,
provided more uniform distribution of CMC values across the
thenar and more tightly clustered cortical sources. It is likely
that the more condensed source clusters simply reflect the
smaller spatial variation of the CMC across the thenar. As
rectification of sEMG may improve detection of motor unit
timing (Halliday and Farmer 2010), the common oscillatory
activity of the motor units could also be better presented,
therefore enhancing detection of the CMC.
Estimates of Coherent Cortical Sources
Implications for Functional Mapping of M1
In accordance with previous studies, the main cluster of the
coherent cortical sources was located in the hand area of the
left rolandic cortex (Conway et al. 1995; Salenius et al. 1997).
In subjects showing significant CMC for most sEMG channels
(⬎90% of all channels), the sources were clustered within a
small volume in the cortex (⬍0.02 cm3 for monopolar sEMG
CMC), whereas more scattered source estimates were observed
in subjects whose CMC barely reached the significance threshold. This negative correlation between the estimated source
volume and CMC level suggests that the spread of the sources
To date, functional magnetic resonance imaging (fMRI) has
been the main tool to pinpoint the rolandic cortex during
noninvasive presurgical evaluation (Bartsch et al. 2006; De
Tiège et al. 2009). Unfortunately, interpretation of fMRI maps
is challenging in patients with altered neurovascular coupling
due to various brain disorders (Bartsch et al. 2006; D’Esposito
et al. 2003; Korvenoja et al. 2006; Krings et al. 2001). In such
cases, MEG may represent an alternative to fMRI, as it provides direct information about neuronal activity. In some
patients with space-occupying lesions, MEG may be superior
Prominent Cortex-Muscle Coherence at ⬃12 Hz with
Rectified sEMGs
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J Neurophysiol • doi:10.1152/jn.00574.2015 • www.jn.org
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SPATIAL VARIABILITY IN CORTEX-MUSCLE COHERENCE
Conclusions
We have shown that the level of coupling between the
rolandic cortex and muscle activity depends on the sEMG
derivation used but is not systematically affected by the site of
the sEMG recording over the muscle. The individual muscle
anatomy therefore does not explain the large interindividual
variability of CMC levels. Nevertheless, HD-sEMG recordings
can facilitate detection of CMC. Monopolar sEMG resulted in
the strongest and most uniform CMC across the thenar and can
thus be recommended for CMC recordings. The effects of
EMG rectification on CMC need further exploration.
ACKNOWLEDGMENTS
We thank Helge Kainulainen for technical support.
GRANTS
This study was supported by the Academy of Finland (Grants 131483 and
263800 to R. Hari and Grant 13266133 to H. Piitulainen) and by the SalWe
Research Program for Mind and Body (Tekes—the Finnish Funding Agency
for Technology and Innovation Grant 1104/10) and the European Research
Council (Advanced Grant 232946 to R. Hari).
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
Author contributions: H.P., A.B., V.J., and R.H. conception and design of
research; H.P. and A.B. performed experiments; H.P., A.B., and M.B. analyzed
data; H.P., A.B., M.B., and R.H. interpreted results of experiments; H.P., A.B.,
and R.H. prepared figures; H.P. and A.B. drafted manuscript; H.P., A.B., M.B.,
V.J., and R.H. edited and revised manuscript; H.P., A.B., M.B., V.J., and R.H.
approved final version of manuscript.
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