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1Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College, Hanover, New Hampshire; 2Department of Physical Therapy, Steinhardt School of Education, New York University, New York, New York; and 3Department of Psychology, Sage Center for the Study of the Mind, University of California–Santa Barbara, Santa Barbara, California
Submitted 17 April 2006; accepted in final form 15 December 2006
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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; Shadmehr and Holcomb 1997, 1999). Recruitment of frontal–parietal, basal ganglia, and cerebellar networks is consistent with a breadth of studies using various visuomotor learning/calibration paradigms (Desmurget et al. 2000; Floyer-Lea and Matthews 2004; Ghilardi et al. 2000; Graydon et al. 2005; Imamizu et al. 2000; Inoue et al. 2000; Krakauer et al. 2004; Martin et al. 1996; Miall and Jenkinson 2005; Smith and Shadmehr 2005) implicating these areas in forming specific functional interactions for adaptive motor control.
The degree of anatomical and functional segregation within this network for different forms of adaptation remains poorly understood. A recent blocked functional magnetic resonance imaging (fMRI) study partly addressed the issue of anatomical specificity to on-line error detection/correction as subjects made 2D reaching movements during directionally inconsistent viscous and visual perturbations (Diedrichsen et al. 2005). The authors noted negligible anatomical segregation between regions activated for "kinematic" and "dynamic" perturbations—that is, the motor cortex, secondary somatosensory cortex, a region along the postcentral sulcus, area 5, and cerebellar lobules V and VIII. Although novel and interesting, the blocked nature of their design did not allow for analysis of functional neural interactions and, although the use of inconsistent perturbations addressed on-line control, they could not attest to issues related to adaptation.
We thus used an event-related fMRI design to investigate whether distinct functional interactions, rather than anatomical segregation, might underlie acquisition of different adaptive behaviors. Subjects performed a target-capture task while adapting to randomly presented position- or velocity-dependent torque perturbations. Unlike the study of Diedrichsen et al. (2005), we kept the mapping between cursor and forearm movement constant (thus avoiding issues pertaining to kinematic vs. dynamic adaptation) but instead manipulated the torque perturbation to the forearm. We expected that any learning associated with the task should be attributed to temporal changes in neural activation patterns. We also hypothesized that if subjects showed evidence of developing perturbation-specific control policies, then this too should be reflected in neural activation—either by the involvement of distinct anatomical regions for each perturbation condition or by the involvement of distinct functional interactions among common regions. We tested these two predictions by analyzing the imaging data using standard subtraction techniques (which would reveal anatomical segregation) and by comparing the blood oxygenation level–dependent (BOLD) signal coherence between common distributed sensorimotor regions (which would reveal distinct functional interactions). We suspected that the anatomical recruitment of cortical and subcortical sensorimotor regions for adaptation to position- and velocity-dependent perturbations should be rather similar, in part based on the finding of Diedrichsen et al. (2005) of modest differences in anatomical segregation between adaptation for kinematic versus dynamic perturbations. However, we hypothesized that a comparison of trial-to-trial BOLD coherence across a set of brain regions for the two types of perturbations might reveal distinct functional neural interactions at the network level.
We investigated two issues. First, we asked whether specificity in functional interactions would be observed during movement preparation versus adaptation. For this, we provided subjects with a perturbation-specific cue during a preparatory period, followed by a "NoGo" (preparation trials) or "Go" (preparation + adaptation trials) signal. We hypothesized that contextual cuing during preparation engages only common functional circuits related to arbitrary visuomotor association processes but implementation of the adaptive strategy (prepare and adapt) necessitates perturbation specificity. Second, we investigated learning-dependent changes in functional interactions. We hypothesized that preparation and adaptation in the latter stage of the learning process would be associated with a relative increase in coherence within a focused set of neural regions involving the frontoparietal cortex, cerebellum, and basal ganglia.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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Fifteen right-handed (Oldfield 1971) individuals (mean age ± SD; 24.7 ± 3.7; 11 females, four males) with no history of neurological impairment participated after signing informed institutional consent.
Setup and procedure
Subjects held a handle connected by a 10-ft. (3.05 m) Delrin rod to a torque motor (Model #SM233BEN16N, Parker Automation), secured against the wall in the scanning suite (Fig. 1 A).
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The cycle from the start of one trial to the start of a subsequent trial consisted of: 2 s to allow the subject to align the cursor to the start position, 2 s to allow the subject to view the target positions, a maximum of 2.5 s for movement execution, and 2 s for knowledge of results. After the end of the feedback epoch, a new start position was immediately specified and a new trial cycle began.
Perturbations and training
Subjects first trained on a null perturbation (motor disabled) for 60 trials (null-1). Immediately afterward they were exposed for 160 trials to one of two equally probable perturbations, presented in a random fashion. One perturbation type was a position-dependent perturbation (100 oz-in. [0.8 Nm] torque in the clockwise direction) enabled if the handle orientation was within ±31.5° from the midway angle between vectors |<|\IMG BORDER=0 WIDTH=6 HEIGHT=3 SRC="$FIGDIR/medium/cjs1164.gif" ALT="Formula "$INLINEFIG_ALIGN|<|CTf|>||>| and |<|\IMG BORDER=0 WIDTH=6 HEIGHT=3 SRC="$FIGDIR/medium/cjs1164.gif" ALT="Formula "$INLINEFIG_ALIGN|<|CTr|>||>|(torque-field range, 63°). Note that our use of the term "positional" refers to this step torque that was enabled and disabled when the specified position was reached. Note too that because subjects’ movements were bidirectional (a counterclockwise out movement and a clockwise return movement) and because the positional perturbation was unidirectional in the clockwise direction, the perturbation was resistive to the subjects’ "out" movement component but assistive for the return component. The other was a velocity-dependent perturbation, a positive viscous torque proportional to the subjects’ velocity (–0.5 oz-in.·deg–1·s–1 [0.004 Nm·deg–1·s–1]). Given that subjects’ movement speed ranged between 150 and 250 deg/s, the peak torque applied to the arm was, on average, similar between the perturbation conditions. After these 160 trials, they completed another null block of 60 trials (null-2). Note that the actual mapping between the handle (forearm) orientation and the cursor remained constant across the experiment. The only component that was perturbed was torque. The moment of inertia of the motor rotor was 9.3 x 10–5 kgm2. The moment of inertia of the rod was 1.6 x 10–3 kgm2 (weight: 2 kg; diameter: 4 cm). The rod was supported at each distal end by an Accrolon 9000 series nonmetallic self-lubricating sleeve bearing (Accro-Seal) and at its middle by a custom-designed plastic ball-bearing, making any friction minimal.
Randomly interspersed "NoGo" trials constituted 25% of each block. The motor was shut off on NoGo trials (i.e., it did not apply a force onto the subjects). Behavioral data, collected on Go and NoGo trials, confirmed that during NoGo trials subjects maintained a static orientation. We cannot exclude the possibility that subjects increased grip force in the NoGo trials, but this seems highly unlikely because the motor was shut off on these trials.
Behavioral measures
Custom-written software was used for graphics, data acquisition (LabVIEW 7; National Instruments, Austin, TX), and analysis (Matlab, The MathWorks, Natick, MA). Motion was sampled at 1,500 Hz using a 4,000-line optical encoder and low-pass Butterworth filtered (10-Hz cutoff) off-line. The movement "out" and movement "back" components were separated by the reversal position. The position data for each movement component were differentiated and movement onsets and offsets were defined. Movement onset for each component was defined as the time at which the velocity exceeded and remained >5% of the peak velocity for >100 ms. Movement offset for each component was defined as the time when the velocity fell and remained <5% of the peak velocity for >100 ms. Four dependent measures were analyzed (pooled across the four starting locations): 1) absolute error, calculated as the absolute angular difference between the vectors |<|\IMG BORDER=0 WIDTH=6 HEIGHT=3 SRC="$FIGDIR/medium/cjs1164.gif" ALT="Formula "$INLINEFIG_ALIGN|<|CTr|>||>| and |<|\IMG BORDER=0 WIDTH=6 HEIGHT=3 SRC="$FIGDIR/medium/cjs1164.gif" ALT="Formula "$INLINEFIG_ALIGN|<|CSr|>||>|(reversal error) and between the vectors |<|\IMG BORDER=0 WIDTH=6 HEIGHT=3 SRC="$FIGDIR/medium/cjs1164.gif" ALT="Formula "$INLINEFIG_ALIGN|<|CTf|>||>| and |<|\IMG BORDER=0 WIDTH=6 HEIGHT=3 SRC="$FIGDIR/medium/cjs1164.gif" ALT="Formula "$INLINEFIG_ALIGN|<|CSf|>||>|(terminal error); 2) variable error, calculated as the SD of error; and 3) time to peak acceleration (TPA) within the first 12.5° of movement. This interval was chosen because it ensured that the positional field was not yet enabled and the viscous field was minimal. Thus TPA reflected anticipatory-based or very early control-based strategies. Data in each condition were fit with an exponential decay function defined by y = a – b * e–t/c, where a is the magnitude of a dependent variable in the last trial, b is the difference between values in the first and last trials, c is the decay constant that represents the number of trials required to attain a roughly 63.2% improvement in performance, and t is the trial number. The decay constant (c) was used as a measure of learning rate (Martin et al. 1996). Error and TPA were analyzed using two-way ANOVA with factors: learning (early, late [mean of the first and last five trials, respectively]) and perturbation (null-1, null-2, positional, viscous). Learning rate was analyzed with a one-way ANOVA with perturbation as a factor. The Student–Newman–Keuls test was used for post hoc analysis. Significance was set at P < 0.05.
Magnetic resonance imaging (MRI) protocol
Imaging was performed using a 1.5-T GE scanner with a standard birdcage head coil. Three fMRI runs, each containing 332 functional volumes were obtained (Run 1: trials 1–60 null-1 and trials 61–93 torque-field; Run 2: trials 1–93 torque-field; Run 3: trials 1–34 torque-field and trials 35–94 null-2). Transitioning between the null and the torque-field conditions within a run ensured that any differences in performance would be directly attributed to the task rather than to any block-to-block effects. The first four functional volumes from each run were discarded to allow longitudinal magnetization to reach equilibrium. The functional runs were collected with: gradient-recalled echoplanar imaging, sensitive to BOLD contrast; TR, 2,500 ms; TE, 35 ms; flip angle, 90°; field of view, 24; 26 slices with a thickness of 3.5 mm and a 1-mm gap; on a GE Signa Horizon LX 1.5 T scanner. Anatomical images collected include: coplanar T1-weighted and a high-resolution image using the SPGR sequence for use in spatial normalization.
Device shielding
Radiofrequency and electromagnetic interference between the electric motor and the scanner were minimized by several means (Chinzei 1999). 1) The motor was housed in specially constructed copper and MUMETAL nesting boxes (Magnetic Shield, Bensenville, IL). 2) The nesting box housing the torque motor was placed as far as possible away from isocenter, within the 1- to 3-Gauss range (zone 4 according to Chinzei 1999). 3) The computer (NI-PXI 8176), digital servoamplifier (Accelus ASP-180-18, Copley Controls), and power supply (PST-070-08-DP-E, Copley Controls) were placed outside the scanner suite (in the technician room). 4) All wires connecting the controllers to the motor were twisted-pair cables and triply shielded using the wires’ own shielding as well as copper mesh and MUMETAL hoses. 5) All shielding materials were earth-grounded. We verified that any device-induced interference into the functional imaging data was negligible by testing individuals (n = 3) under three task conditions as they prosupinated their forearm at a self-paced 1 Hz: 1) with the motor on applying a positive viscous torque as described above, 2) with the motor unplugged, 3) and with the motor out of the room. In each condition, subjects alternated between 20 s of movement and 20 s of rest for three cycles (TR = 2.5 s, 24 functional volumes per movement condition per subject). The imaging data were processed as described above. Qualitatively, no changes in image quality were evident in the functional volumes as a function of whether the motor had current passing through and delivered a torque or was unplugged or was entirely out of the scanning suite. This is illustrated in Fig. 2 (bottom right image in the three panels) by a sample axial slice taken from a randomly selected functional volume of one of the subject's movement conditions. To quantify this, a region of interest (ROI) was defined by a box (1 x 1 x 1 cm) in the left motor cortex based on a movement > rest contrast (Fig. 2, three panels). A signal-to-noise ratio (SNR, mean/variance) was computed within the ROIs for the second through the next-to-last functional volume of each movement-related miniblock (total, 18 volumes per condition). The SNR for each condition is plotted in Fig. 2, bottom right. Preplanned unpaired t-tests between each condition revealed no significant changes in the SNR across the three conditions (Torque vs. Unplugged: t(34) = 1.09, P = 0.29; Out of room vs. Torque: t(34) = –1.78, P = 0.08; Out of room vs. Unplugged: t(34) = –0.55, P = 0.59). These results, as well as qualitative observations, strongly suggest that any possible interference is likely to be a negligible confound to the imaging data.
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The functional imaging data were analyzed with Statistical Parametric Mapping (SPM2; Wellcome Department of Cognitive Neurology, London, UK). Motion correction was performed for each subject to the first functional volume, using a six-parameter rigid-body transformation, and a mean of the corrected images was created. The mean functional image was coregistered to the subject's coplanar T1-weighted image and subsequently coregistered to the subject's high-resolution SPGR anatomical image, using mutual-information coregistration to the high-resolution anatomical scan. The high-resolution anatomical scan was spatially normalized to the Montreal Neurologic Institute template (Talairach and Tournoux 1988) by applying a 12-parameter affine transformation and a nonlinear warping with basis functions. Combined transformation matrices were applied to the functional images. The spatially normalized functional images were smoothed using a 6-mm Gaussian kernel.
Condition-specific differences in the BOLD signal were analyzed with a general linear model approach for event-related fMRI using SPM2. A design matrix with vectors for null (combined null-1 and null-2), positional, and viscous conditions for "NoGo" and "Go" trials was created individually for each of the 15 subjects. Null-1 and null-2 were concatenated into a single column in the design matrix (one column for Go and one column for NoGo trials). This was done because we had no a priori hypotheses regarding differences between these two conditions and no significant behavioral differences were noted. However, combining the two together considerably increased the statistical power of the baseline condition in the imaging analysis. In subtraction methods for imaging analysis, having a robust baseline condition is invaluable and often neglected. Run-to-run regressors were included in the design matrix to account for any nonspecific run-to-run effects. The onset and duration of each event, obtained from time stamps recorded during the experiment, were entered into the model and convolved with the canonical hemodynamic response function (HRF). Between-condition differences in BOLD magnitude were estimated for: a) all "Go" conditions > rest, b) all "NoGo" conditions > rest, c) positional-null > viscous-null, and d) viscous-null > positional-null (contrasts c and d were generated separately for "NoGo" and "Go" trials). The null condition was subtracted out in c and d, resulting in contrasts reflecting only adaptation to perturbations. Note that the NoGo trials in the null condition did not carry the same conditional aspect of motor planning that the perturbation trials had. Therefore subtraction of the null condition from the perturbation conditions ensured that these contrasts would reflect choice planning to each perturbation rather than sensorimotor transformations that remained constant across all conditions and that were not a focus of this investigation. Conversely, in the torque-field conditions, movement preparation was equally required in "Go" and "NoGo" trials, but movement implementation was unique to "Go" trials. Thus activation in the "NoGo" trials represents preparation, whereas that in "Go" trials represents preparation + implementation.
Contrast images were then passed on for random effects analysis at the group level. In addition to between-task contrasts, we correlated performance error with the corresponding magnitude of the BOLD response on a trial-by-trial basis, modeled together for the three perturbation conditions. A threshold magnitude of P < 0.001 (uncorrected) and extent of 10 voxels was used for all imaging analysis.
BOLD coherence
In addition to the traditional BOLD subtraction approach, we were interested in understanding whether functional interactions between distributed neural regions change as a function of learning, adaptive strategies, and processing stages (plan retrieval vs. execution). To address this, we enlisted a long-used approach in neurophysiology—one that has recently been applied to neuroimaging—that involves analysis of the coherence of the BOLD signal between regions of interest (ROIs that we identified from contrasts a and b). Coherence, unlike correlation, singly accounts for signal magnitude and timing, without the complication of interpreting temporal phase lags inherent in cross-correlation methods. Furthermore, coherence-based analysis allows for direct interpretation of functional interactions, which can be assumed only from traditional contrasts (Curtis et al. 2005; Miller et al. 2005; Sun et al. 2004). Finally, event-related coherence is more robust than block-averaging in characterizing learning-dependent changes because coherence captures trial-to-trial neural dynamics that are otherwise obscured by within-block averaging.
Coherence was calculated using Matlab according to the protocol described by D'Esposito and colleagues (Sun et al. 2004), where coherence (Cohxy) is the coherence between the event-related time series of two given voxels (x and y); fxy(
) is the cross-spectrum of x and y; and fxx() and fyy() represent the power spectrum of x and y, respectively, at frequency (Eq. 1). Typically, coherence among neural circuits is dependent on two factors: the bandwidth of the HRF (0–0.15 Hz; Aguirre et al. 1997) and the frequency of the events (in our case, about 0.125 Hz). We therefore chose to analyze coherence within this bandwidth. However, to tailor the bandwidth more precisely to our task while accounting for any slight fluctuations in trial-to-trial event frequency, we computed the mean coherence within a slightly narrower bandwidth (0.1–0.15 Hz). Our choice for this bandwidth is also consistent with data from a previous investigation of coherence at low- and high-frequency bandwidths (Sun et al. 2004). This study found that a low-frequency bandwidth (0–0.2 Hz) is sensitive to coherence between distributed regions, whereas a high-frequency bandwidth (0.2–0.4 Hz) is more sensitive to coherence between spatially adjacent voxels (Sun et al. 2004). Because we were interested in functional interaction between distributed regions, choosing a higher bandwidth would not be appropriate
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, although their individual events were longer than ours.
To generate a set of regions for entry into the coherence analysis we selected classic brain regions known to be involved in sensorimotor control from among those activated in the "Go" > rest (contrast a) and "NoGo" > rest (contrast b) contrasts (see Fig. S1). Two subcortical regions were also added: the left posterior putamen, which was significantly activated in the viscous > positional contrast, and the right dentate. This is justified given the known involvement of these nuclei in motor control. The set of 20 regions is itemized in Table 2. The coherence analysis was performed on a subject-by-subject basis, thus generating 15 coherence values (one per subject) for each pairing of the 20 ROIs, each perturbation condition (positional and viscous), each trial type ("NoGo" and "Go" trials), and for each learning stage (early and late thirds).
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This procedure involves three steps and each step is illustrated in Fig. 3 using a small hypothetical data set. The first step involves organizing the data into a symmetric matrix wherein each cell of the matrix represents the coherence magnitude between imaginary regions a, b, c, d, and e. In Fig. 3 (step 1) each cell is color coded to the magnitude of the coherence value (blue to red corresponding to 0–1 range), with the value also printed into each cell. Note that the diagonal is all ones, indicating that each region is perfectly coherent with itself. We then averaged together all of the symmetric 20 x 20 matrices of coherence values for the 15 subjects to generate a group mean coherence matrix. This was done separately for each task and training period.
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). Note that the distances were calculated across the rows in the matrix, so the distances for any two coherence pairs become normalized to all other pairwise observations. In Fig. 3A, when the example matrix is evaluated with the pdist.m function the result is Fig. 3B. This set of pairwise distances is then passed through the Matlab function linkage.m to create a heirarchical cluster tree that is plotted as a dendrogram using the Matlab function dendrogram.m. In the dendrogram (Fig. 3, step 3) the linkage, or dissimilarity score, is plotted along the x-axis and the regions plotted along the y-axis. Smaller scores represent stronger linkages (or less dissimilarity). Thresholding this score (i.e., vertical line at 0.9 in this figure) allows one to cluster pairings into common families. Thus Fig. 3 (step 3) illustrates that regions c, e, and d are more tightly linked to each other (and form a common cluster shown in red) than a and b, but that b is more similar to this cluster than is a. The dendrogram provides an objective method for determining whether the overall pattern of values within the coherence matrix, across the 20 regions, changes as a function of task or learning.
We were most interested in identifying functional interactions between three major distributed systems: cortical, basal ganglia, and cerebellar. Thus we determined the lowest possible threshold (1.04) that would consistently yield between two and four clusters in all of the late-learning coherence analyses. This threshold was then used for viewing the dendrograms of all conditions. Statistical analysis of cluster solutions is not trivial because of the high dimensionality of the data [(19 x 20)/2 = 190 possible comparisons in each condition]. Multiple comparisons therefore are a suboptimal approach for quantifying differences between conditions (Rencher 2002). Instead, cluster validation is a recommended approach (Rencher 2002) wherein the validity of each cluster is substantiated. Validation of the cluster solutions was performed according to Rencher (2002) by randomly assigning each of the 15 subjects to one of two groups (group A and group B). A linkage analysis was then performed for the 20 voxels of interest on the entire functional time series in real-time (i.e., collapsed across all conditions) for groups A and B. Validity should be reflected as a consistent assignment of clusters for the A and B groups (see RESULTS). Note that this was strictly done for validation of the linkage procedure and not for interpretation of the data.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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Our intent was to train subjects to adapt to two different perturbations. After completing the experiment, each participant reported that the two perturbations were clearly distinct from one another. When asked whether they assumed a particular strategy for either torque-field condition, a typical response for the viscous perturbation was: "I just moved my arm more forcefully to get to the target." Conversely, a typical response for the positional perturbation was: "I had to control my arm from going past the target." Analysis of movement kinematics supported such subjective remarks. Figure 1B shows a representative participant's outward trajectories in the presence of a null perturbation (thick black line), positional perturbation (left), and viscous perturbation (right). A significant perturbation condition main effect for absolute error (Fig. 2, inset) [F(3,42) = 27.3, P < 0.0001, mean squared error (MSE) = 21.8] suggested that of the two, the positional perturbation was more disruptive. Additionally, the number of trials required to attain roughly 63.2% improvement in performance (learning rate) was considerably greater for both torque perturbations relative to the null condition (Fig. 4) (null-1 and null-2: nine trials; viscous: 13 trials; positional: 25 trials). This difference, however, did not reach significance [F(3,42) = 2.1, P = 0.12, MSA = 389].
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To test for direct relationships between performance and brain activity, each subject's error magnitude was correlated with the BOLD magnitude, collapsed across the null, viscous, and positional perturbation conditions (all "Go" trials). Figure 7 shows that activity in three regions was positively correlated with performance error: the right dentate nucleus (x, y, z of local maxima: 15, –51, –30), the left intraparietal sulcus (–30, –51, 54), and the left sensorimotor cortex (–27, –27, 57). In other words, as trial-to-trial error was reduced, activity within these regions also decreased, implicating their involvement in general trial-to-trial learning on our task.
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Subjects first trained in the null condition, wherein they encountered only minimal frictional resistance. The BOLD signal for the "Go" and "NoGo" trials in the null perturbation condition was contrasted with rest to define those areas involved in such a visuomotor transformation (Fig. S11 ). The "Go" > rest contrast yielded activation in cortical and subcortical motor circuits, including bilateral thalamus, bilateral anterior cerebellar cortex, bilateral posterior parietal cortex along the intraparietal sulcus, and the arm representation area of the left primary motor cortex. The "NoGo" > rest contrast yielded an activation pattern similar to that of the above contrast, but with reduced activation of the sensorimotor areas. The "NoGo" contrast additionally yielded activation of the left inferior frontal gyrus, pars triangularis, and pars opercularis that was not observed in the "Go" contrast.
After the null condition, subjects trained on the positional and viscous perturbations, each with an assigned contextual color cue. To define perturbation-specific activations, the positional and viscous conditions were contrasted against each other after subtracting out the null condition from each torque-field condition. This subtraction was intended to eliminate activations exclusively related to visuomotor transformations. It does not eliminate effects related to choosing among context-defined motor responses. The viscous-null > positional-null contrast for the "Go" trials yielded a relative increase in the bilateral putamen, caudate head, anterior cingulate gyrus, right posterior cingulate gyrus, and the left SMA (Table 1 and Fig. 8). The reverse, positional-null > viscous-null, contrast for "Go" trials showed no significant activation at this threshold. These findings suggest a limited degree of additional neural recruitment for the execution of movements in a viscous relative to positional perturbation. Aside from this additional recruitment, analysis of BOLD magnitude suggested that adaptation to either perturbation was not only attributed to overlapping regions, but also that these regions were activated to a similar extent for each perturbation condition. Moreover, neither the viscous-null > positional-null nor the positional-null > viscous-null contrast for the "NoGo" trials showed any significant activation at that threshold, again suggesting that planning for the different adaptive responses was attributed to common anatomical regions activated to similar extents across the perturbation conditions.
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BOLD coherence was analyzed to characterize functional neural interactions on a network level. All regions prescribed to this analysis are listed in Table 2. Of particular interest were interactions between cortical, cerebellar, and basal ganglia systems during 1) the early and late learning stages, 2) between each perturbation condition, and 3) for the contextually cued preparation versus the movement execution epochs of each trial. Figure 9 shows the coherence matrices for each condition with the magnitude of coherence color-coded (warmer = stronger; colder = weaker). The ROIs are labeled along the x–y axes (see Table 2 for x–y label assignments). The values along the diagonal represent a perfect coherence between a given region and itself (value of 1). The matrices were re-sorted to optimize visualization of the data. Re-sorting was performed by associating each matrix with a Laplacian matrix and then sorting the components of a specified eigenvector of the Laplacian (Barnard et al. 1995; Johansen-Berg et al. 2004). The late-learning viscous condition matrix was chosen as the canonical matrix and all other matrices were re-sorted to this ordering. Note the matrices are not symmetrical because movement preparation and execution conditions have been assigned to the top and bottom triangles of the matrix, respectively.
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Linkage analysis computes the relative proximity of each voxel-pair permutation. This proximity measure can be clustered into families sharing similar values and the hierarchical organization of this clustering can be analyzed and visualized as a dendrogram (Fig. 10; see METHODS for details). Figure 10 shows the dendrograms for each condition, the x-axis representing the measure of proximity between each pairing listed on the y-axis. Because linkage computes the distance between the permutations, smaller values along the x-axis represent stronger linkages between the respective regions and larger values represent weaker links between regions. A threshold of 1.04 (see METHODS) was used as a quantitative method of reducing the multidimensional data set into clusters (dashed vertical lines in Fig. 10). This threshold operates much like a P value in descriptive statistics in the sense that linkages falling below this threshold are automatically classified into a common family (shown as gray regions in Fig. 10). It thus follows that the strength of coherence is greater within compared with between clusters.
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The linkage results for the late learning stage in the "Go" trials were characterized by a relative increase in coherence between a premotor–parietal-–putamen network in the viscous condition (Fig. 10A, bottom right) and a premotor–parietal–cerebellar network in the positional condition (Fig. 10A, top right). The black outlines depict the grouping of the largest clusters that contributed to this pattern. Note that this does not imply a noninvolvement of the cerebellum in the viscous condition or a basal ganglia noninvolvement in the positional condition. It is clearly evident that the cerebellum eventually links up with the main family in the viscous condition, although at a more distant linkage score (and the same for the basal ganglia in the positional condition). What this implies is that a common distributed network was involved in both perturbation conditions, although the functional interactions within this common network may have subtly varied as a function of the perturbation condition.
By late learning, the number of clusters was largely reduced. In the "NoGo" trials (Fig. 10B, right), two large families distinctly formed (black outline): a frontoparietal cortical cluster and a basal ganglia cluster. Although these were separate clusters, they were more strongly linked to each other than either one with the cerebellum.
The validity of the clustering method was verified by comparing the rank order of all of the ROIs, across all events for two groups of subjects (see Rencher 2002 and METHODS). The result of this analysis is shown in Fig. 11. The overwhelming similarity in the rank order of the brain sites and their respective linkages in the dendrograms ensures that this method was valid in prescribing clusters (see also Rencher 2002).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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Subjects trained on two very distinct torque perturbations in a randomly interleaved order. Before each trial, subjects were provided with a color-coded guide uniquely linked to each perturbation. In debriefing after the experiment, subjects reported that they readily learned to associate each color with the respective perturbation. The significant reduction in performance error confirmed that individuals learned to adapt in spite of training on a random practice schedule. This was not unexpected and it was previously demonstrated that humans are flexible in learning new skills in a variety of contexts—using arbitrary visual contextual cues to learn different randomly interleaved motor perturbations (Osu et al. 2004) or learning to modulate grasp aperture to perceived sound frequency (audiomotor mapping) (Safstrom and Edin 2006). A control experiment with catch trials included for each perturbation was designed to directly rule out the possibility that subjects deployed a universal cocontraction strategy for both perturbations. Peak angular velocity and signed error were both significantly larger in the catch trials than in the torque-field trials for either perturbation condition, suggesting that at least to a considerable extent, subjects used field-specific strategies by the late learning stage. Empirical evidence supports this finding. For example, cocontraction strategies are predominantly observed only in the first few trials of learning and are generally deployed in cases of far more destabilizing perturbations than the types presented in our experiment (Milner and Franklin 2005). We subsequently discus the neural processes that may underlie the implementation of unique adaptive behaviors.
Neural interactions underlying adaptation
Standard contrasts in our study revealed excess activation, particularly in the basal ganglia and the SMA, in the viscous-positional condition contrast but no excess activation in the reverse contrast. This excess activation was evident only in the "Go" trials and not observed for either contrast in the "NoGo" trials. If analysis ended here, one would conclude that for the most part, common circuits underlie adaptation under different dynamic conditions, although something may have been inherently different about the viscous condition that necessitated additional recruitment of the SMA and basal ganglia. Such a conclusion might even be corroborated by the findings of Diedrichsen et al. (2005), who investigated neural correlates of on-line adaptation for "kinematic" versus "dynamic" perturbations. In that study, Diedrichsen et al. (2005) noted few differences in activation, in their case between a "kinematic" and "dynamic" contrast. Both our initial analysis and that of Diedrichsen et al. (2005) make the assumption that BOLD magnitude alone is sufficient to unveil dynamic, task-specific, neural processes.
However, we also investigated an alternative explanation: that analysis of the temporal dynamics of neural interactions, which are not approachable through analysis of BOLD magnitude, may provide additional information. To this end, we analyzed the coherence of the BOLD signal in a distributed neural network involved in motor control and identified in the standard contrasts. Incorporating the temporal dynamics of the BOLD signal revealed perhaps it is not only the condition-specific difference in the intensity of activation across regions that governs on-line control, but also the functional interactions (coherence) between different regions. Thus coherence, which is more sensitive in detecting subtleties in functional neural interactions, may provide a robust means to identify functional interactions between distributed regions (Garraux et al. 2005; Sun et al. 2004). It was previously suggested that learning is associated with a gradual reduction in within cortical functional interaction and a gradual increase in between cortical–striatal interactions (Toni et al. 2002). Our results substantiate and extend this view by demonstrating that early learning was largely attributed to within cortical, basal ganglia, and cerebellar patterns. Conversely, late learning was characterized by differential functional neural interactions between these same anatomical regions; specifically a preferential interaction of a cerebrocortical–basal ganglia network for adapting to the viscous perturbation and a cerebrocortical–cerebellar network for adapting to the positional perturbation.
BOLD coherence and neural activity
Analytical methods for interpreting brain function using the BOLD signal have progressed considerably over the past decade. A potentially powerful approach recently applied to brain imaging is to compare the temporal changes of the BOLD signal between different brain regions. In other words, one can now ask: "How do regions interact with one another over time?"—rather than asking the traditional question: "Which regions have greater activity?" Analysis of coherence, long used in physiology, offers this window into understanding the functional interactions of neural circuits from MRI data (Curtis et al. 2005; Miller et al. 2005; Sun et al. 2004). Effectively, coherence is the normalized cross-correlation between two waveforms, but in spectral (frequency) space rather than in time. This property gives coherence to two important advantages over cross-correlation: no need to interpret positive and negative correlations that, given the complexity of the HRF, are often difficult to understand and, second, the ability to calculate coherence within the known frequency bandwidth of the HRF and the events, eliminating the need to account for time lags in correlation analysis.
Because of the event-related nature of our design, unavoidably the waveforms submitted to the coherence analysis are considered in "event" time rather than in real time (see also Sun et al. 2004). In other words, this analysis is capturing condition-specific systems level neuronal interactions. It is emphasized that making multiple comparisons [(19 x 20)/2 = 190] per condition to isolate significant local interactions is not the purpose here nor is it the most valid approach (Rencher 2002). Instead, our intent was to understand systems-level patterns in interaction, how they change with task, processing stage, and learning. Linkage is the principled approach for defining such patterns of interactions in a multidimensional data set and in a fully reproducible way (Rencher 2002). The random sampling of the population confirmed the validity of this approach.
Given that the BOLD signal is thought to represent local field potentials, or local neural interactions (Attwell and Iadecola 2002; Logothetis 2003), it is of little surprise that the BOLD pattern was coherent among adjacent regions. In this regard, coherence may have been driven by both shared local field potentials and common functional interactions, possibilities that are difficult to dissociate. Conversely, coherence between spatially segregated regions, but those known to be mono- or polysynaptically connected (Clower et al. 2001, 2005; Eblen and Graybiel 1995; Strick et al. 1998), cannot be explained by simple epiphenomena such as spillover or enhancement of local field potentials among adjacent neural populations but rather reflect the active involvement of a distributed network in a common function.
This said, our results do not imply that cortical–basal ganglia networks always underlie adaptation to viscous perturbations and cerebellar–cortical networks to positional perturbations. Indeed if this were the case, it would be difficult to reconcile the breadth of findings from physiological recordings and imaging studies demonstrating that activity within cortical, cerebellar, and basal ganglia regions can correlate with various movement parameters, depending on the task (DeLong et al. 1984a,b; Fu et al. 1997; Messier and Kalaska 2000; Sergio and Kalaska 2003; Sergio et al. 2005; Turner et al. 2003). Such a redundancy within the sensorimotor system may instead underlie the nervous system's flexibility in preferentially choosing functional circuits that fit more global sensorimotor goals of the task.
Instead, we contend that the differential, perturbation-specific recruitment of common circuits was particularly attributed to the subjects’ adaptation to the sensorimotor goals associated with each perturbation. The TPA results indeed suggest that subjects learned to initiate their movements in an increasingly optimal, perturbation-specific manner. Closer examination of the catch trial experiment data further indicates the presence of aftereffects in the early portion of the movement, which would not be expected if subjects did not develop and implement perturbation-specific strategies or if subjects anticipated catch trials. Further evidence that catch trials, which were randomly interspersed (10%) throughout the perturbed trials, were not anticipated is the overlap in the perturbed and catch trial profiles before the trajectories entered the would-be perturbed zone (gray area, Fig. 6A, left) and, second, by the presence of aftereffects, which clearly would be absent if subjects anticipated catch trials.
A most probable explanation is that subjects learned to predict the angle at which the positional perturbation was engaged and disengaged (as they performed movements from different start positions and to different targets), but not the amplitude of the torque it exerted because the torque magnitude remained constant in this condition. Executing adaptive control in the positional perturbation condition led to a stronger coherence between cortical–cerebellar sensorimotor regions than cortico–basal ganglia regions. This finding is consistent with and supports an extensive literature-based linking of the cerebellum in the prediction of sensorimotor events (Blakemore and Sirigu 2003; Dreher and Grafman 2002; Miall et al. 1993; Nitschke et al. 2003; Nixon 2003; Timmann et al. 2000). In our study, this increased cerebellar–cortical coherence may likely be attributable to the increased effort in predicting the edges of the perturbation step.
Execution of this strategy, however, was unlikely to be appropriate for the viscous perturbation. For example, after repeated training in the viscous condition, subjects may have adapted a strategy wherein the braking of the movement, as it approached the target, could have theoretically been produced by the viscoelastic properties of the muscle rather than by the antagonistic muscles. This strategy has been shown to be used in situations when the agonist force is smaller than the passive viscoelastic tension of the ant(agonists) (Lestienne 1979), which may have been the case toward the end of the movement when the viscous torque was minimal. Clearly, however, this mechanism would not have been feasible in the positional condition, which required a substantial antagonist muscle burst to brake the motion. We thus contend that subjects adapted to the viscous perturbation by scaling their motor output. Indeed, subjects themselves reported that for this condition, they initially increased their force output to counteract the viscous resistance. Unlike in the positional perturbation, the viscous condition revealed a stronger basal ganglia–cortical coherence. The basal ganglia has been repeatedly implicated in scaling processes related to movement (Bergman et al. 1994; Desmurget et al. 2003, 2004; Georgopoulos et al. 1983; Turner et al. 2003; Vaillancourt et al. 2004; Wichmann et al. 1994a,b), which perhaps may also account for some of the hypometric deficits observed in Parkinson's disease.
In summary, we contend that the perturbation-specific motor control policies may have been guided by the engagement of neural circuits selected according to the sensorimotor goals that the subjects found most optimal in adapting to each perturbation.
Neural interactions underlying arbitrary visuomotor associations
We also investigated whether learning-dependent functional connectivity would exhibit specificity for contextually cued recall of adaptive responses to the two perturbations. Our findings suggested that contextual cues were successfully used to plan for the two oncoming perturbations, highlighting the importance of contextual cues in learning multiple adaptive strategies in a random practice schedule (Osu et al. 2004). Coherence was therefore analyzed in the "NoGo" trials to investigate whether contextually cued recall was attributed to the interaction among common or distinct neural circuits. Although no apparent interregional pattern in coherence was noted during early learning, a relative strengthening in the cortical and basal ganglia clusters was noted for both perturbation conditions during late learning. The involvement of these regions in contextually cued recall is in line with reports of increased activation in prefrontal, parietal, and basal ganglia territories in tasks that require the learning of arbitrary visuomotor associations (Boettiger and D'Esposito 2005; Toni and Passingham 1999; Toni et al. 2001) and movement preparation (Alexander and Crutcher 1990; Thoenissen et al. 2002). Importantly, Alexander and Crutcher (1990), who recorded cell activity in the arm representation neurons of the MC, SMA, and putamen while monkeys performed a step-tracking task, reported that the preparatory neuronal activity was perturbation nonspecific, suggesting that this network was engaged in a general plan selection process. This is consistent with other reports attributing the basal ganglia network in part to a role in building and selecting action plans (Gentilucci and Negrotti 1999; Jueptner and Weiller 1998; Kimura et al. 2003; Menon et al. 2000).
Performance and brain activity
Our behavioral data indicated that subjects learned to adapt to both perturbations and, remarkably, achieved this despite training in a random practice schedule. Adaptation was reflected by significant trial-to-trial improvement in absolute and variable error for each perturbation condition, although admittedly large differences in absolute error occurred between the viscous and positional conditions. To identify the neural regions exhibiting performance-related changes, we correlated the BOLD signal with absolute error, collapsed across tasks to avoid the confound of between-task differences in error. Reduction in overall performance error was associated with reduced activity in the left parietal cortex and the right dentate nucleus. This finding is not unexpected considering other imaging studies that similarly showed parietal and cerebellar regions to be modulated over the course of adaptation (Clower et al.
