1Psychiatric Clinic and 2Department of Neurobiology and Biophysics, Institute for Biology III, Albert-Ludwigs-University, D-79104 Freiburg; and 3Neurological Clinic, Albert-Ludwigs-University, D-79106 Freiburg, Germany
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ABSTRACT |
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Feige, Bernd, Ad Aertsen, and Rumyana Kristeva-Feige. Dynamic Synchronization Between Multiple Cortical Motor Areas and Muscle Activity in Phasic Voluntary Movements. J. Neurophysiol. 84: 2622-2629, 2000. To study the functional role of synchronized neuronal activity in the human motor system, we simultaneously recorded cortical activity by high-resolution electroencephalography (EEG) and electromyographic (EMG) activity of the activated muscle during a phasic voluntary movement in seven healthy subjects. Here, we present evidence for dynamic beta-range (16-28 Hz) synchronization between cortical activity and muscle activity, starting after termination of the movement. In the same time range, increased tonic activity in the activated muscle was found. During the movement execution a low-frequency (2-14 Hz) synchronization was found. Using a novel analysis, phase-reference analysis, we were able to extract the EMG-coherent EEG maps for both, low- and high-frequency beta range synchronization. The electrical source reconstruction of the EMG-coherent EEG maps was performed with respect to the individual brain morphology from magnetic resonance imaging (MRI) using a distributed source model (cortical current density analysis) and a realistic head model. The generators of the beta-range synchronization were not only located in the primary motor area, but also in premotor areas. The generators of the low-frequency synchronization were also located in the primary motor and in premotor areas, but with additional participation of the medial premotor area. These findings suggest that the dynamic beta-range synchronization between multiple cortical areas and activated muscles reflects the transition of the collective motor network into a new equilibrium state, possibly related to higher demands on attention, while the low-frequency synchronization is related to the movement execution.
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INTRODUCTION |
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Synchronization between
distributed neuronal activity patterns on a fine temporal scale has
been proposed as a candidate mechanism for integration in the visual
system (Eckhorn et al. 1988; Engel et al.
1992
; Gray and Singer 1989
; Singer and
Gray 1995
), in frontal areas (Abeles et al.
1993a
,b
; Prut et al. 1998
), and in the
visuomotor areas (Roelfsema et al. 1997
). Little,
however, is known about synchronization processes in the motor system
(Baker et al. 1999
; Brown 2000
;
Farmer 1998
). The coherence in the beta frequency range
observed in the human motor system between the electromyogram (EMG) and
cortical activity measured by the magnetoencephalogram (MEG)
(Conway et al. 1995
; Salenius et al.
1997
) or electroencephalogram (EEG) (Halliday et al.
1998
; Mima et al. 2000
) during sustained voluntary muscle contraction was interpreted as a sign of neural coordination, similar to the synchronization observed in monkeys (Baker et al. 1997
; Murthy and Fetz 1992
,
1996
; Riehle et al. 1997
; Sanes
and Donoghue 1993
). These studies (Conway et al.
1995
; Halliday et al. 1998
; Mima et al.
2000
; Salenius et al. 1997
) investigated the
synchronization between human cortical activity and EMG of the active
muscle during maintained isometric muscle contraction. Conway et
al. (1995)
and Salenius et al. (1997)
found the
coherence largely confined to cortical activity in the beta-range (15-30 Hz). They hypothesized that this coherence might reflect binding between synchronized cortical activity in the primary motor
areas and motor output at the spinal level. Since these studies used a
maintained muscle contraction task, it was impossible to assess the
dynamical properties of the synchronization. Another open question is
the localization of the sources of cortical synchronization. Both MEG
studies located the generators of the beta synchronization in the
primary motor cortex. However, since MEG recordings are only sensitive
to tangential current components of active neuronal populations
(Williamson and Kaufman 1981
), as is the case in the primary motor area, possible contributions from sources having mostly
radial components, such as in the premotor area (PMA), may have been
overlooked (Kristeva et al. 1991
). The two studies aiming at investigating the EEG/EMG synchronization (Halliday et
al. 1998
; Mima et al. 2000
) were not able to
locate such sources, due to the lack of spatial resolution with only
two electrodes overlying the contralateral hand area (Halliday
et al. 1998
) or because no source reconstruction technique was
applied (Mima et al. 2000
).
To study whether beta-range and other synchronization between cortical
activity and EMG in the human motor system also occurs under dynamic
conditions and, if so, to examine the spectro-temporal properties of
this synchronization and its relation to the onset and offset of muscle
contraction, we employed a phasic motor task. Subjects performed a
voluntary right index finger movement every 12-25 s
[Bereitschaftspotential paradigm (Kornhuber and Deecke 1965)]. We simultaneously recorded the cortical activity by
high-resolution EEG and the EMG of one of the activated muscles (the
prime mover; Fig. 1). Since the EEG is
sensitive to tangential as well as to radial sources, we could test
whether other cortical areas besides the primary motor cortex become
engaged in the synchronization between cortical activity and EMG.
Moreover, the combination of a dynamic task paradigm and EEG recordings
also allowed us to address another important issue in the study of
motor control: the relation between the cortico-EMG synchronization and
movement-related spectral power changes in the EEG/MEG. It is well
known that a phasic voluntary movement is preceded by cortical
desynchronization mostly in the mu-frequency range (10-13 Hz), whereas
it is followed by postmovement synchronization mostly in the beta-range
(15-30 Hz) (Feige 1999
; Feige et al.
1996
; Pfurtscheller 1992
; Salmelin and
Hari 1994
). The question whether this excess postmovement beta-activity is actually synchronized with the EMG has not yet been
addressed in other studies.
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METHODS |
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Subjects
The experiment was run with seven healthy, right-handed subjects (mean age 28.2 ± 3.2 yr): six males and one female (S3), who had previously given their informed consent.
Experimental paradigm
During the experimental session, subjects were sitting in an electrically shielded, dimly lit room and performed abrupt, self-paced "pulse" movements (rapid flexion followed by extension of the right index finger), starting from light extension position, at irregular intervals of 12-25 s (cf. Fig. 1). They were instructed to be completely relaxed and to avoid any other movement and to fix their gaze on a light-emitting diode in front of them. The subjects were instructed to pay attention to finish the rapid pulse movement in the same position, from which the movement had started. Each subject was given several practice trials prior to the experiment until they reached a consistent EMG pattern for the pulse movement. Electric potentials were recorded from 61 electrode positions equally distributed over both hemispheres on the scalp (band-pass 0-100 Hz; sampling rate 500 Hz; NeuroScan, Herndon, VA). Electrode Cz was used as common recording reference; the ground was on the forehead. The surface EMG was recorded using Ag-AgCl electrodes placed over the pars indicis of the right flexor digitorum muscle (one of the prime mover muscles) and recorded with the same filters and sampling rate as the EEG. The low frequencies were included to investigate whether there was also a low-frequency synchronization between EEG and EMG. The EMG onset was used as a trigger for further analysis. The electro-oculogram (EOG) was recorded to exclude trials with eye movement artifacts. EEG, EMG, and EOG were digitally stored and analyzed off-line. The analysis time was set from 5 s before to 3 s after EMG-onset. Two hundred to 250 artifact-free trials per subject were used for further analysis. After each recording session, the electrode positions and the head contour of the subject were digitized using a three-dimensional (3-D) ultrasound localizing device (ZEBRIS).
Analysis of phase synchronization and phase reference analysis
The synchronization between the EEG and the EMG of the agonist
muscle as a function of frequency and time was quantified by averaging
the complex difference phase factors
ei(
EEG
EMG)
representing the phase difference between EEG and EMG in every single
trial. The amplitude c of the resulting average represents a
measure of the nonuniformity of the distribution of phase differences, i.e., a measure of phase coherence between the EEG and EMG,
by means of the Rayleigh test (cf. Lütkenhöner
1991
; Mardia 1972
; we used Rayleigh's
asymptotic formula for the probability distribution given by
Strutt 1905
, cited after Greenwood and Durand
1955
). Therefore in the following, c will be called
phase coherence. The statistical significance of the phase
coherence c determined from N trials can then be
calculated as
e
N·c2. Note
that the usual definition of coherence includes amplitude covariation
in addition to the stability of phase difference and corresponds to the
square of this value in the case of constant amplitudes (cf.
Bendat and Piersol 1971
; Whalen 1971
).
To localize the sources of EMG-coherent EEG activity, we employed a
new variant of this phase coherence analysis: phase reference analysis
(Feige 1999): if
a(f) is the complex Fourier
coefficient at frequency f, we have
a(f) = |a(f)
|e
i
. For each
frequency, the difference phase factor is multiplied with the
corresponding spectral EEG amplitude and then averaged across
trials: |aEEG(f)
|e
I[
EEG(f)
EMG(f)].
The EMG thereby acts as a phase reference: its phase is subtracted from
the EEG phase, while the EEG amplitude is retained. Thus phase
reference analysis measures the fraction of the EEG that is reliably
synchronized to the EMG. Since this measure, unlike conventional
coherence analysis, preserves both amplitude and phase information of
the EEG, the sources of EMG-coherent EEG activity can be localized by
applying source reconstruction methods to the scalp distributions (or
maps) of the extracted EMG-coherent electrical potentials.
Electric source reconstruction
Source reconstruction of the EMG-coherent EEG maps was performed
on the basis of the individual brain morphology as obtained from MRI.
To establish the spatial relationship between the electrode positions
and the MRI, the digitized head contour was matched with the
head-contour as obtained from MRI by means of a surface-matching algorithm (Huppertz et al. 1998). For structural MRI,
the 3-D dataset with full head coverage and 1 mm3
voxels was acquired using a volume-encoded fast low angle shot pulse
sequence (FLASH) with TR/TE/alpha = 40 ms/60 ms/40°. The source
reconstruction was performed using cortical current density analysis
(CCD) (Ilmoniemi 1991
; Wagner 1998
). The
CCD maps obtained in this way show the current flow distribution on the
cortex, which can account for the potentials measured on the head
surface. The ambiguity in the CCD model was removed by the
"minimum norm constraint." This constraint uses a model
term that is proportional to the square of the strength of the
reconstructed currents. The regularization parameter was determined
according to the
2-criterion. No assumptions
about the number and location of cortical sources were made, except
that all sources were constrained to a surface representing the
cortical gray matter. For each individual subject, the segmented cortex
with all individual gyri and sulci at about 50,000 sampled locations
was used. To account for the shapes of liquor, skull and scalp, a
realistic three-compartment Boundary Element Method model was used as
the volume conductor head model. Only sources with at least 75% of the
strength at the maximum current density itself were considered. Under
these circumstances (sensor distribution, source model used, and color scale) the drop from 100 to 75% happens within a volume of 3 × 3 × 3 cm (Fuchs et al. 1999
). Image segmentation,
volume conductor modeling, source reconstruction, and visualization
were performed using the CURRY software (CURRY 3.0, Philips Research,
Hamburg, Germany).
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RESULTS |
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Since movement-related EEG rhythms occurring at fixed locations
and in a defined functional situation can have a broad or a narrow
spectrum, depending on the subject (Feige et al. 1996), we examined for each individual subject the entire frequency range between 0 and 45 Hz over a time window running from 5 s before to
3 s after EMG onset (Fig. 2). This
frequency × time plane approach differs from the
conventional method of examining only the data filtered in the most
responsive frequency bands (Pfurtscheller 1992
;
Salmelin and Hari 1994
). The synchronization between the EEG and the EMG of the agonist muscle as a function of frequency and
time was quantified by the phase coherence. Figure 2A shows the frequency × time distributions of EEG-EMG phase
coherence for two (of 7) investigated subjects. To facilitate the
interpretation, the movement-related frequency × time
distributions of baseline-relative spectral power changes in EEG (Fig.
2B) and EMG (Fig. 2C) are shown for comparison.
Observe that each subject exhibited two patches of EEG-EMG coherence
with different spectro-temporal properties (Fig. 2A): an
early, low-frequency coherence (demarcated by a dotted white frame) and
a later, high-frequency coherence (solid white frame). The
spectro-temporal properties of these two successive instances of
EEG-EMG coherence for all seven investigated subjects are summarized in
Table 1. The low-frequency coherence
ranged from 2 to 14 Hz (with a maximum at 5 Hz) and started immediately after EMG onset. Probably, this low-frequency coherence between cortical activity and EMG represents a functional state of the oscillatory network related to the pulse movement execution. Following the movement, there is a functional change in the network state, characterized by the high-frequency coherence. This beta-range coherence lasted 1-2 s, ranged from 16 to 28 Hz (with a maximum between 19 and 24 Hz), and started after the "pulse" movement, which lasted approximately 400 ms. This coherent beta-range activity occurring after movement termination can, in fact, be observed in
single trials, as is demonstrated in the three trials shown in Fig.
3. Note that coherent beta-range activity
was present only after (Fig. 3D), but not before the
movement (Fig. 3C). Interestingly, the increased tonic EMG
after movement termination was not described in previous
Bereitschaftspotential studies. Possibly, this activity is related to
the active muscles reaching the new equilibrium state after conclusion
of the active movement (Wachholder 1928
).
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The postmovement high-frequency coherence between cortical activity and
EMG should be distinguished from the well-known postmovement cortical
beta-synchronization that has been described extensively in the EEG
(Pfurtscheller 1992) and MEG (Feige et al.
1996
; Salmelin and Hari 1994
) literature. This
becomes apparent from a comparison between the frequency × time distributions of EEG-EMG phase coherence (Fig. 2A)
and movement-related EEG spectral power differences (Fig.
2B). The EEG spectral power distributions indeed show the well-known cortical postmovement beta-synchronization, i.e., the elevation of beta-range EEG-power immediately after movement
termination. In all subjects the area (in frequency × time) over which this elevation extends included the patch of
EEG-EMG coherence (solid white frames in Fig. 2, A and
B), but was distinctly larger (especially covering higher
frequencies). Similarly, Fig. 2C demonstrates that also the
EMG exhibits beta-range spectral power enhancement immediately after
the voluntary movement. As in the EEG, this enhancement includes the
spectral band of EEG-EMG coherence, but it is also distinctly broader,
especially toward higher frequencies. Therefore only part of the
spectral power enhancement known as postmovement beta-synchronization
is actually coherent to part of the postmovement EMG.
To localize the cortical sources of EMG-coherent EEG activity, we
employed a new variant of phase coherence analysis: phase reference
analysis (Feige 1999) (cf. METHODS). Since
this measure, unlike conventional coherence analysis, preserves both
amplitude and phase information of the EEG, the sources of EMG-coherent EEG activity can be localized by applying source reconstruction methods
to the scalp distributions (or maps) of the extracted EMG-coherent
electrical potentials (Fig.
4A). Thus we reconstructed for
all subjects the EMG-coherent EEG map at the maximum postmovement high-frequency coherence (i.e., the peak in the solid white frame in
Fig. 2A) and at the maximum of the low-frequency coherence during the movement (i.e., the peak in the dotted white frame in Fig.
2A) by applying cortical current density analysis (cf. METHODS), using the individual brain anatomy
derived from the subjects' magnetic resonance imaging (MRI). All seven
subjects exhibited extended cortical current density sources for the
high-frequency coherence comprising tangential activity in the
contralateral primary motor cortex (cMI) but, in contrast to earlier
reports, the extended source area included additional radial activity
in the contralateral premotor area (cPMA; Fig. 4, A and
B). Besides the tangential activity in MI and the radial
activity in cPMA, in two of the subjects the source area included
additional radial activity in the contralateral parietal area (cPA). In
fact, never before were so many motor areas shown to engage in coherent
activity so late after termination of an active movement. The finding
that multiple motor areas are simultaneously involved is supported by
the original high-frequency EMG-coherent EEG maps as shown for one of
the subjects in Fig. 4A: the electric field distribution suggests contributions not only from a tangential source (within cMI)
but also from a radial source (within the cPMA). This activation of
multiple motor areas can be explicitly seen also in the tangential (cMI) and radial (cPMA) orientation of the individual current vectors
(Fig. 4B). To compare the sources of the beta-range
coherence with those of the low-frequency coherence during the
movement, a source reconstruction of the EMG-coherent EEG map at the
maximum low-frequency coherence (i.e., the peak in the dotted white
frame in Fig. 2A) was also performed. The reconstruction of
the low-frequency coherence maps showed an extended source area
including again the primary motor cortex and the premotor area (cf Fig.
4D). However, in the low-frequency EMG-coherent EEG maps, in
addition more medial parts of the premotor area were engaged. The
finding that the primary motor, premotor, and medial premotor areas are
simultaneously involved is also supported by the complex pattern of the
original EMG-coherent EEG maps shown in Fig. 4C.
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DISCUSSION |
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The present results are the first to show that motor cortical
areas and the EMG synchronize their joint activity in a dynamic fashion
in systematic relation to phasic voluntary movements. Low-frequency
(5 Hz) synchronization, starting at movement onset, is followed by
high-frequency (
23 Hz) synchronization after movement termination,
lasting for 1-2 s. This synchronization dynamics may reflect changes
in functional network state related to phasic voluntary movements.
Beta-range synchronization between cortical activity and EMG had
previously been observed during maintained motor tasks (Brown
2000
; Conway et al. 1995
; Halliday et al.
1998
; Mima et al. 2000
; Salenius et al.
1997
), but never before under dynamic conditions with a phasic
motor task. The close tuning of the EEG-EMG synchronization in all
investigated subjects around
23 Hz, a frequency at which also
coherence between human single motor unit spike trains was reported
(Farmer et al. 1993
), suggests that this may be a
preferred frequency for integration of distributed activity in the
motor system.
Comparison between the frequency × time distributions
of EEG-EMG coherence (Fig. 2A) and movement-related EEG
spectral power difference (Fig. 2B) revealed clear
differences in the spectro-temporal extent of the two postmovement
synchronization phenomena. This indicates that the postmovement
beta-synchronization of the EEG/MEG, presumably reflecting the internal
synchronization of relatively large cortical areas, is composed of at
least two components: one that is coherent with the EMG (cf. Fig.
2A) and one that is not (the remainder). Moreover, the two
components cover different frequency ranges. This finding that the
postmovement cortical beta-synchronization is of a composite nature
implies that the notion that it reflects idling motor cortex
(Pfurtscheller 1992) or inhibition (Salmelin and
Hari 1994
) needs to be revised. Instead, it lends support to
our earlier hypothesis that postmovement cortical beta-synchronization
plays an active role in motor control, possibly integrating distributed
activity between the cortex and the muscle (Brown 2000
;
Feige et al. 1996
; Kilner et al. 1999
).
The high-resolution EEG used in this study enabled us to localize
generators of the beta-range EMG-synchronized cortical activity not
only in the contralateral primary motor areas MI but also in the PMA.
The contribution of the premotor areas to the generation of the EEG-EMG
synchronization was overlooked in earlier MEG studies, presumably
because the sensitivity of the MEG is restricted to tangential sources.
The dynamic synchronization of the premotor areas and muscle activity
may be important for postural stabilization after movement. One
possible mechanism for this may be related to the predominantly
inhibitory action of premotor areas on pyramidal tract neurons as
recently shown (Tokuno and Nambu 2000).
In all subjects we observed a joint participation of the contralateral MI area and the PMA. In two of the subjects, the contralateral PA engaged in the coherent EEG-EMG activation as well, suggesting the existence of inter-individual differences in the human motor system.
The contralateral primary motor and premotor areas were found to generate also the low-frequency cortico-EMG coherence during the voluntary movement. The activation of these two areas with the additional participation of the more medial parts of the premotor area may represent a functional state of the oscillatory motor network related to the movement execution.
It is interesting to note that the beta-range EEG/EMG synchronization
occurred during the transition of the motor system into a new
equilibrium state when the attentional demands of the motor tasks are
higher because the subjects were instructed to pay special attention to
finish the rapid pulse movement in the same position from which the
movement has started. During this transition, there was an increased
tonic activity in the active muscle (cf. Fig. 3B). Also
Baker et al. (1997) and Kilner et al.
(1999)
presented evidence for beta-range cortico-EMG coherence
during a stationary phase of a movement paradigm (a hold phase after a
precision grip task), but not during active movement periods. Likewise,
Conway et al. (1995)
, Salenius et al.
(1997)
, and Halliday et al. (1998)
demonstrated
such coherent activity during maintained motor contraction and
suggested that it might reflect a low-effort contraction maintenance rhythm. These findings suggest that the dynamic beta-range
synchronization between multiple cortical areas and activated muscles
reflects the transition of the collective motor network into a new
equilibrium state, possibly related to higher demands on attention.
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ACKNOWLEDGMENTS |
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We thank Prof. C. H. Lücking for helpful discussions and C. Sick and T. Ball for experimental and analysis help. We are indebted to Drs. Leonardo Cohen, Bernhard Conway, Manfred Fuchs, Alexa Riehle, Jerome Sanes, Andrew Schwartz, and Mario Wiesendanger for constructive comments on an earlier version of the manuscript.
This work was supported in part by grants from the Deutsche Forschungsgemeinschaft and the Research Fund of the Albert-Ludwigs-University Freiburg.
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FOOTNOTES |
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Address for reprint requests: R. Kristeva-Feige, Neurological Clinic, Albert-Ludwigs-University, Breisacher Straße 64, D-79106 Freiburg, Germany (E-mail: kristeva{at}nz11.ukl.uni-freiburg.de).
Received 27 March 2000; accepted in final form 1 June 2000.
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REFERENCES |
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