1Human Motor Control Section, Medical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland 20892-1428; 2Institut National de la Santé et de la Recherche Médicale, Centre d'Exploration et de Recherche Médicales par Emission de Positons, 69003 Lyon, France; and 3Department of Radiology, Fukui Medical School, Shimoaizuki 23, Yoshida, Fukui, Japan
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ABSTRACT |
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Deiber, Marie-Pierre, Manabu Honda, Vicente Ibañez, Norihiro Sadato, and Mark Hallett. Mesial motor areas in self-initiated versus externally triggered movements examined with fMRI: effect of movement type and rate. The human frontomesial cortex reportedly contains at least four cortical areas that are involved in motor control: the anterior supplementary motor area (pre-SMA), the posterior SMA (SMA proper, or SMA), and, in the anterior cingulate cortex, the rostral cingulate zone (RCZ) and the caudal cingulate zone (CCZ). We used functional magnetic resonance imaging (fMRI) to examine the role of each of these mesial motor areas in self-initiated and visually triggered movements. Healthy subjects performed self-initiated movements of the right fingers (self-initiated task, SI). Each movement elicited a visual signal that was recorded. The recorded sequence of visual signals was played back, and the subjects moved the right fingers in response to each signal (visually triggered task, VT). There were two types of movements: repetitive (FIXED) or sequential (SEQUENCE), performed at two different rates: SLOW or FAST. The four regions of interest (pre-SMA, SMA, RCZ, CCZ) were traced on a high-resolution MRI of each subject's brain. Descriptive analysis, consisting of individual assessment of significant activation, revealed a bilateral activation in the four mesial structures for all movement conditions, but SI movements were more efficient than VT movements. The more complex and more rapid the movements, the smaller the difference in activation efficiency between the SI and the VT tasks, which indicated an additional processing role of the mesial motor areas involving both the type and rate of movements. Quantitative analysis was performed on the spatial extent of the area activated and the percentage of change in signal amplitude. In the pre-SMA, activation was more extensive for SI than for VT movements, and for fast than for slow movements; the extent of activation was larger in the ipsilateral pre-SMA. In the SMA, the difference was not significant in the extent and magnitude of activation between SI and VT movements, but activation was more extensive for sequential than for fixed movements. In the RCZ and CCZ, both the extent and magnitude of activation were larger for SI than for VT movements. In the CCZ, both indices of activation were also larger for sequential than for fixed movements, and for fast than for slow movements. These data suggest functional specificities of the frontomesial motor areas with respect not only to the mode of movement initiation (self-initiated or externally triggered) but also to the movement type and rate.
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INTRODUCTION |
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The identification and role of the mesial frontal cortical areas
involved in human motor control are topics of continued debate. Initially, the supplementary motor area (SMA) was considered to be the
only motor field within area 6 of the medial wall (Penfield and
Welch 1951; Woolsey et al. 1952
). Recently,
however, several functionally distinct motor fields on the mesial
surface of the hemisphere have been recognized (for a review, see
Picard and Strick 1996
). One of the central questions
about the SMA concerns its role in self-initiated movements as opposed
to movements triggered by external stimuli. Preferential involvement of
the SMA in self-initiated movements has been suggested by the results
of several electrophysiological and lesion experiments in monkeys
(Chen et al. 1995
; Mushiake et al. 1991
;
Okano and Tanji 1987
; Passingham 1987
;
Thaler et al. 1988
, 1995
; Wise 1984
).
Data from electroencephalographic (Jahanshahi et al.
1995
; Papa et al. 1991
) and functional
neuroimaging studies in humans (Larsson et al. 1996
;
Rao et al. 1993
; Tyszka et al. 1994
;
Wessel et al. 1997
) are also consistent with this idea.
However, the results of a study by Remy et al. (1994)
,
who observed greater blood flow in the SMA with externally triggered movements, have been controversial. In addition to the mode of movement
initiation, other variables in motor control may modulate the activity
of the SMA. In particular, the SMA is said to have a role in motor
sequences (Jenkins et al. 1994
; Mushiake et al. 1990
, 1991
; Shibasaki et al. 1993
; Tanji
and Shima 1994
). Further, the sensitivity of the SMA to
movement rate is a controversial issue, with some studies showing
significant negative dependence at higher rates (MacKinnon et
al. 1994
; Sadato et al. 1996b
), and others
reporting nonsignificant negative (Blinkenberg et al. 1996
) or positive (Schlaug et al. 1996
) dependence.
Picard and Strick (1996) reviewed the anatomic and
physiological data obtained from nonhuman primates and humans to
formulate a functional anatomic classification of the mesial cortical
areas. In their analysis of functional neuroimaging studies in humans, they distinguished between simple tasks, which require basic
organization of movement, and complex tasks, which make additional
motor or cognitive demands. They concluded that humans have at least
four distinct mesial motor regions: anterior SMA (pre-SMA), posterior SMA (SMA proper, or SMA), and, in the anterior cingulate cortex, the
rostral cingulate zone (RCZ) and the caudal cingulate zone (CCZ)
(Picard and Strick 1996
). In the superior frontal gyrus, the SMA proper, lying caudal to the level of the anterior commissure (VAC line), is activated mainly by simple tasks, and the pre-SMA, lying
rostral to the VAC line, is activated during relatively more complex
tasks. In the cortex of both banks of the cingulate sulcus, from the
genu of the corpus callosum (Brodmann area 32) to the posterior border
of Brodmann area 24, the large RCZ is activated by complex tasks, and
the smaller CCZ is activated during simple tasks.
On the basis of Picard and Strick's (1996)
classification of the mesial cortical areas according to the
simple/complex dichotomy, we reexamined the role of each mesial area in
self-initiated versus externally triggered movements. In addition, to
assess the interaction between factors that modulate activity in the
SMA, we introduced two other movement variables: type of movements
(repetitive finger movements of a single digit versus sequential finger
movements) and rate of movements (slow, ~0.25 Hz, vs. fast, ~1 Hz).
We used functional magnetic resonance imaging (fMRI) to achieve better spatial resolution of the mesial cortical areas and to allow
single-subject analysis.
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METHODS |
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Subjects
We studied nine normal volunteers (8 men and 1 woman), aged
26-52 (mean, 35) years. The subjects were all right-handed as measured
by the Edinburgh Inventory (Oldfield 1971). The protocol was approved by the Institutional Review Board, and all subjects gave
their written informed consent for the study.
Tasks
The subjects performed two motor tasks, each of which had a
distinct mode of initiation: self-initiated (SI; rate decided by the
subject) and visually triggered (VT; rate imposed by a signal).
According to Jahanshahi et al. (1995), the term
self-initiated is more suitable than "self-paced" because in the
self-initiated mode the desired rate of movement was specified, and on
each trial the subjects had to decide when to initiate the movement to
maintain the desired rate. A visual signal was used to keep the rhythm and number of movements constant in the SI and VT tasks. The subjects wore goggles, and in the SI task, each movement elicited a red flash of
light. The sequence of flashes generated in the SI task was recorded
and played back in the VT task. Thus the rate of movement in the VT
task was yoked to that generated in the SI task.
Two types of movements, both involving digits of the right hand (digit
2, index finger; digit 3, middle finger; digit 4, ring finger; digit 5, little finger), were performed: a repetitive movement in which the
thumb was opposed to the index finger (2-2-2-2..., FIXED) and a sequence movement in which the thumb was
opposed to each of the other four fingers
(2-3-4-5-5-4-3-2-2-3-4-5..., SEQUENCE). Each type
of movement was performed at two different rates: a slow rateone
movement approximately every 4 s (~0.25 Hz,
SLOW)
and a fast rate
one movement approximately every
second (~1 Hz, FAST). In the SI task, the subjects were
instructed to generate movements at an average rate centered ~0.25 or
1 Hz. They were asked specifically not to pursue a very regular rate
but rather to introduce some irregularity in the rhythm of their
movements to minimize automaticity in the SI task and anticipatory
behavior in the VI task.
Eight movement conditions were tested: self-initiated, repetitive movement, slow rate (SI, FIXED, SLOW); visually triggered, repetitive movement, slow rate (VT, FIXED, SLOW); self-initiated, repetitive movement, fast rate (SI, FIXED, FAST); visually triggered, repetitive movement, fast rate (VT, FIXED, FAST); self-initiated, sequence movement, slow rate (SI, SEQUENCE, SLOW); visually triggered, sequence movement, slow rate (VT, SEQUENCE, SLOW); self-initiated, sequence movement, fast rate (SI, SEQUENCE, FAST); and visually triggered, sequence movement, fast rate (VT, SEQUENCE, FAST). A visual control condition (VISUAL) also was included: when the sequence of flashes recorded during the SI, FIXED, FAST condition was played back, the subjects were asked not to make any movement. The order of conditions was pseudorandomized across subjects, with the constraint that the SI tasks had to be performed before the VT tasks within a movement type and rate, in accordance with the yoked experimental design described earlier.
Subjects were taught to perform a brisk and precise tap of the thumb to the fingers. The sequence and repetition rate of the finger movements were monitored by an electrically equipped glove. A session consisted of an OFF-ON cycle of three rest periods and three movement periods (3 repetitions of the same movement condition), and began with a rest period. Each period was 30 s long so that the performance of one session lasted 3 min. One session was performed for each movement condition without replication. Subjects practiced each movement condition once before the experiment started.
Magnetic resonance imaging
Images were obtained by a whole-body 1.5 T magnetic resonance imaging (MRI) scanner (Signa, General Electric, Milwaukee, WI), equipped with a full-head Medical Advance coil permitting complete isotropic coverage. Head motion was minimized by placing tight but comfortable foam padding around the subject's head. High-resolution MR images were obtained for anatomic reference. A three-dimensional, T1-weighted sequence (TR/TE/flip: 100 ms/5 ms/70°) was used to obtain 15 contiguous high-resolution sagittal images of 6-mm thickness, with a 24-cm field of view and a 256 × 256 matrix. The images covered the mesial structures, the left hemisphere (contralateral to the movements), and part of the right hemisphere. The interhemispheric fissure served as the reference for positioning the sagittal slices, with slices on the left and right sides of the brain separated by the fissure. High-resolution echo planar imaging scans then were obtained in the same planes with a T2*-weighted acquisition (TR/TE/flip: 3000 ms/40 ms/90°), which produced a 64 × 64 matrix with a 24-cm field of view and 3.75-mm in-plane resolution. In each hemisphere, analysis was restricted to the two slices covering the mesial cortical areas. A time-course series of 60 images/slice was acquired for each trial, in an OFF-ON cycle paradigm of 30 s of rest and 30 s of movement.
Data analysis
To remove motion artifacts, the fMRI time series from each
subject were realigned according to the method of Friston et al. (1995a), with the first image of each slice used as a
reference. Within-plane spatial smoothing was applied with a
two-dimensional Gaussian kernel of 5.6 × 5.6 mm of full width at
half-maximum (FWHM). No smoothing was applied in the interplane
direction. No spatial normalization of the data was performed because
the brain was only partly sampled. Therefore the data were analyzed on
an individual basis. The fMRI time series were analyzed by statistical
parametric mapping (SPM; SPM software, Wellcome Department of Cognitive
Neurology, London, UK). The effect of global differences in scan
intensity was removed by scaling each scan in proportion to its global
intensity. Statistical analysis was performed for each condition in a
general linear model, in which regionally specific activation is
explored as to how well the reference waveform fits to the observed
time series of the fMRI signal (i.e., hemodynamic response) at each and
every voxel (Friston et al. 1995b
). The reference
waveform was obtained by smoothing a time-dependent sensorimotor
parameter of interest (i.e., a box-car waveform with 0s for rest epoch
scans and 1s for movement epoch scans) with a Gaussian kernel of a
delay and dispersion of the square root of 8 s, modeling the
hemodynamic response function. The time-series fMRI data also were
smoothed over observation (time) by use of the same Gaussian kernel as
the hemodynamic response function. Thus with the use of matrix
notation, the model can be expressed as
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(1) |
At the same time, the intensity of the signal in the activated areas
was assessed by measuring the amplitude of the fitted (or modeled)
reference waveform at each and every voxel. The fitted reference
function was obtained by
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(2) |
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(3) |
A region-of-interest (ROI) approach was used to describe the results
over the nine subjects. On the high-resolution MRI slices, four ROIs
were traced on the medial wall of the hemisphere for each subject:
pre-SMA, SMA, RCZ, and CCZ (Fig. 1). In
each hemisphere, the pre-SMA and SMA regions were traced on the more
medial slice, and the RCZ and CCZ regions were traced on the two more
medial slices. The regions were delimited according to the analysis of Picard and Strick (1996), who reviewed the results of
positron emission tomography (PET) studies on activation in the medial wall of the human brain. First, the anatomic landmarks of
Talairach and Tournoux (1988)
were drawn on the
appropriate high-resolution MRI midsagittal slice: AC-PC line, VAC
line, and VPC line. Measurements of the brain were taken
(anteroposterior dimension, maximal height from AC-PC plane). ROIs then
were traced as follows: pre-SMA, region rostral to the VAC line, above
the cingulate sulcus, extending anteriorly up to 17.1% of the
anteroposterior brain dimension; SMA, region between the VAC and VPC
lines and above the cingulate sulcus; CCZ, region including the cortex
on both banks of the cingulate sulcus, extending rostrally to the VAC
line
3.7% and caudally
9.4% of the anteroposterior brain
dimension; RCZ, region including the cortex on both banks of the
cingulate sulcus, adjacent to CCZ and extending rostrally
28.6% of
the anteroposterior brain dimension (Fig. 1). The number of pixels,
including both hemispheres, in the four ROIs (mean ± SD of 9 experiments) was 650 ± 84 for the pre-SMA, 533 ± 95 for the
SMA, 775 ± 117 for the RCZ, and 402 ± 70 for the CCZ.
[According to Talairach and Tournoux (1988)
, the brain
is 175 mm in total anteroposterior length. Given that measurement, the
proportional measurements are determined from the numerical boundaries
given in Figs. 4 and 5 of Picard and Strick (1996)
. The
pre-SMA anterior boundary is 30 mm rostral to VAC (i.e., 17.1% of 175 mm, the total anteroposterior brain dimension), the CCZ anterior
boundary is ~ 6.5 mm rostral to VAC (i.e., 3.7% of 175 mm), the
CCZ posterior boundary is ~16.5 mm caudal to VAC (i.e., 9.4% of 175 mm), and the RCZ anterior boundary is 50 mm rostral to VAC (i.e.,
28.6% of 175 mm)].
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The fMRI images were interpolated to have the same pixel size as the
high-resolution MRI. To avoid distortion of the functional images, the
high-resolution images were coregistered to the mean of the fMRI time
series by use of the algorithm of Woods et al. (1993),
and the ROI templates were applied to the Z, cluster, and
%CSI images. The ROIs were used for two purposes: a descriptive analysis, in which the results of the individual cluster analysis were
reported and which resulted in histograms showing the number of
subjects having significant activation in each ROI and each condition
and a quantitative analysis of activation differences between
conditions, which was performed separately on the extent of activation
and on the %CSI during movement versus rest periods. The extent of
activation, computed from the Z maps, was expressed as the
number of pixels in which Z > 3.09. Analysis of the
%CSI during movement periods versus rest periods was performed from the %CSI maps on the maximum value within each ROI. For RCZ and CCZ,
measurements from the two medial slices within each hemisphere were
averaged. Results were analyzed by repeated measures ANOVA, with a
Greenhouse-Geisser correction.
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RESULTS |
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Task performance
Intervals between finger taps or intertap intervals (ITIs) and movement frequency are shown in Table 1. No movement was recorded during the control condition (VISUAL), but small-amplitude movements that did not trigger an electrical signal from the glove cannot be excluded. Movement frequency was calculated for each subject in each condition as follows: the number of movements performed in each movement period was computed as the number of ITIs plus 1, and the sum of ITIs gave the movement time (the first ITI corresponding to the beginning of a 30-s rest period to the first movement was always discarded); movement frequency was obtained by dividing the number of movements by the movement time (in seconds). For each condition, the number of movements and the movement time in each of the three movement periods were added, to calculate the movement frequency. SLOW movements were performed at a mean frequency of 0.31 Hz and FAST movements at 0.69 Hz. SEQUENCE movements were performed slightly faster (mean, 0.53 Hz) than FIXED movements (mean, 0.47 Hz), a result solely attributable to the fast movements. The frequencies of the movements in the SI and VT tasks were very similar (SLOW: 0.32 Hz in SI, 0.31 Hz in VT; FAST: 0.70 Hz in SI, 0.67 Hz in VT). A repeated measures ANOVA, with RATE (SLOW, FAST), MOVEMENT (SEQUENCE, FIXED), and TASK (SI, VT) as within-subject factors, was performed on the movement frequency data. There was a significant effect of RATE (F = 129.76, P < 0.001) and MOVEMENT (F = 12.01, P < 0.01) on movement frequency. However, as expected because of the study design, movement frequency was not significantly affected by TASK.
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The movements were performed with relative irregularity as shown by the large difference between the maximum and minimum values of the ITIs (Table 1). Accuracy was measured only in the SI mode because of limitations of the study design. No errors were reported in FIXED movements. In SEQUENCE movements, on average, 96.9% of the responses were correct at the SLOW rates and 97.8% were correct at the FAST rates.
Descriptive analysis
Figures 2 and 3 show the results of the cluster analysis in the left paramedial slice of four individual subjects in each condition. The cluster analysis revealed significant activation in the mesial cortex for the movement conditions (Z > 3.09, P < 0.05), with differing distributions depending on ROIs, hemisphere, and condition. Examination of activity distribution using the ROI approach revealed that location of the activity peak in the mesial cortex was quite variable among subjects and conditions. However, VT movements frequently were associated with maxima of activity in the most posterior mesial regions, i.e., in the SMA or CCZ. In SI sequential movements, the four mesial structures were activated in most subjects, whereas in SI fixed movements, activation was generally less extended and did not always include all four mesial regions. Variability across subjects regarding the mesial activation extent was even higher for VT movements. In addition to the mesial cortex, foci of activity also could be found independently of the movement condition in the visual cortex, cerebellum and thalamus (Figs. 2 and 3). Histograms of the distribution of activation over the nine subjects are shown in Fig. 4. The mesial structures were activated in very few subjects in the control condition, that is, when flashes were presented but no movement was performed (VISUAL). In all movement conditions, subjects showed more constant bilateral activation of the mesial structures with the SI task than with the VT task. The difference in activation between the SI task and the VT task was large in the SEQUENCE, SLOW movements, especially for the contralateral pre-SMA and RCZ, whereas the difference was much smaller in the SEQUENCE, FAST movements. Mesial structures were activated in fewer subjects with the FIXED, SLOW movements than with the other movements. In all conditions, activation in the pre-SMA was usually more ipsilateral than contralateral.
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Quantitative analysis
For measurements of the extent of activation and %CSI, two distinct questions were posed: are the movement conditions different from the baseline condition (VISUAL) and how do the movement conditions differ from each other? To answer the first question, we performed a repeated measures ANOVA with CONDITION (8 movement conditions and 1 control condition) as the within-subject factor. We sought to answer the second question by performing a repeated measures ANOVA with the following within-subject factors: ROI (pre-SMA, SMA, RCZ, CCZ), TASK (SI, VT), MOVEMENT (FIXED, SEQUENCE), RATE (SLOW, FAST), and HEMISPHERE (left, right).
EXTENT OF FUNCTIONAL ACTIVATION. The extent of functional activation was assessed as the number of pixels with Z > 3.09. In a comparison of movement conditions with the control condition, the extent of activation was significantly affected by CONDITION (F = 10.66, P < 0.001). Moreover, contrast analysis showed that the extent of activation was significantly larger for the eight movement conditions than for the control condition (56 vs. 4 pixels, F = 22.37, P < 0.001).
In a comparison between movement conditions, there was no significant effect of ROI and HEMISPHERE. The extent of activation was affected significantly by TASK (SI: 77 pixels, VT: 35 pixels; F = 19.11, P < 0.01), MOVEMENT (FIXED: 43 pixels, SEQUENCE: 69 pixels; F = 31, P < 0.001), and RATE (SLOW: 45 pixels, FAST: 66 pixels; F = 9.48 P < 0.05). There was a significant interaction of ROI and HEMISPHERE (F = 8.99, P < 0.01), ROI and TASK (F = 5.43, P < 0.05), and ROI, TASK, and RATE (F = 4.8, P < 0.05). A repeated measures ANOVA was performed for each ROI on the extent of activation for the eight movement conditions. The results are shown in Fig. 5 and summarized in Table 2. In the pre-SMA, there was a significant effect of TASK, with more extended activation for SI (116 pixels) than for VT (30 pixels; F = 25.21, P < 0.01). RATE had also a significant effect, as there was more extended activation with fast movements than with slow movements (85 vs. 61 pixels, F = 6.76, P < 0.05). Last, there was a significant HEMISPHERE effect, with more extended activation in the hemisphere ipsilateral to the hand movement (left: 49 pixels, right: 97 pixels; F = 9.51, P < 0.05). There was a significant interaction between HEMISPHERE and MOVEMENT (F = 6.74, P < 0.05). In the SMA, the extent of activation was significantly affected by MOVEMENT (FIXED: 57 pixels, SEQUENCE: 104 pixels; F = 7.28, P < 0.05), and there was a significant interaction of HEMISPHERE and TASK (F = 6.73, P < 0.05). In the RCZ, the TASK had a significant effect on the extent of activation, with SI (42 pixels) being greater than VT (14 pixels) (F = 10.1, P < 0.05). In the CCZ, the extent of activation was significantly affected by TASK (SI: 54 pixels, VT: 31 pixels; F = 51.04, P < 0.001), MOVEMENT (FIXED: 24 pixels, SEQUENCE: 60 pixels; F = 23.6, P < 0.01), RATE (SLOW: 30 pixels, FAST: 54 pixels; F = 6.5, P < 0.05), and HEMISPHERE (left: 60 pixels, right: 24 pixels; F = 15.91, P < 0.01).
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PERCENTAGE OF CHANGE IN SIGNAL INTENSITY. The change in signal intensity during movement periods was calculated relative to rest periods as a percentage. Repeated measures ANOVA were performed on the maximum values. In a comparison of movement conditions with the control condition, the %CSI was affected significantly by CONDITION (F = 3.2, P < 0.05). However, contrast analysis did not show a significant difference among the eight movement conditions and the control condition (0.78 vs. 0.63%, F = 2.3, P > 0.05).
In a comparison between movement conditions, there was a significant effect of ROI (pre-SMA: 0.92%, SMA: 0.96%, RCZ: 0.66%, CCZ: 0.57%; F = 5.41, P < 0.05) and TASK (SI: 0.89%, VT: 0.66%; F = 7.02, P < 0.05). There were also significant interactions between HEMISPHERE and TASK (F = 10.78, P < 0.05) and between ROI and MOVEMENT (F = 4.12, P < 0.05). A repeated measures ANOVA was performed for each ROI on the %CSI for the eight movement conditions. The results are shown in Fig. 6 and summarized in Table 2. In the pre-SMA, there were significant interactions of HEMISPHERE and TASK (F = 6.99, P < 0.05) and of RATE and MOVEMENT (F = 11.56, P < 0.05). In the SMA, none of the within-subject factors had a significant effect on the %CSI. In the RCZ, the TASK had a significant effect (SI: 0.77%, VT: 0.54%; F = 8.41, P < 0.05). There was also a significant interaction of HEMISPHERE and TASK (F = 6.74, P < 0.05). In the CCZ, the %CSI was significantly affected by TASK (SI: 0.63%, VT: 0.51%; F = 11.33, P < 0.05), MOVEMENT (FIXED: 0.5%, SEQUENCE: 0.64%; F = 29.09, P < 0.01), RATE (SLOW: 0.51%, FAST: 0.63%; F = 10.18, P < 0.05), and HEMISPHERE (left: 0.63%, right: 0.52%; F = 17.68, P < 0.01). There were significant interactions of TASK, RATE, and MOVEMENT (F = 8.28, P < 0.05) and of HEMISPHERE, TASK, RATE, and MOVEMENT (F = 5.69, P < 0.05).
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DISCUSSION |
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In the present study, we focused on the mesial wall of the
cerebral hemispheres to examine the participation of the motor areas in
self-initiated movements and movements triggered by a visual signal.
First, we discuss the anatomic basis of our work and describe the
technical limitations inherent in this context. Because in humans there
is still no absolute identification of separate motor fields in the
mesial wall, we elected to use the more recent classification of
Picard and Strick (1996), which is based primarily on
the analysis of several functional imaging studies. As these
investigators anticipated, their work provides a practical framework
for studying the role of the medial wall motor areas. They mainly have
used data from group PET studies for their analysis, which have in
common the reference frame of Talairach and Tournoux
(1988)
. Conversely, our approach is based on individual
examination of fMRI images. We have chosen to keep intact as much as
possible the high spatial resolution provided by this technique, and
thus we have resorted to spatial normalization, which might have been
problematic because the brain was only partially sampled. However, we
applied the landmarks' conventions of Talairach and Tournoux
(1988)
in each brain to delimit the anatomic ROIs according to
Picard and Strick's (1996)
scheme. Our calculations for
the ROI boundaries were based on the visual examination of Figs. 4 and
5 in the paper of Picard and Strick (1996)
because no
numerical measures were provided. We are aware that such an interpretation can lead to some error and that our ROI delimitation in
reference to the study of Picard and Strick (1996)
is
not absolute.
Motor paradigm and performance
There is abundant literature on changes in regional cerebral blood
flow (rCBF) as well as in movement-related potentials in relation to
the self decision on a motor response (Deiber et al. 1991,
1996
; Frith et al. 1991
; Playford et al.
1992
; Praamstra et al. 1995
, 1996
; Touge
et al. 1995
; Van Oostende 1997
). In these studies, a free-selection condition, characterized by the subject's own decision about "what to do," generally is opposed to a cued condition, in which the subject is instructed on the movement by a
signal; in both conditions, the precise timing of the movements is
determined externally by a pacing stimulus or also can be decided by
the subject. In contrast, our question related to the effect of the
movement initiation mode on brain activation, that is, the aspect of
timing or "when to do" something. The movements we tested were
all instructed, that is, the subjects knew in advance what to do. Only
the way in which they timed their movements was variable: it was either
their own decision (self-initiated) or dictated through a visual
trigger signal (visually triggered). In the present discussion, we will
restrict our review of the literature to studies focused on the
initiation mode of instructed movements; this is distinct from the
topic of movement selection. In the self-initiated mode, we focused on
the volitional aspect of making the movements by asking the subjects to
attempt a slightly irregular rate centered around a given value. We
wanted to avoid establishing automaticity or rhythmicity that
potentially could obscure differences between the SI and VT modes.
Moreover, strict regularity in movement performance likely would
generate undesirable anticipatory behavior in VT movements, with
specific effects on brain activity. In terms of movement frequency, our
choice of testing a slow and a fast rate was motivated by the study of
Sadato et al. (1996b)
, which showed a clear difference
in SMA blood flow between 0.25- and 1-Hz movements. However, as shown
in Table 1, the difference in the frequency of slow and fast movements
was smaller than expected, with slow movements performed at ~0.31 Hz
and fast ones at ~0.69 Hz. This observation, along with the fact that
only two movement rates were tested, limits the interpretation of the
rate effect observed in the present study.
Because an fMRI study is primarily a single-subject technique, we consider it essential to show how each subject behaved on the different tasks tested. Histograms of the results (Fig. 4) were based on cluster maps, which essentially assert the significance of activation by its intensity and spatial extent within each task for every subject. The results clearly showed more intense activation for SI tasks than for VT tasks; visual stimulation without any detectable movements was associated with minimal activity. Activation was largely bilateral, with the hemisphere contralateral to the movements generally more activated than the ipsilateral hemisphere, except for the pre-SMA. The difference between SI and VT movements was smaller for sequential movements performed at a fast rate. In a sequence, the decision about the next movement can be made during the interval between movements for both SI and VT tasks. When the sequence must be performed at a fast rate, timing preparation in SI is minimal, and one can argue that for fast sequences, the SI and VT tasks are not very different. This interpretation also should apply to the fixed movements at a fast rate; however, the difference between SI and VT was not so small for those movements. An alternative explanation is that when the movement is sufficiently complex and temporally demanding, as it is in the sequence performed at a fast rate, the difference between the SI and VT tasks tends to disappear because of the additional processing role of the mesial motor areas in movement complexity and high-rate movement production.
Quantitative analysis focused on the extent of activation (Fig. 5) and the percentage of change in signal intensity (Fig. 6). These measurements might correspond to neural recruitment and neural activity rates, respectively, but the relation between CBF and neuronal discharge rates remains too uncertain for us to give any definite conclusion. From a theoretical point of view, one has to be aware that with the use of spatial smoothing, an increase in signal intensity could artificially produce an increase in activation extent, the intensity value of a pixel being smeared to its nearest neighbors. However, considering the increase range of the fMRI signal obtained (~1% on average, see Fig. 5) and the small FWHM used (5.6 mm in-plane), the effect of the Gaussian filter on data dispersion can be estimated as being negligible. This is supported further by the observation that a motor parameter could affect significantly the extent of activation without affecting the increase in signal intensity (Table 2).
In confirmation of the descriptive approach, the main result of the quantitative analysis is that, compared with the VT movements, the SI movements were associated with a larger extent and intensity of activation in the mesial motor regions. Moreover, significant interaction between TASK (SI vs. VT) and ROI (SMA, pre-SMA, RCZ, and CCZ) was observed for the extent of activation, which means that the effect of the task differed according to the mesial structure involved (Table 2). Additionally, for all regions taken together, the extent of activation was also sensitive to movement type (FIXED vs. SEQUENCE) and rate (~0.31 vs. ~0.69 Hz), with larger values for sequential movements and faster rates (Table 2).
Pre-SMA and SMA
Our measures showed predominant changes in the extent of activation with no main effect on intensity of activation. Only the pre-SMA was affected significantly by the nature of the task, with SI movements inducing more extended activation than VT movements. The SMA showed a trend for such a differential response, although it did not reach significance. On the other hand, the SMA was affected by the movement type, with sequential movements associated with a larger extent of activation than fixed movements.
EXPERIMENTAL DATA.
Some neurophysiological studies in monkeys have addressed the issue of
specificity of the premotor areas in self-initiated versus externally
triggered movements, most of which comparing the SMA with the lateral
premotor area (Mushiake et al. 1991; Okano and
Tanji 1987
; Romo and Schultz 1987
). However,
their goal differed from ours on a basic point: movement rates in the
two modes of selection were not matched. In the self-initiated mode, the monkeys had to "learn" to respond at their own pace within a
time window, whereas in the triggered mode, a cue was presented intermittently. In those studies requiring performance of simple motor
tasks, both the SMA and the premotor area were similarly active
regardless of the generating mode. However, long lead neurons, which
are more abundant in the SMA, were observed mainly before self-paced
movements (Okano and Tanji 1987
). Neuronal modulation in
the SMA was found to occur earlier for self-initiated than for
externally triggered movements (Thaler et al. 1988
).
Mushiake et al. (1991)
contrasted a self-initiated task
in which the monkeys had to remember a predetermined sequence with a
visually triggered task in which the sequence of touch pads was
visually guided; the self-initiated mode cannot be considered as purely
self-paced because the time to start the whole sequence was cued.
Results showed that the SMA was more active in the self-initiated than in the triggered mode, whereas the premotor area showed the inverse behavior. Altogether, the results of the aforementioned studies suggest
some preferential participation of the SMA in self-initiated movements,
especially when more complex movements are involved. However, the
studies were all conducted before the description of the SMA
proper/pre-SMA subdivision, and thus no clear parallel can be drawn
with our data as to which part of the SMA is concerned.
HUMAN ELECTROPHYSIOLOGY AND FUNCTIONAL IMAGING.
Studies of movement-related potentials preceding movements show that
these potentials indeed are modulated by the mode of motor initiation.
Papa et al. (1991) recorded the Bereitschaft potential
occurring before wrist movements in self-paced and externally cued
conditions. The Bereitschaft potential was present only in self-paced
conditions, suggesting different cortical areas for the generation of
self-paced and stimuli-triggered movements. Jahanshahi et al.
(1995)
confirmed the absence of Bereitschaft potential in
externally triggered movements when the rate of presentation of the
trigger stimulus was very variable. With more regular trigger presentation, the amplitude of the Bereitschaft potential was smaller
for externally triggered than for self-initiated movements. If the
Bereitschaft potential mainly reflects SMA activity (Deecke and
Kornhuber 1978
; Ikeda et al. 1992
; Lang
et al. 1991
), then its absence or reduced amplitude in
externally cued movements suggests minor activation of the SMA in this
type of movement, which is consistent with our data. However, different
components can be distinguished in the Bereitschaft potential, and
their cortical generators remain an object of debate
(Böcker et al. 1994
; Bötzel et al.
1993
; Neshige et al. 1988
; Toro et al.
1993
). Moreover, the spatial resolution of scalp-recorded
electrical potentials is too weak to achieve definite distinction
between the pre-SMA and the SMA proper.
Rostral and caudal anterior cingulate cortex
In contrast with the pre-SMA and the SMA, for which the extent of activation was more sensitive than the magnitude of activation, both variables were affected similarly in the RCZ and CCZ. Consequently, activation in the cingulate cortex is discussed with reference to the extent and intensity together. In both RCZ and CCZ, self-initiated movements induced greater activation than did visually triggered movements, and activation was consistently larger in the left hemisphere (contralateral to the movements). In addition, in the CCZ, sequential movements and faster movements were both associated with more activation.
In monkeys, Shima et al. (1991) found two distinct
movement-related foci in the cingulate cortex that were associated with self-paced and signal-triggered movements. Using a simple key-press task and matching the execution rate of the two types of movements, they found that more neurons responded to the self-paced motor task in
the anterior than in the posterior cingulate cortex. Long-lead type of
activity (500 ms to 2 s before movement) was observed mainly
before the self-paced movements and was more frequent in the anterior
cingulate cortex. According to Picard and Strick (1996)
,
the anterior cingulate cortex described by Shima et al. (1991)
is equivalent to the rostral cingulate motor area and
would correspond to the RCZ in humans, whereas the posterior cingulate cortex is equivalent to both dorsal and ventral cingulate motor areas
and would correspond to the human CCZ. Our data showed that both parts
of the human cingulate cortex were more active in self-paced than in
triggered movements. No direct comparison was made between the rostral
and the caudal zones to determine whether, as suggested by Shima
et al. (1991)
, the RCZ has a greater role in self-paced movements.
In most of the functional imaging studies that have examined the effect
of the movement initiation mode on brain activation, the boundaries of
the anterior cingulate cortex were not clearly delineated, and little
attention was given to this mesial structure (Rao et al.
1993; Tyszka et al. 1994
). Remy et al.
(1994)
defined an "upper anterior cingulate cortex"
region whose anteroposterior coordinates covered both RCZ and CCZ. They
did not report any significant changes of activity in this region with
different modes of movement initiation. Wessel et al.
(1997)
observed that, as in the pre-SMA, activation in the RCZ
was significantly larger with the self-paced than with the
metronome-paced task. They found a converse result in the CCZ, but the
movement rate was faster for the metronome-paced task than for the
self-paced task. In view of our own results, which show higher activity
in the CCZ with the self-paced movements as well as with the faster
rate, it is possible that the effect of rate is predominant over the effect of movement initiation mode (self-initiated versus visually guided) in the CCZ, in contrast with the pre-SMA (see Pre-SMA and
SMA).
There are very few studies in which the activity of the anterior
cingulate cortex is examined in relation to the complexity and rate of
motor tasks. Shibasaki et al. (1993), contrasting a
simple finger sequence with a more complex finger sequence, executed at
a self-paced rate of 2 Hz, found no difference in rCBF in the anterior
cingulate cortex. Our finding of an effect of movement type in the CCZ
by contrasting a sequence movement with a single repetitive movement
suggests that the critical factor is the sequential nature of the
movement, and not its intrinsic complexity, as studied by
Shibasaki et al. (1993)
. Recently, Picard and
Strick (1997)
examined 2-deoxyglucose uptake in the mesial cortex of monkeys during the performance of remembered simple sequences
of reaching movements. The arm areas of the pre-SMA and SMA proper
showed radioisotope uptake, but it was the dorsal cingulate motor area,
which is equivalent to the CCZ in humans, that showed the most intense
and extensive 2-deoxyglucose uptake. These results further suggest that
this region is involved more than any other medial area in the
preparation and execution of highly practiced, remembered sequences of
movements. Concerning movement frequency, Sadato et al.
(1996b)
observed a peak of activity in the region we termed the
CCZ at very slow movement rates (0.25 and 0.5 Hz), which correlated
with the activity in the SMA proper, and a monotonic decrease in rCBF
at higher repetition rates. Because we only tested relatively slow
rates, it is difficult to draw any parallel with our study.
It is interesting to note that, of all the mesial regions we studied,
the CCZ is the only one with a significant response to every variable
tested in the present study, that is, TASK, MOVEMENT, and RATE. Furthermore the signal
increase contralateral to the movements was significantly larger than
the ipsilateral one. The CCZ is likely to correspond to the dorsal
cingulate motor area in the monkey (Picard and Strick
1996), which projects directly to the spinal cord (Dum
and Strick 1991
; He et al. 1995
). The dorsal
cingulate motor area is the only cingulate motor area in the monkey
that lacks interconnections with the dorsolateral prefrontal cortex
(Lu et al. 1994
), an observation that correlates in
humans with the dissociation observed between activation of the CCZ and that of the prefrontal cortex (for a review, see Picard and
Strick 1996
). Those propositions suggest that the CCZ might be
functionally closer to the executive motor system than to a supramotor
center, which might explain why the signal there is so responsive to
each of the relatively simple motor features tested in this study, without preference for any of them. Our analysis also revealed that the
CCZ was the region in which the intensity of signal change was the
smallest, suggesting both a low threshold and low magnitude for the
correlations reported. The other mesial regions showed more selectivity
in their activity. Self-initiated movements induced larger activation
in the more rostral mesial areas, that is, the pre-SMA and the RCZ. The
movement type was the only variable that affected the SMA proper, with
sequential movements inducing larger activation than simple repetitive
finger movements.
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ACKNOWLEDGMENTS |
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The authors thank Dr. S. P. Wise for comments on an earlier version of this manuscript and B. J. Hessie and D. G. Schoenberg for skillful editing.
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FOOTNOTES |
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Address for reprint requests: M. Hallett, Clinical Director, NINDS, NIH, Bldg. 10, Room 5N226, 10 Center Dr. MSC-1428, Bethesda, MD 20892-1428.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 20 July 1998; accepted in final form 11 February 1999.
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REFERENCES |
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