Brain (2000), 123, 2475–2490
Multimodal EEG analysis in man suggests
impairment-specific changes in movement-related
electric brain activity after stroke
T. Platz,1 I. H. Kim,1 H. Pintschovius,1 T. Winter,1 A. Kieselbach,1 K. Villringer,2 R. Kurth2 and
K.-H. Mauritz1
1Klinik
Berlin, Department of Neurological Rehabilitation,
Freie Universität and 2Department of Radiology,
Universitätsklinikum Benjamin Franklin, Freie Universität
Berlin, Germany
Correspondence to: Dr T. Platz, Klinik Berlin, Kladower
Damm 223, 14089 Berlin, Germany
Summary
Movement-related slow cortical potentials and eventrelated desynchronization of alpha (alpha-ERD) and beta
(beta-ERD) activity after self-paced voluntary triangular
finger movements were studied in 13 ischaemic supratentorial stroke patients and 10 age-matched control
subjects during movement preparation and actual
performance. The stroke patients suffered from central
arm paresis (n ⍧ 8), somatosensory deficits (n ⍧ 3) or
ideomotor apraxia (n ⍧ 2). The multimodal EEG analysis
suggested impairment-specific changes in the movementrelated electrical activity of the brain. The readiness
potential of paretic subjects was centred more anteriorly
and laterally; during movement, they showed increased
beta-ERD at left lateral frontal recording sites. Patients
with somatosensory deficits showed reduced alpha-ERD
and beta-ERD during both movement preparation and
actual performance. Patients with ideomotor apraxia
showed more lateralized frontal movement-related slow
cortical potentials during both movement preparation
and performance, and reduced left parietal beta-ERD
during movement preparation. We conclude that (i)
disturbed motor efference is associated with an increased
need for excitatory drive of pyramidal cells in motor and
premotor areas or an attempt to drive movements through
projections from these areas to brainstem motor systems
during movement preparation; (ii) an undisturbed
somatosensory afference might contribute to the release
of relevant cortical areas from their ‘idling’ state when
movements are prepared and performed; and (iii) apraxic
patients have a relative lack of activity of the mesial
frontal motor system and the left parietal cortex, which
is believed to be part of a network subserving ideomotor praxis.
Keywords: event-related desynchronization; arm; apraxia; hemiparesis; deafferentation
Abbreviations: alpha-ERD ⫽ event-related desynchronization of alpha activity; beta-ERD ⫽ event-related desynchronization
of beta activity; HEOG ⫽ horizontal electrooculogram; MRP ⫽ movement-related DC potentials; PCA ⫽ principal components
analysis; VEOG ⫽ vertical electrooculogram
Introduction
Stroke in man can result in different impairments affecting
motor control, such as central paresis, deafferentation,
apraxia, visuoconstructional deficits and neglect. Even simple
movements indicate different behavioural disturbances for
each of these impairments. For example, hemiparesis results,
even after clinical recovery, in reduced efficiency of various
basic motor abilities, such as aiming, dexterity and steadiness
(Platz et al., 1999); while these patients are able to perform
most motor tasks, they still show an increased demand for
time and corrections (Platz et al., 1994). It seems that the
final efferent pathway that is affected (the pyramidal tract)
causes behavioural deficiencies in different aspects of basic
© Oxford University Press 2000
motor control. Deafferentation, on the other hand, seems to
affect especially the feedback-guided aspects of basic motor
control, e.g. the homing-in phase of aimed movements, which
guarantees final precision (Platz and Mauritz, 1997), and the
ability to maintain a constant force with static motor tasks
(Rothwell et al., 1982). Ideomotor apraxia affects motor
behaviour at a higher level of organization. The spatiotemporal characteristics of the movements of ideomotor
apraxic patients are altered qualitatively; for example, they
are unable to reproduce the characteristic features of gestures
flawlessly (Poizner et al., 1990; Platz and Mauritz, 1995).
In addition to learning about the behavioural consequences
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T. Platz et al.
of stroke, it is of interest to assess any related changes in
the functional activity of the brain. Activation studies with
imaging techniques have indicated functional cortical
reorganization after motor stroke (Brion et al., 1989; Chollet
et al., 1991; Weiller et al., 1992; Cramer et al., 1997; Cao
et al., 1998; Seitz et al., 1999); movements of the recovered,
formerly paretic hand after stroke were shown to be associated
with bilateral activation of the motor and non-motor
association cortex and the contralateral cerebellum, in
addition to areas symmetrical to those activated by movements of the unaffected hand, i.e. the primary sensorimotor
cortex.
The aim of the present study was to describe the
electrophysiological correlates of changed cerebral motor
control in different clinical impairments after stroke, namely
hemiparesis, deafferentation and ideomotor apraxia, by means
of multimodal EEG. The high temporal resolution of this
technique—a few milliseconds—allows the separate assessment of brain activity related to movement preparation and
movement execution. In addition, different electrical activities
of the brain can be analysed from a given data set: slow
movement-related DC potentials (MRPs) and event-related
desynchronization (ERD) of rhythmic brain activity, at
frequencies in both the alpha band (alpha-ERD) and beta
band (beta-ERD). These different analyses reveal different
patterns of spatiotemporal cerebral activation related to
movement, and might therefore reflect different neural
mechanisms related to motor control. Movement-related slow
cortical potentials are generated by the coherent synaptic
activity of cortical neurones; excitatory postsynaptic
potentials at the apical dendrites of pyramidal cells with their
source in deeper layers near or at the soma are the most
likely candidates for the generation of these slow cortical
potentials (Bierbaumer et al., 1990). Shifts in slow negative
MRPs start ~1.5 s before movement onset [negativity of
Bereitschaft (readiness)], with a fairly wide bilateral
distribution and a maximum at the midline close to the
vertex, at least with self-paced, consciously performed finger
movements. They become more lateralized shortly before
and during movement, with maxima at recording sites
overlying the sensorimotor cortex contralateral to the moving
hand (negativity of performance). MRPs have been shown
to be modified in amplitude and distribution in relation to
specific task demands (Kornhuber and Deecke, 1965; Lang
et al., 1994). Apart from these slow potential changes,
rhythmic sensorimotor activity in the alpha and beta bands
becomes desynchronized as early as 2 s before movement
onset; desynchronization continues during movement. Early
alpha- and beta-ERD in the preparatory period before
movement onset has been described as more lateralized
than ERD during movement or MRPs during movement
preparation (Pfurtscheller and Berghold, 1989; Pfurtscheller
and Neuper, 1994; Stancák and Pfurtscheller, 1996). The
central beta rhythm has a slightly more anterior focus than
the central alpha rhythm, indicating different expression of
these rhythmic brain activities in the motor and somatosensory
cortex (Pfurtscheller et al., 1994; Salmelin et al., 1995).
Characteristic of rhythms within the alpha and lower beta
band, occurring not only over the sensorimotor area but also
over visual and auditory cortex, is their blockade (or ERD)
when the corresponding area becomes activated. This might
indicate a shift from an ‘idling’ to an ‘active’ state of cortical
areas involved in a given task (Pfurtscheller and Neuper,
1994). Neurally, desynchronized alpha-band activity is
believed to be a correlate of increased cellular excitability in
interconnected thalamocortical systems (Steriade and Llinas,
1988). Thus, it might be assumed that ERD reflects changes
in local interactions between pyramidal neurones and
interneurones controlling the frequency components of the
EEG, while slow movement-related potentials would
represent the response of cortical neurones to afferent signals
(Pfurtscheller and Lopes da Silva, 1999).
In the present study, two approaches were combined: (i)
the assessment of different subgroups of stroke patients with
clinically distinct impairments; and (ii) the multimodal EEG
analysis of movement-related brain activity with high time
resolution, providing comprehensive information about the
electrical activity of the brain before and during movement.
This combination was chosen to explore the possibility of
impairment-specific changes in movement-related electrical
activity of the brain after stroke. The study should answer
the question whether any changes in the electrical activity
of the brain (MRP, alpha-ERD and/or beta-ERD) occur in
stroke patients immediately before and/or during simple
movements, and whether any of these changes are associated
with a particular impairment of interest (hemiparesis,
deafferentation, ideomotor apraxia).
Material and methods
Subjects
Ten healthy subjects (five female and five male; mean age
54.3 years, SD 5.1 years) served as a reference group. They
had no history of brain disease, no clinical signs of central
or peripheral nervous system disorder, and no orthopaedic
conditions relevant to upper extremity movements.
Between 1996 and 1999, we recruited 13 patients after a
first unilateral supratentorial ischaemic stroke in the subacute
to early chronic phase during inpatient rehabilitation treatment
at the department of neurological rehabilitation of the Freie
Universität, Berlin (for patient details, see Table 1). The
patients’ mean age was 54.9 years (SD 6.5 years) and, on
average, they were investigated 8.8 weeks (SD 7.3 weeks)
after stroke. Patients were selected if they belonged to any
of three groups of interest at the time of the investigation,
i.e. if they presented with (i) mild to moderate central arm
paresis without somatosensory deficits [referred to here as
the ‘hemiparesis’ (HE) group]; (ii) somatosensory deficits of
the arm (reduced sensation to light touch and/or positional
change) but without signs of overt paresis (‘deafferentation’
(SE) group]; or (iii) ideomotor apraxia, as ascertained by
Movement-related brain activity after stroke
2477
Table 1 Characteristics of stroke patients
Patient
Age
(years)
Gender
Time after
Cerebral lesion
stroke (weeks)
P.E.
57
M
6
G.U.
53
F
20
M.U.
50
M
9
B.E.
58
M
6
G.R.
66
M
4
S.T.A.
61
F
12
M.O.
49
M
4
Incomplete L MCA stroke, affecting frontal
areas
R posterior limb of internal capsule,
putamen, white matter under precentral gyrus
R putamen and head of caudate, anterior
limb of internal capsule
R posterior limb of internal capsule
G.A.
58
M
3
R posterior limb of internal capsule
HE-L
P.A.
47
M
7
R inferior frontal and postcentral gyrus
SE-L
H.E.
59
M
6
SE-L
S.T.E.
55
M
5
R posterior part of posterior limb of internal
capsule
R thalamus
K.L.
59
M
28
Incomplete L MCA stroke, predominantly
temporal and lower frontal and parietal areas
IM
S.A.
42
F
5
Incomplete L MCA stroke, predominantly
temporal and lower frontal and parietal areas
IM
L posterior limb of internal capsule,
putamen, extending laterally
L anterior and posterior limb of internal
capsule, extending laterally
L posterior limb of internal capsule
Group
Clinical presentation
HE-R
Mild to moderate paresis,
normal sensation
Moderate paresis, normal
sensation
Mild to moderate paresis,
normal sensation
Mild to moderate paresis,
normal sensation
Mild to moderate paresis,
normal sensation
Mild to moderate paresis,
normal sensation
Mild hemiparesis, normal
sensation
Mild hemiparesis, normal
sensation
Moderate reduction of LT, TH
and ST
Mild reduction of LT and TH
HE-R
HE-R
HE-R
HE-L
HE-L
HE-L
SE-L
Moderate reduction of LT, TH,
PS and ST
Mild ideomotor apraxia, nonfluent aphasia, mild
sensorimotor paresis
Moderate ideomotor apraxia,
global aphasia, mild
sensorimotor paresis
F ⫽ female; M ⫽ male; R ⫽ right hand affected; L ⫽ left hand affected; HE ⫽ hemiparesis; SE ⫽ somatosensory deficits; IM ⫽
ideomotor apraxia; LT ⫽ sense of light touch; TH ⫽ thermal sense; PS ⫽ position sense; ST ⫽ stereognosis.
typical parapraxic performance errors with symbolic and
non-symbolic gestures after verbal command and with
imitation (Platz and Mauritz, 1995) when the non-paretic
ipsilesional arm was tested [‘ideomotor apraxia’ (IM) group].
The patients had to be able to perform the experimental task
(see below) in order to be included in the study.
All subjects gave informed consent to participation in the
study, which had received approval from the Ethics
Committee of the Freie Universität Berlin.
Procedure
Motor tasks
A triangular trajectory movement was performed by the
subject’s index finger. This movement was chosen because
it has been shown previously to be sensitive to motor control
deficits in both hemiparetic and apraxic patients, i.e. it reflects
both motor execution and cognitive–motor aspects of motor
control (Platz et al., 1994; Platz and Mauritz, 1995). The
triangular movement consisted of a movement from the
resting position upwards and outwards to a position that
represented the tip of a triangle; from there downwards and
outwards to the outer angle; and then back to the starting
position, which represented the inner angle of an imagined
triangle. The triangular movement was performed twice in
sequence with a target rate of 1 Hz for single movement
elements, resulting in a movement time of ~6 s. Only one
finger was moved at any time, all other fingers resting on
the plate. Movements were self-initiated, and the interval
between movement sequences was intended to range from 8
to 16 s. Subjects were requested not to move their eyes
before and during movements, but to look at a fixation point.
Five blocks of movements, each with 30 task repetitions,
were recorded (for control subjects there were five blocks
for each hand, the hands being alternated after each block).
Hemiparetic patients and deafferented patients performed the
motor task with their affected hand, i.e. the hand contralateral
to the affected half of the brain. Ideomotor apraxic patients
performed the task with their ipsilesional (left) hand to
avoid any confounding by paresis or somatosensory deficits
affecting the contralesional hand. The healthy control subjects
performed the task with either hand.
The motor tasks were performed with the subject’s moving
hand positioned on a plate; an integrated touch-switch
underneath the index finger generated an 8 bit electrical
impulse when it was lifted. These impulses were recorded
simultaneously with the surface EMG over the extensor
digitorum communis muscle and the EEG. In addition, data
from a 2D miniature accelerometer (model EGAXT2-C-5;
Entran, Fairfield, NJ, USA), fixed to the tip of the index
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T. Platz et al.
Fig. 1 Electrode positions on a standardized realistic head model
reconstructed from an MRI when viewed from above. The same
view is used in Figs 2–5. The electrodes are positioned according
to the extended international 10–20 system.
finger, was recorded with a 500 Hz analogue–digital (AD)
conversion rate. These data were analysed off-line. We
recorded the intra-individual mean and coefficient of variation
for the duration of the triangular movements, and the maximal
tangential acceleration during each movement segment of
a triangle.
EEG recording and primary data analysis
The subject was seated in a semi-reclining chair. Movementrelated potentials were recorded using a multichannel EEG
device (SynAmps amplifier and Scan software (NeuroScan,
Sterling, Va., USA). Skin preparation with Abralyt Light (Falk
Minow Services, Munich, Germany) resulted in impedance
⬍3 kΩ at all recording sites. Twenty-seven Ag–AgCl sinter
electrodes were spaced on the scalp in a montage modified
from the international 10–20 system (FP1, FP2, F7, F3, FZ,
F4, F8, FC5, FC1, FC2, FC6, T3, C3, CZ, C4, T4, CP5,
CP1, CP2, CP6, T5, P3, PZ, P4, T6, O1, O2) (Fig. 1) (Jasper
1958; Herrmann et al., 1989). Vertical (VEOG) and horizontal
(HEOG) electrooculograms were recorded separately. Data
were recorded continuously with DC filtering to 100 Hz and
an AD conversion rate of 500 Hz, with either A1 or A2 as
the reference electrode (ipsilateral to the moving hand).
Movement onset was marked off-line according to a rectified
surface EMG recording of the extensor digitorum communis
muscle. Data sweeps were generated for a period from 5 s
before until 18 s after movement onset. Baseline correction
was performed using the interval from 2500 to 2000 ms
before movement onset. Blink artefacts were removed from
raw data using VEOG data and the artefact-reduction Scan
software algorithm (NeuroScan). Individual sweeps were
inspected off-line and were rejected from the analysis if one
of the following conditions was fulfilled: (i) the resting period
before movement onset was shorter than 8 s; (ii) the EEG
signals were greater than 50 µV or less than –50 µV; (iii)
HEOG indicated horizontal eye movements; (iv) one or more
electrodes showed any other artefact.
For the analysis of DC potentials, intra-individual
averaging of sweeps was performed; on average 53 accepted
sweeps were averaged (patients, 52 sweeps; controls, 54
sweeps; t-test, P ⫽ 0.793). Averaged sweeps were corrected
for residual drifts by a linear detrend algorithm (followed by
a second baseline correction), transformed to a common
average reference (based on 19 electrodes of the 10–20
system), and reduced in size to the period of interest from
3 s before movement onset to 5 s after movement onset. All
further analyses of DC potentials were based on common
average reference data.
For the analysis of ERD in the alpha band, data were
baseline-corrected to a baseline period from 3500 to 2500 ms
before movement onset and transformed to a common average
reference (based on 19 electrodes of the 10–20 system). The
alpha frequency with the largest movement-related change
of power at C3 or C4 (contralateral to the moving limb) was
considered the individual’s µ frequency; this most reactive
frequency was determined by comparing a period of 4 s
before movement onset (5000 to 1000 ms before movement
onset) with a period of 4 s during movement (0–4000 ms
after movement onset); a distinct responsive peak in the
alpha band was usually present; the mean µ frequency for
control subjects was 10.2 Hz (SD 1.1 Hz) and for patients it
was 9.5 Hz (SD 0.9 Hz) (t-test, P ⫽ 0.060). Data were then
bandpass-filtered from 1 Hz below to 1 Hz above the
individual µ frequency, and were rectified. Intra-individual
averaging of sweeps was then performed. For the analysis
of ERD in the beta band, a broader and standardized bandpass
digital filter was used from 14.5 to 24.5 Hz, because the
distribution of the power of beta-band activity was broader
than the distribution of the power of alpha-band activity and
was usually without a discernible and distinct peak. After
filtering, the data were rectified, and intra-individual
averaging of sweeps was performed. These bandpassed intraindividual averages (for the alpha and beta bands) were
transformed to ERD data by dividing each data point for any
electrode by the mean of the baseline data (3.5–2.5 s before
movement) for that electrode; this resulted in relative scores
for each electrode and data point indicating the degree of
either event-related synchronization (score ⬎1.0) or ERD
(score ⬍1.0).
Advanced and statistical data analysis
Movement-related time periods of interest
For each individual, electrode and data aspect (DC, alphaERD, beta-ERD) scores for dependent variables were
Movement-related brain activity after stroke
calculated as mean scores for prespecified time periods: NB1,
early negativity of Bereitschaft (readiness) from 750 to
500 ms before movement onset; NB2, late negativity of
Bereitschaft from 250 to 0 ms (immediately before movement
onset); NP1, negativity of performance during the first second
of movement; NP2, negativity of performance during the
second and third seconds of movement. The distinction
between early and late negativity of Bereitschaft was based
on published data showing that an early negativity of
Bereitschaft, starting up to 2 s before movement onset,
evolves more gradually than a late negativity of Bereitschaft
immediately before movement onset with a steeper slope
(Tamas and Shibasaki, 1985). The selection of periods was
confirmed post hoc; i.e. modelling a moving dipole revealed
a relatively stable localization of the equivalent dipole during
both NB1 and NP1, whereas there was a gradual shift from
the first to the second position during NB2. Negativity of
performance was divided into two phases in order to assess
the temporal evolution of MRPs during performance. Later
during movement (NP2), the data were more variable than
the data relating to the first second of movement, and
thus the statistical analysis rarely revealed any significant
intergroup effects; consequently, these data (NP2) are not
presented in detail. The main analyses were therefore based
on the two phases of relatively stable electrocortical activity
distributions, namely NB1 and NP1.
Data reduction
EEG analysis as described above included 27 electrodes, two
time periods of interest and three types of analysis (DC,
alpha-ERD, beta-ERD), giving 162 parameters per person.
Accordingly, data reduction was warranted. Rather than any
self-selected data reduction, more formal (and presumably
more meaningful) procedures for data reduction were
performed. For DC potentials, the equivalent dipole
reconstruction was performed using the Advanced Source
Analysis (ASA) software (ANT Software, Enschede, The
Netherlands). The small number of electrodes and the wide
distribution of task-related surface negativity (DC potentials)
(Fig. 2) excluded the possibility of accurate localization of
the centres of activity in every single subject. However, by
means of meaningful data reduction, dipole reconstruction
should facilitate the detection of significant differences in
generator configuration between subjects. The volume
conductor effects were accounted for by a spherical head
model with three shells representing the inner and outer
surfaces of the skull and the skin surface. A single fixed
dipole was fitted; this dipole model assumed that the surface
electric field could be explained by a single equivalent dipole
with constant position and orientation over the entire time
window of interest (i.e. NB1, NP1) and varying source
strength; information from 27 electrodes was then represented
by seven dipole parameters (x, y and z positions; x, y and z
normalized orientation moments; and average dipole
strength); three normalized orientation moments (with scores
2479
ranging from 1 to –1) were chosen as opposed to two
orientation angles because of their ease of interpretation. The
fit was carried out separately for every subject and period of
interest (NB1, NP1). Starting with a dipole positioned in the
centre of the spherical head model, the free parameters of
the dipoles were repeatedly adjusted in such a way that the
residual variance (RV), i.e. the squared sum of the differences
between the measured and simulated EEG field data, became
minimal. The individual fitting process continued until all
predetermined stopping criteria were reached. These were (i)
reduction of RV to below 0.1%; (ii) position shift smaller
than 0.1 mm; and (iii) orientation changes smaller than 1°.
The goodness of fit of the model was assessed by the amount
of the variance that was explained, i.e. the proportion of the
signal variance that was explained by the dipole (1 – RV).
The seven estimated dipole parameters for each individual and
period of interest were then used for further statistical analysis.
For ERD data, equivalent dipole reconstruction (with ASA
software) is not possible. Principal components analysis
(PCA) was therefore used. The purpose of PCA is to explain
as much as possible of the total variation in the data with as
few factors (the principal components) as possible. Because
data reduction was our primary interest, and in view of the
signal-to-noise ratio in the ERD data for the individuals in
our study, we considered it appropriate to explain
approximately 75% of the variance in the data set and to
enter no principal component that explained less than 5% of
the total variance. To help in the interpretation of the principal
components, an orthogonal (varimax) rotation was applied.
PCA was performed separately for alpha-ERD and betaERD, based on the data set consisting of the data for 10
healthy subjects, both hands, all 27 electrodes and the time
periods of primary interest (NB1, NP1). Estimated factor
weights for the PCA were then used to estimate factor scores
for each individual (healthy subjects and patients) and time
period of interest; these factor scores were then used for
further statistical analysis.
Statistical analysis
Data for the 10 healthy subjects served as a reference. Data
for each patient group (hemiparesis, deafferentation and
ideomotor apraxia) were compared with data for healthy
individuals; t-tests for independent groups and unequal
variances were performed. Data for hemiparetic subjects
(eight data sets: four patients with right hemiparesis and four
patients with left hemiparesis) were compared with pooled
data for either hand of healthy subjects (20 data sets); data
for other patient groups (left hand assessed) were compared
with corresponding data for healthy subjects (10 data sets
with the left hand assessed).
Results
Motor performance
For each individual, we recorded the duration of the triangular
movements and the maximal tangential acceleration of each
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T. Platz et al.
movement segment of a triangle. The groups were similar
with regard to movement duration (P ⬎ 0.20). Only the
hemiparetic patients showed a tendency to have a somewhat
shorter movement duration and showed greater variability of
movement duration with repeated movements (mean, control
subjects 2.90 s, hemiparetic subjects 2.75 s, P ⫽ 0.0841;
coefficient of variation, control subjects 0.12, hemiparetic
subjects 0.23, P ⫽ 0.0050). In addition, for all three segments
of the triangular movement the hemiparetic subjects had
lower maximal acceleration scores than control subjects
(maximal acceleration of first segment: control subjects 6.2 U,
hemiparetic subjects 3.2 U, P ⫽ 0.0006). Patients with
ideomotor apraxia had a reduced maximal acceleration for
the first movement segment only; a less pronounced tendency
was observed in patients with somatosensory deficits (control
subjects, left hand 6.1 U; ideomotor apraxic subjects 1.8 U,
P ⫽ 0.0001; deafferented patients 4.2 U, P ⫽ 0.0811).
Dipole analysis of movement-related DC
potentials
Goodness of fit
Overall, modelling a single fixed dipole for each individual
for the readiness period (from 750 to 500 ms before movement
onset) resulted in a mean goodness of fit (proportion of
variance explained) of 0.65 (95% confidence interval 0.59–
0.70); modelling a single fixed dipole during the first second
of movement resulted in a mean goodness of fit of 0.74
(95% confidence interval 0.68–0.80). This indicates a
considerable degree of variance explained by the individual
models; equally, overfitting seems unlikely. Although single
fixed dipoles cannot be regarded as accurate localizing
information for focal brain activity in a given experiment,
they do represent valid descriptors of more widely distributed
MRPs and are thus suitable for numerical comparisons of
groups. Dipole analysis data for subgroups are presented
in Table 2.
Hemiparetic patients
Comparing the dipole analysis data for eight hemiparetic
subjects with 20 data sets for control subjects (10 right arm,
10 left arm) revealed differences, especially during the
readiness potential period. On average, the dipole during
the readiness period was at a higher location (z position:
hemiparesis 57.7 mm, controls 23.2 mm, P ⫽ 0.0042) and
the dipole was more anteriorly (y) and more laterally (x)
oriented (y orientation: hemiparesis 0.29, controls 0.03, P ⫽
0.0504; x orientation: hemiparesis 0.25, controls –0.02, P ⫽
0.0506), while its strength was reduced (magnitude:
hemiparesis 30.2 nAm, controls 46.8 nAm, P ⫽ 0.0111).
This corresponds to a more anteriorly centred and more
lateralized map of MRPs (Fig. 2, HE, Readiness). During
movement, only a tendency towards a more posterior
localization of the dipole among hemiparetic patients was
noted (y position: hemiparesis 0.5 mm, controls 7.8 mm,
P ⫽ 0.0702).
Patients with somatosensory deficits
Dipole analysis among patients with left-sided somatosensory
deficits did not reveal significant differences compared with
controls subjects (left arm). Numerically, the dipole was,
on average, positioned less laterally and more posteriorly.
Equally, the MRP map suggests a more posteriorly centred
and less lateralized distribution of DC potentials (Fig. 2, SE,
Readiness and Performance).
Patients with ideomotor apraxia
For two ideomotor apraxic patients performing the task
with their left, non-paretic limb, dipole analysis indicated
for both the readiness period and the first second of
movement a more lateral position of the single fixed dipole
(x position: readiness, ideomotor apraxia –20.7 mm, controls
(left arm) –0.3 mm, P ⫽ 0.0656; performance, ideomotor
apraxia –33.1 mm, controls –21.6 mm, P ⫽ 0.0535). In
addition, the dipole was oriented more anteriorly during
movement (y orientation: ideomotor apraxia 0.30, controls
0.06, P ⫽ 0.0182). The MRP map similarly indicates a
more anteriorly centred and more lateralized distribution
of movement-related negativity, with a lack of negativity
at left lateral recording sites (Fig. 2, IM, Readiness and
Performance).
Principal components analysis of ERD in the
alpha band
Principal components analysis in the alpha band
Three factors accounted for 74% of the standardized variance
(a fourth factor would have accounted for less than an
additional 5%). Factors 1, 2 and 3 explained (after varimax
rotation) 8.4, 6.4 and 5.1 of the variance, respectively. We
preferred to retain relatively few components because our
primary purpose was data reduction for statistical analysis.
Factor 1 correlated highly with the frontocentral and
posterior electrodes [correlation ranged from 0.52 to 0.88
(O1)], which showed little or no movement-related
desynchronization, or even a movement-related increase in
alpha activity (synchronization) among healthy subjects (Figs
3 and 4, CO). Factor 2 correlated highly with bilateral central
and right centroparietal electrodes [C3, CZ, C4, CP2, CP6, P4;
correlation ranged from 0.63 to 0.88 (C4)]; these electrodes
showed the most prominent task-related alpha-ERD. Factor
3 correlated especially with the left frontal and lateral
electrodes [FP1, F7, FC5, T3, CP5, F8; correlation ranged
from 0.57 to 0.87 (F7)]; at these recording sites a minor
degree of alpha-ERD was observed. By the use of
standardized scoring coefficients for the three components
and each electrode derived from data for the control subjects,
Movement-related brain activity after stroke
2481
Table 2 Dipole analysis of movement-related DC potentials of 13 stroke patients and 10 control subjects
Variable
Control
L (n ⫽ 10)
Readiness
Position
Orientation
Orientation
R (n ⫽ 10)
L (n ⫽ 4)
R (n ⫽ 4)
Deafferentation
L (n ⫽ 3)
Apraxia
L (n ⫽ 2)
x
y
z
x
y
z
0.3 (31.2)
15.3 (20.5)
25.8 (30.0)
–0.03 (0.23)
0.08 (0.17)
0.96 (0.04)
47.1 (23.5)
–1.0 (16.3)
12.6 (10.4)
20.5 (42.8)
0.0 (0.20)
–0.02 (0.28)
0.74 (0.61)
46.5 (11.7)
–4.7 (12.3)
6.6 (9.5)
53.3 (19.3)
0.03 (0.15)
0.31 (0.42)
0.83 (0.30)
35.9 (10.3)
10.5 (36.5)
19.3 (32.0)
62.1 (23.4)
0.46 (0.27)
0.28 (0.45)
0.63 (0.39)
24.5 (12.4)
–1.3 (8.6)
–9.8 (23.0)
25.3 (18.6)
0.01 (0.17)
0.12 (0.47)
0.42 (0.98)
37.0 (12.4)
–20.7 (3.1)
7.8 (2.4)
–26.8 (55.5)
0.20 (0.24)
0.34 (0.24)
0.88 (0.15)
69.8 (39.7)
x
y
z
x
y
z
–21.6 (15.1)
7.1 (13.3)
34.3 (17.5)
0.01 (0.16)
0.06 (0.18)
0.79 (0.59)
110.7 (60.7)
14.5 (7.2)
8.5 (11.8)
31.5 (14.4)
0.06 (0.11)
–0.03 (0.24)
0.96 (0.05)
154.1 (130.5)
–14.1 (8.2)
–4.7 (4.0)
31.7 (16.4)
–0.06 (0.13)
0.18 (0.34)
0.92 (0.16)
120.4 (10.5)
16.6 (7.9)
5.6 (7.3)
36.3 (24.0)
0.14 (0.11)
0.10 (0.31)
0.94 (0.04)
108.9 (44.0)
–6.2 (12.6)
–3.6 (13.9)
31.7 (5.1)
–0.05 (0.12)
0.30 (0.57)
0.53 (0.78)
101.7 (45.8)
–33.1 (3.0)
9.1 (0.30)
28.7 (20.3)
–0.13 (0.08)
0.30 (0.06)
0.94 (0.03)
80.7 (17.8)
Magnitude
Performance
Position
Hemiparesis
Magnitude
Data are subgroup averages (SD) of dipole parameters. Single dipoles were characterized by seven parameters of a 3D vector reflecting
their position within the head model (x, lateral distance; y, anterior–posterior distance; z, vertical distance from the centre of the spherical
head model in mm), their normalized orientation moments (expressed in the same coordinate system; scores range from 1 to –1), and
their average strength in nAm (Magnitude). L ⫽ left; R ⫽ right.
individual factor scores were calculated; subgroup mean
values are given in Table 3.
[mean alpha-ERD at C3, C4, CP2, CP6: control subjects (left
arm), readiness 0.88, performance 0.80; deafferented subjects,
readiness 1.12, performance 0.97] (Fig. 4, CO and SE,
Readiness and Performance).
Hemiparetic patients
Statistical analysis of group differences between eight
hemiparetic patients and 20 data sets for 10 control subjects
(left and right arms) confirmed a negative increase in factor
3 among hemiparetic subjects during the first second of
movement (factor 3: hemiparesis –1.32, controls 0.00, P ⫽
0.0610). The effect was similar for left and right hemiparetic
patients (factor 3: left hemiparesis –1.15, right hemiparesis
–1.48). As already noted, factor 3 was especially correlated
with scores for predominantly left frontal and lateral
electrodes. Hemiparetic subjects had a pronounced alphaERD at these recording sites (mean alpha-ERD at FP1, F7,
FC5, T3, CP5, F8: control subjects 0.95, hemiparetic subjects
0.81) (Fig. 4, CO and HE, Performance).
Patients with somatosensory deficits
These patients had higher positive values of factor 2 both
during movement preparation and during performance (factor
2: readiness, deafferentation 1.45, controls 0.15, P ⫽ 0.0586;
performance, deafferentation 0.68, controls –0.41, P ⫽
0.0866). This effect continued during the later stages of
performance (factor 2, 1–3 s after movement onset:
deafferentation 0.90, controls –0.25, P ⫽ 0.0089). Factor 2
was highly correlated with scores for central electrodes and
right centroparietal electrodes. Accordingly, alpha-ERD was
reduced for deafferented patients at recording sites where
movement-related alpha-ERD can be expected typically
Patients with ideomotor apraxia
No statistically significant differences were found between
the two ideomotor apraxic patients and the 10 control subjects
(left arm), even though the topographical map indicated a
reduced alpha-ERD at left centroparietal recording sites
(Fig. 4, CO and IM, Readiness and Performance).
Principal components analysis of ERD in the
beta band
Principal components analysis in the beta band
Six components were necessary to account for 77% of the
standardized variance (any further component would have
accounted for ⬍4% of additional variance); the variance
explained by the six factors (after varimax rotation) was 5.6,
3.7, 3.6, 3.3, 2.8 and 2.0, respectively. Factor 1 was highly
correlated with mesiocentral electrodes (FC1, FC2, FC5, CZ,
CP1, CP2; correlation ranged from 0.69 to 0.89), factor 2
with left lateral centroparietal electrodes (FC5, C3, T3, CP5,
P3; correlation ranged from 0.62 to 0.76), factor 3 (among
others) with occipital electrodes (O1, O2; correlation 0.69
and 0.86, respectively), factor 4 with frontopolar-frontal
electrodes (FP1, FP2, F3, F4, F8; correlation 0.53–0.84),
factor 5 with right lateral-central electrodes (FC6, C4, CP6;
correlation 0.62, 0.71 and 0.82, respectively) and factor 6
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T. Platz et al.
Fig. 2 Movement-related DC potentials. Evoked potentials are time-locked to the onset of triangular
finger movements performed with the left hand. Group average activities for the period from 750 to
500 ms before movement onset (Readiness) and for the period from movement onset to 1000 ms after
movement onset (Performance) are plotted as 27-channel topographical EEG maps. Blue denotes
movement-related positive drifts and red denotes movement-related negative shifts of slow cortical
potentials, based on common average reference data. Isoelectric lines are separated by 0.4 µV. CO ⫽
grand average of 10 control subjects; HE ⫽ grand average for four left hemiparetic patients; SE ⫽
grand average for three patients with somatosensory deficits; IM ⫽ grand average for two patients with
ideomotor apraxia.
Movement-related brain activity after stroke
2483
patient groups. Therefore, detailed results are presented only
for these three factors in Table 4.
Hemiparetic patients
Statistical analysis of group differences between the eight
hemiparetic patients and the 20 data sets for the 10 control
subjects (left and right arms) confirmed a reversal of sign
with a considerable positive value for factor 3 among the
hemiparetic subjects during movement preparation (factor 3:
readiness, hemiparesis 0.45, controls –0.19; P ⫽ 0.0267).
This indicates a movement-related increase in beta activity
(event-related synchronization, ERS) at occipital recording
sites for hemiparetic patients [mean beta-ERD at O1 and O2:
control subjects 0.968 and 0.962, respectively (⫽ ERD),
hemiparetic subjects 1.03 and 1.02 (⫽ ERS)] (Fig. 5, CO
and HE, Readiness). The hemiparetic patients’ beta-ERD
during movement was not significantly different from that
of the control subject.
Patients with somatosensory deficits
Fig. 3 For alpha-ERD and beta-ERD, principle components
analysis (PCA) revealed three and six factors, respectively, that
accounted for ~75% of the variance within each data set. For
each electrode, the figure shows the factor with which the
electrode has the highest correlation (all correlations ⬎0.50).
Factors 1–3 in the PCAs of both alpha-ERD and beta-ERD
revealed significant group differences; no significant group
differences were observed for factors 4–6 in the PCA for betaERD.
with temporoparietal electrodes (T5, PZ, P4; correlation 0.80,
0.59 and 0.49, respectively) (Fig. 3). Factors 1, 2, 5 and 6
represented information related to movement-related
desynchronization at frontal, central and parietal recording
sites, while factors 3 and 4 represented information related
to the paucity of movement-related desynchronization at
prefrontal and occipital recording sites (Fig. 5, CO). Only
factors 1–3 could differentiate between control subjects and
These patients had higher positive values for factor 1 during
movement preparation compared with control subjects (left
arm), indicating a lack of beta-ERD at mesiocentral recording
sites [factor 1: readiness, deafferentation 2.25, controls 0.72,
P ⫽ 0.0128; mean beta-ERD for FC1, FC2, FC5, CZ,
CP1, CP2 during readiness: control subjects (left arm) 0.88;
deafferented subjects 0.98] (Fig. 5, CO and SE, Readiness).
In addition, values for factor 3 had a reversed sign, negative
values during movement indicating a minor degree of
desynchronization at occipital recording sites among
deafferented patients, which was not present in control
subjects [factor 3: performance, deafferentation –0.35,
controls 0.17, P ⫽ 0.0500; mean beta-ERD at O1 and
O2 during performance: control subjects (left arm) 0.99;
deafferented subjects 0.97]; this effect was found even
during the later stages of performance (factor 3, 1–3 s after
movement onset: deafferentation –0.39, controls 0.13,
P ⫽ 0.0200) (Fig. 5, CO and SE, Performance).
Patients with ideomotor apraxia
ERD of the two ideomotor apraxic patients and 10 control
subjects (left arm) differed significantly during movement
preparation (only); apraxic patients had higher positive values
for factor 2 and more negative scores for factor 3 (readiness:
factor 2, apraxia 1.03, controls 0.20, P ⫽ 0.0374; factor 3,
apraxia –0.67, controls 0.02, P ⫽ 0.0687). This indicates
that the apraxic patients lacked beta-ERD at left lateral
centroparietal recording sites, whereas their ERD was
somewhat pronounced at occipital recording sites [mean betaERD at FC5, C3, T3, CP5 and P3 during readiness: control
subjects (left arm) 0.95, ideomotor apraxic patients 1.00;
mean beta-ERD at O1 and O2 during readiness: control
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T. Platz et al.
Fig. 4 Movement-related desynchronization of alpha-band activity (alpha-ERD). Relative changes in
alpha-band activity are time-locked to the onset of triangular finger movements performed with the left
hand. Group average activities for the period from 750 to 500 ms before movement onset (Readiness)
and for the period from movement onset to 1000 ms after movement onset (Performance) are plotted
as 27-channel topographical EEG maps of proportional mean alpha activity compared with the baseline
interval from 3.5 to 2.5 s before movement onset. Blue denotes movement-related synchronization, and
thus a proportional increase in alpha activity (values ⬎1.0); red denotes movement-related
desynchronization, and thus a proportional decrease in alpha-band activity (values ⬍1.0) compared
with baseline alpha activity. Colour steps and lines are separated by proportional differences of 0.024.
For abbreviations, see caption of Fig. 2.
Movement-related brain activity after stroke
2485
Table 3 Alpha-ERD in 13 stroke patients and 10 control subjects
Variable
Control
L (n ⫽ 10)
Readiness
Factor 1
Factor 2
Factor 3
Performance
Factor 1
Factor 2
Factor 3
Hemiparesis
Deafferentation
L (n ⫽ 3)
Apraxia
L (n ⫽ 2)
R (n ⫽ 10)
L (n ⫽ 4)
R (n ⫽ 4)
0.38 (0.79)
0.15 (0.73)
0.07 (0.85)
0.12 (0.71)
0.46 (0.68)
–0.02 (1.09)
0.47 (0.43)
–0.26 (0.05)
0.30 (0.66)
0.43 (0.35)
0.88 (0.12)
–0.16 (1.32)
0.90 (0.63)
1.45 (0.69)
0.73 (0.82)
0.24 (0.09)
–0.26 (0.80)
0.04 (0.45)
0.27 (1.45)
–0.41 (1.06)
–0.06 (1.01)
–0.22 (1.24)
–0.13 (1.30)
0.06 (1.26)
0.45 (0.38)
–1.26 (0.60)
–1.15 (1.51)
–0.31 (0.54)
–0.18 (0.51)
–1.48 (1.93)
1.30 (1.47)
0.68 (0.68)
0.34 (1.11)
0.63 (0.11)
–1.26 (1.04)
0.24 (0.67)
Data are subgroup averages (SD) of factor scores.
subjects (left arm) 0.98, ideomotor apraxic patients 0.95]
(Fig. 5, CO and IM, Readiness).
Baseline differences in alpha and beta EEG
activity
Event-related desynchronization was calculated relative to
baseline data. Thus, changes between groups could partially
reflect differences in baseline EEG activity (as opposed to
differences in EEG before and during movement). Therefore,
it seemed important to include a comparison of baseline
activities in the different groups. Mean amplitudes of alpha
and beta activity during baseline (3500–2500 ms before
movement onset) differed from the control subjects’ data
only for patients with somatosensory deficits (alpha: control
subjects 1.87 µV, deafferentation 0.88 µV, P ⫽ 0.0569;
beta: control subjects 1.21 µV, deafferentation 0.84 µV,
P ⫽ 0.0780).
Next, we sought to determine whether the effects
demonstrated for ERD in patients with somatosensory deficits
would still be present when these baseline differences were
accounted for. For this purpose, a subgroup of control subjects
with similar (P ⬎ 0.40) baseline amplitudes of alpha (n ⫽
6) and beta (n ⫽ 7) activity was chosen and their ERD was
compared with that of patients with somatosensory deficits.
The pattern of altered alpha-ERD and beta-ERD, as described
in the sections headed Patients with somatosensory deficits,
was still present in these comparisons; P values were reduced
because of the smaller number of subjects (alpha-ERD: factor
2: readiness, deafferentation 1.45, controls 0.33, P ⫽ 0.0837;
performance, deafferentation 0.68, controls –0.26, P ⫽
0.1312; beta-ERD: factor 1: readiness, deafferentation 2.25,
controls 1.04, P ⫽ 0.0373; factor 3: performance,
deafferentation –0.35, controls 0.36, P ⫽ 0.0822).
Effect of motor performance on EEG activity
Patients differed from control subjects with regard to maximal
acceleration during movement. We therefore sought to
investigate the degree to which motor behaviour among
control subjects influenced EEG activity and thus whether
differences in motor behaviour, independently of any clinical
impairment, could explain differences in EEG activity. Simple
regression models for control subjects’ maximal acceleration
when using their right hand revealed no influence on EEG
parameters during performance (dipole parameters, factors
1–3 for alpha-ERD, factors 1–3 for beta-ERD; for all
univariate regression models P ⬎ 0.25). Thus, at least
within the intended low range of movement variation of the
experiment and with regard to the EEG parameter analysed,
no major influence of movement variation on EEG parameters
was found among the control subjects.
Discussion
General considerations
Both clinical experience and previous research support the
notion of impairment-specific behavioural motor deficits; by
classifying patients according to their clinical presentation it
is possible to demonstrate various patterns of associated
changes in motor behaviour (Platz et al., 1994; Platz and
Mauritz, 1995, 1997). It was, therefore, of interest to
investigate whether this clinically oriented functional
classification of stroke patients was associated with a
differential pattern of movement-related electrical activity of
the brain, and thus with a differential pattern of functional
cerebral reorganization. The strength of the method is its high
temporal resolution, which allows the separate assessment of
movement preparation and movement execution and makes
it possible to distinguish slowly evolving movement-related
potentials and sensorimotor rhythms, and thereby different
types of electric brain activity that are closely related to
neural function. In line with the available literature (for
review, see Lang et al., 1994; Pfurtscheller and Lopes da
Silva, 1999), different spatiotemporal patterns of electrical
activity of the brain were found in the present study, on the
basis of the same raw EEG data (Figs 2, 4 and 5): in healthy
subjects, slow cortical movement-related potentials were
widespread, with a midline centre at the vertex during
movement preparation, and became more lateralized during
movement execution. ERD in the alpha and beta bands,
however, was expressed bilaterally, with centres over the
2486
T. Platz et al.
Fig. 5 Movement-related desynchronization of beta-band activity (beta-ERD). Relative changes of betaband activity are time-locked to the onset of triangular finger movements performed with the left hand.
Group average activities for the period from 750 to 500 ms before movement onset (Readiness) and for
the period from movement onset to 1000 ms after movement onset (Performance) are plotted as
27-channel topographical EEG maps of proportional mean beta activity compared with the baseline
interval from 3.5 to 2.5 s before movement onset. Blue denotes movement-related synchronization, and
thus a proportional increase in beta activity (values ⬎1.0); red denotes movement-related
desynchronization, and thus a proportional decrease in beta-band activity (values ⬍1.0) compared with
baseline beta activity. Colour steps and lines are separated by proportional differences of 0.024. For
abbreviations, see caption of Fig. 2.
Movement-related brain activity after stroke
2487
Table 4 Beta-ERD in three stroke patients and 10 control subjects
Variable
Control
L (n ⫽ 10)
Readiness
Factor 1
Factor 2
Factor 3
Performance
Factor 1
Factor 2
Factor 3
Hemiparesis
R (n ⫽ 10)
L (n ⫽ 4)
R (n ⫽ 4)
Deafferentation
L (n ⫽ 3)
Apraxia
L (n ⫽ 2)
0.67 (0.94)
0.20 (1.00)
0.02 (0.80)
0.77 (0.67)
–0.52 (1.02)
–0.39 (0.92)
0.19 (0.56)
0.01 (0.52)
0.47 (0.71)
0.84 (0.88)
0.01 (0.68)
0.42 (0.32)
2.25(0.57)
–0.39 (0.45)
–0.70 (0.70)
0.67 (0.09)
1.03 (0.20)
–0.67 (0.26)
–0.42 (1.08)
0.17 (0.98)
0.24 (0.78)
–0.45 (1.02)
–0.15 (1.10)
0.10 (1.50)
–1.18 (0.74)
0.12 (1.44)
0.26 (1.01)
–0.04 (1.24)
–0.13 (0.71)
0.29 (1.07)
0.20 (0.32)
0.36 (0.40)
–0.35 (0.19)
0.51 (0.47)
0.99 (0.55)
–0.35 (0.43)
Data are subgroup averages (SD) of factor scores.
sensorimotor cortices already apparent during movement
preparation. Furthermore, while alpha desynchronization was
recorded primarily at central and parietal electrodes, beta
synchronization was also recorded simultaneously at
frontomesial recording sites.
The results obtained also demonstrate that this type of
investigation can be performed with stroke patients who
show deviations from the pattern documented for age-matched
healthy subjects, as we have described above. More
interestingly, the results support the hypothesis that changes
in the electrical activity of the brain differ according to the
specific impairments that occur after a stroke. Groups of
hemiparetic, deafferented and ideomotor apraxic patients
showed different patterns of changes in the electrical activity
of the brain compared with healthy subjects. Each of these
groups showed its own picture of changes in either preparatory
or execution-related electrical activity in the brain. We will
summarize these patterns and propose possible functional
interpretations. While they are encouraging, these results and
interpretations must be considered to be preliminary and
subject to confirmation because they are based on a limited
number of patients; however, as previously shown for
Parkinson’s disease (i.e. Jahanshahi et al., 1995; Pfurtscheller
et al., 1998), these results firmly indicate the potential of a
non-invasive method, with high temporal resolution, for the
investigation of specific aspects of altered motor control
after stroke.
Impairment-specific changes
Motor performance
While movement duration was largely similar across groups,
the patients—especially those with hemiparesis—had lower
maximal acceleration scores for their triangular movements.
It could therefore be argued that differences in the electrical
activity of the brain observed between the groups might be
explained by differences in motor behaviour. This, however,
seems unlikely, as among the control subjects no systematic
effect of variation of maximal acceleration during finger
movements on EEG parameters could be substantiated within
the set of experimental data. Accordingly, the alternative
hypothesis is more likely—that differences in the electrical
activity of the brain between groups reflect more fundamental
changes in cortical motor control.
Hemiparesis
Patients with hemiparesis showed differences from control
subjects mostly during movement preparation. Since
movement-related slow DC potentials generally showed a
wide unipolar distribution, dipole analysis cannot be
considered to be a descriptor of a small activated area, but
rather a descriptor of a more global field. Accordingly, it
was used for the statistical confirmation of topographical
differences in MRPs between groups. In hemiparetic subjects,
the MRP map (Fig. 2, HE, Readiness) and dipole analysis
indicated a more laterally (contralateral to the moving limb)
and frontally centred distribution of the Bereitschaftspotential
(readiness potential). This might indicate increased
importance of the motor and premotor cortex in movement
preparation in this patient group. More specifically,
hemiparetic subjects may have an increased need for
excitatory postsynaptic drive of pyramidal cells (Bierbaumer
et al., 1990) and thus need an increased neural effort (Rösler
et al., 1993) in motor areas during movement preparation.
Alternatively, these changes in the electrical activity of the
brain might reflect compensatory measures that are recruited
in order to drive movement through projections from these
areas to brainstem motor systems. Such mechanisms in motor
areas might resemble a neural attempt to compensate for
reduced ability to produce highly selective and efficient force
impulses via the primary motor cortex and the pyramidal
system, as reflected in the reduced maximal acceleration
scores of the hemiparetic patients’ index finger movements.
Another effect discriminated between the hemiparetic patients
and control subjects during movement preparation: while
control subjects had, on average, a minor beta-ERD also at
occipital recording sites, the hemiparetic patients showed a
rather slight event-related synchronization (increase in beta
activity) at these sites. This may indicate an accentuated
‘idling’ state in visual areas during movement preparation in
hemiparetic subjects.
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T. Platz et al.
During movement, few differences between hemiparetic
and control subjects were noted. There was increased alphaERD at predominantly left frontal and lateral recording sites
for both left and right hemiparetic subjects. This might
indicate accentuated activation of the left frontolateral cortex
during movement in hemiparetic subjects. Its relevance might
be related to attention to performance (Jueptner et al., 1997),
internal language for the guidance of finger movements (Seitz
and Roland, 1992) or otherwise increased effort needed to
perform a willed action (Frith et al., 1991), as suggested
previously for hemiparetic stroke patients with increased
lateral prefrontal activation (Weiller et al., 1992).
Deafferentation
The MRP map (Fig. 2, SE) and dipole analysis suggested a
more posteriorly centred distribution of DC potentials both
during movement preparation and movement, as well as a
less lateralized distribution during movement; these effects
were not corroborated statistically. However, analysis of ERD
did indicate statistically significant differences compared
with control subjects. Among control subjects, alpha-ERD
occurred bilaterally, with a maximum at centroparietal
electrodes during both movement preparation and execution.
In patients with somatosensory deficits, however, it was
absent during movement preparation and was considerably
reduced during movement execution. A similar effect with a
lack of ERD was documented for beta-ERD at mesiocentral
recording sites, and could be corroborated statistically during
movement preparation. These findings might indicate a lack
of reduction of an idling state before and during movement.
It might further be speculated that intact somatosensory input
contributes to movement-related alpha-ERD and, to a lesser
degree, to beta-ERD in corresponding cortical areas. The
quantitative difference in the effect with respect to alphaERD and beta-ERD might be explained as follows: there is
reason to believe that movement-related alpha-ERD occurs
predominantly in postcentral somatosensory cortical areas
that receive the main somatosensory input, while beta-ERD
might occur more strongly in frontal motor areas (Salmelin
and Hari, 1994) that receive somatosensory input to a minor
degree (Wiesendanger et al., 1985).
Ideomotor apraxia
It has been proposed that the brain network specifically
involved in ideomotor praxis includes (i) the inferior parietal
lobe, containing representations of learned skilled
movements; (ii) the supplementary motor area and basal
ganglia, which transcode these representations into a motor
programme; and (iii) the motor cortex, which finally
implements these motor programmes (Heilmann and
Gonzalez Rothi, 1993). Ideomotor apraxia is believed to
result from the disconnection or disruption of any of these
structures except the motor cortex. In the two cases reported
here, damage to the left parietal lobe might have caused
apraxia. Maps of MRPs of these two ideomotor apraxic
patients and dipole analysis revealed a more lateralized
distribution, during both movement preparation and
movement execution. A possible explanation of this
observation is reduced activity of the affected lateral frontal,
temporal and parietal areas of the left hemisphere. The
distribution of the readiness potential (Fig. 2, IM) especially
suggests readiness an additional relative reduction in the
activity of the mesial frontal motor system, which is thought
to be part of the network related to praxis, and consequently
a relative preponderance of the lateral motor system
(Passingham, 1997). Numerically, alpha-ERD was reduced
at left parietal recording sites during movement preparation
and execution; the same was true of beta-ERD during
movement preparation. This effect was, however, statistically
significant only for beta-ERD during movement preparation.
Accordingly, both MRPs and ERD showed alterations in
movement-related brain activity that might be linked to areas
that have been proposed to be involved in ideomotor praxis,
namely the left parietal and mesial frontal areas.
Conclusions
The stroke patients in this study were selected on the basis
of clinically assessed impairments. Each impairment was
associated with specific alterations in the electrical activity
of the brain during movement preparation and/or movement
execution. On the basis of these results, one could argue that
a disturbed motor efference, as with central paresis, is
associated with an increased need for excitatory drive of
pyramidal cells in the motor and premotor areas during
movement preparation and/or for compensatory measures
recruited in order to drive movement through projections
from these areas to brainstem motor systems, as well as
possibly an increased attentive state of the left lateral
prefrontal cortex during movement. Movement-related
desynchronization of rhythmic brain activity in the alpha and
beta bands was reduced among patients with somatosensory
deficits. Accordingly, the undisturbed somatosensory
afference might contribute to the release of motor and
somatosensory areas from their idling state when movements
are prepared and performed. The more lateralized, frontally
centred, movement-related slow cortical potentials and the
reduced ERD over left parietal areas in apraxic patients may
indicate a relative lack of activity of the mesial frontal motor
system and the left parietal cortex, which are believed to be
part of a network subserving ideomotor praxis. Because of
the small number of individuals investigated, the present
findings are considered preliminary, especially with regard
to patients with somatosensory deficits and ideomotor apraxia.
Acknowledgements
The project was supported by grant MA 1782 from the
Deutsche Forschungsgemeinschaft.
Movement-related brain activity after stroke
2489
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Received January 31, 2000. Revised July 17, 2000.
Accepted August 24, 2000