The Journal of Neurophysiology Vol. 80 No. 4 October 1998, pp. 2162-2176
Copyright ©1998 by the American Physiological Society

Motor Subcircuits Mediating the Control of Movement Velocity: A PET Study

Robert S. Turner¹, Scott T. Grafton^{1, 2}, John R. Votaw², Mahlon R. Delong¹, and John M. Hoffman^{1, 2}

¹ Department of Neurology and ² Department of Radiology, Emory University School of Medicine, Atlanta, Georgia 30322

	ABSTRACT

Abstract Introduction Methods Results Discussion References

Turner, Robert S., Scott T. Grafton, John R. Votaw, Mahlon R. DeLong, and John M. Hoffman. Motor subcircuits mediating the control of movement velocity: a PET study. J. Neurophysiol. 80: 2162-2176, 1998. The influence of changes in the mean velocity of movement on regional cerebral blood flow (rCBF) was studied using positron emission tomography (PET) in nine healthy right-handed adults while they performed a smooth pursuit visuomanual tracking task. Images of relative rCBF were obtained while subjects moved a hand-held joystick to track the movement of a target at three different rates of a sinusoidal displacement (0.1, 0.4, and 0.7 Hz). Significant changes in rCBF between task conditions were detected using analysis of variance and weighted linear contrasts. The kinematics of arm and eye movements indicated that subjects performed tasks in a similar manner, particularly during the faster two tracking conditions. Significant increases in rCBF during arm movement (relative to an eye tracking only control condition) were detected in a widespread network of areas known for their involvement in motor control. The activated areas included primary sensorimotor (M1S1), dorsal and mesial premotor, and dorsal parietal cortices in the left hemisphere and to a lesser extent the sensorimotor and superior parietal cortices in the right hemisphere. Subcortically, activations were found in the left putamen, globus pallidus (GP), and thalamus, in the right basal ganglia, and in the right anterior cerebellum. Within the cerebral volume activated with movement, three areas had changes in rCBF that correlated positively with the rate of movement: left M1S1, left GP, and right anterior cerebellum. No movement-related sites had rCBF that correlated negatively with the rate of movement. Regressions of mean percent change (MPC) in rCBF onto mean hand velocity yielded two nonoverlapping subpopulations of movement-related loci, the three sites with significant rate effects and regression slopes steeper than 0.17 MPC·cm¹·s¹ and all other sites with nonsignificant rate effects and regression slopes below 0.1 MPC·cm¹·s¹. Moreover, the effects of movement per se and of movement velocity varied in magnitude independently. These results confirm previous reports that movement-related activations of M1S1 and cerebellum are sensitive to movement frequency or some covarying parameter of movement. The activation of GP with increasing movement velocity, not described in previous functional-imaging studies, supports the hypothesis that the basal ganglia motor circuit may be involved preferentially in controlling or monitoring the scale and/or dynamics of arm movements. The remaining areas that were activated equally for all movement rates may be involved in controlling higher level aspects of motor control that are independent of movement dynamics.

	INTRODUCTION

Abstract Introduction Methods Results Discussion References

Functional-imaging studies using positron emission tomography (PET) have identified cerebral areas related to the control of movement as reflected by changes in regional cerebral blood flow (rCBF) associated with the performance of movement tasks. For a wide variety of actions, there is an increase in rCBF in primary sensorimotor cortex (M1S1), dorsal, ventral, and mesial premotor cortices, and superior parietal cortex in addition to anterior cerebellum (Colebatch et al. 1991; Deiber et al. 1991; Grafton et al. 1991; Roland 1984; Roland et al. 1980). With improvements in scanner resolution and refined image analysis techniques, movement-related activations also can be detected in many of the subcortical nuclei of the motor circuit including posterior putamen, globus pallidus, ventral thalamus, red nucleus, and midbrain (Winstein et al. 1996). A major challenge remains to distinguish functionally defined subcircuits within the extended network of cerebral areas implicated in motor control.

In the search for unique functional subcircuits within the motor-control areas, a large number of imaging studies have examined cognitive aspects of motor control (Decety et al. 1994; Deiber et al. 1991, 1996; Grafton et al. 1996a; Stephan et al. 1995; Tyszka et al. 1994). It could be argued, however, that a parcellation of motor-control areas according to their relations to fundamental parameters of movement (e.g., the direction and the scale of movement) takes logical precedence over studies concerning relations to more abstract cognitive aspects of motor control. Interpretational ambiguities arising from cognitive motor studies also argue for a systematic study of the neural control of simple movement parameters using functional imaging. For instance, experiments that manipulate simple kinematic parameters of movement have a strong advantage because one instruction can be given for all scans, creating in the subject a more uniform motor set, mental state, and attentional load. Also in studies manipulating simple movement parameters, subject compliance, and task performance can be measured objectively by recording limb and eye movement.

Recent studies using PET (Blinkenberg et al. 1996; Dettmers et al. 1995, 1996b; Jenkins et al. 1997; Sadato et al. 1996; VanMeter et al. 1995; Winstein et al. 1996) and functional magnetic resonance imaging (fMRI) (Dettmers et al. 1996a; Rao et al. 1996; Sadato et al. 1997; Schluag et al. 1996; Wexler et al. 1997) have begun to sort out the differential sensitivity of elements of the motor system to low-level parameters of movement. Among the several studies that assessed the influence of movement rate there is general agreement that the activation of M1S1 and cerebellum increases with increasing movement frequency (Blinkenberg et al. 1996; Rao et al. 1996; Sadato et al. 1996, 1997; Schluag et al. 1996; Wexler et al. 1997).

Two recent PET studies surveyed the whole brain for regional activity correlated with movement scale-related parameters and reported somewhat divergent findings for dynamic force and velocity. Cerebral activations associated with generating different levels of isometric force with the index finger were investigated by Dettmers et al. (1995). The main finding was increased activity in a wide variety of cortical and subcortical structures as the level of dynamic force increased. Increasing force increased the activation of M1S1, cingulate motor areas, superior parietal cortex, thalamus, and cerebellar vermis. Some motor-control areas, including the basal ganglia and lateral cerebellum, were activated equally for all levels of finger force. [Wexler et al. (1997) reported similar results in an fMRI study of force-related cortical activity.] Another study used a variation of the Fitts task to examine the functional anatomy of the speed/accuracy trade-off during continuous reciprocal reaching (Fitts 1954; Winstein et al. 1996). In that study, only the ventral premotor cortex and anterior cerebellum had increasing activations as the velocity of movement increased. There is little agreement between the two studies as to which areas were related preferentially to the manipulated scaling parameter even though dynamic force and movement velocity share common underlying physiological covariates (e.g., the level of agonist muscle activation). Both studies, however, do support the idea that a subset of the areas activated during a movement task may be involved in regulating a low-level task parameter such as force or velocity of movement.

Studies in nonhuman primates performing a variety of arm-movement tasks have found neural discharge that correlates with movement force, velocity, or extent in many motor-related cortical areas (Ashe and Georgopoulos 1994; Bauswein et al. 1991; Cheney and Fetz 1980; Crutcher and Alexander 1990; Evarts 1968; Fu et al. 1993; Hepp-Reymond et al. 1978, 1994; Hore and Flament 1988; Kalaska et al. 1989; Kurata 1993; Smith 1979; Werner et al. 1991), in the cerebellum (Fu et al. 1997a; Mano and Yamamoto 1980; van Kan et al. 1993), and in the basal ganglia (Crutcher and DeLong 1981; Georgopoulos et al. 1983; Liles 1985; Turner and Anderson 1997). Although these studies have revealed detailed information about the discharge of single neurons in specific brain regions, they do not provide an efficient way to assess simultaneously the relative sensitivity of many cerebral regions to kinematic or dynamical parameters.

The goal of this study was to identify the neural anatomy associated with the control of the velocity of movement. We conducted PET studies in normal volunteers as they performed a continuous visuomanual tracking task designed to manipulate the rate of arm movements (movement velocity and the number of reversals) while holding constant the extent of movement. We found that within the cerebral volume that was activated with movement of the arm (relative to no movement) a small subset of areas (M1S1, the basal ganglia, and cerebellum) had changes in rCBF correlated with the rate or velocity of movement. Other areas that have been implicated in the neural control of movement, including premotor and parietal cortices, were activated equally for all movement rates. Some of these results have been presented previously in abstract form (Turner et al. 1996).

	METHODS

Abstract Introduction Methods Results Discussion References

Subjects

Nine healthy adults (58 ± 12 yr, mean ± SD, range 44-79; 7 male, 2 female) were recruited for the study from the general population. (Subjects in this age range were recruited to serve as an age-matched control population for a functional-imaging study of parkinsonian subjects.) Subjects were excluded if, according to a medical interview, they had a history of neurological disease, psychiatric disease, or hypertension or if they were taking any prescription medication. All subjects were right-handed by self report. Subjects gave written informed consent in accordance with the institutional Human Investigation Committee before participating in the study.

Apparatus and behavioral tasks

The subjects were positioned supine on the scanner bed with the right upper arm placed on a flat surface at the subject's side, the shoulder abducted 30° and elbow flexed 90°. The subject's right hand was fixed in a padded plastic splint attached to a gimbal-mounted joystick so as to allow medial and lateral longitudinal rotations of the shoulder. The arm and joystick were hidden from the subject behind a black curtain.

A video monitor was suspended over the scanner bed and tilted to face the subject (Fig. 1). During each tracking task, a "target" (a solid white circle, 1.5 cm diam) moved horizontally across the monitor according to a sinusoidal time/position function at one of three frequencies (0.1, 0.4, and 0.7 cycles/s) at a constant gain (peak-to-peak displacement of 20 cm). The subject was instructed to match the position and movement of the target as closely as possible with an on-screen cursor (a hollow red 1.5-cm square) controlled by movements of the hand-held joystick. Longitudinal rotations of the shoulder caused the cursor to move along the same horizontal track as the target, whereas vertical movement of the joystick had no effect. To follow the target's 20-cm displacement across the monitor, a matching 20-cm displacement of the joystick was required. Before the first PET scan, subjects practiced the tracking task at each target rate until tracking errors stabilized.

View larger version (17K): [in this window] [in a new window]   FIG. 1. Setup of experiment. Subject is shown lying supine on scanner bed (axial view) with right elbow resting on the bed. Medial and lateral longitudinal rotations of the shoulder joint were translated into left and right movements of a cursor across a computer monitor mounted above the subject. Joystick attached to the subject's hand and the curtain preventing subject's vision of the hand are not shown. Computer controlled target and the subject controlled cursor moved along the same horizontal line.

Along with the three arm-movement tasks, subjects performed a control task in which the target moved at 0.4 Hz and the subject was instructed to follow the movement of the target with only the eyes. The subjects were told not to move or even think about moving the arm. Eye tracking was chosen as a control condition because all subjects chose to follow movements of the target with their eyes as a natural strategy during performance of the arm movement tasks. The use of eye tracking as a control condition allowed for subtraction of cerebral activations related to visual perception and oculomotor tracking of the target.

Silver/silver chloride surface electrodes were placed periorbitally to allow electrooculographic (EOG) recording of horizontal movements of the eyes (gain = 1,000, band-pass filtering 0.1-100 Hz). Joystick position and EOG signals were digitized at 250 Hz.

Imaging

Eight PET scans were performed in a counterbalanced presentation of two repetitions of each of four tracking conditions: control (eye) tracking, and arm tracking at 0.1, 0.4, and 0.7 Hz, and then in the reverse order. Subjects began one of the tracking tasks 10 s before the initiation of each PET scan and continued for the duration of the scan. Digitized data for kinematic analysis were saved for the first 60 s of a scan.

Images of rCBF were acquired with the use of a modified autoradiographic method (Herscovitch et al. 1983; Raichle et al. 1983). A bolus of H₂¹⁵O (45 mCi) was injected intravenously into the left arm simultaneous with the initiation of the tracking task and 10 s before the initiation of a 90-s scan. Images of radioactivity were used to estimate rCBF (Mazziotta et al. 1985).

The rCBF images were acquired with a Siemans ECAT 951 tomograph. The scanner collects 31 contiguous 3.375-mm-thick slices with an intrinsic resolution of ~5 mm full width half-maximum (FWHM). Images were collected parallel to the canthomeatal line, and subjects were positioned in the scanner so that the field of view covered the vertex. Thus the inferior cerebellum was not included. Images were reconstructed with a calculated attenuation and a 0.3-cm ramp filter, then smoothed with a three-dimensional Gaussian filter to an isotropic resolution of 11.8 mm FWHM.

Kinematics analysis

Task performance was measured for the 60-s record obtained for each scan. Joystick velocity was derived by digital low-pass filtering (5-Hz cutoff) and differentiation of the position signal (Hamming 1983) and mean absolute velocity of movement was computed. The mean extent of movement was found by computing the average of the difference between positional extremes for each movement cycle. The mean temporal error (phase lead or lag) in tracking performance was computed by finding the temporal shift that minimized the sum of positional error and velocity error relative to target position and velocity.

Records of horizontal eye position (HEOG) were converted to an inferred gaze position by regressing HEOG records onto target position to obtain global offset and gain factors for all HEOG records from a subject. Horizontal gaze records were plotted and inspected to ensure subject compliance.

rCBF image analysis

Image processing was performed on a SUN Ultra 1 workstation. For spatial normalization, a within-subject alignment of PET scans was performed using an automated registration algorithm (Winstein et al. 1996; see Woods et al. 1998a for details of implementation and validation). A mean image of the coregistered PET scans was coregistered to a PET reference atlas generated from 18 normal subjects, centered in Talairach coordinates using an affine transformation with 12 df; (Talairach and Tournoux 1988; see Woods et al. 1998b for details of implementation and validation). Coregistered PET images were smoothed to a final isotropic resolution of 15 mm FWHM and normalized to each other using proportionate global scaling within a volume composed of any pixel where nonzero data were available in all scans for all subjects.

Statistical comparisons of globally normalized rCBF were performed using repeated measures two-way analyses of variance (ANOVAs) and weighted linear contrasts (Neter et al. 1990; Woods et al. 1996). A t-map image of significant effects was calculated on a voxel-by-voxel basis by weighting the scans as a function of the task factors. Within areas of significant activation, local maxima were identified in the t-map, and maximal t and P values and mean rCBF values were tabulated for each comparison. Raw rCBF values were converted to values of mean percent change in rCBF (MPC) relative to the mean rCBF at the site during control scans: MPC = (activation scan rCBF  mean control rCBF)/(mean control rCBF). The following three planned comparisons were evaluated.

MOVEMENT. A two-way ANOVA was used to identify areas demonstrating an increase in rCBF under all arm-movement conditions relative to rCBF under the control condition. All 72 scans were included according to two main effects: task (n = 2 categories, all movement scans weighted +1 vs. eye only control scans weighted 3) and subject (n = 9). Regions with significant effects were identified by searching for loci at which the statistical contrast yielded P < 0.001 (t  3.58, df = 36) in 50 contiguous voxels (75 mm³).

MOVEMENT RATE. A two-way ANOVA was used to identify areas that had increased activation as the rate of movement increased. The 54 arm-movement scans were included according to two main effects: rate (n = 3, weighted according to target rate: 0.1 Hz = 1, 0.4 Hz = 0, 0.7 Hz = +1) and subject (n = 9). This weighting scheme resulted in an analysis mathematically identical to a linear regression of normalized rCBF onto target rate after accounting for intersubject variability. Scans from the eye-only control condition were not included in this analysis, thereby allowing the y intercept of the regression line (essentially, a measure of the rate-independent movement-related activation of an area) to differ from zero.

To compensate for the multiple nonindependent comparisons inherent in this statistical analysis, sites of significant rate-related activation were identified according to the principles outlined by Friston et al. (1994) using a global threshold for significance of P < 0.05. This analysis takes into account both the magnitude and the spatial extent of putative sites of activation, and it compensates for the multiple nonindependent comparisons performed within the volume of the search space. Two separate tests were performed using different search volumes. First, sites with significant rate effects were identified within the cerebral volume that demonstrated at least a nominal movement-related activation (threshold t = 3.0, P < 0.005). Second, the whole gray-matter volume was searched for significant rate effects.

INVERSE MOVEMENT RATE. A two-way ANOVA was used to identify areas that had decreased activation as the rate of movement increased. The 54 arm-movement scans were analyzed exactly as described for movement rate except the weights for the rate effect were inverted (0.1 Hz = +1, 0.4 Hz = 0, 0.7 Hz = 1).

The anatomic locations of significant effects were determined according to their position relative to landmarks visible in a coregistered MRI image. The positions of nuclear boundaries in the vicinity of the basal ganglia and thalamus were guided further by the coregistration of line drawings from a high-resolution atlas of the human forebrain (Schaltenbrand 1977).

	RESULTS

Abstract Introduction Methods Results Discussion References

Task performance

Figure 2 shows the performance of one subject during representative 10-s epochs of each of the four task conditions. For the three arm-movement conditions (Fig. 2, bottom 3 rows), movements of the subject's hand approximated those of the target both in position (Fig. 2, left) and velocity (Fig. 2, middle). The subject synchronized reversals in the direction of hand movement with those of the target; this indicates that the subject was employing a predictive strategy. The subject's gaze followed the movement of the target under all four conditions (Fig. 2, right). When tracking at the slowest target rate (0.1 Hz), a pattern of intermittent accelerations and decelerations around the target velocity was evident (Fig. 2, 2nd row, middle). This pattern could be taken to suggest that the subject was tracking the target with an error-based, nonpredictive strategy under the 0.1-Hz condition. Further analysis using the Fourier technique revealed that intermittent accelerations of similar frequency and of greater magnitude were present under the 0.4- and 0.7-Hz conditions even though they were less obvious when the hand was moving at higher velocities. (Note the different scales in Fig. 2 for the plots of velocity under the 0.1-, 0.4-, and 0.7-Hz conditions.) Furthermore, the temporal error in the subject's tracking was very small under the 0.1-Hz condition (8-ms lag) compared with estimates of the visuomotor reaction time (~200 ms), and the temporal error changed only slightly for the 0.4- and 0.7-Hz conditions (to 0 and 14 ms phase leads, respectively).

View larger version (43K): [in this window] [in a new window]   FIG. 2. Task performance during a representative 10 s of each of the 4 behavioral conditions for 1 subject. For each condition (1 row per condition), plots show the subject's hand position, hand velocity, and gaze position [inferred from electrooculographic (EOG) recordings]. Position or velocity of the target is shown in gray for comparison. Top: control eye-only condition. During the control condition, the subject's gaze followed the target while the hand remained stationary. Bottom: active tracking. During the active tracking conditions, the position of the hand matched the target position closely. Intermittent acceleration and decelerations of the hand around the target velocity were present under all tracking conditions but were most evident under the 0.1-Hz condition because of the low velocities of the target and hand.

For the most part, subjects performed the tracking tasks with a low degree of spatial and temporal error. The mean velocity of movement matched the mean velocity of the target at all target rates (Fig. 3A) both for the mean performance across subjects ( and ) and for all nine subjects individually (···). There was a slight tendency for the mean extent of movement to undershoot the extent of target movement (20 cm) during medium and fast tracking conditions (Fig. 3C), and this effect was accentuated in one subject (subject 4). The variability between subjects in mean movement extent also increased with movement rate. The mean extent of movement across subjects, however, never deviated >0.5 cm from the target extent. The temporal error in tracking was greatest under the slow tracking condition (mean phase lag = 40 ms behind the target, Fig. 3B). The phase lag under the slow tracking condition was markedly longer (121 and 126 ms) in two subjects (subjects 4 and 8); this suggests these two did not use a predictive strategy under the 0.1-Hz tracking condition. There was no appreciable temporal error during the two faster tracking conditions (Fig. 3B).

View larger version (16K): [in this window] [in a new window]   FIG. 3. Mean task performance of all 9 subjects during the 3 tracking rates. A: movement velocity. Mean velocity of the hand matched very closely that of the target both for the mean across subjects () and for all subjects individually (···). (Performance values for individual subjects were computed as the mean across the 2 replications for each of the tracking 3 rates.) B: temporal error. Subjects tended to lag behind the position of the target under the 0.1-Hz condition, especially in 2 subjects. Under the 0.4- and 0.7-Hz conditions, movements of the hand were well time-locked with those of the target. C: movement extent. Mean extent of movement declined slightly as target rate increased as did the variance between subjects in movement extent.

Regional cerebral blood flow

MOVEMENT VERSUS EYE-ONLY CONTROL. Significant movement-related increases in rCBF were found in a wide swath of cortex surrounding the left central sulcus (blue areas in Fig. 4, left), in the left basal ganglia and thalamus (Fig. 4, middle), and in the right anterior cerebellum (Fig. 4, right). Additional smaller areas of activation were observed in the right precentral gyrus, right superior parietal lobule, and right basal ganglia (putamen/globus pallidus border). Peaks within these activated regions were found at a constellation of loci that has been implicated in the control of arm movements in many previous functional-imaging studies (Table 1).

View larger version (55K): [in this window] [in a new window]   FIG. 4. Significant movement-related (blue) and rate-related (yellow) activations. Axial views of t-maps for the PET movement and movement-rate comparisons (P < 0.005) are shown superimposed on a magnetic resonance image (MRI) from 1 representative subject in Talairach coordinates. A: cortex. Movement-related activity covered a wide expanse of cortex in the left hemisphere including primary sensorimotor, dorsolateral and mesial premotor, and dorsal parietal cortices (according to radiological convention the right hemisphere is to the left). Movement-related activity also was observed in the right sensorimotor and parietal cortices, ipsilateral to the moving arm. Within this wide expanse, rate-related activity was restricted to a small band of cortex surrounding the left central sulcus. B: basal ganglia/thalamus. Movement-related activity was observed in skeletomotor-related portions of the left basal ganglia and thalamus and in the right basal ganglia. Rate-related activity was restricted to the left posterior globus pallidus. C: cerebellum. A large portion of the right anterior cerebellum was activated with movement. Rate-related activity was observed in a band covering the mesial portions of the cerebellar movement-related activation.

Two distinct activation peaks were found near the central sulcus in what previous studies have identified as the arm-related portion of sensorimotor cortex. The more dorsomedial site (item 1 in Table 1) may correspond with the shoulder representation described in previous studies (Colebatch et al. 1991; Fink et al. 1997; Grafton et al. 1993), whereas the ventrolateral maximum (item 7 in Table 1) may correspond with the representation of the distal hand. Maxima in the frontal cortices also were identified in the mesial and dorsolateral premotor cortices. Activated sites in the parietal cortices were found both ipsilateral and contralateral to the moving arm.

Maxima in the subcortical activations were observed at two distinguishable sites in the left thalamus corresponding to the approximate locations of the ventral lateral and ventral anterior thalamic nuclei (Fig. 4, middle). Additional maxima were observed in the lentiform nucleus (border between putamen and pallidum) ipsilateral to the working arm. The anterior lobe of the cerebellum ipsilateral to the working arm was also highly activated during movement as were the deep cerebellar nuclei (Fig. 4, right).

MOVEMENT RATE. Rate-related increases in rCBF were detected in only three restricted regions (Table 2) within the widespread cerebral volume that was activated with movement: left M1S1 (yellow area in Fig. 4, left; Fig. 5), left posterior globus pallidus (Fig. 4, middle; Fig. 6, left), and right anterior cerebellar vermis and intermediate zone (Fig. 4, right; Fig. 6, right). Two additional areas were identified as rate-related when the search for rate effects was broadened to include cerebral areas that were not activated significantly with movement (Table 2, *) including a small site in the left precentral gyrus (Fig. 5, ) and visual cortices bilaterally (data not shown). A monotonic increase in rCBF with increasing movement rate at these sites suggested that these areas were involved in a special way in regulating or monitoring the velocity or frequency of arm or target movements.

View larger version (132K): [in this window] [in a new window]   FIG. 5. Rate-related activations in cortex. Sites at which regional cerebral blood flow (rCBF) increased significantly with the rate of movement (blue regions, P < 0.005). Activation in primary sensorimotor cortex extended from the anterior bank of the central sulcus () posterior to include the postcentral sulcus (maximum t-value at Talairach coordinates 22, 36, 63). Small rate-related activation on the precentral gyrus (, Talairach coordinates 45, 1, 51) was ventral and lateral to the region of premotor cortex that was activated significantly with movement.

View larger version (27K): [in this window] [in a new window]   FIG. 6. Rate-related activations in the basal ganglia and cerebellum. Left: basal ganglia/thalamus. Coronal view shows that rate-related activity was restricted to a small region in the posterior globus pallidus (maximum t-value at Talairach coordinates 22, 16, 3). Nuclear boundaries were determined by the coregistration of line drawings from Schaltenbrand and a population average MRI atlas in Talairach coordinates. Scale = 10 mm. Right: cerebellum. Axial view shows that rate-related activity was concentrated in an anteroposterior band including the vermis and right intermediate zone of the anterior cerebellum (maximum t-value at Talairach coordinates 9, 52, 13). Line drawing was derived from a population average MRI atlas in Talairach coordinates. Scale = 20 mm.

In cortex, the largest area of significant rate-related activation extended in a band from the left superior parietal lobule (Fig. 5; Talairach coordinates: 24, 38, 64) to the anterior bank of the central sulcus (Talairach coordinates: 15, 22, 63). The local maximum in the t-map for this activation was situated in the left postcentral gyrus, but the anterior extension of the activated region included coordinates that previous studies have identified as the center of the proximal arm representation in M1S1 (Colebatch et al. 1991). The small rate-related activation in the precentral gyrus (Table 2, item 4; Fig. 5, ) was positioned just ventral and lateral outside of the large area of precentral gyrus that was activated with movement (Table 1, item 3).

The rate-related activation in the left basal ganglia was centered in the middle-posterior portion of the external globus pallidus (Fig. 6, left). This activation appeared to include most of the posterior aspects of medial and lateral globus pallidi, and it spread laterally into the very medial portions of the posterior putamen. In the anterior cerebellum, the rate-related activation was strongest in the vermis and the intermediate zone of the right hemisphere (Fig. 6, right). Less-intense rate-related activations extended into the right lateral and left intermediate zones and the midbrain tegmentum. The limited field of view of the PET detector array prevented imaging of the caudal portions of the cerebellum.

Significant inverse relations between rCBF and arm velocity (i.e., higher rCBFs for lower velocities) were not found within the movement-activated regions. When the search space was broadened to include all gray matter, three locations were found to have significant inverse rate effects: the left precuneus, left middle frontal gyrus, and right middle temporal gyrus (Table 2). These three areas also had slightly reduced mean rCBFs during movement relative to the control condition (i.e., negative MPCs).

The regions that had significant rate-related activations appeared to be unique and distinct among the brain areas that were activated during movement. At the three primary rate-related loci, rCBFs increased by 0.18% for every 1-cm/s increase in mean velocity (as measured by regression analysis, "Slope" in Table 2, white text on black background in Fig. 7A and  in Fig. 7B). All of the loci identified as movement related (Table 1) that were not close to rate-related sites had MPC/velocity slopes <0.1 (black text and  in Fig. 7B). In addition, the strength of the velocity effect (i.e., the slope of the MPC/velocity relation) was independent of the magnitude of the movement effect (i.e., MPC, Fig. 7A). Areas showing significant rate effects had movement effects that were distributed across the range of observed movement effects. Thus it is unlikely that some areas appeared to be non-rate-related merely because movement-related activations saturated the hemodynamic response for the range of movement velocities employed.

View larger version (26K): [in this window] [in a new window]   FIG. 7. Distributions of the effects of movement and movement velocity on rCBF. A: distribution of velocity effects vs. movement effects for individual loci. Effects of velocity on rCBF [mean percent change (MPC) in rCBF per 1 cm/s change in velocity, MPC·cm1·s1) were divided into 2 distinct distributions: 1 for sites with significant rate effects (white text on black background) and the other for sites with nonsignificant rate effects. Only overlap was for the small rate-related locus in premotor cortex that was not activated significantly with movement (*). Notice that there was no apparent relation between the effects of movement per se (abscissa) and movement velocity (ordinate) as would be expected if movement-related saturation of the hemodynamic response was an important factor limiting the effects of velocity on rCBF. Data were extracted for all tracking conditions in all subjects for movement- and rate-related loci identified in Tables 1 and 2. Movement-activated loci were omitted if located within 1 cm of a rate-activated site. B: histogram of velocity effects. Lack of overlap between significant () and nonsignificant () velocity effects is emphasized when shown in histogram form. Again, the only overlap was for the small premotor site that was not activated with movement (*).

At each of the rate-related loci identified, rCBF values showed a monotonic increase with the velocity of movement (Fig. 8). The detailed form of that relationship differed, however, between the cerebellar site and the basal ganglia and motor cortical sites. At the cerebellar site, the rate effect was steep across the slow range of velocities tested (5-15 cm/s) but showed signs of saturation in the fast range (15-25 cm/s). The opposite was true at M1S1 and GP sites where the relation between rCBF and velocity was weak for slow velocities, whereas there was a strong increase in rCBF in the fast velocity range (Fig. 8). No obvious behavioral correlate accounted for the apparent difference in the effects of movement rate at different sites.

View larger version (27K): [in this window] [in a new window]   FIG. 8. Relation between mean velocity and mean rCBF for rate-activated sites. For each site, the mean velocity across subjects for the 3 tracking rates is plotted vs. the mean change in rCBF (relative to rCBF in the control task, , ±SE). Gray lines show least squares linear regression lines fit to the data for each site. Notice that the velocity-related increase in rCBF at the cerebellar site showed evidence of saturation, whereas velocity-related increases in rCBF at M1S1 and globus pallidus sites were enhanced in the higher range of velocities.

It is clear from Fig. 8 that the rate-independent activation of an area (i.e., the y intercept of the regression lines) often differed from zero and varied independent of the rCBF/velocity relation. It is also worth noting in Fig. 8 that rate effects were equally if not more evident in the fast range of velocities, within which range performance data showed no evidence for different performance strategies (0.4- and 0.7-Hz conditions, Fig. 3). This observation strengthens the view that rate effects could not be accounted for by changes in motor strategy alone.

	DISCUSSION

Abstract Introduction Methods Results Discussion References

A fundamental approach to understanding the neural control of movement lies in determining the degree and nature of functional segregation within and between the multiple motor-control areas of the brain. The present study furthers this approach by identifying a small collection of motor-control areas where activity (as measured by regional cerebral blood flow) is correlated with the velocity or rate of movement. We infer that these few areas are involved preferentially in controlling or monitoring the rate, scale, or dynamics of arm movements. The remaining movement-related areas, including premotor, parietal, and some cerebellar sites, had significant activations that were not correlated with the velocity of movement. These areas may be involved in aspects of neural control that are independent of low-level parameters of movement.

Task performance

Subjects performed the tracking task with a high degree of temporal and spatial accuracy. There were some indications that the slow tracking condition (0.1 Hz) was performed in a different manner than the faster two conditions. Under the slow condition, hand velocities typically showed a pattern of intermittent accelerations and decelerations that was reminiscent of the velocity profiles observed during visuomotor tracking that relies on visual feedback (i.e., nonpredictive tracking) (Miall et al. 1986). It is likely, however, that most of our subjects employed a similar strategy for all tracking rates. A Fourier analysis of the velocity error data indicated no change in the dominant frequency and an actual increase in the magnitude of subjects' velocity error with increasing movement rate. In addition, nonpredictive tracking is characterized by a temporal delay of >100 ms (Poulton 1981; Viviani and Terzuolo 1982), whereas in the present study, the mean temporal delay across subjects was 40 ms during the slow tracking condition. Finally, even if the 0.1-Hz condition was performed using a different strategy, effects of movement velocity on rCBF (the primary effects of interest in this study) were also evident in the higher range of velocities where performance was most consistent.

Velocity effects

The restricted number of regions with changes in rCBF that correlated with velocity was something of a surprise to us. Although movement-related activations were observed in most of the cerebral areas implicated in the control of arm movements in previous functional-imaging studies, only three areas within this mosaic had activations that were related significantly to the rate of movement. Movement-activated loci were distributed into two nonoverlapping subpopulations: those with significant rate effects with slopes (i.e., from regressions of MPC in rCBF onto mean velocity) >0.17 MPC·cm¹·s¹, and those with nonsignificant rate effects and slopes <0.1 MPC·cm¹·s¹. The magnitudes of movement effects and rate effects varied independently across the population of movement-activated loci; this supports the notion that the rate-related activation of a region was a separate and distinct process from the movement-related activation of a region. Even though it is unlikely that neural control of any one aspect of movement, including rate or velocity, involves one exclusive area or set of areas (Remy et al. 1994; Roland and Zilles 1996), the present results argue that there is a strong preferential involvement of the areas identified here in regulating or monitoring movement rate or scale.

Outside of the movement-activated regions, only two areas had significant positive rate-related activations. The small rate-related activation of left precentral gyrus will be discussed later (see CORTEX). The activation of visual areas with increasing movement rate was most likely a product of the sensitivity of visual areas to the velocity of moving visual stimuli (Corbetta et al. 1990) combined with the fact that our control condition used only the 0.4-Hz target rate. This type of effect has been described previously for other arm movement paradigms (Winstein et al. 1996).

Several ambiguities arise in the interpretation of the rate-related activations. Potentially confounding factors range from low-level kinematic parameters to high-level task factors and peculiarities of the group of subjects studied. At the low level, it was physically necessary that variations in the mean velocity of movement covaried with either the frequency at which changes in direction were made (i.e., for movements of constant extentthe design used in the present study) or the amplitude of movement (i.e., for movements at a constant reversal frequency). By using a task that required continuous movement of the arm, we avoided the additional potential confound of variations in the fraction of time spent moving during a scan, a factor demonstrated to have a strong influence on the activation of motor structures (Grafton et al. 1996b).

Because mean velocity and the frequency of movement reversals covaried in the present study, it is quite possible that some of the apparent velocity-related changes (particularly those in M1S1 and cerebellum) reflected an involvement in processes that operate before or during each change in movement direction (e.g., braking of an ongoing arm movement, switching between different muscle synergies, or synchronizing movement with the visual stimulus). A tremendous amount of nonhuman primate, clinical, and functional-imaging data implicate M1S1 and cerebellum in the specification of movement direction (reviewed, e.g., in Georgopoulos 1995). [Although the discharge of neurons in motor-related portions of the basal ganglia often reflects the direction of movement, lesion and clinical studies do not support the concept that the basal ganglia contribute substantially to the specification of movement direction (Crutcher and Alexander 1990; Crutcher and DeLong 1984; Georgopoulos et al. 1983; Turner and Anderson 1997).] The higher the frequency of changes in movement direction, the greater the activation one would expect in cerebral areas that are involved in the specification of movement direction.

Previous functional-imaging studies have manipulated movement rate while either holding constant or not controlling movement velocity (Blinkenberg et al. 1996; Jenkins et al. 1997; Rao et al. 1996; Sadato et al. 1996, 1997; Schluag et al. 1996; VanMeter et al. 1995; Wexler et al. 1997). Although all of these studies described rate-related activations of M1S1 and cerebellum (and additional areas in 2 of the studies), no previous study has reported a rate-related activation in the basal ganglia.

It is likely that the proprioceptive sensory consequences of increasing movement velocity contributed to the rate-related activations observed in the present study. Proprioceptive sensory input has access to all of the structures demonstrating rate-related activations in the present study (DeLong et al. 1985; Fetz et al. 1980; Murphy et al. 1978; Thach 1979), and neural activity in proprioceptive pathways is scaled with the velocity of movement (Grill and Hallett 1995; Matthews 1981). Also, in the present study, a substantial portion of the rate-related activation of cortex was located over the postcentral gyrus, a principle site for the processing of proprioceptive inputs. It bears consideration, however, that proprioception is an ingrained and essential component in the control of normal limb movements (Gandevia and Burke 1992) and even a transient loss of proprioceptive input induces a widespread alteration in the organization of the sensorimotor cortices (Donoghue et al. 1990; Sadato et al. 1995). Thus it is questionable whether a distinction between movement execution and proprioceptive reafference is meaningful when using a technique with low temporal and spatial resolution such as PET in the analysis of normal motor control.

Task conditions and subject instructions were kept constant across the three arm-movement conditions. In spite of this, uncontrolled high-level aspects of task difficulty such as a subject's sense of effort and attentional load may have covaried with movement velocity. Those factors were not controlled with the present paradigm nor were they in most previous studies of movement rate. It is worth noting, however, that one study that did manipulate both movement rate and task difficulty while studying cortical activations found the effects of rate were restricted to M1S1, whereas the effects of movement difficulty were widespread (Wexler et al. 1997).

All of the subjects in this study were >40 yr old, well beyond the mean age of subjects in most functional-imaging studies of normal motor control. Subjects were screened for overt neurological and psychiatric problems by medical interview, but more subtle age- or disease-related factors (e.g., altered vascular responsivity, hypertension, or cerebral pathology) may not have been detected without more extensive testing. Thus although the present results are robust for the group of subjects studied, their absolute generality remains to be determined.

An interesting interpretation of the rate-related activations observed in the present study is that they reflect an underlying involvement of the activated areas in controlling the global scale of movement. The view that movement direction and movement scale often are planned independently is supported by a variety of experimental approaches including chronometric studies (Bock and Arnold 1992; Bonnet et al. 1982; Ghez et al. 1990; Rosenbaum 1980) as well as psychophysical studies of variability in the specification of direction and extent of arm movements (Bock and Arnold 1980; Flanders et al. 1992; Ghez et al. 1990, 1997; Gordon et al. 1994; Messier and Kalaska 1997; Pine et al. 1996). This view is further supported by observations of neuropathologic conditions (e.g., Parkinson's disease) in which the control of movement scale is impaired while the control of movement direction is spared (Godaux et al. 1992). In addition, electrophysiological studies of neural discharge in the motor cortices of nonhuman primates have reported that the neural coding of movement direction and of movement scale (extent or velocity) follow different time courses (Fu et al. 1995). These observations have led to the proposal that different neural circuits may contribute to the specification of movement direction and movement scale (Gordon et al. 1994). Although admittedly speculative at this point, the velocity-related activations observed in the present study may reveal components of the motor control subcircuit that is involved preferentially in regulating movement scale.

BASAL GANGLIA. The location of the rate-related activation in the left basal ganglia corresponds fairly well with that reported previously for the portions of medial and lateral globus pallidus related to somatomotor function (DeLong and Georgopoulos 1981; Parent 1986). In primates, the somatomotor circuit through the basal ganglia includes most of the postcommissural putamen, the ventrolateral portions of medial and lateral segments of the globus pallidus, and the subthalamic nucleus, and this circuit exits the basal ganglia via inhibitory projections from the medial pallidum to the motor thalamus and brain stem (Alexander et al. 1990; Parent and Hazrati 1995).

Previous PET studies that manipulated the frequency of movement did not describe rate-related activations in basal ganglia structures (Blinkenberg et al. 1996; Jenkins et al. 1997; Sadato 1996; VanMeter et al. 1995). (Studies of movement rate using fMRI restricted their focus to cortical structures.) The most obvious and appealing explanation for the absence of a basal ganglia activation in previous studies is that the tasks in those studied did not manipulate movement velocity. Other factors that differ between the present and previous studies include the use of smooth pursuit movements (vs. intermittent movement tasks in previous studies), the use of proximal arm movements (vs. wrist and finger movements in most previous studies), and the use of older subjects. There is no reason at present, however, to think any of these latter factors would influence the activation of basal ganglia differentially.

In the present study, nearly all stages in the cortical-basal ganglia-thalamocortical motor circuit showed a significant activation with movement. It may be asked, then, why were rate-related activations not present at all stages of the basal ganglia motor circuit (i.e., in lateral putamen, a recipient of M1S1 afferents and source of pallidal afferents, or in motor thalamus, a target of pallidal efferents and source of afferents to M1)? As is true with all negative results in PET data, interpreting the absence of effects in the present data are fraught with difficulties both because of the numerous sources of variance in the PET data and because of the indirect nature of the relationship between rCBF and neural discharge (Jueptner and Weiller 1995, review). We may conclude, however, that the globus pallidus is part of a network of areas involved in movement scaling some portions of which network may not have been detected in the present study.

Although previous functional-imaging studies do not describe an effect of movement velocity, frequency, or force on activity in basal ganglia structures, the present results are congruent with other lines of evidence implicating the sensorimotor portion of the globus pallidus in the control of movement scaling. In nonhuman primates, the movement-related discharge of single pallidal neurons often is correlated with the extent or velocity of movement (Georgopoulos et al. 1983; Turner and Anderson 1997; but see Brotchie et al. 1991; Mink and Thach 1991a). Other studies of nonhuman primates have reported that disruption of normal basal ganglia outflow by electrical stimulation, reversible inactivation, or permanent lesion affected the speed and/or the metrics of trained arm movements while preserving the directional accuracy of the movements (Alamy et al. 1996; Horak and Anderson 1984a,b; Hore and Villis 1980; Inase et al. 1996; Kato and Kimura 1992; Mink and Thach 1991b).

Human pathologies of the basal ganglia often are associated with hypo- and hyperkinetic movement disorders, and it has been proposed that these symptoms also may arise from the basal ganglia's involvement in regulating various aspect of the scale of movement (Berardelli et al. 1996; Hallett and Khoshbin 1980; Jordan et al. 1992; Kunesch et al. 1995; Praamstra et al. 1996; Stelmach et al. 1989; Thompson et al. 1988; Wascher et al. 1997). The success of pallidotomy as a treatment for Parkinson's disease brings that interpretation of the basal ganglia pathology data into question, however (Baron et al. 1996; Dogali et al. 1995; Laitinen et al. 1992; Marsden and Obeso 1994). Nonetheless, the results of the present study do reinforce the general view that information processed in the globus pallidus reflects, in part, the velocity or scale of movement.

CEREBELLUM. This study, as well as previous functional-imaging studies (Dettmers et al. 1995; Sabatini et al. 1993; Sadato et al. 1996; VanMeter et al. 1995; Winstein et al. 1996), implicates mesial cerebellar structures in the control of low-level parameters of movement. Recording studies in the cerebellum of nonhuman primates have demonstrated relations of single unit discharge to scale-related parameters of arm movements (Fu et al. 1997a,b; Mano and Yamamoto 1980; van Kan et al. 1993). Other studies have implicated the cerebellum in compensating for the interaction torques generated during multijoint reaching movements (Bastian and Thach 1995; Bastian et al. 1996), compensating for the extraneous loads imposed by surrounding tissue (Krauzlis and Lisberger 1994), regulating the level of dynamic force output (Dettmers et al. 1995), and processing motor error (Ebner et al. 1996; Jueptner et al. 1995). The cerebellum also receives strong proprioceptive sensory input (Bower 1997). Because these factors likely covary with the velocity of movement and the frequency of reversals in movement direction, it is not surprising that the present study demonstrated a strong rate-related activation in the cerebellum.

It is interesting to note that the anterior lateral cerebellar cortex was strongly activated during performance of the tracking task but, unlike the cerebellar vermis and intermediate zone, showed no evidence of being rate related. This result supports the concept that the lateral cerebellar cortex is involved in higher aspects of visuomotor control independent of movement kinematics, whereas medial cerebellar territories are involved in controlling the specifics of movement execution (Kim et al. 1994; Mushiake and Strick 1993).

CORTEX. The rate-related activation in a region surrounding the left central sulcus corresponds with the approximate location in M1S1 that many previous studies reported to be activated with movements of the proximal arm (Colebatch et al. 1991; Fink et al. 1997). The importance of sensory feedback in this task is emphasized by the fact that the maximum t-value in this activation was located in the postcentral gyrus and that the region of significant activation extended posteriorly to include the postcentral sulcus (Fig. 5).

It may be seen as surprising that a more widespread velocity-related activation of cortical areas was not observed in the present study. Various neurophysiological studies have described an influence of force, velocity, or amplitude on the discharge of neurons in mesial, dorsal, and ventral premotor and parietal cortical areas (Ashe and Georgopoulos 1994; Bauswein et al. 1991; Crutcher and Alexander 1990; Fu et al. 1993; Hepp-Reymond et al. 1994; Kurata 1993; Smith 1979; Werner et al. 1991) along with the well-studied influence of those parameters in M1S1 (Cheney and Fetz 1980; Evarts 1968; Hore and Flament 1988; Kalaska et al. 1989 to cite a few). One might predict from these studies that scaling-related increases in rCBF would be found in most if not all of the cortical areas implicated in motor control.

Substantial data, however support the view that cortical areas involved in motor control are organized in a relative hierarchy such that M1S1 is involved more in low-level aspects of motor control and premotor and parietal areas are involved more in aspects of movement planning independent of movement kinematics and dynamics. Even though discharge related to low-level parameters may be found in many cortical areas, comparisons have shown consistently that the prevalence and strength of relations to the purely motor aspects of a task are greatest in the discharge of neurons in M1 (Alexander and Crutcher 1990; Johnson et al. 1996; Kalaska et al. 1990; Mushiake et al. 1991; Tanji and Kurata 1982; Weinrich and Wise 1982). Recent studies show convincingly that purely motor aspects of a task are represented more commonly in the neural activity of M1 than in dorsal premotor cortex (Scott et al. 1997; Shen and Alexander 1997). The discharge of neurons in S1 also is reported consistently to be closely correlated with low-level parameters of movement (Fromm and Evarts 1982; Jennings et al. 1983; Riehle and Requin 1995; Wannier et al. 1986). The present results are consistent with these neurophysiological studies and with the results of lesion studies (Passingham 1993) in concluding that, among the cortical areas commonly activated with movement, M1S1 is most closely involved in the low-level kinematic and dynamical aspects of movement.

A small rate-related activation was located in the precentral gyrus in a region the functional identity of which is defined poorly. There is no consensus for the position of the boundary between the human equivalents of the macaque dorsal and ventral premotor areas (PMd and PMv, respectively) (Fink et al. 1997; Matelli et al. 1985, 1991). It has been proposed that this boundary lies at the dorsal/ventral position of the frontal eye fields (Fink et al. 1997; Grafton et al. 1997), similar to its position in the macaque. The rate-related activation in precentral gyrus was located at the same approximate dorsal/ventral level as the human frontal eye fields (Paus 1996) and thus was in an ambiguous region between the PMd and the putative location of PMv.

There are several possible interpretations of the fact that this premotor cortical area was not activated with movement per se but showed a modest scaling of rCBF with increasing movement rate. Because it was situated immediately adjacent to the movement-activated area of PMd, this activation could arise if the PMd activation increased in size as movement rate increased even though its level of activation remained constant. Effects of movement frequency on the area of functional activation have been described previously for cortical motor areas (Rao et al. 1996; Sadato et al. 1997; Wexler et al. 1997). Alternatively, the rate-related premotor site may be a subdivision of the human premotor cortex that is functionally distinct from the large medial area that is strongly activated with the visuomotor tracking task. Finally, given this site's close proximity to the reported location of the human frontal eye field (Paus 1996) and given the fact that our eye only tracking condition did not control for the rate of eye movement, this activation may reflect the influence of oculomotor tracking at different rates.

It is reasonable to conclude from the present study that low-level kinematic or dynamical parameters of the tracking task had a strong influence on the functional activation of M1S1, globus pallidus, and medial cerebellar cortexcerebral areas that belong to the basal ganglia-motor cortical and the cerebellar-motor cortical circuits. Other motor areas (including premotor and parietal cortical areas and the lateral anterior cerebellum) were affected weakly if at all by the rate or velocity of movement even though those areas were activated strongly with movement. These results support the long-standing hypothesis that the neural motor control system has at least some degree of hierarchical organization (Jackson 1875). Movement velocity and/or some covarying kinematic or dynamical parameters of movement engage elements of the motor control system that have relatively direct access to the spinal motor apparatus and that are characterized as being involved in low-level aspects of motor control (Alexander et al. 1990). The movement-activated regions not affected by the rate of movement include brain areas, such as the premotor and parietal cortices, thought to be involved in integrative or abstract aspects of motor control (Kalaska et al. 1983; Tanji and Kurata 1985). There is no reason to suppose that the velocity-related activations observed here reflect an exclusive involvement in controlling the velocity of movement, both because other physiologically relevant task factors covaried with velocity in the present study and because it is unlikely that activity in a brain area would "code" a single physical parameter (Fetz 1992). Previous reports of activation in motor cortex and cerebellum related to the rate of movement (Blinkenberg et al. 1996; Rao et al. 1996; Sadato et al. 1996, 1997; Schluag et al. 1996; VanMeter et al. 1995) make it especially likely that behavioral or physiological covariates of movement rate account, at least in part, for the activations of the M1S1 and cerebellum. The novel velocity-related activation of globus pallidus, in contrast, supports the role hypothesized for the basal ganglia motor circuit in the control of movement velocity, extent, or overall movement scaling (Berardelli et al. 1996; Georgopoulos et al. 1983; Hallett and Khoshbin 1980; Horak and Anderson 1984b; Turner and Anderson 1997). Future studies are required to further dissociate the cerebral activation effects of movement scaling parameters such as velocity and extent from effects related to the frequency of movement reversals. These results will be critical for understanding the compensatory changes in activity that occur in patients with movement disorders who are unable to perform at speeds identical to normal subjects.

	ACKNOWLEDGEMENTS

  We thank R. P. Woods for providing image analysis software.

  This work was supported in part by the PET Imaging Center, Emory University School of Medicine, Georgia, and by grants from the Dana Foundation and the National Institutes of Health.

	FOOTNOTES

  Address for reprint requests: R. S. Turner, Dept. of Neurology, WMRB 6000, Emory University, Atlanta, GA 30322.

  Received 19 February 1998; accepted in final form 23 June 1998.

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