Abstract

We previously demonstrated that predictive motor timing (i.e., timing requiring visuomotor coordination in anticipation of a future event, such as catching or batting a ball) is impaired in patients with spinocerebellar ataxia (SCA) types 6 and 8 relative to healthy controls. Specifically, SCA patients had difficulties postponing their motor response while estimating the target kinematics. This behavioral difference relied on the activation of both cerebellum and striatum in healthy controls, but not in cerebellar patients, despite both groups activating certain parts of cerebellum during the task. However, the role of these two key structures in the dynamic adaptation of the motor timing to target kinematic properties remained unexplored. In the current paper, we analyzed these data with the aim of characterizing the trial-by-trial changes in brain activation. We found that in healthy controls alone, and in comparison with SCA patients, the activation in bilateral striatum was exclusively associated with past successes and that in the left putamen, with maintaining a successful performance across successive trials. In healthy controls, relative to SCA patients, a larger network was involved in maintaining a successful trial-by-trial strategy; this included cerebellum and fronto-parieto-temporo-occipital regions that are typically part of attentional network and action monitoring. Cerebellum was also part of a network of regions activated when healthy participants postponed their motor response from one trial to the next; SCA patients showed reduced activation relative to healthy controls in both cerebellum and striatum in the same contrast. These findings support the idea that cerebellum and striatum play complementary roles in the trial-by-trial adaptation in predictive motor timing. In addition to expanding our knowledge of brain structures involved in time processing, our results have implications for the understanding of BG disorders, such as Parkinson disease where feedback processing or reward learning is affected.

INTRODUCTION

Precise timing is essential in everyday life, as it is an integral part of many motor skills and behaviors (Hore & Watts, 2005; Iacoboni, 2001) and its disruption in a number of neurological disorders can have a devastating effect on those afflicted with the condition (Harrington & Haaland, 1999). Studies of motor timing have shown that this important function is controlled primarily by the cerebellum and BG (Ivry & Spencer, 2004; Matell & Meck, 2004; Mauk & Buonomano, 2004; Gibbon, Malapani, Dale, & Gallistel, 1997; Ivry & Keele, 1989); however, most of these experiments employed rhythmic activity, time estimation, or time production paradigms. Although this particular type of motor timing may be well suited to investigate the neural structures implicated in temporal processing alone, most everyday motor activities actually require predictive timing: visuomotor coordination in response to anticipated changes of stimuli in the environment (i.e., catching or hitting a ball, grabbing moving objects, turning the steering wheel, etc.). In this context, predictive motor timing can be envisioned as having two components, the temporal processing per se and holding or postponing the motor response until the appropriate moment to act. To date, postponement of motor responses has been a measure of interest mainly in response inhibition paradigms (e.g., suppression of a prepotent response tendency), using go/no-go or Stroop-like tasks. Some of these studies, which included healthy controls or patients with movement disorders, have found striatum as well as cerebellum to be involved in response inhibition in different capacities (Vink, Kaldewaij, Zandbelt, Pas, & du Plessis, 2015; Ye et al., 2015; van der Salm et al., 2013; Chao, Luo, Chang, & Li, 2009; Aron & Poldrack, 2005; Mostofsky et al., 2003). Typically, striatal activation was related to the actual response inhibition and expectations about the probability of a stop signal to occur (Vink et al., 2015; van der Salm et al., 2013; Aron & Poldrack, 2005), whereas cerebellar activation was mainly associated with “go” trials (Mostofsky et al., 2003). However, in most studies on response inhibition, participants react to external cues (“go” or “no-go” signals), whereas in predictive motor timing, these “go/no-go” cues are self-generated. Thus, there is a scarcity of studies investigating the neural substrate of predictive motor timing with a focus on the specific roles played by cerebellum and BG, in particular.

There is a debate in the literature as to whether cerebellum and striatum are involved in timing of short or long intervals, as well as in continuous or discontinuous tasks. For instance, both cerebellum and BG were shown to be associated with the perception of short temporal intervals (Jahanshahi, Jones, Dirnberger, & Frith, 2006; Ferrandez et al., 2003; Nenadic et al., 2003; Harrington, Haaland, & Hermanowicz, 1998; O'Boyle, Freeman, & Cody, 1996). However, the striatum is also known to be activated during processing of long temporal intervals (Meck, 2005; Meck & Benson, 2002). Thus, although the role of cerebellum seems confined almost exclusively to the estimation of millisecond to second-range intervals, the BG are engaged in the neural control of a wider range of time (e.g., seconds), perhaps due to its strong connections with the frontal regions involved in higher cognitive processes such as attention, memory, control (Coull, Cheng, & Meck, 2011).

Indeed, in a previous study (Bares et al., 2011), we found that both striatum and cerebellum were necessary in a predictive motor timing task that required temporal processing over a period of several seconds. However, given that predictive motor timing requires both temporal processing and postponement of motor response until the right time to act, one cannot dissociate the activation due to temporal processing from that due to postponement of the motor response per se, if only the current trials are taken into account in the analysis. In contrast, if a trial-by-trial analysis is employed, accounting for changes in brain activity in the current trial as a function of the outcome in the immediately preceding trial, the roles of the cerebellum and BG in predictive motor timing could be better teased apart. To do so, in the current paper, we used the neuroimaging data collected in a previous study (Bares et al., 2011), and we conducted a new analysis, which focused on the trial-by-trial changes in the brain activity as healthy controls and SCA patients performed a predictive motor timing task. We hypothesized that changes in striatum would reflect both the cognitive requirements of the task as well as postponement of motor action, whereas cerebellum would be involved primarily in correct temporal estimation.

METHODS

Participants

For this study, patients diagnosed as having a genetically defined spinocerebellar ataxia (SCA) type 6 (n = 5) or type 8 (n = 4) were recruited from the ataxia outpatient clinic at the University of Minnesota (five women, mean age = 49.1 years, SD = 10.7 years), together with 12 healthy controls (HC; age-matched) with no neurological or other health problems (five women, mean age = 51.5 years, SD = 12.5 years). The patients (Table 1) with SCA types 6 and 8 have clinical syndromes involving predominantly the cerebellum and are well characterized both clinically and genetically (Maschke et al., 2005). Before testing, the clinical status of the patients was scored on the International Cooperative Ataxia Rating Scale of the World Federation of Neurology (Trouillas et al., 1997). All participants were right-handed (Oldfield, 1971) and did not have clinically apparent depression on the Montgomery and Asberg Depression Rating Scale (Montgomery & Asberg, 1979). None of the participants had a history of color blindness or evidence of cognitive decline. All participants were remunerated for their participation after giving informed consent. The study was approved by the institutional review board of the University of Minnesota.

Table 1. 

Detailed Description of the SCA Patients

No.Age (years)SexSCA TypeDisease Duration (years)Walking AidWFNa
Total ScorePosture, GaitLimb AtaxiaSpeechOculomotor Dysfunction
63 11 Walker 50 17 23 
43 15 Cane 41 16 16 
30 11 Cane 25 10 
43 10 None 28 11 11 
47 20 Chair 54 27 18 
45 15 Walker 42 20 13 
52 11 None 13 
62 None 23 
55 11 Cane 24 14 
No.Age (years)SexSCA TypeDisease Duration (years)Walking AidWFNa
Total ScorePosture, GaitLimb AtaxiaSpeechOculomotor Dysfunction
63 11 Walker 50 17 23 
43 15 Cane 41 16 16 
30 11 Cane 25 10 
43 10 None 28 11 11 
47 20 Chair 54 27 18 
45 15 Walker 42 20 13 
52 11 None 13 
62 None 23 
55 11 Cane 24 14 

M = male; F = female; SCA = spinocerebellar ataxia; WFN = World Federation of Neurology score.

a

Range 0–100. The higher the total score, the more severe ataxia (Trouillas et al., 1997).

Behavioral Task

We employed the same experimental paradigm as that reported elsewhere (Bares, Lungu, Husarova, & Gescheidt, 2010; Bares et al., 2007). Specifically, participants performed a predictive motor timing task in which they had to respond via button-press to intercept a circular target that moved from left to right on a computer screen. Their action launched a small fireball from a virtual cannon located on the lower right side of the screen. The fireball moved upward, at constant speed (20.0 cm/sec), and served to intercept the moving target (Figure 1A). The objective was to launch the fireball at the optimal time to hit the target when it passed through the interception zone (not visible to the participants), which was always in the same location on the upper right side of the screen. If the participant were successful, both the fireball and target appeared to explode (the red dots in Figure 1B); if the participant failed to intercept the target, no explosion animation occurred. The diameter of the target was 1 cm, and the diameter of the fireball was 0.3 cm. The target was launched from the left side of the screen at three different angles of movement relative to the horizontal plane of the screen (0°, 15°, and 30°) and followed a linear trajectory (the dotted yellow lines in Figure 1A, not visible to the participants) toward the interception zone. The target could have one of three different movement types, constant velocity, deceleration, and acceleration, and three different travel speeds, slow (complete travel time across the screen: 3.5 sec), medium (complete travel time across the screen: 3.0 sec), and fast (complete travel time across the screen: 2.5 sec). The target movement was thus characterized by any combination of the three variables (type, speed, angle), giving a total of 27 different kinematic combinations.

Figure 1. 

The prediction motor timing task. (A) A target ball (green) was launched from the left side of the screen on one of the three linear trajectories (yellow dotted lines, not visible to the participants) at various speeds, angles, and acceleration rates toward a “target” zone (the intersection point of the three trajectories). Participants launched a cannonball (lower right side of the screen) by pressing a button. The cannonball traveled upward at a constant speed. The goal was to intercept the target ball in the target zone. (B) If the action was correctly timed, the cannonball intercepted the target ball and both exploded (red dots). (C) The distribution of trial types by blocks during the motor timing task. There were six blocks, two for each type of movement (accelerated, constant, decelerated) presented in pseudorandom order across participants. Within each block, there were 54 trials resulting in six presentations of each of the nine trial types (originating from all combination of the three types of speeds and three angles).

Figure 1. 

The prediction motor timing task. (A) A target ball (green) was launched from the left side of the screen on one of the three linear trajectories (yellow dotted lines, not visible to the participants) at various speeds, angles, and acceleration rates toward a “target” zone (the intersection point of the three trajectories). Participants launched a cannonball (lower right side of the screen) by pressing a button. The cannonball traveled upward at a constant speed. The goal was to intercept the target ball in the target zone. (B) If the action was correctly timed, the cannonball intercepted the target ball and both exploded (red dots). (C) The distribution of trial types by blocks during the motor timing task. There were six blocks, two for each type of movement (accelerated, constant, decelerated) presented in pseudorandom order across participants. Within each block, there were 54 trials resulting in six presentations of each of the nine trial types (originating from all combination of the three types of speeds and three angles).

For the actual experiment, blocks of trials were organized on the basis of movement type (either constant, acceleration, or deceleration) within which the combination of speed and angle was randomized. There were six blocks of trials, two for each movement type, containing 54 trials bounded by 20-sec break periods. The total duration of the interception task was 18 min 27 sec. Each combination of speed and angle was presented six times during each block of a given movement type, for a total of 12 presentations of a particular trial type (movement type, speed, and angle) during the entire experiment. Before the imaging session, participants were given one block to practice the task. This block had 54 trials during which each combination of a particular trial type was presented twice and the total duration of the practice was 3 min 26 sec. Participants were seated 60 cm in front of a computer monitor for the practice block; during scanning, they performed the task lying supine in the scanner, seeing the screen through a mirror mounted on the coil and pressing the button on an MR-compatible button box.

Analysis of Behavioral Data

The dependent variable in behavioral analysis was the outcome of each individual trial, which was binary (Hit vs. Error); therefore, we could not use parametric methods to analyze the behavioral results. As such, we employed nonparametric test designed to handle nominal data, such as test of proportion or chi-square to compare the distribution of trial types among each group and the difference between groups. To account for inflated chi-square values due to the high number of trials, we used Phi and Cramer's V corrections. For each trial type, participants were successful in their attempt provided that they had pressed the button during an optimal temporal window while the target was moving on the screen. The lower bound of this temporal window varied between 605 and 1924 msec and its upper bound varied between 789 and 2100 msec from the trial onset, depending on the trial type. The median optimal hitting point (middle point of the temporal window), for all trials, was at 1467 msec from the trial onset. Whenever participants pressed the button too early or too late relative to the boundaries of the temporal window, their action resulted in an error. Thus, trials were classified as hits, early errors, and late errors based on whether participants pressed the button within, before, or after this temporal window, respectively. Trials in which participants did not press the button by the time the target cleared the screen were classified as omissions.

To demonstrate that participants actively adapted their strategy on a trial-by-trial basis, we performed the following steps. First, we calculated the middle point in the optimal time window for each trial type. Then, for each trial, we computed the difference between the actual RT and this middle point, and we expressed this difference in percentage relative to the window's temporal width. As such, successful trials (hits) had values between −50% and +50% (depending on whether the button press occurred before or after the middle point in the optimal time window); conversely, early errors had values below −50% and late errors, above +50%. This variable allowed for the quantification of the magnitude of deviation from the “optimal hit-point” independent of the target parameters, primarily the speed (i.e., the width of the optimal window was smaller for the trials in which target moved fast). Next, a discrete variable with five categories was used to classify the speed change across successive trials: extreme decrease (from 3.5 to 2.5 sec), average decrease (from 3.5 to 3 sec or from 3 to 2.5 sec), no change (same speed), average increase (from 2.5 to 3 sec or from 3 to 3.5 sec), and extreme increase (from 2.5 to 3.5 sec) in speed.

Similarly, we computed a discrete variable with five categories representing the magnitude of change in target's trajectory angle across successive trials. Finally, evidence for the trial-by-trial strategy was obtained using random-effects GLM models with the speed or angle change category and group as independent factors and the deviation from the optimal point as the dependent variable. In addition, the same dependent variable was employed in a random-effects GLM with the group, current and previous trial type (based on outcome: hit, early error, and late error) as independent factors.

Imaging Parameters of the fMRI Experiment

All participants were scanned during the task using a 3-T Whole Body MR System (MAGNETOM Trio, Siemens Medical Systems, Erlangen, Germany). Before the fMRI run, 144 or 160 (depending on the participant's head width) FLASH structural images were acquired in slices of 1-mm thickness in sagittal plane (256 × 256 mm), yielding a spatial resolution of 1 × 1 × 1 mm for the anatomical volume. The imaging parameters of the anatomical sequence had a repetition time and echo time of 13 and 4.92 msec, respectively, with a flip angle of 25°. Then, whole-brain fMRI was performed using an EPI sequence measuring BOLD signal. A total of 30 functional slices per volume were acquired for all participants in all runs. These slices, which were acquired in transversal plane with a 30° positive tilt around y axis (to cover the whole brain and the cerebellum), were in ascending order and interleaved. They had a thickness of 3 mm, an in-plane resolution of 3 × 3 mm (matrix size = 64 × 64), and a field of view of 192 × 192 mm, with a 1-mm gap between them to avoid cross-talking. A complete scan of the whole brain was acquired in 2000 msec (repetition time); the flip angle was 75°, and echo time was 30 msec. A total number of 556 volumes were acquired during the functional run.

Preprocessing of fMRI Data

Brain Voyager QX (Brain Innovation B.V., Maastricht, the Netherlands) software was used for fMRI data preprocessing and analysis. The functional bidimensional images of every participant were preprocessed to correct for the difference in time slice acquisition (slice scan time correction). In addition to linear detrending, a high-pass filter of three cycles per time course (frequency domain) was applied to the corrected 2-D slices. Then, the functional series was preprocessed to correct for possible motion artifacts in any plane of the tridimensional space and to ensure that the movements in any plane did not exceed 3 mm. Later, these functional images were then used to reconstruct the 3-D functional volume for every participant and every run, which was aligned with the corresponding 3-D anatomical volume, and both were normalized to standard Talairach space (Talairach & Tournoux, 1988). Finally, spatial smoothing using a Gaussian kernel at 7 mm FWHM was applied to the 3-D functional data.

Analysis of fMRI Data

We used a rapid event-related block design for our experiment. Each behavioral event, which lasted between 2.5 and 3.5 sec (corresponding to the maximum travel time of the moving target across the screen), was classified according to the behavioral outcome (hit, early error, and late error) in both the current and previous trials. As such, we had nine events (early error after early error, hit after early error, late error after early error, early error after hit, hit after hit, late error after hit, early error after late error, hit after late error, and late error after late error). These events describe all possible combinations of behavioral outcomes from one trial to the next. These predictors were entered as fixed factors in single participant GLM; then, the parameter estimates of this GLM model were subsequently entered into a second level of analysis corresponding to a random-effect GLM model that was used for group analysis (Penny & Holmes, 2003). The statistical parameters of this latter model were estimated voxelwise for the entire brain, and activation maps were computed for various contrasts between the predictors. In analyzing the neuroimaging data, we first characterized brain activation patterns for various statistical contrasts only for the healthy individuals, and then we performed group comparisons using the same contrasts. We were interested in the following contrasts: (1) brain activation during current trial as a function of outcome in the previous trial (hit vs. error); (2) brain activation during successive successful trials versus successive errors (hits followed by hits vs. errors followed by errors, of the same kind), and (3) brain activation during hits and late errors versus the early errors in the current trials when they followed early errors. The first contrast will reveal the impact of immediate past outcome on the current brain activity; the second contrast will highlight brain regions involved in maintaining a successful strategy; the last contrast will highlight the brain activation related to the postponement of the motor response from one trial to the next, regardless of trial duration. When displaying the activation maps corresponding to these contrasts for the HC group only, we employed the false discovery rate (q < 0.05) correction, for the whole brain, at the millimeter isotropic resolution and a minimum cluster size of 108 adjacent significant voxels (corresponding to a volume of 108 mm3). For group differences, we displayed the activation map using the same cluster size and a statistical threshold for each voxel in the cluster of at least p < .001 (uncorrected), adjusted to correspond to p < .05 family-wise error. It is worth noting that, in most cases, this threshold turned out to be more conservative than the false discovery rate of q < 0.05. In addition, we also conducted additional detailed ROI analyses in various clusters of interest that we obtained as a result of the above-mentioned contrasts. For this, we used a GLM random-effects analysis with group and type of trial as independent factors and the BOLD signal expressing the magnitude of the contrast of interest averaged over all voxels in the ROI as dependent measure. To this end, we employed the SPSS (Statistical Package for Social Sciences, SPSS, Inc., Chicago, IL) software.

RESULTS

Behavioral Results

As reported previously, SCA patients performed worse than healthy controls (Figure 2A) as revealed by both the proportion and chi-square tests (Bares et al., 2011). Not only was the proportion of hits significantly lower in patients than in healthy controls (χ2 = 321.63, df = 1, N = 6804; Phi = 0.21, and Cramer's V = 0.21; p < .001), but the patients had a smaller proportion of hits than early (χ2(1) = 104.40, p < .001) and late errors (χ2(1) = 16.83, p < .001). In contrast, healthy controls had a higher proportion of hits than any of the other two error types (χ2(1) = 191.51, p < .001 for early errors and χ2(1) = 293.45, p < .001 for late errors).

Figure 2. 

(A) The proportion of hits, early errors, and late errors for the two groups in the current trials. (B) The proportion of hits, early errors, and late errors in the current trials as a function of previous trial type for the two groups.

Figure 2. 

(A) The proportion of hits, early errors, and late errors for the two groups in the current trials. (B) The proportion of hits, early errors, and late errors in the current trials as a function of previous trial type for the two groups.

The trial-by-trial performance of the two groups is shown in Figure 2B. The proportion and chi-square tests performed both within each group as well as comparing the two groups revealed that healthy controls had a significantly higher proportion of hits in the current trials than SCA patients, regardless of the outcome in the previous trial (χ2(1) = 78.98, p < .001 after early errors; χ2(1) = 137.15, p < .001 after hits and χ2(1) = 84.72, p < .001 after late errors). This suggests that the performance difference between healthy controls and SCA patients reported earlier (Bares et al., 2011) is a very stable one, and it does not depend, at least for healthy controls, on the outcome in the previous trial. In addition, the healthy controls were more likely to succeed than to make either an early error or a late error regardless of the previous trial type trial (all ps < .01). In contrast, the SCA patients tended to produce more early errors than hits regardless of previous trial type; early errors were more frequent than hits after hits and late errors (all ps < .01), but these proportions were the same when the previous trial was an early error (χ2(1) = 2.75, p = .1).

The random-effects GLM assessing the change in deviation from the optimal hitting point revealed a significant main effect for the change in speed, F(4, 76) = 109.57, p < .001, as well as an interaction effect Group × Change in speed, F(4, 76) = 24.88, p < .001 (red and blue bars in Figure 3A). This indicated that the more extreme the change in speed across successive trials, the larger the change in deviation from the optimal hitting point in the current trial. In addition, this effect had a significantly larger magnitude among SCA patients than healthy controls. Although this result reflects participants' trial-to-trial adaptation to the change in speed, it does not inform us about the success of the adaptation strategy. In this context, the same GLM model applied to hit ratios revealed a significant interaction between group and hit ratio, F(4, 76) = 2.59, p < .05 (the points and dotted lines in Figure 3A). This suggests that healthy controls maintained their successful strategy despite the change in speed from one trial to the next (e.g., successful adaptation), whereas SCA patients performed significantly worse as the speed of the target ball decreased across successive trials. This apparently counterintuitive finding reflects a deficit in SCA capacity to postpone their action long enough to obtain a hit.

Figure 3. 

(A) The change in deviation from the optimal hitting point across successive trials (red and blue bars, left vertical axis) and average hit ratio (dotted lines, right vertical axis) as a function of change in target speed. (B) The change in deviation from the optimal hitting point across successive trials as a function of outcomes in the current and preceding trials, presented separately for each of the two groups.

Figure 3. 

(A) The change in deviation from the optimal hitting point across successive trials (red and blue bars, left vertical axis) and average hit ratio (dotted lines, right vertical axis) as a function of change in target speed. (B) The change in deviation from the optimal hitting point across successive trials as a function of outcomes in the current and preceding trials, presented separately for each of the two groups.

Regarding the change in angle across successive trials, the GLM analysis revealed a significant main effect for angle change, F(4, 76) = 4.41, p < .005, and a significant Group × Angle change interaction effect, F(4, 76) = 2.72, p < .05. This indicates that, for healthy controls, changes in angle did not affect the change in deviation from the optimal hitting point in the current trial, whereas SCA patients tended to anticipate or delay their reaction depending on whether the angle decreased or increased from one trial to the next, respectively. However, the hit ratio within each group and the group differences in hit ratio were not significantly affected by the angle change across successive trials [F(4, 76) = 2.02, p = .1 for the main effect of angle and F(4, 76) = 0.84, p = .51 for the Group × Angle change interaction effect].

The GLM analysis with group, current and previous trial type (hit, early error, and late error) as independent factors revealed that, for both groups, the change in deviation from the optimal hitting point in the current trial depended on the outcome in both the previous and current trials, F(4, 66) = 4.45, p < .005 (Figure 3B). Specifically, the magnitude of the deviation from the optimal hitting point increased as the discrepancy in outcome across successive trials increased (i.e., from early to late errors or vice versa; see Figure 3B), in line with the findings related to the changes in speed alone.

In summary, these behavioral results indicate not only that the two groups are different in terms of their general performance but also that their trial-by-trial strategy is different: Specifically, the SCA patients tend to make more early errors than the other type, suggesting that they have trouble suppressing or holding their motor response long enough to improve their task performance.

Imaging Results

Given that in the neuroimaging data analysis we relied on the natural jitter between different event types, created by the variability in participants' performance (e.g., occurrence of hits, early and late errors), it was important to assess the magnitude of the ISI between events. On average, the ISI for all participants and all trials (grouped by trial type, 27 categories) was 28.67 sec, with a median of 16.5 sec.

Bilateral anterior putamen was strongly activated in healthy controls whenever the previous trial was a hit, regardless of the outcome in the current trial (Figure 4; Table 2). The same contrast, but comparing the two groups, revealed overlapping activation in the same location in bilateral putamen (Figure 4; Table 2). In both of these clusters, the brain activation was significantly higher in healthy controls than in SCA patients in the current trial, when the previous trial was a hit, regardless of the outcome in the current trial [F(1, 19) = 28.58, p < .001; η2 = 0.60, observed power = 0.92 for right putamen and F(1, 19) = 33.34, p < .001; η2 = 0.64, for a post hoc power = 0.99 for left putamen] (bar graphs in Figure 4). This finding suggests that, in healthy individuals, the putamen acts as a relay of past successes.

Figure 4. 

Brain activation comparing current trials preceded by hits versus errors in HC only and in the comparison HC > SCA. The bar graphs represent the change in BOLD signal (mean; SEM) for each trial type and group in clusters from the left and right putamen.

Figure 4. 

Brain activation comparing current trials preceded by hits versus errors in HC only and in the comparison HC > SCA. The bar graphs represent the change in BOLD signal (mean; SEM) for each trial type and group in clusters from the left and right putamen.

Table 2. 

Brain Activation during Current Trials When It Was Preceded by a Hit versus an Error

Activated RegionsTalairach Coordinates (Peak of Activation)Volume (mm3)Maximum t Value
Healthy Controls Only (df = 11) 
Right putamen 21 2967 10.52 
Right parahippocampal gyrus (BA 35) 18 −16 −8 332 9.79 
Right anterior cerebellum (culmen) −37 177 8.15 
Left precuneus (BA 7) −76 49 231 8.37 
Left precentral gyrus (BA 6) −18 −19 67 173 9.33 
Left putamen −18 3093 9.06 
 
Healthy Controls vs. SCA Patients (df = 19) 
Right premotor cortex (BA 6) 63 16 189 5.71 
Right inferior parietal lobule (BA 40) 42 −46 61 227 4.41 
Right putamen 24 19 2257 5.54 
Right superior parietal lobule (BA 7) 18 −52 64 244 4.98 
Left precuneus (BA 7) −70 55 2086 6.40 
Left putamen −27 −2 2578 6.14 
Left superior parietal lobule (BA 7) −21 −58 64 202 4.61 
Activated RegionsTalairach Coordinates (Peak of Activation)Volume (mm3)Maximum t Value
Healthy Controls Only (df = 11) 
Right putamen 21 2967 10.52 
Right parahippocampal gyrus (BA 35) 18 −16 −8 332 9.79 
Right anterior cerebellum (culmen) −37 177 8.15 
Left precuneus (BA 7) −76 49 231 8.37 
Left precentral gyrus (BA 6) −18 −19 67 173 9.33 
Left putamen −18 3093 9.06 
 
Healthy Controls vs. SCA Patients (df = 19) 
Right premotor cortex (BA 6) 63 16 189 5.71 
Right inferior parietal lobule (BA 40) 42 −46 61 227 4.41 
Right putamen 24 19 2257 5.54 
Right superior parietal lobule (BA 7) 18 −52 64 244 4.98 
Left precuneus (BA 7) −70 55 2086 6.40 
Left putamen −27 −2 2578 6.14 
Left superior parietal lobule (BA 7) −21 −58 64 202 4.61 

Mastering the predictive motor timing task requires one to maintain a successful performance in the current trial after a hit in the previous trial. Comparing the brain activity during a successful (hits after hits) versus a failed strategy (errors after errors, of the same kind) yielded activation in left putamen (lentiform nucleus) both when considering only then healthy controls as well as when comparing them with the SCA patients (Figure 5; Table 3). A detailed ROI analysis in the left putamen cluster (bar graph in Figure 5) indicates that the difference between the two groups is specific to trials where a successful strategy was maintained (i.e., hits after previous hits) and not to trials where the motor strategy was maintained but resulted in a failure (successive errors of the same kind) [F(2, 38) = 9.54, p < .001; η2 = .33; observed power = 0.97; for the Group × Trial type interaction]. However, the same contrast performed in the healthy controls group only revealed activation in a vast network including bilateral striatum, cerebellum, and many froto-parieto-temporo-occipital regions that are typically considered to be part of the attentional and action monitoring networks (Table 3). A detailed ROI analysis indicated that in the right putamen and left dentate nucleus there were significant Group × Trial type interaction effects [F(2, 38) = 5.79, p < .01; η2 = .23; observed power = 0.4; for right putamen and F(2, 38) = 3.80, p < .05; η2 = .17; observed power = 0.66; for left dentate]. These effects did not have a large enough magnitude to surpass the statistical threshold imposed brainwise when comparing the two groups.

Figure 5. 

Brain activation comparing current trials in successive hits versus successive errors of the same kind (early after early and late after late errors, respectively) in HC group only and in the comparison HC > SCA. The bar graphs represent the change in BOLD signal (mean; SEM) for each trial type and group in the cluster from the left putamen.

Figure 5. 

Brain activation comparing current trials in successive hits versus successive errors of the same kind (early after early and late after late errors, respectively) in HC group only and in the comparison HC > SCA. The bar graphs represent the change in BOLD signal (mean; SEM) for each trial type and group in the cluster from the left putamen.

Table 3. 

Brain Activation Comparing the Activity in the Current Trials in Successive Hits with that in Successive Errors of the Same Kind (Early after Early and Late after Late Errors, Respectively)

Activated RegionsTalairach Coordinates (Peak of Activation)Volume (mm3)Maximum t Value
Healthy Controls Only (df = 11) 
Right posterior cerebellum (declive) 24 −61 −20 3434 7.97 
Right inferior occipital gyrus (BA 18) 36 −82 −5 2794 9.34 
Right middle temporal gyrus (BA 39) 30 −61 22 372 7.34 
Right middle occipital gyrus (BA 19) 30 −79 10 143 5.44 
Right putamen 18 2590 9.52 
Right thalamus 24 −16 16 201 7.71 
Right inferior occipital gyrus (BA 18) 27 −88 −14 112 5.81 
Right anterior cingulate cortex (BA 32) 21 29 22 350 10.55 
Right mid-cingulate gyrus (BA 24) −4 49 121 5.84 
Left posterior cingulate gyrus (BA 31) −34 34 3476 7.13 
Left posterior cingulate gyrus (BA 30) −9 −52 901 7.93 
Left paracentral lobule (BA 31) −19 43 794 6.60 
Left anterior cerebellum (dentate nucleus) −15 −55 −26 167 6.00 
Left putamen −24 −2 3412 9.90 
Left medial frontal gyrus (BA 8) −18 26 40 141 8.33 
Left middle occipital gyrus (BA 18) −27 −82 −2 504 6.45 
Left precuneus (BA 7) −24 −73 40 241 6.29 
Left fusiform gyrus (BA 19) −21 −61 −5 1317 7.32 
Left anterior cerebellum (culmen) −30 −34 −20 144 7.57 
Left middle temporal gyrus (BA 39) −36 −73 10 646 7.94 
 
Healthy Controls vs. SCA Patients (df = 19) 
Left putamen −27 −2 396 7.75 
Activated RegionsTalairach Coordinates (Peak of Activation)Volume (mm3)Maximum t Value
Healthy Controls Only (df = 11) 
Right posterior cerebellum (declive) 24 −61 −20 3434 7.97 
Right inferior occipital gyrus (BA 18) 36 −82 −5 2794 9.34 
Right middle temporal gyrus (BA 39) 30 −61 22 372 7.34 
Right middle occipital gyrus (BA 19) 30 −79 10 143 5.44 
Right putamen 18 2590 9.52 
Right thalamus 24 −16 16 201 7.71 
Right inferior occipital gyrus (BA 18) 27 −88 −14 112 5.81 
Right anterior cingulate cortex (BA 32) 21 29 22 350 10.55 
Right mid-cingulate gyrus (BA 24) −4 49 121 5.84 
Left posterior cingulate gyrus (BA 31) −34 34 3476 7.13 
Left posterior cingulate gyrus (BA 30) −9 −52 901 7.93 
Left paracentral lobule (BA 31) −19 43 794 6.60 
Left anterior cerebellum (dentate nucleus) −15 −55 −26 167 6.00 
Left putamen −24 −2 3412 9.90 
Left medial frontal gyrus (BA 8) −18 26 40 141 8.33 
Left middle occipital gyrus (BA 18) −27 −82 −2 504 6.45 
Left precuneus (BA 7) −24 −73 40 241 6.29 
Left fusiform gyrus (BA 19) −21 −61 −5 1317 7.32 
Left anterior cerebellum (culmen) −30 −34 −20 144 7.57 
Left middle temporal gyrus (BA 39) −36 −73 10 646 7.94 
 
Healthy Controls vs. SCA Patients (df = 19) 
Left putamen −27 −2 396 7.75 

Finally, we found significant group differences regarding the postponement of motor response from one trial to the next in the right posterior putamen and right anterior cerebellar lobe (Figure 6; Table 4), but only the cerebellar cluster surpassed the statistical threshold when considering only the healthy controls. In all regions found when comparing the groups, healthy controls had a higher activation level than SCA patients (p < .001). A detailed analysis of the BOLD signal in cerebellar and striatal clusters showed that, indeed, these regions are specifically activated in healthy individuals whenever the motor response was postponed for a longer duration (i.e., transition from early errors to hits or late errors) as compared with a shorter one (i.e., from a previous hit or late error to a current early error) [F(1, 19) = 5.44, p < .001 for the Group × Current trial type interaction for right putamen cluster and F(1, 19) = 15.51, p < .001 for right cerebellum]. This finding indicates that the successful postponement of a motor response during the estimation of target kinematics depends on a network that includes both the cerebellum and striatum. Thus, we expanded our previous results (Bares et al., 2011), which did not account for the previous trial outcome, and we showed that these two key structures also act in tandem when dynamic adaptation of motor response is required from one trial to the next.

Figure 6. 

Brain activation comparing current hits and late errors versus early errors following early errors in HC group only and in the comparison HC > SCA. The bar graphs represent the change in BOLD signal (mean; SEM) for each trial type and group in clusters from the right cerebellum and putamen.

Figure 6. 

Brain activation comparing current hits and late errors versus early errors following early errors in HC group only and in the comparison HC > SCA. The bar graphs represent the change in BOLD signal (mean; SEM) for each trial type and group in clusters from the right cerebellum and putamen.

Table 4. 

Brain Activation Comparing Current Hits and Late Errors versus Early Errors following Early Errors

Activated RegionsTalairach Coordinates (Peak of Activation)Volume (mm3)Maximum t Value
Healthy Controls Only (df = 11) 
Right middle temporal gyrus (BA 21) 54 −13 −8 142 5.56 
Right inferior occipital gyrus (BA 18) 30 −85 −5 799 5.89 
Right anterior cerebellum (culmen) 33 −46 −17 118 5.29 
Right middle occipital gyrus (BA 18) 27 −88 13 228 6.09 
Right cerebellum (anterior lobe) 21 −46 −23 110 5.33 
Right posterior cingulate (BA 23) −49 25 264 5.42 
Left anterior cingulate (BA 24) −6 32 −2 234 7.57 
Left middle occipital gyrus (BA 18) −27 −85 −2 718 9.33 
Left middle occipital gyrus (BA 19) −42 −73 1115 10.07 
 
Healthy Controls vs. SCA Patients (df = 19) 
Right middle temporal gyrus (BA 21) 54 −13 −14 317 5.38 
Right inferior temporal gyrus (BA 20) 57 −25 −17 182 5.55 
Right fusiform gyrus (BA 19) 45 −70 −11 113 4.82 
Right middle occipital gyrus (BA 18) 24 −82 −2 420 4.81 
Right cerebellum (culmen) 21 −46 −23 252 4.99 
Right anterior cingulate gyrus (BA 24) 18 −7 40 270 5.12 
Right putamen 25 −13 13 116 5.99 
Right precuneus (BA 7) −61 61 210 4.57 
Left precuneus (BA 7) −18 −70 49 304 5.05 
Left inferior occipital gyrus (BA 17) −18 −91 −8 155 4.53 
Left middle occipital gyrus (BA 18) −27 −85 −2 372 5.27 
Activated RegionsTalairach Coordinates (Peak of Activation)Volume (mm3)Maximum t Value
Healthy Controls Only (df = 11) 
Right middle temporal gyrus (BA 21) 54 −13 −8 142 5.56 
Right inferior occipital gyrus (BA 18) 30 −85 −5 799 5.89 
Right anterior cerebellum (culmen) 33 −46 −17 118 5.29 
Right middle occipital gyrus (BA 18) 27 −88 13 228 6.09 
Right cerebellum (anterior lobe) 21 −46 −23 110 5.33 
Right posterior cingulate (BA 23) −49 25 264 5.42 
Left anterior cingulate (BA 24) −6 32 −2 234 7.57 
Left middle occipital gyrus (BA 18) −27 −85 −2 718 9.33 
Left middle occipital gyrus (BA 19) −42 −73 1115 10.07 
 
Healthy Controls vs. SCA Patients (df = 19) 
Right middle temporal gyrus (BA 21) 54 −13 −14 317 5.38 
Right inferior temporal gyrus (BA 20) 57 −25 −17 182 5.55 
Right fusiform gyrus (BA 19) 45 −70 −11 113 4.82 
Right middle occipital gyrus (BA 18) 24 −82 −2 420 4.81 
Right cerebellum (culmen) 21 −46 −23 252 4.99 
Right anterior cingulate gyrus (BA 24) 18 −7 40 270 5.12 
Right putamen 25 −13 13 116 5.99 
Right precuneus (BA 7) −61 61 210 4.57 
Left precuneus (BA 7) −18 −70 49 304 5.05 
Left inferior occipital gyrus (BA 17) −18 −91 −8 155 4.53 
Left middle occipital gyrus (BA 18) −27 −85 −2 372 5.27 

DISCUSSION

In the current study, we investigated the brain activation specific to trial-by-trial adaptation of motor performance in an interception task requiring precise predictive motor timing in healthy participants and SCA patients with abnormal cerebellar function. Our behavioral results showed that SCA patients were not only worse than healthy controls in terms of their general performance in the task, but also that their trial-by-trial strategy was different due to their tendency to make more early errors than the other type. In addition, patients were affected more than healthy controls by the changes in speed from one trial to the next, reacting with a greater magnitude of change in their motor strategy and with a significant worsening of performance as the speed decreased in successive trials. This result suggests that cerebellar dysfunction is associated with impairment in suppressing or holding a motor response long enough to improve task performance. In probing the neural substrate of this behavior, we focused on the dynamic interaction between the cerebellum and BG because the joint activation of both structures is necessary for successful performance in healthy individuals (Bares et al., 2011). Our principal findings related to BG–cerebellum interaction during predictive motor behaviors are the following: (1) reward-based activation of the BG was reduced in SCA patients, which suggests that it is dependent on cerebellar input; (2) trial-to-trial adjustments required to maintain a successful strategy were related to activation of a large neuronal network in healthy controls, including clusters in cerebellum, bilateral putamen, but also regions from the visual attention and action monitoring networks (Table 3); and (3) the postponement of the motor response during evaluation of target's kinematic properties, the key to success in the motor timing task, was associated with joint activation of the right cerebellum and putamen in addition to other temporo-occipital regions.

The increased striatal activation in the current trials, following hits compared with errors in the preceding trials (Figure 4), indicates that the striatum is involved in conveying the reward value or positive feedback from one trial to the next. This is consistent with the role of the striatum in providing a feed-forward model for the future actions (Husarova et al., 2013; Bastian, 2006), a conveyer of positive feedback in motor learning (Wachter, Lungu, Liu, Willingham, & Ashe, 2009) or reward value, in general (Berns, McClure, Pagnoni, & Montague, 2001). Although reciprocal connections between the cerebellum and BG have been recently documented (Bostan, Dum, & Strick, 2013), we know little of the functional significance of this connection. The two structures may have a fundamentally different relation to reward during learning with the striatum being activated primarily for immediate rewards whereas the cerebellum is involved with future rewards (Tanaka et al., 2004). Our data suggest that immediate reward-related activation in the BG during motor behavior is dependent in part on normal input from the cerebellum. This interpretation was supported by the results of a post hoc effective connectivity analysis between bilateral clusters in cerebellum and striatum, which indicated that following hits, but not errors, the information “flowed” from cerebellar to striatal clusters. In addition, the fact that in all contrasts we found significant between-group differences in different parts of putamen confirms our hypothesis that striatum sits at the crossroads between cognitive and motor processes in this motor timing task.

We chose a behavioral task that not only tested the ability of participants to integrate a prediction about the perception of target movement with precise timing of the motor response but was also very similar to naturalistic everyday behaviors, which require the integration of sensory and motor prediction, such as in catching, shooting, playing tennis, and many other activities. However, the prediction of perceptual events is rarely an end in itself and is often part of a greater goal that involves concomitant temporal processing and the control of motor behavior. Motor control literature posits the existence of adaptive internal models that are used by our brain to make predictions about the state of the body and the environment and calibrate movements to accurately reach a goal (Shadmehr, Smith, & Krakauer, 2010). Although our task and experimental design are different from those typically employed in the motor adaptation and motor control domains, our results are consistent with reports from this literature. Specifically, our cerebellar findings are consistent with previous results, which indicate that the cerebellum is involved in integrating sensory information and constructing predictions, as well as predictive control of motor commands that can be further processed by the cerebral cortex (Shadmehr et al., 2010; Tseng, Diedrichsen, Krakauer, Shadmehr, & Bastian, 2007). Consistent with this integrative approach is also the activity in the parahippocampal gyrus, which is a known site for implicit spatial scene recognition (Bastin et al., 2013; Howard, Kumaran, Olafsdottir, & Spiers, 2011; Rajimehr, Devaney, Bilenko, Young, & Tootell, 2011). It is conceivable that, in our predictive timing task, participants had to quickly and implicitly recognize various target movement patterns to facilitate the fast implementation of the strategy from one trial to the next. It is interesting to note that the strategy that enabled the optimal trial-by-trial adaptation depended on the relative change in target kinematic parameters across successive trials (Figure 3). This is consistent with previous findings indicating that humans employ adaptive strategies based on the statistical properties of the environment (Semrau, Daitch, & Thoroughman, 2012; Fine & Thoroughman, 2007).

Action-monitoring processes, such as response inhibition and error detection, are also important parts in our interception task, especially for the trial-by-trial adjustments used to improve performance. A number of cortical and some subcortical structures of brain are involved in these functions; several frontal regions, especially the (right) inferior frontal gyrus, SMA, pre-SMA with included DLPFC, and the anterior cingulate gyrus, play an important role in response inhibition (Mayer et al., 2012; Sharp et al., 2010; Nakata et al., 2008; Aron et al., 2007; Aron & Poldrack, 2005; Aron, Robbins, & Poldrack, 2004), and deficits in response inhibition have also been demonstrated after lesions of the BG (Thoma, Koch, Heyder, Schwarz, & Daum, 2008). Impairments in response inhibition are a major component of many of the cognitive disturbances seen in Parkinson disease in particular and the tendency toward compulsive behaviors (Gauggel, Rieger, & Feghoff, 2004; Lees & Smith, 1983). In this context, it is interesting to note that obsessive compulsive behaviors are part of the cognitive syndrome seen in some patients with disease of the cerebellum (Schmahmann, 2004). Our finding that joint activation of the striatum and cerebellum was associated with superior performance in response inhibition suggests that response inhibition in cerebellar disorders may be related primarily to disruption of cerebellar-striatal networks. The fact that the documented paired activation was ipsilateral may appear to be puzzling when viewed from the perspective of the known direct anatomical connections between these structures. However, recent data have shown there are ipsilateral and contralateral disynaptic connections from the BG to cerebellum via the thalamus (Hoshi, Tremblay, Feger, Carras, & Strick, 2005) and reciprocal connections from the cerebellum to the BG via pontine nuclei (Bostan, Dum, & Strick, 2010). Therefore, we see no inconsistency with known anatomy in documenting ipsilateral paired activation, especially given the fact that the effective connectivity analysis revealed strong ipsilateral functional connections between these structures, with information “flowing” from cerebellum to striatum. Furthermore, we know so little about dynamic brain activity during complex behaviors that it may not be prudent to dismiss activation that appears not to conform to our concept of how the brain works.

It is worth mentioning that in all contrasts we found no region in which activity was significantly higher in SCA patients than in healthy controls, indicating a general hypoactivation during motor timing for this clinical population, which was also seen when analyzing the trial-by-trial strategy. This may be the result of inherent limitations of our study design. One might argue that brain structures and systems outside the cerebellum may also be affected in SCA (Maschke et al., 2005) and thus confound the interpretation of our results. Although it is impossible to completely discount this objection, the phenotypes in the participants we studied (SCA types 6 and 8) are generally regarded as having a rather pure cerebellar syndrome (Maschke et al., 2005). We rather believe that our results reflect perturbations in networks that involve cerebellum, especially its connection with striatum. In support of this conjecture is the fact that, although SCA type 8 is associated with pathological abnormalities in the cerebellum alone, some patients with this disorder also have Parkinsonian features, presumably as a result of the disrupted cerebellum–striatum connections. In SCA type 6 individuals, the nigrostriatal dysfunction is described, as well.

Feed-forward control and correctly estimating future actions or states of the motor system are critical for fast and coordinated movements. Here we showed that cerebellar dysfunction in spinocerbellar ataxia patients extends to BG dopaminergic circuits, as well, leading to a failure of predictive feed-forward control mechanism and inaccurate estimation of motor commands consequences (Ebner & Pasalar, 2008). The striatum is the critical input structure of the BG, and it is crucial to both motor control and learning (Humphries & Prescott, 2010). Our results may have also implications for understanding not only for the diseases of the cerebellum but also for the diseases of BG with dopamine dysfunction like Parkinson disease, where feedback processing or reward learning is observed and the fine movement coordination is affected.

Acknowledgments

This work was supported by NIH grant NS40106, MH065598, the Department of Veterans Affairs, the Brain Sciences Chair, a Proshek-Fulbright grant, the Academia Medica Pragensis Foundation, and a “CEITEC-Central European Institute of Technology” project (CZ.1.05/1.1.00/02.0068) from the European Regional Development Fund.

Reprint requests should be sent to James Ashe, Department of Neuroscience, University of Minnesota, 4-134 Molecular and Cellular Biology Building, 420 Washington Ave. S.E., Minneapolis, MN 55455, or via e-mail: ashe@tc.umn.edu.

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