J Neurophysiol 97: 1527-1545, 2007.
First published November 29, 2006; doi:10.1152/jn.01160.2006
0022-3077/07 $8.00
Neural and Electromyographic Correlates of Wrist Posture Control
Aaron J. Suminski1,
Stephen M. Rao2,
Kristine M. Mosier3 and
Robert A. Scheidt1,4
1Department of Biomedical Engineering, Marquette University; and 2Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin; 3Department of Radiology, Indiana University School of Medicine, Indianapolis, Indiana; and 4Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
Submitted 31 October 2006;
accepted in final form 26 November 2006
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ABSTRACT
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In identical experiments in and out of a MR scanner, we recorded functional magnetic resonance imaging and electromyographic correlates of wrist stabilization against constant and time-varying mechanical perturbations. Positioning errors were greatest while stabilizing random torques. Wrist muscle activity lagged changes in joint angular velocity at latencies suggesting trans-cortical reflex action. Drift in stabilized hand positions gave rise to frequent, accurately directed, corrective movements, suggesting that the brain maintains separate representations of desired wrist angle for feedback control of posture and the generation of discrete corrections. Two patterns of neural activity were evident in the blood-oxygenation-level-dependent (BOLD) time series obtained during stabilization. A cerebello-thalamo-cortical network showed significant activity whenever position errors were present. Here, changes in activation correlated with moment-by-moment changes in position errors (not force), implicating this network in the feedback control of hand position. A second network, showing elevated activity during stabilization whether errors were present or not, included prefrontal cortex, rostral dorsal premotor and supplementary motor area cortices, and inferior aspects of parietal cortex. BOLD activation in some of these regions correlated with positioning errors integrated over a longer time-frame consistent with optimization of feedback performance via adjustment of the behavioral goal (feedback setpoint) and the planning and execution of internally generated motor actions. The finding that nonoverlapping networks demonstrate differential sensitivity to kinematic performance errors over different time scales supports the hypothesis that in stabilizing the hand, the brain recruits distinct neural systems for feedback control of limb position and for evaluation/adjustment of controller parameters in response to persistent errors.
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INTRODUCTION
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Tool use is an important aspect of human development, and meaningful interaction with our environment frequently requires both the stabilization of hand-held objects about desired limb postures and the movement of such objects between stable postures. Recent experimental evidence suggests that distinct neural mechanisms may be involved in the planning and control of simple motor actions such as reaching with the arm and stabilizing it against environmental loads (Kurtzer et al. 2005
; Lackner and Dizio 1994; Scheidt et al. 2004). The neural mechanisms subserving reaching and trajectory control have received a great deal of recent attention, whereas study of the central mechanisms supporting final position control and its optimization within a given task context have received much less attention. Here we describe a set of experiments using a novel, MR-compatible robot designed to examine both the central and peripheral mechanisms contributing to the control of wrist posture.
When the position of a limb is actively maintained, unexpected changes in the load elicit a series of changes in muscle activity (Evarts and Tanji 1976; Evarts and Vaughn 1978; Lee and Tatton 1975; Marsden et al. 1978) termed the M1, M2, and M3 responses (Lee and Tatton 1975). The M1 component has the shortest latency occurring 
25 ms after a perturbation and is caused by the monosynaptic (segmental) reflex response to muscle stretch. It is followed in time by the M2 and M3 responses, which occur at latencies between 40 and 100 ms. Single-unit recordings in primates demonstrate that these "long loop" reflex pathways are mediated by supraspinal pathways including neural populations in both the primary sensorimotor cortex (Evarts and Tanji 1976; Thach 1978) and the cerebellum (Strick 1978, 1983; Thach 1978). These neuromuscular responses are important in the compensation for unexpected perturbations because both the segmental (Sinkjaer and Hayashi 1989) and the transcortical "reflex" responses (Evarts and Fromm 1981; Evarts and Tanji 1976; Miall et al. 1993; Strick 1978) have been implicated in the moment-by-moment (closed-loop or "on-line") feedback control of joint position.
However, subjects may invoke alternative strategies to maintain the position of the limb. These strategies include impedance control via voluntary co-activation of antagonist muscles about the joints (i.e., cocontraction), whereby intrinsic muscle mechanical properties provide an immediate mechanical response to the perturbation (Loeb et al. 1999; Nichols and Houk 1976), or they can also generate discrete, feedforward, corrective movements compensating for the approximate mean of the perturbation (Fagg et al. 1998; Haaland and Harrington 1989). These strategies are not mutually exclusive but rather are complementary in two ways. First, they act to reduce performance errors over different timescales (ranging from the short-latency mechanical responses of antagonist co-activation and reflex action to the reduction of persistent errors by discrete adjustment of behavioral goals). Second, they provide the flexibility in motor output needed to respond to task-dependent constraints on accuracy and muscular effort and thus may provide the behavioral basis for optimality in human motor control (Scott 2004; Todorov and Jordan 2002). That is, by monitoring long-term trends in task-relevant variables, an optimal neural controller can adjust feedback parameters (including the feedback control setpoint) to optimize those aspects of motor output of greatest importance given the task at hand (Scott 2004). Much is yet unknown about the neural mechanisms mediating wrist position control (i.e., posture stabilization), including what aspects of environmental perturbation are compensated on a moment-by-moment basis in this task (hand force, position, or both) and what performance variables might cause subjects to invoke a discrete corrective action during stabilization.
Functional imaging techniques provide a valuable tool for studying the central neural networks contributing to motor behavior in humans. The majority of functional magnetic resonance imaging (fMRI) studies of motor behavior have heretofore been unable to probe the mechanisms involved in the control of limb position due to the lack of devices available to perturb human movement in a controlled manner within the MR environment. Instead, research has focused on eye-hand coordination (Miall et al. 2000, 2001), visuomotor adaptation (Flament et al. 1996; Imamizu et al. 2000), force control (Dai et al. 2001; Ehrsson et al. 2001; Thickbroom et al. 1998; Vaillancourt et al. 2003), and the feedforward and feedback control of joystick-mediated cursor movement (Seidler et al. 2004). Notable exceptions include studies by Shadmehr and colleagues, who have used both PET and fMRI to study the neural correlates of motor learning (Nezafat et al. 2001), memory consolidation (Shadmehr and Holcomb 1997), and trajectory errors (Diedrichsen et al. 2005) as subjects performed reaching movements while holding the handle of a two-joint robot. Similarly, Desmurget and colleagues used PET to investigate the neural mechanisms related to the correction of movement trajectories during a pointing task (Desmurget et al. 2001). But given that limb trajectory and final position control might be served by distinct neural mechanisms (Ghez et al. 2004; Kurtzer et al. 2005; Scheidt et al. 2004), a design limitation of those studies is that the tasks and neural imaging techniques used were not well-suited for decoupling the control of trajectory from final posture due to the long integration periods associated with the hemodynamic and metabolic dynamics associated with fMRI and PET. Thus there is a need to reevaluate whether limb stabilization excites neural pathways during feedback control of position which are anatomically and functionally distinct from those involved in the generation of discrete compensatory responses.
Here we describe experiments designed to investigate the central and peripheral mechanisms contributing to the control of wrist position using fMRI and electromyography (EMG), respectively. Subjects grasped the handle of a novel MR-compatible robot both in and out of a MR scanner and stabilized their right hand against constant (CT) and random (RT) extensor torque perturbations having the same mean value. Stabilization was preceded and followed by brief periods of passive manipulation of the hand during which subjects were instructed to relax. Vision of the wrist was precluded at all times, forcing subjects to rely strictly on proprioceptive feedback during stabilization. We anticipated that EMG would demonstrate that the two wrist extensor loads elicit different combinations of compensatory responses throughout the stabilization period, promoting feedback control of limb position regardless of load (evidenced by the recruitment of long-loop reflexes), while encouraging an increase in limb impedance when the load was uncertain. We also thought it possible that subjects might adjust their behavioral goals by generating discrete, feedforward, corrective movements if ongoing feedback control failed to meet subjective accuracy requirements. We therefore hypothesized that at least two, distinct, neural mechanisms contribute to the stabilization of wrist position in the presence of persistent environmental perturbations. A first mechanism likely mediates the on-line control of endpoint position (not torque) via feedback control. Because feedback control attempts to adjust motor commands to cancel deviations of the limb from its desired state, regions contributing to feedback control of wrist position will likely show increased blood-oxygen-level-dependent (BOLD) response during periods wherein limb positioning errors are present independent of the level of force generated (i.e., throughout RT stabilization as well as during the passive movements that occurred before and after stabilization). Ideally, temporal variations in the BOLD response in these regions should correlate with variations in wrist position on a moment-by-moment basis. A second mechanism likely monitors performance over a longer timeframe than the feedback mechanisms, generating discrete, conditional, corrective actions when feedback control fails to eliminate persistent errors. Regions contributing to this higher-order evaluation of performance are expected to show increased BOLD response during both RT and CT stabilization regardless of whether positioning errors are present as well as variations in BOLD response that reflect changes in positioning errors with a longer temporal integration period. Consistent with these expectations, the data suggest that when stabilizing the hand against environmental perturbations, subjects recruit distinct brain networks for the ongoing regulation of limb position (i.e., feedback control) and for the evaluation of feedback control and the feed-forward planning and execution of internally generated corrective actions.
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METHODS
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Ten healthy volunteers participated in each of the two sessions of this study (5 female; mean age = 27.5 yr, range = 21–38). All subjects were right-handed according to the Edinburgh Handedness Inventory (Oldfield 1971). Subjects were excluded if they had significant neurological, psychiatric, or other medical history or were taking psychoactive medications. Additional exclusion criteria were specific to MR scanning: pregnancy, weight inappropriate for height, ferrous objects within the body, low visual acuity, and a history of claustrophobia. Written informed consent was obtained from each subject in accordance with institutional guidelines approved by the Medical College of Wisconsin and Marquette University in accord with the Declaration of Helsinki.
Description of robotic tool
A MR-compatible manipulandum with integrated pneumatic actuator was developed to exert computer controlled torques about the subject's right wrist (Fig. 1A). The device monitors wrist position (within 0.05°) and wrist torque (within 0.001 Nm). Air pressure within the actuator is sensed by a Honeywell 26PC series pressure transducer (Honeywell International, Morristown, NJ), and digitized with a National Instruments PCI-6036E multifunction data-acquisition system (National Instruments, Austin, TX). Analog measurements of pressure within the actuator are amplified and Nyquist filtered at a cutoff frequency of 20 Hz. Joint angle is measured with an Agilent HEDM-6540, 3-channel, Mylar film optical encoder (Agilent Technologies, Palo Alto, CA), paired with a Measurement Computing PCI-QUAD-04 incremental encoder driver (Measurement Computing, Middleboro, MA). Robot control is achieved using custom hardware and software designed to use the XPC target, real-time operating system (Mathworks, Natick, MA). Wrist angle and actuator pressure data were acquired at the control loop rate of 1,000 Hz. Experiments demonstrating the compatibility of the robot and MR scanner are presented in APPENDIX A.
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FIG. 1. A: schematic representation of the 1 degree of freedom pneumatic manipulandum illustrating how the subject interfaces to the device. B: a single trial was conducted in 5 phases. During the 30 s prior to stabilization (phase 1), the subject was instructed to relax while the robot held the hand in a comfortable posture of 40° flexion. Three seconds prior to the start of stabilization (phase 2), the robot moved the relaxed hand to the target posture (20° flexion) and held it there until the onset of the stabilization period. During stabilization periods (phase 3), subjects were instructed to hold their wrist steady at a comfortable target angle of 20° flexion. At the end of the stabilization period, the subject was instructed to relax, and the robot again moved the passive hand to its resting position at 40° flexion (phase 4) after which resting electromyograms (EMG) were monitored for 3 s (phase 5). C: subjects performed the wrist-stabilization task in a blocked design experiment that alternated between periods of rest and active stabilization. During phase 3, subjects stabilized against either constant (gray line) or pseudo-random (black line) extensor torque perturbations. Each of the 2 trial types was presented 1 time in pseudorandom order during a run (e.g., subjects could not predict the perturbation on the next trial), along with 30-s periods of inactivity (rest) preceding and following each stabilization period.
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Experimental procedures
Subjects participated in two experimental sessions designed to evaluate the electromyographic and neural correlates of wrist stabilization. To allow for the collection of EMG data from task-relevant muscles, subjects performed the experiment while inside a mock MR scanner located at Froedert Memorial Lutheran Hospital in Milwaukee, WI. Each subject also performed the identical stabilization experiment in a separate session while simultaneously undergoing fMRI scanning in a 1.5T General Electric Signa scanner (General Electric Healthcare, Milwaukee, WI) equipped with a three-axis local gradient head coil and an elliptical endcapped quadrature radiofrequency coil at Froedert.
In both sessions, subjects rested supine in the scanner with their head constrained by foam padding to reduce head motion inside the head coil. With arms at their sides, subjects grasped the robot handle with their dominant hand. The handle's axis of rotation was aligned with that of the wrist, and the frame of the device was secured to both the subject's forearm and the inner wall of the scanner bore for support. A single trial was conducted in 5 phases (Fig. 1B). During the 30 s prior to stabilization (phase 1), the subject was instructed to relax while the robot held the hand in a comfortable resting posture of 40° flexion (
r). Three seconds prior to the start of stabilization (phase 2), the robot moved the relaxed hand to the target posture (20° flexion) and held it there until the onset of the stabilization period. The purpose of this phase was to provide subjects a salient haptic cue of the desired wrist angle about which they were to stabilize. During the 30 s stabilization periods (phase 3), subjects were instructed to hold their wrist steady at the target angle during two experimental conditions in which the device was programmed to apply either a predictable, constant extensor torque about the wrist (CT, mean = 1.2 Nm) or unpredictable, pseudo-random extensor torques composed of band-limited Gaussian "white" noise with a high-frequency cutoff of 1.6 Hz (RT; 1.2 ± 1.1 Nm; mean ± SD both here and elsewhere). At the end of the stabilization period, the subject was instructed to relax, and the robot again moved the passive hand to its resting position at 40° flexion (phase 4) after which resting EMG continued to be monitored for 3 s (phase 5). Direct view of the wrist was precluded and subjects received no visual feedback of hand motion during stabilization phase 3. Instead subjects were provided with a stationary visual fixation target that was visible using prism glasses and was back-projected onto a screen located at their feet. The fixation target moved in concert with the hand during passive movement phases 2 and 4 and thus provided an implicit visual representation of the desired wrist angle during stabilization phase 3. Subjects received experimental instructions between trials via the back-projection screen.
Both sessions consisted of a blocked design experiment that alternated between periods of rest and active stabilization. Each of the two trial types was presented one time in pseudo-random order during a run, along with 30-s periods of inactivity (rest) preceding and following each stabilization period (Fig. 1C). Thus there were 60 s of rest between stabilization periods within each run to minimize the occurrence of fatigue. Each subject performed 10 of these runs in each session. During actual imaging runs, whole-brain images were acquired using a single-shot, blipped gradient-echo echo-planar pulse sequence (19 contiguous sagittal 7-mm slices, TE = 40 ms, TR = 2.5 s, 90° flip angle, FOV = 24 cm, 64 x 64 matrix, 3.75-mm in-plane resolution); 72 whole-brain images were acquired in each run. BOLD contrast was used to image the hemodynamic related changes in the brain occurring during the two stabilization tasks. Before functional imaging, high-resolution three-dimensional (3D) spoiled gradient recalled at steady-state T1-weighted anatomic images were collected for anatomic localization and co-registration (TE = 5 ms, TR = 24 ms, 40° flip angle, slice thickness = 1.2 mm, FOV = 24 cm, 256 x 192 matrix).
EMG data collection and analysis
In the mock scanning session, EMGs were recorded from 10 wrist and arm muscles using differential surface electrodes while subjects stabilized their wrists (Delsys DE-2.1 electrodes and Delsys Bagnolli 16 system; Delsys, Taunton, MA). Monitored muscles included: wrist and finger flexors and extensors (flexor carpi radialis, FCR; flexor carpi ulnaris, FCU; flexor digitorum superficialis, FDS; extensor carpi radials, ECR; extensor carpi ulnaris, ECU; and extensor digitorum communus, EDC), an elbow flexor and extensor (the short head of biceps, BIC; and lateral head of triceps, TRI), and a shoulder flexor and extensor (anterior deltoid, AD; and posterior deltoid, PD). EMG signals were band-pass filtered between 10 and 450 Hz, amplified (x1000), and sampled at 1,000 Hz using a National Instruments PCI-6036E multifunction data acquisition system (National Instruments). Residual offsets were subsequently removed from the digitized EMGs, which were then rectified and filtered at 4 Hz with a zero-phase low-pass filter (4th-order Butterworth). To allow for comparison of EMG activity across the study population, each subject's muscle activities were subsequently normalized by the peak value of the rectified and filtered activity recorded from that muscle during a series of maximum voluntary isometric contractions (MVIC). Each subject performed a total of 12 MVIC trials prior to the start of the stabilization experiment: two each of maximal isometric wrist flexion, wrist extension, elbow flexion, elbow extension, shoulder flexion, and shoulder extension. The peak magnitude of EMG activity for each muscle was defined as the largest value of EMG after signal processing as described in the preceding text.
The resulting normalized EMG time series were averaged across trials within each trial type for each subject to obtain an estimate of each muscle's activity during both CT and RT stabilization. We used a cross-correlation technique (Neilson 1972) to evaluate the temporal relationship between changes in wrist joint angle (d/dt) and individual muscle activities during stabilization. Significance was evaluated by comparing the correlation magnitude to an estimate of the 95% confidence interval bounding zero correlation (Box et al. 1994). We then characterized the coordination between muscles at each joint by estimating the degree of antagonist muscle co-activity (CoA) at the wrist, elbow, and shoulder joints using a measure also known as "wasted contraction" (Thoroughman and Shadmehr 1999). We considered the AD and PD as shoulder antagonists, BIC and TRI as elbow antagonists, and the FCR and ECR muscles as wrist antagonists. For each pair of antagonist muscles and at each sampling instant, the minimum value of the two normalized EMG signals was selected to yield a time varying co-activity signal which represents the magnitude of normalized EMG that is equal and opposite in the antagonist muscle pair
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Finally, we computed for each subject and for each muscle at each joint, the amount of EMG activity exceeding the co-activity value computed for that joint. This value, which we call the excess-activity (ExA), estimates the amount of EMG above and beyond that which contributes to coactivation about the joint, roughly estimating the amount (magnitude) of phasic muscle activity that may contribute to moment-by-moment feedback compensation for the imposed loads
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Behavioral data analysis
Time series of joint angle and joint angular velocity were low-pass filtered at a cutoff frequency of 10 Hz to reduce encoder state transition artifacts. Stabilization was evaluated using five kinematic and one dynamic performance measures. First, we computed objective stabilization error 
o(nT) as the difference between the actual and desired (target) hand position
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where t is the target wrist angle (20° flexion) and (nT) is the instantaneous wrist angle at sample instant nT. To compare objective performance across stabilization conditions, we then computed the root mean square (RMS) value of this time series throughout each 30-s trial
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where N is the total number of data samples during each stabilization period (i.e., 30,000).
We next quantified drift in the instantaneous joint angle equilibrium position by fitting a first-order polynomial to the joint angle time series data over the final 20 s of each trial. Drift was considered significant in those trials where the slope of the regression line was statistically different from zero. This polynomial defined the subjective target angle s(nT) as the instantaneous reference angle about which small corrections were made, and was used to estimate subjective stabilization error s(nT) as the RMS deviations about s(nT)
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For comparing subjective performance across stabilization conditions, we computed its RMS throughout each 30-s trial
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where again, N is the total number of data samples during stabilization (30,000).
We also identified the onset and direction of discrete corrective movements made by subjects during both RT and CT stabilization (Fig. 3A, top). Here we wished to identify relatively rapid movements that were not a direct mechanical consequence of moment-by-moment fluctuations in wrist torque generated to oppose the perturbation. Thus for each stabilization condition for each individual subject, we removed the trial-averaged wrist angle trajectory from every individual trial to obtain a "corrected" wrist angle time series (C, Fig. 3A, middle). We considered as discrete corrections only those movements wherein the angular velocity of corrected wrist angle trajectories (dC/dt) exceeded 5°/s, occurring 
1 s after the start of a stabilization period (Fig. 3A, bottom). Both the total number of trials where at least one discrete corrective movement was detected and the total number of discrete movements across all trials were counted. In addition, we classified each correction movement as being correctly or incorrectly directed based on the relationship of the wrist angle and the objective target position t at the instant angular velocities exceeded the threshold.
We then constructed an estimate of state estimation errors q(nT) during phases 2–4 of each trial (i.e., during stabilization as well as during the preceding and following passive movement phases). Here we assume that during trial phases 2 and 4, passive movement of the wrist induced a discrepancy between actual limb position and that expected given the recent history of motor output, whereas during trial phase 3, state estimation errors would arise from load-induced deviations from the subjective target angle s(nT). Specifically
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The time series of q(nT) were then used to compute RMS values of state estimation errors on a moment-by-moment basis (i.e., within a 2.5-s integration window emulating the temporal sampling of the functional imaging pulse sequence, TR)
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where m is the total number of TR sampling instants during phases 2–4 of the trial and N is the number of wrist angle sampling instants in each TR integration period (i.e., 2,500).
Finally, we computed RMS wrist torque values, again on a moment-by-moment basis (i.e., within a 2.5-s integration window as in Eq. 8) and on a trial-by-trial basis (during phase 3 as in Eq. 6). Because wrist angular deviations about the desired target angle were small, torque values were estimated to be proportional to the transduced actuator pressure.
fMRI data analysis
Functional MR images were generated and analyzed within the Analysis of Functional NeuroImages (AFNI) software package (Cox 1996). The three images at the beginning and ending of each run were discarded to allow for equilibration of the magnetic field. For each subject, individual run image time series were concatenated into one large dataset and then aligned in three-dimensional space to minimize the effects of head motion; an interactive, linear, least-squares method was used for this purpose (AFNI program 3dVolreg) (Cox 1996). Registration yielded six movement indices per functional imaging run. The across-subjects average head movement for each of the rotation indices were 0.23 ± 0.12, 0.19 ± 0.24, and 0.35 ± 0.34° (rotations in the superior-inferior, anterior-posterior, and left-right planes, respectively), whereas average translational head movement was 0.40 ± 0.29, 0.24 ± 0.31, and 0.28 ± 0.24 mm (translation in the superior-inferior, anterior-posterior, and left-right direction). No subjects were excluded from further analysis due to head motion. A voxel-wise multiple linear regression analysis was used to determine the amount of fMRI signal contrast between the two task conditions (stabilization against constant CT or random RT torques), and the resting baseline. The two input reference functions consisted of a time series (having the value 1 during each stabilization period of a given stimulus type and 0 otherwise) convolved with a 
-variate function to model the dynamics of the hemodynamic response. A second multiple regression analysis was performed to determine the amount of fMRI signal contrast between the two task conditions and the resting baseline, but only during the middle third of stabilization trials (that is, RTM and CTM). This analysis focuses on activation patterns during stabilization itself disregarding activations due to transient events at the beginning and end of trial phase 3. In both regression analyses, the time series of head motion indices (obtained from the spatial registration process) were included in the model of resting baseline to reduce the potential for false positives due to stimulus correlated motion.
The resulting functional images for RT, CT, RTM, and CTM were interpolated to obtain a volumetric grid having 1-mm3 voxel volumes, coregistered, and then converted into the Talairach stereotaxic coordinate space (Talairach and Tournoux 1988). To facilitate group analysis, the Talairached functional images were spatially blurred using a 4-mm Gaussian full-width half-maximum filter to compensate for inter-subject anatomical variability. T-tests were performed on a voxel-wise basis on the regression fit coefficients for RT, CT, RTM, and CTM to identify regions showing greater activation during stabilization as compared with rest. In addition, a mixed-model ANOVA with post hoc t-tests (treating subjects as a random factor) was used to identify regions showing differences in level of activation between the RT and CT conditions. In all across-subject analyses, a cluster-size and thresholding technique was used to correct for multiple comparisons in the group analysis. Appropriate cluster size (554 µl, 5.6 voxels) and individual voxel P value thresholds (P = 0.005) were estimated by performing 5,000 iterations in a Monte-Carlo simulation using the AlphaSim tool included within the AFNI toolkit (Cox 1996). The location of activated regions in group statistical parametric maps was obtained using the Talairach atlas (Talairach and Tournoux 1988) for cerebral activations and the Schmahmann atlas (Schmahmann 2000) for activations in the cerebellum and its nuclei. Cortical activations were visualized using CARET (Van Essen et al. 2001) (http://brainmap.wustl.edu/caret).
To evaluate the pattern of neural activity due to stabilization, we extracted for each individual subject the average BOLD time series from each of 18 ROIs in which significant activation was observed in either RT or CT relative to rest (see Table 1). We then computed the percentage BOLD signal change (PSC) relative to a local baseline as a function of time for both RT and CT trials. For this calculation, the local baseline was defined by fitting a first-order polynomial through data from the two 30-s rest periods immediately preceding and after each stabilization period for each individual subject and ROI. PSC time series for the population were created by averaging individual RT and CT time series within ROI and across subjects.
On visual examination of the PSC time series for each ROI and each trial type, two distinctive patterns (or features) of the time series became evident. First, some ROIs demonstrated great sensitivity to load type and considerable sensitivity to passive movement of the wrist during trial phases 2 and 4. Second, some ROIs demonstrated elevated activity during stabilization against both load types and no sensitivity to passive motion of the wrist. Finally, some ROIs demonstrated some characteristics of each of the two patterns. To quantify these observations, we defined a two-element feature vector 
= {
, 
M} summarizing the average increase in PSC induced by passive movement in phase 2 () and the difference in PSC between RT and CT trials during stabilization (M). The feature was the average of four consecutive TR samples from each of the RT and CT trials, beginning two points (i.e., 2 TR) prior to the start of the stabilization period. The M feature was computed as the difference between the average RT and CT PSC time series during the middle 20-s (8 TR) of stabilization. We then classified each ROI into one of two clusters based on similarity of BOLD PSC activation captured by using a k-means clustering technique (Johnson and Wichern 2002) within the Minitab 14 statistical computing environment (Minitab, College Park, PA). K-means clustering is a nonheirarchical clustering technique used to classify data into K groups when the groups are initially unknown. Data were assigned to a cluster by minimizing the Euclidain distance within the feature space between the data and corresponding cluster centroids, which are initialized a priori but are allowed to adapt. Values of extracted from the primary sensorimotor cortex and presupplementary motor area (pre-SMA)/rostral cingulate zone (RCZ) ROIs were used to define the initial value of the centroids of the clusters. Results of the cluster analysis were insensitive to the particular seed regions selected initially.
Because stability of control requires that parametric adjustment of an optimal feedback controller progress more slowly than error compensation provided by the controller itself (cf. Widrow and Walach 1996), we performed a final set of BOLD correlation analyses designed to identify brain regions explicitly involved in the moment-by-moment and long-term correction for kinematic and/or kinetic performance errors. Here, we performed four separate AFNI regression analyses using input reference functions corresponding to the magnitude of RMSTR(q(mN)), RMSTRIAL(o), and RMS torque both on a trial-by-trial and TR-by-TR basis (ErrorTrial, ErrorTR, TorqueTrial, and TorqueTR). Each input reference function was created from error or pressure data measured during the corresponding stabilization run within the MR-scanner. The value at each (TR) sampling instant for reference functions that varied on a trial-by-trial basis was set equal to the RMS error (torque) value computed throughout the corresponding stabilization period (30-s integration window). The value at each (TR) sampling instant for reference functions that varied on a moment-by-moment basis was set equal to the RMS error (torque) value computed during that TR sampling period (2.5-s integration window). In all cases, the input reference time series were convolved with a -variate function to model the temporal filtering properties of the hemodynamic response. Additional reference time series separately identifying periods of RT and CT stabilization were included in each AFNI regression analysis to account for the average activation due to stabilization within each task. Again, time series of head motion indices were included in the model of resting baseline to reduce the potential for false positives due to stimulus correlated motion. The resulting functional images were interpolated, co-registered, warped into a sterotactic coordinate frame, and blurred to facilitate between subject analyses as described above. Voxelwise, one-sided t-tests were used to identify regions where activation was significantly modulated by ErrorTrial, ErrorTR, TorqueTrial, or TorqueTR. In each case, we restricted our search to regions identified as being active during stabilization against either RT or CT perturbations (Fig. 5A), thus applying a reduced cluster size threshold of 262 µl (2.6 voxels) and individual voxel statistical threshold of P = 0.005 to correct for multiple comparisons in the AFNI statistical analyses.
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FIG. 5. A: functional activation maps for the study population showing the regions of interest (ROIs) activated during periods of either random (RT) or constant (CT) torque stabilization. Colored areas indicate regions that were shown to be significantly active in these contrasts at the P < 0.05 level of significance (corrected for multiple comparisons). ROIs categorized into cluster I by the k-means cluster analysis are shown in red and ROIs placed into Cluster II are shown in blue. Top: activations mapped onto inflated representations of the cerebral hemispheres; bottom: subcortical activations in the basal ganglia and thalamus (left: z = 6; center-left: z = 1), anterior cerebellar cortex (center right: z = –12), and right dentate nucleus (right: z = –20). B: Percent signal change (PSC) and cluster analysis of blood-oxygen-level-dependent (BOLD) time series from RT (black lines) and CT (gray lines) stabilization periods. The shaded regions about each time series indicate ±2 SE. The scale bars above the PSC plots indicate the time intervals used to calculate the components of = {, M}. The feature was the average of 4 consecutive data points from each of the RT and CT trials, beginning 2 points prior to the start of the stabilization period. The M feature was computed as the difference between the average RT and CT PSC time series during the middle 20 s (8 TR) of stabilization. Two distinct patterns were observed. ROI time series taken from M1/S1/PMd are representative of the first pattern of activation (cluster 1). Activations in cluster 1 are sensitive to perturbation type in that RT trials show increased activity over the duration of the 30-s stabilization period, whereas CT trials have increased activation only at the beginning and end of the trials when the hand is moved passively and the perturbation changes ON-OFF state. Activities taken from the right middle frontal gyrus (BA 46) are representative of the second pattern of activation (cluster 2). Cluster 2 ROIs show parallel increases in BOLD PSC with little differentiation between RT and CT stabilization trials. Many of these regions are also insensitive to passive movement. The ROI time series taken from the cerebellum (CBLM) is representative of ROIs near the boundary between cluster groups 1 and 2. These ROIs show some sensitivity to stabilization type, but little sensitivity to passive movement. C: group separation and membership for the 18 ROIs considered in the k-means cluster analysis was visualized by plotting the coordinates of each ROI within the –plane. Cluster 1 membership is indicated by the red circles and cluster 2 membership is indicated by the blue squares (see also Table 1 for a description of ROI location). Regions showing significant sensitivity to passive movement, i.e.,  0, are indicated by filled symbols. These include left M1/S1/PMd, IPL (BA 5 and 7), and SMA/CCZ. M1/S1, primary sensorimotor cortex; L IPL (5,7), left inferior parietal lobule (BA 5,7); L IPL (40), left inferior parietal lobule/supramarginal gyrus; L ANG, left angular gyrus/inferior parietal lobule; SMA, supplementary motor area; CBLM, bilateral cerebellar cortex and right dentate nucleus; BG, basal ganglia and thalamus; R BA 46, right middle/inferior frontal gyrus; pSMA/RCZ, presupplementary motor area and rostral cingulate zone; R IPL (40), right inferior parietal lobule/supramarginal gyrus.
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Statistical testing of behavioral and EMG performance measures
Objective RMS stabilization errors were averaged within subject by trial type. Planned, paired t-tests were used to test the null hypothesis that the mean RMS errors from the two stabilization conditions were equal (CT – RT = 0) in both the MRI and mock-MRI experimental sessions. Next for each subject and each load condition, we averaged the objective RMS stabilization errors from the first two trials, the second pair of trials, on up to the last pair of trials (trials 9 and 10) to yield five average RMS error scores. We then used separate, repeated-measures ANOVA for the RT and CT tasks to determine whether task performance improved throughout the experimental sessions. At the wrist, planned, paired t-tests were used to evaluate differences in the magnitude of FCR and ECR muscle activity between RT and CT stabilization tasks. We then computed five estimates of average muscle co-activity values about the wrist, elbow, and shoulder joints: phase 2 (averaged across CT and RT trials), phase 3 (RT), phase 3 (CT) as well as during phases 4 and 5 (both of which were averaged across CT and RT trials). At each joint, one-way, repeated-measures ANOVA were used to test the null hypothesis that the mean muscle co-activity was equal across phases 2–5 of the stabilization trial. Dunnet's post hoc t-test was used to detect significant differences in co-activity during phases 2–4 from the control condition (i.e., from phase 5). Post hoc Tukey t-tests were also used to compare RT stabilization co-activity values across joints. Finally, we performed three-way, repeated-measures ANOVA and post hoc Tukey t-tests to compare muscle excess-activity, ExA, across trial phases (2–5), muscles, and load type at the wrist. Statistical testing was carried out within the Minitab computing environment (Minitab). Effects were considered statistically significant at the 
= 0.05 level.
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RESULTS
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The frequency content of both the constant and random pressure perturbations were within the bandwidth of the manipulandum (Suminski and Scheidt 2004), and thus the perturbations were consistent from one trial to the next within subjects and across the study population (see Fig. 2A, PCT and PRT). Despite this consistency, wrist angle time series were highly variable both within and across subjects. During stabilization, wrist angle deviations from the target were variably compensated within a given trial, and subjects were able to recover the desired reference position only on average across many trials (Fig. 2B). Not unexpectedly, subjects were less able to maintain steady hand posture while perturbed by band-limited pseudo-random torques than by constant torques. Planned, paired t-test found greater objective RMS wrist angle errors in RT versus CT trials in both the MRI (T10 = 7.62, P < 0.0005) and mock-MRI (T10 = 11.94, P = 0.0005) experimental sessions. Thus subjects demonstrated similar behavioral trends in both sessions. In the MRI session, RMS wrist angle errors did not differ as a function of trial number, providing no evidence of performance improvements with extended practice in either stabilization task [CT: F(4,41) = 0.81, P = 0.524; RT: F(4,41) = 0.37, P = 0.823]. We also observed significant positional drift over time in 91% of RT trials (88 of 96) and 75% of CT trials (72 of 95). The absolute magnitude of this drift was 0.155 ± 0.104°/s and 0.017 ± 0.028°/s in the RT and CT cases, respectively. In both torque conditions, the drift was evenly distributed about the target angle and appeared to vary randomly from one trial to the next. Similar trends were observed in the mock-MRI experimental session.
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FIG. 2. A: measured bellows pressure for both a representative single subject (top, S3) and the subject population tested within the MR scanner experiment (bottom, MRI) while stabilizing against either constant (PCT) or random (PRT) torque perturbations. B: wrist angle error for both a representative single subject (top, S3) and the population of the MR scanner experiment (bottom, MRI). For the representative subject (top), light gray lines indicate the subject's performance on individual trials, whereas heavy black lines denote the mean performance of the subject across trials. For the population (bottom), light gray lines indicate the mean performance of individual subjects and the heavy black lines denote the mean performance of the population.
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In the MRI experimental session, we observed discrete corrective movements (Fig. 3A) in joint angle trajectories in 95% of RT trials (91 of 96) and 19% of CT trials (18 of 95). Of these corrective movements, 51% and 62% were flexion movements in the RT and CT cases, respectively. The majority of corrective movements were directed appropriately to reduce positioning errors (56.1 and 80% of movements in the RT and CT cases, respectively; (shaded bars in Fig. 3, B and C, represent correctly directed movements). Again, similar trends were observed in the mock-MRI session.
Figure 4A compares kinematic performance and EMG activity during both RT and CT stabilization for a representative subject in phases 2–5 of the mock-MRI experiment. Preprogrammed (commanded) changes in wrist torque (
c) elicited changes in wrist angle () and wrist angular velocity (d/dt) despite instruction to hold the hand steady at the target angle. Whereas the RT perturbations in phase 3 elicited joint angular deflections that persisted throughout the stabilization period, averaging 16.95 ± 3.07° peak to peak across subjects and trials, CT perturbations elicited only transient hand deflections averaging 6.07 ± 2.07° peak to peak across subjects and trials. At the wrist, increases in both flexor FCR and extensor ECR activities were consistently observed in both torque conditions. Across subjects, average FCR activity increased by 13.1 ± 5.3% of its MVIC level in the RT case relative to rest, whereas it increased by only 7.4 ± 3.5% in the CT case. Activities were greater in the RT case (T10 = 6.35, P < 0.0005). Average ECR activity increased by 10.6 ± 4.3% of its MVIC level in the RT case relative to rest, whereas it increased by only 2.6 ± 1.5% in the CT case. Again, activities were greater in the RT case (T10 = 7.18, P < 0.0005). Not surprisingly, the variability of EMG activity was also consistently much greater in the RT case for both muscles. Although not shown in Fig. 4, we similarly examined the synergistic flexors FDS and FCU and found their patterns of activation to mirror those of FCR closely. Cross-correlation analyses between the activations of FCR and each of these flexors yielded single peaks with no time lag. Similarly, the synergistic extensors EDC and ECU mirrored activations in ECR closely. Consequently, we limited further analysis of wrist musculature to FCR and ECR.
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FIG. 4. EMG and kinematic data demonstrated that subjects utilize a combination of reciprocal activation and antagonist muscle co-activation to stabilize the wrist. A: kinematic and EMG data from a representative subject during phases 2–5 (bar at top) of RT (black traces) and CT (gray traces) trials in the mock-MRI experiment. Phase 2 consists of the time period where subjects are passively moved from a resting position (40° wrist flexion) to the stabilization target angle (20° wrist flexion) 3 s prior to the start of stabilization. During phase 3, subjects actively stabilize their wrist for 30 s against either a CT or RT perturbation. Phase 4 consists of passive movement of the subject from the stabilization target angle back to the resting position, and during phase 5, subjects are instructed to rest quietly. Preprogrammed changes in commanded wrist torque (c) elicited changes in wrist angle () and wrist angular velocity (d/dt) despite instructions to hold the hand steady at the target (initial) joint angle. Note the rapid changes in and d/dt during phases 2 and 4 while subjects are being passively moved. EMG activity in both flexor carpi radialis (FCR) and extensor carpi radialis (ECR) was negligible during the passive movement and resting states (phases 2, 4, and 5). Activity in both FCR and ECR was significantly greater in RT trials as compared with CT trials. In the CT case, transient increases in co-activation C measured at the onset of stabilization approach a steady-state value within 500 ms. In the RT trials, co-activity C increased dramatically over both baseline and that estimated during CT trials. Time series of selected kinematic and EMG variables on an inflated time scale (gray bar in A) is shown in A, right. Rapid increases in commanded wrist torque c elicit rapid wrist extensions (dashed vertical markers) that led increases in FCR activity and decreases in ECR activity, both with relatively short latencies (single arrowheads). Increased extensor activity consistent with a strategy of increased co-activity is evident much later (asterisk). B: averaged population data demonstrated that antagonist muscle co-activity C was greatest at the wrist during stabilization (phase 3) in both torque tasks as compared with rest (phase 5) although it was significantly greater in RT trials compared with CT trials. Co-activity was also present at the elbow and shoulder but only during RT trials. Co-activity at the wrist was greater than at the elbow and shoulder on RT trials. Horizontal bars indicate significant differences between stabilization conditions at the P < 0.05 level. Hatched bars, estimates of co-activity during phases 2, 4, and 5; white and gray bars, estimates of co-activity during periods of active stabilization (phase 3) against CT and RT, respectively. Error bars indicate ±2 SEs of the mean. C: cross-correlation analysis of d/dt and FCR (black area) or ECR (gray area) for a representative subject. Significant correlations were observed with EMG both lagging/leading changes in d/dt. The horizontal dashed lines represent the 95% confidence interval about 0 correlation. Peaks of significant FCR correlations with d/dt were observed at –320 ms (b) and 60 ms (d), and the peaks of ECR correlations with d/dt were observed at –310 ms (a), 23 ms (c), and 405 ms (e).
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At the wrist, ANOVA and post hoc Dunnet's t-tests found a significant effect of trial phase on muscle co-activity values [F(4,66) = 61.33, P < 0.0005; Fig. 4B] with elevated co-activation during phase 3 in both torque conditions relative to rest (phase 5; RT: P < 0.0005; CT: P = 0.031). Post hoc t-tests also showed that antagonist phase 3 co-activation at the wrist was greater on RT trials as compared with CT trials (P < 0.05). Elevations in co-activity were also observed at the elbow [F(4,66) = 11.83, P < 0.0005] and at the shoulder [F(4,66) = 8.27, P = 0.0005] but only during phase 3 of the RT condition (P < 0.0005 for both elbow and shoulder). Post hoc t-tests also found that co-activation in the RT condition was greater at the wrist than either the elbow (T10 = 6.21, P < 0.0005) or the shoulder (T10 = 5.71, P < 0.0005). There was no difference in magnitude of co-activation in the RT condition between the elbow and shoulder (T10 = 0.49, P = 0.633). No significant EMG activity was observed during passive movement (phases 2 and 4) as compared with rest at any joint.
Examination of the ExA's revealed that during stabilization, the magnitude of muscle activations potentially contributing to long-loop feedback compensation for positioning errors varied by trial phase [F(3,138) = 14.64; P < 0.0005] and muscle [F(1,138) = 22.32; P < 0.0005] and not by trial type [F(1,138) = 0.36; P = 0.552]. Post hoc Dunnet's t-test found elevated excess EMG activations during active stabilization relative to rest (phase 3: P < 0.0005) but not during periods where the wrist was passively moved (phase 2: P = 0.084; phase 4: P = 0.757). Additional post hoc Tukey's t-tests reveled that the magnitude of this phasic muscle activation was greater in the FCR that in the ECR (P < 0.0005), a finding consistent with the expected activation patterns given the biased perturbations used in this study.
We next performed a correlation analysis between changes in wrist angle and measured EMG responses in the RT condition to evaluate the contributions of reflex-mediated responses to wrist stabilization (Fig. 4C). For the wrist flexor FCR, we consistently observed increased EMG activity lagging wrist extension (i.e., increases in d/dt by the convention used in Fig. 4) by 59.0 ± 44.2 ms (range: 20–178 ms; Fig. 4C, d). The timing of this load-dependent activity was within the range of delays expected for long-loop reflex compensation for muscle stretch (Evarts and Vaughn 1978; Matthews 1981; Strick 1978). For the wrist extensor ECR, we consistently observed decreased EMG activity lagging wrist extension by 40.3 ± 23.7 ms (range: 17–93 ms; Fig. 4C, c). The sign and latency of these EMG changes are consistent with an unloading response (Sinkjaer et al. 2000). We also observed an increase in ECR activity with lag of 484.8 ± 118.3 ms (range: 387–675 ms; Fig. 4C, e) in 5/10 subjects. This later response is consistent with a strategy of voluntary co-activation about the wrist because no contemporaneous decrease in flexor activity is observed. Finally, for both muscles, we observed that EMG led d/dt by 350 ms (Fig. 4C, a and b); this correlation between changes in EMG activity and later changes in d/dt are obligated by the compliance of the pneumatic device. These relationships are evident on closer examination of individual EMG responses (Fig. 4A, right, inflated time scale).
Functional imaging
We sought to identify the neural correlates of wrist stabilization mediated by long-loop reflex (feedback) control and muscle co-activation strategies during experiments conducted within the MR scanner. Changes in BOLD signal intensity relative to rest were correlated with either periods of deterministic CT or unpredictable RT wrist stabilization in many cortical and subcortical regions thought to contribute to movement and stabilization of the upper extremity (Table 1, pcorrected < 0.05; Fig. 5A). We examined the time series of BOLD activations for each ROI identified in Table 1 during stabilization against each load type for each subject. Two basic patterns of activation became evident. The first, an example of which is shown in Fig. 5B for the ROI spanning the primary sensorimotor cortex (M1/S1), was characterized by sustained increases in percentage BOLD signal change (PSC) during RT stabilization and only transient increases in PSC during passive movement of the hand (i.e., starting before the onset of CT stabilization). The second pattern, an example of which is shown for the right middle frontal gyrus (BA 46), was characterized by a parallel increase in PSC during both stabilization conditions with no sensitivity to passive movement. A third example, shown for the cerebellum ROI, shows some sensitivity to stabilization type, but little sensitivity to passive movement. Using features which quantify these subjective observations, we performed a k-means cluster analysis to identify those ROIs demonstrating common patterns of sensitivity to trial type and/or passive movement (see METHODS). Two distinct groups were identified: those ROIs that were sensitive to both trial type and passive movement (cluster 1; red regions in Fig. 5A), and those demonstrating increased BOLD activation over the duration of the trial, and largely insensitive to passive movement (cluster 2; blue regions in Fig. 5A). Group separation and membership was visualized by plotting the coordinates of each ROI within the plane (Fig. 5C) where cluster 1 membership is indicated by red circles, and cluster 2 membership is indicated by blue squares (see also Table 1). Cluster 1 contained ROIs located in left precentral, postcentral, middle frontal gyri, including primary motor cortex (M1, BA 4), primary sensory cortex (S1, BA 2 and 3), and the dorsal premotor cortex (PMd). Additional cortical ROIs with membership in cluster 1 included the left superior/inferior parietal lobule (SPL/IPL, BA 5 and 7), right precentral gyrus (PMd and PMv, BA 6), middle temporal gyrus (MTG, BA 39), bilateral medial frontal gyrus (SMA, BA 6), and the caudal cingulate gyrus (CCZ) (Picard and Strick 2001). There were no subcortical ROIs contained in cluster 1. Cluster 2 contained ROIs located in right PMd, bilateral SMA, and the cingulate gyrus (although these ROIs were located rostral to the vertical anterior commissure line and are thus considered to be located in pre-PMd), pre-SMA, and the rostral cingulate zone (RCZ) (Picard and Strick 2001). In addition, Cluster II contained cortical ROIs located in the left superior frontal gyrus (SFG, BA 8), medial frontal gyrus (MeFG, BA 8), angular gyrus, bilateral IPL (BA 40), middle frontal gyrus (MiFG, BA 10 and 46), inferior frontal gyrus (IFG, BA 45, 46 and 47), supramarginal gyrus, superior temporal gyrus (STG, BA 22) and insula (BA 13). Cluster II contained subcortical ROIs in the basal ganglia, cerebellum, and thalamus. The ROI in the left basal ganglia included activation of the putamen, globus pallidus, and caudate body, whereas the cerebellar ROI was localized in the bilateral cerebellar cortex lobules IV, V, and VI (Schmahmann 2000), vermis, and the right dentate nucleus (DN). ROIs in the thalamus exhibited activations in the left ventral posterior lateral (VPL) nucleus, ventral lateral (VL), ventral posterior medial (VPM), ventral anterior (VA), medial dorsal (MD) nuclei, and left pulvinar. Regions showing significant sensitivity to passive movement, i.e., 0, [F(18,162) = 2.37; pcorrected <0.05] are indicated by filled symbols. These include left M1/S1, SPL/IPL (BA 5 and 7), and SMA/CCZ. The left M1/S1, SPL/IPL (BA 5 and 7), and right STG (BA 39) were also significantly modulated by task type, with M >0 [F(18,162) = 3.94; P < 0.05].
We next evaluated whether the activations identified in the preceding analysis were related to compensation for kinematic errors, generation of wrist torques, or both. We constructed contrasts wherein one-sided t-test (corrected for multiple comparisons) evaluated whether BOLD activation changes were correlated with RMS state estimation errors or RMS torques either on a trial-by-trial or a TR-by-TR basis (ErrorTrial, ErrorTR, TorqueTrial, and TorqueTR; Fig. 6). The ErrorTrial contrast (Fig. 6; blue regions; pcorrected <0.05) reveals areas of activation in the left IPL (BA 40, A and B), angular gyrus (A and B), RCZ (BA 32, A), and right MiFG (BA 46, C). Alternatively, the ErrorTR contrast (Fig. 6; red regions; pcorrected <0.05) reveals areas of elevated BOLD activation in the left precentral (M1, BA 4), postcentral (BA 3 and 5), middle frontal (PMd, BA 6), right precentral gyri (PMv, BA 6), bilateral SMA proper and CCZ similar to regions with membership in cluster 1 (A–C). Additional cortical activations were observed in right MeFG (BA 8, C), supramarginal gyrus, IFG (BA 46, C), STG (BA 22, C), bilateral IPL (BA 40, B and C), and insula (BA 13, C). Subcortical activations were observed in the left putamen, globus pallidus (medial and lateral), VL, VPL, VPM nuclei of the thalamus (D), the right cerebellar cortex (lobule IV, V, and VI; E and F) and the red nucleus. Activation of the left IPL in the ErrorTrial contrast were located more posterior to those activated regions of IPL in the ErrorTR contrast. There were no overlapping activations in the ErrorTrial and ErrorTR contrasts. Importantly, no regions were found to be active in contrasts examining differences between TorqueTrial or TorqueTR and resting baseline.
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FIG. 6. Functional activation maps for the study population showing those regions wherein the BOLD response correlated significantly (P < 0.05, corrected for multiple comparisons) with state estimation errors on both a moment-by-moment (ErrorTR, red regions) and a trial-by-trial basis (ErrorTrial, blue regions). Activations in the ErrorTR contrast are localized primarily within regions identified to have membership in cluster 1 including the left precentral (M1, BA 4), postcentral (BA 3 and 5), middle frontal (PMd, BA 6), right precentral gyri (PMv, BA 6), bilateral SMA proper and CCZ (A–C). Other cortical and subcortical activations correlating with moment-by-moment changes in error were observed in cluster 2 regions including the right MeFG, supramarginal gyrus, IFG, STG, cerebellar cortex (E; z = –13) bilateral IPL, insula, and left VL, VPL, VPM nuclei of the thalamus (D; z = 9). Alternatively, the ErrorTrial contrast reveals only cluster 2 cortical activation in the left IPL, angular gyrus, RCZ (BA 32, and right MiFG in A–C). No BOLD correlations with wrist torque were observed on either a trial-by-trial or a moment-by-moment basis.
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Although not shown, we similarly evaluated whether BOLD activations were correlated with moment-by-moment fluctuations in objective stabilization errors on a TR-by-TR basis [i.e., by replacing s(nT) with o(nT) in Eq. 7]. We found activation regions similar to those displayed in red in Fig. 6, although activation within M1 did not reach statistical significance using the thresholding and clustering criteria reported above. In contrast, when we evaluated whether BOLD activations were correlated with subjective stabilization errors on a trial-by-trial basis RMSTRIAL(s), we found no regions with significant activation. Thus distinct neural networks appear to be involved in the processing of separate estimates of kinematic performance errors over short and long time frames.
Next, we performed a BOLD contrast highlighting brain regions that were differentially active in the random and constant torque stabilization tasks. Based on the behavioral and EMG results obtained in the mock-MRI study, this contrast is expected to identify brain regions acting to elevate peripheral joint co-activity as well as regions involved in the feedback stabilization of the wrist, exclusive of regions contributing similarly to the compensation for average extensor torques which were common to both tasks. A mixed-model ANOVA treating subjects as the random factor contrasted the BOLD responses to stabilization against random and constant torque perturbations (RT > CT, Fig. 7, purple ROIs; pcorrected <0.05). This contrast reveals areas of elevated activation in left primary sensorimotor cortex extending into PMd proper, rostral portions of IPL (BA 40) and SPL (BA 5), and bilateral SMA proper and CCZ (A–C). Additional cortical activations are also observed in the left STG (BA 41, A), right primary sensorimotor cortex (B and C), PMd, and the bilateral insula (A and C). Subcortical activations were observed in the left MD, bilateral VL, VPL, and VA nuclei of the thalamus (D) and bilateral basal ganglia (caudate/putamen/globus pallidus; left hemisphere activation shown in D), and the left red nucleus. Bilateral activations are observed in anterior cerebellar cortex (E and F), while activation is also evident in the right dentate nucleus (F). In contrast, no regions of elevated activity were found in a mixed-model ANOVA contrasting CT > RT.
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FIG. 7. Functional activation maps for the study population showing the RT > CT (purple regions) and the RTM and CTM contrasts (green regions). Colored areas indicate regions that were shown to be significantly active in these contrasts at the P < 0.05 level of significance (corrected for multiple comparisons). The RT > CT contrast reveals activations largely lateralized in left primary sensorimotor cortex (extending into PMd proper), SMA proper, and rostral portions of IPL in A and B. Activations observed in the RTM AND CTM contrast were observed in left pre-PMd, inferior frontal gyrus, bilateral pre-SMA, superior/middle temporal gyrus, and the right superior frontal gyrus as shown in A–C and are representative of the generation of discrete corrective movements. Activations in the RT > CT contrast also include subcortical regions: left thalamus and basal ganglia (D; z = 7), bilateral anterior cerebellar cortex lobule IV, V, and VI (E and F, z = –10), and the right dentate nucleus (F, z = –20).
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Finally, we performed an analysis highlighting brain regions that were commonly active throughout the stabilization period in both torque conditions, disregarding activations that may have arisen due to passive movement of the hand and/or other transient events at the beginning and end of trial phase 3. Therefore we limited this final BOLD analysis to the middle third of the stabilization time series. We constructed a contrast including all voxels wherein t-test (corrected for multiple comparisons) found BOLD activations to exceed their resting values. We then identified those regions having similar activations in both stabilization conditions (i.e., the intersection of the group activation maps for RTM and CTM; Fig. 7, RTM and CTM; green ROIs, pcorrected <0.05). This contrast reveals areas of elevated activation in left pre-PMd, IFG (BA 10, 45, 46, and 47; A), STG (BA 22, A), bilateral IPL, STG, MTG (A and C) pre-SMA (A–C), and right superior frontal gyrus (BA 8, B).
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DISCUSSION
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Subjects performed two sets of identical experiments, both in and out of a MR scanner, wherein they stabilized their wrists against predictable and unpredictable torque perturbations. Kinematic and EMG data revealed that subjects compensated for the different perturbations using a flexible combination of three simple strategies. These included: the modulation of joint viscoelasticity via co-activation of agonist/antagonist muscle pairs spanning the wrist, elbow, and shoulder joints, spinal and supraspinal reflex action, and the generation of discrete corrective movements. Whereas cocontraction and spinal reflex action are considered to be peripheral response mechanisms that can be influenced by descending motor commands (Porter and Lemon 1995), supraspinal (long-loop) reflex action and the generation of discrete corrective movements both depend on transcortical neural processing (Evarts and Tanji 1976; Haaland and Harrington 1989; Strick 1983). We sought to test the hypothesis that during stabilization of the wrist, distinct neural mechanisms contribute to the closed-loop feedback control of wrist position and the evaluation/adjustment of controller parameters (e.g., the desired reference wrist angle) in response to persistent performance errors. We expected that BOLD signal activity in regions mediating on-line feedback control of endpoint position would be sensitive to moment-by-moment variations in state estimation errors, not torque, consistent with their putative role in the optimal feedback control of movement (Scott 2004). We expected BOLD signal activity in brain regions that monitor and adjust the performance of the feedback controller to be sensitive to variations in performance errors over a much broader time frame. This prediction derives from the control theoretic consideration that stability of nested control loops (such as those proposed here) requires that supervisory updating of behavioral goals and/or feedback control laws must have lower bandwidth (i.e., process information at a slower rate) than the closed-loop feedback controller being monitored and adjusted (cf. Widrow and Walach 1996
TOP
ABSTRACT
INTRODUCTION
) and (C) in 56.1% of RT trials wherein discrete movements were observed (
). In both cases, incorrectly directed movements are superimposed (not stacked) in white.