Abstract

The hippocampus and cerebellum play a role in the process of temporal memory formation. The interaction between these brain regions during the prediction of motor executions nevertheless remains unclear. Using fMRI, we show here that the hippocampus and cerebellum are co-activated during a timing-dependent task that requires accurate prediction timing of finger movements following preceding visual cues, but not during 2 control tasks: a reaction task requiring identical coordination of individual and combined fingers without predicting the motor timing, or an imagery task. In addition, functional connectivity analyses reveal that the hippocampus showed increased functional connectivity with the bilateral hemispheres of the cerebellum. These results suggest that hippocampal–cerebellar interplay occurs during spatio-temporal prediction of movements on the basis of visuomotor integration.

Introduction

Predicting the timing of motor sequences on the basis of spatial information is required for the precise execution of complex motor output, such as playing a musical instrument following the directions of a conductor. Spatio-temporal prediction, that is, the anticipation of perceptual events for the execution of the timed motor responses (motor timing) or perceptual judgments, can be achieved by the use of temporal information (e.g., the velocity of an object) and spatial information (e.g., the location of an object) during a task (Assmus et al. 2003; Coull and Nobre 2008; O'Reilly et al. 2008).

The hippocampus and cerebellum underlie spatial and temporal coding in the formation of memories. Examples of spatial coding include spatial navigation and adaptation of goal-oriented behavior (Burguière et al. 2005; Rochefort et al. 2011). Specifically, Rochefort et al. (2011) showed that the spatial coding of place cells was impaired only in the condition requiring egocentric navigation (spatio-temporal memory) of the maze task when protein kinase C-dependent plasticity was inhibited at the Purkinje cell level. In addition, lateralized regions within hippocampus and cerebellum interact during sequential route-based navigation and map-based navigation in humans (Iglói et al. 2010, 2012). For temporal coding, the hippocampus and cerebellum cooperate in controlling temporal aspects of learning-dependent motor output such as trace conditioning (Koekkoek et al. 2003; Takehara et al. 2003; Hoffmann and Berry 2009; Wikgren et al. 2010; MacDonald et al. 2011) and cued fear conditioning (Scelfo et al. 2008; Ruediger et al. 2011). The interaction between the hippocampus and cerebellum during spatio-temporal prediction, however, is not yet fully understood.

We hypothesize that the hippocampus and cerebellum are co-activated and show significant functional connectivity during spatio-temporal prediction, but not during performance of coordinated movements only (i.e., without their predictive timing) or during motor imagery, even though these tasks may by themselves activate either the cerebellum (Hanakawa 2002; Meister et al. 2004; Ito, 2008) or the hippocampus (Ghaem et al. 1997; Sacco et al. 2006; Bird et al. 2010).

We used functional magnetic resonance imaging (fMRI) to measure the brain activity during a modified Serial Interception Sequence Learning (SISL) task (Gobel et al. 2011) that allows to distinguish motor coordination from predictive and reactive timing functions. In addition, we included control conditions with imagined movements to investigate the involvement of the hippocampal and cerebellar systems in the planning of imagined versus executed motor actions. We hypothesized that the hippocampus interacts functionally with both hemispheres of the cerebellum to establish the spatio-temporal prediction of actual movements, but not reactive motor tasks and imagery.

Materials and Methods

Subjects

Sixteen healthy right-handed subjects (age 18–26, 3 females) participated in the experiments. None of the volunteers played musical instruments or rhythmic games on a regular basis. All subjects gave their informed consent prior to the study. The experiment was approved by the ethics committee of the Department of Psychology, the University of Amsterdam, The Netherlands.

Set-up, Conditions, and Experimental Protocol

Setup

The subjects performed modified versions of the SISL task (Gobel et al. 2011), requiring precise timing of actual or imagined finger movements of the left nondominant hand. The task was administered in the MRI scanner (Philips, 3.0-Tesla Achieva). Subjects had to actually press buttons, or imagine pressing buttons, exactly timed to the moment that upward moving black circles (referred to as moving markers) overlapped with corresponding fixed white circles in the top row (referred to as target markers) (Fig. 1). The moving markers appeared at the bottom of the screen, moved upward with constant velocity (2.8 ms per pixel), overlapped with the target markers at the top row and kept moving up until they disappeared off the screen. The moving markers could appear either one by one or in pairs of 2 simultaneously. The diameter of the target markers was 20% of the middle third of the display width. The diameter of moving markers was 20 pixels smaller than that of the target marker so as to fit completely inside the target markers and not to interfere with the moments of the complete overlap between markers observed by subjects. The task was programmed in Matlab (R2010a) using Psychtoolbox 3. The visual stimuli were provided by using LCD projection onto a screen that was visible through a mirror attached to the head coil. Finger responses of the left hand were acquired using a 4-button box (from the little finger to the index finger) (Current Design, HHSC-1 × 4-CL).

Figure 1.

Experimental conditions. Subjects were subjected to 6 different conditions. (a) Corresponding finger movement condition. The subjects press assigned buttons when 1 or 2 moving markers overlap with the target markers. The purpose of the condition is to extract activity related to prediction timing and motor coordination. (b) 4-Finger movement condition. The subject presses all 4 buttons when any of the moving markers overlap with the target markers. Performance depends on timing of motor output. (c) Flash marker condition. Participants are instructed to press the buttons corresponding to the target markers as soon as the target markers flash. As the participants cannot anticipate the timing to press the buttons, the condition requires only finger coordination. (d) Mental rehearsal condition. The purpose of the condition is to imagine movements that require motor coordination and prediction timing. (e) Observation condition. This is the control condition of the mental rehearsal condition. (f) Rest condition. The subject stares at the fixation point. Colored dashed lines and arrows represent the expected locations to press buttons.

Figure 1.

Experimental conditions. Subjects were subjected to 6 different conditions. (a) Corresponding finger movement condition. The subjects press assigned buttons when 1 or 2 moving markers overlap with the target markers. The purpose of the condition is to extract activity related to prediction timing and motor coordination. (b) 4-Finger movement condition. The subject presses all 4 buttons when any of the moving markers overlap with the target markers. Performance depends on timing of motor output. (c) Flash marker condition. Participants are instructed to press the buttons corresponding to the target markers as soon as the target markers flash. As the participants cannot anticipate the timing to press the buttons, the condition requires only finger coordination. (d) Mental rehearsal condition. The purpose of the condition is to imagine movements that require motor coordination and prediction timing. (e) Observation condition. This is the control condition of the mental rehearsal condition. (f) Rest condition. The subject stares at the fixation point. Colored dashed lines and arrows represent the expected locations to press buttons.

Conditions

Each participant was presented 6 different task conditions, to distinguish spatio-temporal prediction, motor coordination, and motor imagery in the main effect analysis of the fMRI data. The main experimental condition requiring spatio-temporal prediction was the Corresponding Finger condition (Fig. 1a). Control conditions consisted of the 4-Finger condition (Fig. 1b), Flash Marker condition (Fig. 1c), Mental Rehearsal condition (Fig. 1d), Observation condition (Fig. 1e), and Rest condition (Fig. 1f). In the Corresponding Finger condition (Fig. 1a), subjects were asked to press the corresponding button or buttons at the moment when the moving markers overlapped with the target markers. In this condition, either a single or 2 simultaneously moving markers could overlap at a given moment. In the 4-Finger condition (Fig. 1b), visual stimuli were exactly the same as those for the Corresponding Finger condition, but all 4 buttons had to be pressed simultaneously when any moving marker overlapped with any of the target markers. Thus, it eliminated the coordination of individual finger movements while maintaining timing. In the Flash Marker condition (Fig. 1c), the target markers flashed for 150 ms at random time intervals from 480 to 1070 ms (average temporal interval: 770 ms). The subjects were asked to respond to the white flashing target markers by pressing the corresponding buttons as soon as they detected them. The sequence of flashing of the target markers was exactly the same in as in the Corresponding Finger condition, but 1) its timing was independent of that of the sequence of upward streaming markers and hence could not be predicted from it, 2) the timing of marker flashes was randomly jittered unlike the fixed timing of the sequence in the Corresponding Finger condition, and 3) used a larger range of stimulus intervals than in the Corresponding Finger condition. We thus maintained the same number of button presses between the Flash Marker Condition and the Corresponding Finger condition but eliminated spatio-temporal prediction only for the Flash Marker condition. In the Mental Rehearsal condition (Fig. 1d), exactly the same visual stimuli were provided as in the Corresponding Finger condition, but now subjects were instructed to imagine pressing the buttons, rather than actually pressing them. In the Observation condition (Fig. 1e), the identical moving markers were presented as in the Corresponding Finger condition, but the white target markers were not displayed, and the subjects were instructed to merely observe the flow of markers without any actual pressing or motor imagery. And finally in the Rest condition (Fig. 1f), participants were instructed to stare at the crosshair fixation point in the middle of the black screen. All conditions were preceded by text-based cues, which appeared on the screen for a duration of 3 s. The sequence of appearance of moving markers followed the same order for all conditions (except the Rest condition during which no markers were shown): AC-B-AD-BC-CD-BD-C-A-AB-D (A: left-most trajectory, B: second trajectory, C: third trajectory, D: right-most trajectory). The average temporal interval between appearances was 600 ms (range 500–660 ms) for all conditions except the Flash Marker condition in which the intervals jittered differently, unpredictably and within a larger range to eliminate the timing aspect. The sequence appeared 4 times in one trial. No feedback to indicate the correct responses was given during the task.

Concertedly, the 6 conditions allowed us to assess different aspects of motor behavior including spatio-temporal prediction, motor coordination, and motor imagery. 1) “Spatio-temporal prediction” (subtracting the Flash Marker condition (Fig. 1c) from the Corresponding Finger condition (Fig. 1a)); the Corresponding Finger condition and the Flash Marker condition were the same in terms of the visual input and the number of button presses made, but they differed with respect to the possibility to predict and plan the exact timing of the movements ahead, which was only possible in the Corresponding Finger condition. 2) “Motor coordination” (subtracting the 4-Finger condition (Fig. 1b) from the Corresponding Finger condition (Fig. 1a)); the Corresponding Finger condition and the 4-Finger condition were the same in terms of visual input, but they differed in that the fingers only had to be moved and coordinated separately in the Corresponding Finger condition (i.e., requiring visuomotor integration at the level of individual finger movements). 3) “Motor imagery” (subtracting the Observation condition (Fig. 1e) from the Mental Rehearsal condition (Fig. 1f)); the Mental Rehearsal condition and the Observation condition were the same in terms of visual input and the number of button presses made (i.e., zero), but they differed in that brain activity related to imagined movements was evoked during the Mental Rehearsal condition, but not during the Observation condition.

Protocol

Participants performed 4 scanning runs. Each run contained 15 blocks (block duration = 33 s; 1 run = 8.25 min; total duration = 33 min) and in each run, all conditions occurred twice except the Rest condition, which occurred 5 times. The order of the 4-Finger condition, Flash Marker condition, Mental Rehearsal condition, and Observation condition was pseudo-randomized within a run to counteract any order effect (Supplementary Fig. 1).

Quantification of Behavioral Performance

We calculated the average percentage of correctly timed responses (correct responses) for each trial during the Corresponding Finger condition and 4-Finger condition. A motor response was regarded correct if it was made within an interval of −150 to 150 ms of the time of perfect overlap of target and moving marker; 150 ms was the time needed for the center of the moving markers to move from the edge to the center of the target markers. Response time (Prediction time) was calculated based on the correct button responses only. For the Flash Marker condition, responses were reactive rather than predictable and were made after observing the flashing stimulus; we quantified performance as the average reaction time of correct responses and as the number of correct responses, in the time window determined as the temporal interval from 100 ms after the onset of one marker flash to the onset of another marker flash (average temporal interval 770 ms, range 480 < t < 1070 ms). The button responses that were performed with the correct button but recorded within 100 ms after the marker flashes were regarded as false alarms and disregarded; the false alarm rate in the time window from 0 to 100 ms was 0.64%, corresponding to 2.4 ± 1.9 (std) per block. We performed an analysis of variance (ANOVA) on the behavioral results, using condition as a within-subject variable.

Imaging Parameters and Acquisition

Functional and structural MR scans were recorded using a 3 T scanner (Philips, 3.0-Tesla Achieva) at the Spinoza Center, University of Amsterdam. For functional MRI scans, whole-brain functional T2*-weight MRI data were acquired using a gradient-echo planar imaging sequence (55 transverse slices with 0 mm gap, ascending slice acquisition; voxel size 2.5 × 2.5 × 2.5 mm3; repetition time (TR) 3179 ms, echo time (TE) 29.93 ms; flip angle 80°; field of view (FOV) 200 × 200 mm2). For the structural MR scans, T1-weighted images were acquired (220 slices; TR 8.2 ms; TE 3.8 ms; inversion time 670.4 ms; FOV 240 × 188 mm2, matrix size 240 × 187; flip angle 8°; voxel size 1 × 1 × 1 mm3). Head movements were minimized by restraining the subject's head using a sponge cushion inside the 32-channel head coil.

fMRI Data Preprocessing

All data analyses were performed using SPM8 (Wellcome Trust Centre for Neuroimaging, University College London, UK). Images were realigned using iterative rigid body transformation, coregistered to the structural MRI images, normalized to the MNI standard brain template (the Montreal Neurological Institute), and smoothed with a 5-mm full-width at half maximum (FWHM) Gaussian kernel. A high pass filter (128 s) was applied.

To obtain accurate spatial normalization of the cerebellum, the spatial unbiased atlas template (SUIT) toolbox (Diedrichsen 2006) was used, separate from and in parallel to the whole-brain analysis: specifically, after the functional images were realigned and coregistrered to the structural images, the cerebellum and brainstem in the structural image were isolated and warped into SUIT space. The functional images were resliced into the SUIT atlas space (resolution 1 mm isotropic) with the mask created by the isolation process of the structural scan to avoid contamination of BOLD signals outside of cerebellum such as the visual cortex. These functional images, dedicated to cerebellum-specific analysis, were then smoothed at 5 mm FWHM, as those dedicated to the whole-brain analysis.

Task-Dependent Activation

Statistical testing was conducted to analyze data on an individual basis and across subjects. First-level analyses were computed using general linear models and convolution with the canonical hemodynamic response function for different experimental conditions. The following contrasts were calculated: spatio-temporal prediction (Corresponding Finger condition (Fig. 1a)—Flash Marker condition (Fig. 1c)), motor coordination (Corresponding Finger condition (Fig. 1a)—4-Finger condition (Fig. 1b)), and motor imagery (Mental Rehearsal condition (Fig. 1d)—Observation condition (Fig. 1e)). To avoid any residual movement effects, the design matrix included the 6 head motion regressors (translations and rotations). In addition, to take into account learning effects of sequential finger movements in the Corresponding Finger condition, Flash Marker condition, and 4-Finger condition, we performed a parametric modulation analysis (Büchel et al. 1998) based on the averaged performance rates, reaction times, and variance of reaction times. The parametric modulators and their temporal derivatives were added into the design matrix as parameters of no interest. Individual fixed-effects statistical parameter maps were generated from the linear contrasts. For the group analysis, each individual subject's first-level contrast images were entered into a second-level random-effect analysis. The contrasts were estimated at 1-sample t-test (uncorrected, P < 0.0005) with spatial extent threshold determined by family-wise error (FWE) correction at cluster size at P < 0.05. The activated regions were identified on the basis of the anatomy toolbox containing probabilistic cytoarchitectonic maps (Eickhoff et al. 2005) and the spatially unbiased atlas for the cerebellum (Diedrichsen et al. 2009).

Functional Connectivity Analysis

Psychophysiological interaction (PPI) analyses (Friston et al. 1997) were used to evaluate functional connectivity between hippocampus or cerebellum (the seed area) with the rest of the brain including the cerebellum during spatio-temporal prediction, to exclude the possibility of independent co-activation of 2 different regions in the main effect analysis. To perform the analysis, we defined a linear model using 3 regressors: 1) the main effect for the Corresponding Finger condition versus Flash Marker condition as a context variable, 2) the activity of the seed, and 3) the PPI between the first regressor (context variable) and second regressor (the activity from the seed region). Seed regions were defined as the voxels within a 5 mm-radius sphere around the local maxima in the left hippocampus and cerebellar regions of the contrast (Corresponding Finger condition – Flash Marker condition) in the group random-effects analysis (uncorrected, P < 0.0005, spatial extent threshold using FWE correction at cluster level at P < 0.05), excluding nonhippocampal and noncerebellar regions on the basis of the anatomy toolbox (Eickhoff et al. 2005). The signal time courses within the seed regions were adjusted using the F-contrast of all conditions to exclude the possible effects explained by contrast columns and extracted as their first eigenvector. The parametric modulators, their temporal derivatives, and head motion regressors used in the main effect were included in the model. The contrasts estimated from the individual PPI analysis were analyzed at a second-level 1-sample t-test (uncorrected, P < 0.0005, spatial extent threshold using FWE correction at cluster level at P < 0.05).

As an additional analysis, to reveal possible hippocampal involvement during the motor imagery contrast, we tested functional connectivity from the coordinates of the cerebellar regions found in the main effect analysis of the motor imagery contrast, to examine the strength of the functional connectivity toward hippocampus during motor imagery. Following the extraction and F-contrast adjustment of the signal time course from the cerebellar seed regions during motor imagery, we conducted the same procedures as we described above for the PPI analysis of spatio-temporal prediction.

Results

Behavior

Spatio-Temporal Prediction

The percentage of correct responses gradually increased over the trials from 40% to 70%, showing learning of task-related visuomotor integration (F(7,120) = 2.35, P < 0.05, ANOVA) (Fig. 2a). The distribution of the prediction time for the correct button responses skewed toward negative (i.e., predictive) reaction times and its mean saturated at −55 ms before the exact moment when the moving marker overlapped with the target marker (F(7,120) = 3.77, P < 0.05, ANOVA) (Fig. 2b). Even if we restricted the analysis to single-finger responses only, thereby excluding the possibly confounding effect of coordination of movements across fingers in the 2-finger responses, we still observed that the prediction time shifted slightly toward −60 ms (F(7,120) = 3.94, P < 0.05, ANOVA), reflecting establishment of prediction timing. The averaged variance of the prediction time across trials did not change across trials (F(7,120) = 1.46, P = 0.1881, ANOVA). In the 4-Finger condition, the percentage of correct responses was stable over the trials (F(7,120) = 0.65, P = 0.7162, ANOVA) but the percentage of correct responses started at a higher level at the first trial (∼57% when compared with ∼40% in the Corresponding Finger condition) (Supplementary Fig. 2a). The prediction time was ∼−50 ms at the start of the first trial and did not significantly change over the trials (F(7,120) = 1.03, P = 0.4154, ANOVA) (Supplementary Fig. 2b). The averaged variance of the prediction time was stable across trials (F(7,120) = 0.92, P = 0.4939, ANOVA). In the Flash Marker condition, the percentage of correct responses increased from 63% to 84% F(7,120) = 2.89, P < 0.05, ANOVA), while the reaction time shifted from ∼600 to 500 ms (F(7,120) = 4.01, P < 0.001, ANOVA) (Supplementary Fig. 3a,b). The averaged variance of the reaction time in Flash Marker condition was not significantly different across trials (F(7,120) = 1.58, P = 0.1489, ANOVA). Overall, there were only few responses between 0 and 100 ms (false alarm rate: 0.64%, corresponding to 2.4 ± 1.9 (STD) per block).

Figure 2.

Behavioral results for the Corresponding Finger condition. (a) Percentage of correct responses. The average percentage of correct responses increases across trials indicating learning-dependent prediction timing of sequential motor responses (F(7,120) = 2.35, P < 0.05, ANOVA). Error bars indicate standard errors of the mean (SEM). (b) Prediction time of movements. The mean decrease and the probability distribution saturates at a value of 55 ms before the moment of marker overlap (F(7,120) = 3.77, P < 0.05, ANOVA), indicating that spatio-temporal prediction for the motor execution occurs. Prediction time 0 represents the moment of complete overlap between target and motion markers. White dot: median. Black line: 25th–75th percentile. Gray-colored area: probability distribution of prediction time.

Figure 2.

Behavioral results for the Corresponding Finger condition. (a) Percentage of correct responses. The average percentage of correct responses increases across trials indicating learning-dependent prediction timing of sequential motor responses (F(7,120) = 2.35, P < 0.05, ANOVA). Error bars indicate standard errors of the mean (SEM). (b) Prediction time of movements. The mean decrease and the probability distribution saturates at a value of 55 ms before the moment of marker overlap (F(7,120) = 3.77, P < 0.05, ANOVA), indicating that spatio-temporal prediction for the motor execution occurs. Prediction time 0 represents the moment of complete overlap between target and motion markers. White dot: median. Black line: 25th–75th percentile. Gray-colored area: probability distribution of prediction time.

Neuroimaging

To reveal increments in (co)activation related to spatio-temporal prediction, motor coordination, and motor imagery we compared the Corresponding Finger condition to the Flash Marker condition, the Corresponding Finger condition to the 4-Finger condition, and the Mental Rehearsal condition to the Observation condition, respectively. For the prediction-related increment of activity, Figure 3a presents a map of the hippocampal and cerebellar regions that were significantly more strongly activated in the Corresponding Finger condition when compared with the Flash Marker condition. It shows activation of the bilateral hemisphere and central vermis of cerebellar lobule VI, the left side of hippocampus, and visual cortex (V1, Cuneus, and V2) (for details, see Table 1). The right side of hippocampus was activated but failed to reach the threshold (FWE correction at cluster level at P < 0.05). The activity related to spatio-temporal prediction was not biased by the amount of motor execution, because the total number of button presses between the Corresponding Finger condition and Flash Marker condition did not differ (t(254) = −1.84, P = 0.0664, 2-sample t-test).

Table 1

Activations for spatio-temporal prediction, motor coordination, and motor imagery

Region t-value Cluster size (voxels) MNI coordinate
 
z-score 
x y z 
(1) Spatio-temporal prediction 
 Primary visual cortex (V1) L 11.81 5964 −2 −76 −1 5.84 
 Cuneus R 11.33  −82 31 5.74 
 Secondary visual cortex (V2) R 11.3  −78 13 5.73 
 Hippocampus (Subiculum) L 7.64 115 −22 −32 −9 4.81 
 Cerebellum lobule VI (Hem) L 7.2 64 −18 −64 −17 4.67 
 Cerebellum lobule VI (Vermis) 6.23 73 −72 −27 4.31 
 Cerebellum lobule VI (Hem) R 5.27 45 18 −60 −19 3.9 
(2) Motor coordination 
 Cerebellum lobule VI (Hem) L 9.53 644 −26 −62 −23 5.34 
 Cerebellum lobule VI (Hem) R 9.08 600 24 −68 −23 5.22 
 Supplementary motor cortex R 8.37 360 51 5.03 
 Primary somatosensory cortex L 8.29 705 −46 −38 57 5.01 
 Precuneus 8.17 489 −66 55 4.97 
 Primary somatosensory cortex R 7.4 379 46 −30 61 4.73 
 Cuneus R 6.62 331 −78 31 4.46 
 Premotor cortex R 6.61 110 30 −6 57 4.46 
 Cerebellum lobule V (Vermis) R 6.41 30 −54 −7 4.38 
 Cerebellum lobule VIIIa (Hem) R 6.32 64 22 −62 −49 4.35 
(3) Motor imagery 
 Lingual gyrus (hOC3v, V3v) R 10.65 2206 24 −84 −7 5.6 
 Primary visual cortex (V1) R 9.5  −86 −5 5.33 
 Thalamus R 9.41 152 20 −12 −1 5.31 
 Caudate L 7.35 296 −16 -26 21 4.72 
 Middle occipital gyrus R 7.04 92 40 −66 4.61 
 Inferior parietal lobule L 6.05 167 −52 −30 43 4.24 
 Supplementary motor area L 5.09 97 −2 55 3.82 
 Cerebellum lobule VI (Hem) L 5.75 68 −32 −54 −29 4.12 
 Superior parietal lobule (7A) L 5.6 123 −26 −60 61 4.05 
 Cerebellum lobule VI (Hem) R 5.56 63 38 −58 −25 4.03 
Region t-value Cluster size (voxels) MNI coordinate
 
z-score 
x y z 
(1) Spatio-temporal prediction 
 Primary visual cortex (V1) L 11.81 5964 −2 −76 −1 5.84 
 Cuneus R 11.33  −82 31 5.74 
 Secondary visual cortex (V2) R 11.3  −78 13 5.73 
 Hippocampus (Subiculum) L 7.64 115 −22 −32 −9 4.81 
 Cerebellum lobule VI (Hem) L 7.2 64 −18 −64 −17 4.67 
 Cerebellum lobule VI (Vermis) 6.23 73 −72 −27 4.31 
 Cerebellum lobule VI (Hem) R 5.27 45 18 −60 −19 3.9 
(2) Motor coordination 
 Cerebellum lobule VI (Hem) L 9.53 644 −26 −62 −23 5.34 
 Cerebellum lobule VI (Hem) R 9.08 600 24 −68 −23 5.22 
 Supplementary motor cortex R 8.37 360 51 5.03 
 Primary somatosensory cortex L 8.29 705 −46 −38 57 5.01 
 Precuneus 8.17 489 −66 55 4.97 
 Primary somatosensory cortex R 7.4 379 46 −30 61 4.73 
 Cuneus R 6.62 331 −78 31 4.46 
 Premotor cortex R 6.61 110 30 −6 57 4.46 
 Cerebellum lobule V (Vermis) R 6.41 30 −54 −7 4.38 
 Cerebellum lobule VIIIa (Hem) R 6.32 64 22 −62 −49 4.35 
(3) Motor imagery 
 Lingual gyrus (hOC3v, V3v) R 10.65 2206 24 −84 −7 5.6 
 Primary visual cortex (V1) R 9.5  −86 −5 5.33 
 Thalamus R 9.41 152 20 −12 −1 5.31 
 Caudate L 7.35 296 −16 -26 21 4.72 
 Middle occipital gyrus R 7.04 92 40 −66 4.61 
 Inferior parietal lobule L 6.05 167 −52 −30 43 4.24 
 Supplementary motor area L 5.09 97 −2 55 3.82 
 Cerebellum lobule VI (Hem) L 5.75 68 −32 −54 −29 4.12 
 Superior parietal lobule (7A) L 5.6 123 −26 −60 61 4.05 
 Cerebellum lobule VI (Hem) R 5.56 63 38 −58 −25 4.03 

L, left; R, right. P < 0.0005, uncorrected, spatial extent threshold is determined by family-wise error (FWE) correction for the multiple comparison at cluster level (P < 0.05).

In contrast, the motor coordination-related and motor imagery-related increments of activity were only found in the cerebellum (motor coordination: bilateral hemisphere in cerebellar lobule VI, right vermis of central region of lobule V, and right hemisphere in cerebellar lobule VIIIa; motor imagery: bilateral hemisphere in lobule VI) (Fig. 3b,c).

Figure 3.

Functional MRI of hippocampal and cerebellar activity during spatio-temporal prediction, motor coordination, and motor imagery. Spatio-temporal prediction (a) was assessed by the contrast Corresponding Finger condition—flash marker condition, motor coordination (b) was assessed by the contrast Corresponding Finger condition—4-Finger condition, and motor imagery (c) was assessed by the contrast mental rehearsal condition—observation condition. To allow comparison of hippocampal activation across the different contrasts, the x, y, and z planes are kept constant. The x and y coordination of cerebellar activations are displayed on the Figure. Note that the hippocampus was only activated during spatio-temporal prediction.

Figure 3.

Functional MRI of hippocampal and cerebellar activity during spatio-temporal prediction, motor coordination, and motor imagery. Spatio-temporal prediction (a) was assessed by the contrast Corresponding Finger condition—flash marker condition, motor coordination (b) was assessed by the contrast Corresponding Finger condition—4-Finger condition, and motor imagery (c) was assessed by the contrast mental rehearsal condition—observation condition. To allow comparison of hippocampal activation across the different contrasts, the x, y, and z planes are kept constant. The x and y coordination of cerebellar activations are displayed on the Figure. Note that the hippocampus was only activated during spatio-temporal prediction.

Functional Connectivity

To test whether the left hippocampal regions activated in the spatio-temporal prediction contrast show functional connectivity with the cerebellar regions, we used PPI analysis using the clusters within the left hippocampus found for this contrast. We defined the hippocampal activity as the average of the activation in 5 mm radial spheres surrounding the peaks of activation of the left side ([−22, −32, −9]), masking out nonhippocampal tissue. After analyzing the connectivity at the level of individual subjects, we calculated the main effect at the group level using 1-sample t-tests (uncorrected P < 0.0005, Spatial extent threshold: FWE correction at cluster level at P < 0.05). The cerebellar regions found in the PPI analysis from the left hippocampus seed region were the bilateral hemispheric lobules VI, right hemispheric region in lobule VIIa Crus I, left hemispheric region of lobule VIIIb (uncorrected P < 0.0005, Spatial extent treshold: FWE correction at cluster level, P < 0.05) (Fig. 4). In addition to the cerebellar regions, functional connectivity was found for the angular gyrus, inferior occipital gyrus, inferior parietal lobule, inferior temporal gyrus, middle frontal gyrus, middle occipital gyrus, premotor cortex, primary visual cortex (V1), ventral V4 (V4v), and superior parietal lobule (see Table 2).

Table 2

Functional connectivity from hippocampus in the spatio-temporal prediction

Seed region: left hippocampus [−22, −32, −9]
 
Region t-value Cluster size (voxels) MNI coordinate
 
z-score 
x y z 
Angular gyrus R 8.86 1004 28 −54 45 5.16 
Superior parietal lobule (7A) R 7.33  30 −66 55 4.71 
Superior parietal lobule (7A) R 7.12  30 −58 67 4.64 
Superior parietal lobule (7A) L 7.93 309 −28 −68 55 4.9 
Inferior parietal lobule L 6.82 398 −42 −40 45 4.53 
Middle occipital gyrus L 6.8 173 −24 −72 25 4.53 
Middle frontal gyrus R 6.67 148 40 59 4.48 
Primary visual cortex (V1) R 6.53 513 14 −92 −1 4.43 
Cerebellum lobule VIIa Crus I (Hem) R 6.37 33 46 −56 −31 4.37 
Inferior occipital gyrus L 6.18 104 −40 −56 −5 4.29 
Inferior temporal gyrus R 6.17 96 42 −56 4.29 
Primary visual cortex (V1) L 6.16 153 −8 −90 4.29 
Middle occipital gyrus L 6.1 158 −34 −84 4.26 
Superior parietal lobule (7A) L 6.02 50 −26 −54 69 4.23 
Premotor cortex L 5.77 70 −44 −2 51 4.12 
Ventral V4 (V4v) L 5.53 151 −34 −80 −13 4.02 
Premotor cortex L 5.49 94 −30 −8 65 
Cerebellum lobule VI (Hem) L 5.34 69 −30 −56 −27 3.94 
Cerebellum lobule VI (Hem) R 5.13 35 36 −62 −27 3.84 
Cerebellum lobule VIIIb (Hem) L 4.87 38 −16 −62 −49 3.71 
Seed region: left hippocampus [−22, −32, −9]
 
Region t-value Cluster size (voxels) MNI coordinate
 
z-score 
x y z 
Angular gyrus R 8.86 1004 28 −54 45 5.16 
Superior parietal lobule (7A) R 7.33  30 −66 55 4.71 
Superior parietal lobule (7A) R 7.12  30 −58 67 4.64 
Superior parietal lobule (7A) L 7.93 309 −28 −68 55 4.9 
Inferior parietal lobule L 6.82 398 −42 −40 45 4.53 
Middle occipital gyrus L 6.8 173 −24 −72 25 4.53 
Middle frontal gyrus R 6.67 148 40 59 4.48 
Primary visual cortex (V1) R 6.53 513 14 −92 −1 4.43 
Cerebellum lobule VIIa Crus I (Hem) R 6.37 33 46 −56 −31 4.37 
Inferior occipital gyrus L 6.18 104 −40 −56 −5 4.29 
Inferior temporal gyrus R 6.17 96 42 −56 4.29 
Primary visual cortex (V1) L 6.16 153 −8 −90 4.29 
Middle occipital gyrus L 6.1 158 −34 −84 4.26 
Superior parietal lobule (7A) L 6.02 50 −26 −54 69 4.23 
Premotor cortex L 5.77 70 −44 −2 51 4.12 
Ventral V4 (V4v) L 5.53 151 −34 −80 −13 4.02 
Premotor cortex L 5.49 94 −30 −8 65 
Cerebellum lobule VI (Hem) L 5.34 69 −30 −56 −27 3.94 
Cerebellum lobule VI (Hem) R 5.13 35 36 −62 −27 3.84 
Cerebellum lobule VIIIb (Hem) L 4.87 38 −16 −62 −49 3.71 

L, left; R, right. P < 0.0005, uncorrected, spatial extent threshold is determined by family-wise error (FWE) correction for the multiple comparison at cluster level (P<0.05).

Figure 4.

Hippocampal–cerebellar functional connectivity during spatio-temporal prediction. Activations represent the functional connectivity with the seed regions in the context of spatio-temporal prediction (Seed region: left hippocampus ([−22, −32, −9] (uncorrected P < 0.0005, Cluster size threshold FWE correction at cluster level (P < 0.05)).

Figure 4.

Hippocampal–cerebellar functional connectivity during spatio-temporal prediction. Activations represent the functional connectivity with the seed regions in the context of spatio-temporal prediction (Seed region: left hippocampus ([−22, −32, −9] (uncorrected P < 0.0005, Cluster size threshold FWE correction at cluster level (P < 0.05)).

The common regions identified in both main effect analysis and PPI analysis were the bilateral cerebellar lobule VI and primary visual cortex (V1). For the inverse connectivity analyses, PPI analysis from the 3 cerebellar seed coordinates based on the main effect analysis in the spatio-temporal prediction condition did not show any significant clusters in hippocampus (uncorrected P < 0.0005, Spatial extent: FWE correction at cluster level, P < 0.05). In addition, a seed voxel placed in the right side of cerebellum based on the motor imagery-related activation similarly yielded a functional connectivity in the left hippocampus that remained below threshold (uncorrected P < 0.0005, Spatial extent: FWE correction at cluster level, P < 0.05).

Discussion

In this study, we have adapted a finger movement task to tease apart different aspects of motor control including spatio-temporal prediction, finger coordination, and imagination of movements. We show that subjects display prediction and improved accuracy across trials and that the prediction-related contrast in the main effect analysis shows co-activation of hippocampus and cerebellum. Functional connectivity analyses reveal that the hippocampal activations significantly interact with specific cerebellar regions, only in the context of spatio-temporal prediction of fine motor skills.

Behavioral data

The increase in correct responses and the restricted distribution of the predictive response time in the Corresponding Finger condition reflect the improvement of motor control and establishment of spatio-temporal prediction with timed motor executions. The response probability shows that the motor timing of the button responses becomes stable following the initial trial and slightly antecedent to the moment of marker overlap, indicating establishment of the prediction. Establishment of spatio-temporal prediction in response to perceptual events during a task can be categorized as a type of learning-dependent timing (in this case “learning-dependent prediction timing”), as it requires movements to be timed to ongoing visual input; that is, the movement can be prepared or predicted, ahead of time.

A performance improvement and similar antecedent responding are not observed in the 4-Finger movement condition that requires prediction timing, but not individual control of fingers or finger combinations. In contrast, in the Flash Marker condition requiring reactive instead of predicted movements of individual and combined fingers, the performance improved across trials and the average reaction times became faster upon overlap of the moving markers with the target markers, but did not become predictive, that is, antecedent, to the flashing, indicating absence of prediction learning based on timing aspects of the task. The changes may thus reflect simply improvement of reaction timing in the context of the task. Motor execution improvements based on temporal prediction of the motor executions in the Flash Marker condition cannot occur, owing to the randomized, jittered, and larger interstimulus interval range between marker flashes, such that the timing of the flashes is unpredictable and the subjects passively react to, but cannot predict, the timing of flashes. Spatial prediction may still have occurred to a degree during the Flash Marker condition, as the sequence of responses was repeated, but since the temporal prediction is no longer possible, the integration of spatial and temporal prediction cannot occur. Also, the low percentage of correct button responses in the time window from 0 to 100 ms after marker overlap (0.64%) is consistent with a reactive rather than predictive timing in this condition.

Brain activation

We measured brain activation in the 6 variations of the task to extract the responsible regions during spatio-temporal prediction, motor coordination, and motor imagery. Previous research that addressed cerebellar regions involved in temporal processing has shown results partly overlapping with ours, that is, lobule VI, VII, and superior vermis (as reviewed in Spencer and Ivry 2013). The differences may reflect the different demand of the task used in that study; some tasks require explicit timing, that is, the overt estimation or discrimination of the duration of perceptual events, while our task requires implicit timing, that is, the indirect use of spatial-temporal information in responding to visual stimuli corresponding to each button. The difference in findings implies that different mechanisms may be recruited to process explicit versus implicit timing within the cerebellum. We report here that both left posterior hippocampus and several cerebellar regions (i.e., the bilateral hemisphere and central vermis of cerebellar lobules VI) showed activity only during the spatio-temporal prediction aspect of the task. The activated regions found in lobule VI agree with part of the regions found in a previous study, which showed the neural correlate of motor and sensory timing (Bares et al. 2007, 2011; Aso et al. 2011) (Fig. 3).

In contrast to prediction-related activation, the coordination of individual and combined fingers did not induce joint activation of the cerebellum and hippocampus, but solely of the cerebellum (bilateral hemisphere in cerebellar lobule VI, right vermis of central region of lobule V, and right hemisphere in cerebellar lobule VIIIa). Similarly, for imagined timed movements, the cerebellum, but not the hippocampus, was activated (bilateral hemisphere in lobule VI) although cortical areas (superior and inferior parietal areas and supplementary motor cortex) observed during motor coordination contrast and motor imagery were consistent with previous neuroimaging research of complex motor coordination (Sadato et al. 1997; Witt et al. 2008) and motor imagery (Parsons et al. 1995; Gerardin et al. 2000; Richter et al. 2000; Kosslyn et al. 2001). Lobule V and VI have been observed as part of a sensorimotor network, with especially lobule V involved in arm and finger movements (Grodd et al. 2001; Habas et al. 2009; Stoodley and Schmahmann 2010; Stoodley et al. 2012). These data are in line with outcomes of fMRI studies employing imagery tasks involving specific timing and sequential finger coordination, such as imagery of playing a piano and a finger-tapping task (Hanakawa 2002; Meister et al. 2004). Possibly, the cerebellum participates in motor imagery for forming and using internal models so as to facilitate predictions during cognitive control, regardless of actual performance (Ito 2008). The lack of hippocampal activation during our motor imagery-timing task suggests that a hippocampal contribution is only needed for real as opposed to imagined timing of finger movements. Yet, the hippocampus appears to be activated during mental navigation and motor imagery of locomotion (Ghaem et al. 1997; Sacco et al. 2006; Bird et al. 2010). Future studies will have to elucidate to what extent the difference in results reflects a difference in exact requirements of timing and/or motor domains. The different conditions of our task matched in terms of visual-temporal input and motor output, but differed only in terms of the demands placed on visuomotor integration, allowing us to extract brain activation relevant for spatio-temporal prediction, motor coordination, or motor imagery separately. In addition, our finding that parts of lobule VI were activated during spatio-temporal prediction is in line with the notion that our task included not merely motor output but also a cognitive aspect of the prediction of a complex motor sequence (Cross et al. 2013).

To confirm whether the hippocampal and cerebellar activations observed in the main effect analysis interact with each other in spatio-temporal prediction, we conducted a psychophysiological interaction analysis (Friston et al. 1997). As we hypothesized, the functional connectivity analyses in spatio-temporal prediction showed that the left hippocampus connect extensively to several cerebellar lobules in the hemispheres; the left hippocampus connects to the bilateral hemispheric lobules VI, right hemispheric region in lobule VIIa Crus I, and left side of lobule VIIIb. Participation of lobule VII and VIII is consistent with previous research in the context of spatio-temporal prediction in the perceptual judgment task (O'Reilly et al. 2008). In addition to the cerebellar regions, functional connectivity in hippocampus was found for visuomotor system (the angular gyrus, inferior occipital gyrus, inferior parietal lobule, inferior temporal gyrus, middle frontal gyrus, middle occipital gyrus, primary visual cortex (V1), ventral V4 (V4v), premotor cortex, superior parietal lobule). The premotor and parietal cortices are reported to involve in the implicit timing with cerebellum (Coull and Nobre 2008). Indeed, a recent cerebellar-patient study with the perceptual judgment task that demands spatio-temporal prediction shows that the cerebellum acts to recalibrate predictive models from perceptual cues (Roth et al. 2013).

The main effect analysis and the functional connectivity analysis share lobule VI as the common cerebellar regions, in addition to the primary visual cortex. As cerebellar lobule VI is commonly found activated in both sensorimotor and cognitive processes (Kelly and Strick 2003; Stoodley and Schmahmann 2009, 2010; Stoodley et al. 2012), our results may indicate that the hippocampus contributes to establish spatio-temporal prediction by integrating complex motor execution and specific timing onsets in a network of several brain regions responsible for spatio-temporal processing while the cerebellum may contribute to optimize the spatio-temporal prediction that is highly required for accomplishing the task. To our knowledge, our data are the first demonstration of a hippocampal–cerebellar interaction during spatio-temporal prediction.

Functional implications of hippocampal–cerebellar interaction

Information about hippocampal–cerebellar functional interaction is scant. In the spatial domain, cerebellar impairment leads to malfunction of the spatial code (as determined by place cells) in the hippocampus, causing disorientation in a maze in circumstances where self-motion information from the cerebellum is required to navigate in the maze (Rochefort et al. 2011). Spatial processing recruits lateralized regions within hippocampus and cerebellum; when sequential route-based navigation is required, the right cerebellar lobule VIIa and left hippocampus appear to form a functional loop together with the prefrontal cortex, whereas when map-based navigation is required the left cerebellar lobule VIIa and right hippocampus form a circuit with the parietal cortex (Iglói et al. 2010, 2012).

In the temporal domain, however, little direct physiological evidence for the interaction of hippocampus and cerebellum has been reported although many studies including animal and human patient studies show either hippocampal or cerebellar involvement in processes of temporal information (Ivry 1996; O'Reilly et al. 2008; Grube et al. 2010; MacDonald et al. 2011). A recent study reported the synchronization of theta oscillations between hippocampus (CA1) and cerebellum in eyeblink conditioning (Hoffmann and Berry 2009) indicating that the hippocampus modulates the function of the cerebellum. In addition, eye blink conditioning in an fMRI study in humans showed that hippocampal activation is identified only during trace conditioning (the temporal gap between off set of conditioned stimulus and onset of unconditioned stimulus) while the cerebellar lobule VI activates in both trace conditioning and delay conditioning (in the latter, the termination of the conditioned and unconditioned stimulus occurs simultaneously) (Cheng et al. 2008). From a theoretical aspect, a computational model of learning of adaptive timing proposes the interaction between the internal clock of the hippocampus and that of the cerebellum; the neural circuits of the hippocampus may modulate attention of cues, while the timing circuit (internal clock) of the olivocerebellar system may control timing of motor planning and execution (Grossberg and Merrill 1996; De Zeeuw et al. 2011). The hippocampal clock may thus cooperate with the cerebellar clock and its forward modeling (Wolpert et al. 1998), that is, the internal model of motor timing, to learn the interval between the moving markers and the timing of motor execution and to predict the upcoming perceptual events.

Like for spatial processing, we found a lateralization of the interaction between hippocampus and cerebellum as the left side of hippocampus, the region implicated in spatio-temporal memory formation (Iglói et al. 2010, 2012), was more strongly and extensively activated than its right counterpart in the main effect analysis; anatomically, the cerebellum connects preferentially with cerebral regions in a contralateral manner. Our results reveal functional interaction using left hippocampal seeds each with both ipsi- and contralateral cerebellar regions. Although we found hippocampal–cerebellar interaction during spatio-temporal prediction in the PPI analysis, a directional information flow (causal relationship) about hippocampal–cerebellar functional interaction remains obscure because PPI analysis cannot infer causality (Friston et al. 1997; O'Reilly et al. 2012). Classical studies using animal experiments have reported a bi-directional projection between cerebellum and hippocampus (Whiteside and Snider, 1953; Heath and Harper, 1974; Snider and Maiti, 1976; Newman and Reza, 1979), yet anatomical connectivity between these structures remains ill understood; at the least, it appears multisynaptic, offering possibility for widespread and bilateral functional connectivity. Future analyses will need to use methods for determining the directional causality between hippocampus and cerebellum during spatio-temporal prediction, such as dynamic causal modeling (Friston et al. 2003) or Granger causality, neither of which was suited for the current study design; alternatively, intervention studies using transcranial magnetic stimulation may provide a means to unravel causal relationships, although current tools allow only to stimulate relatively superficial cortical and cerebellar areas.

In conclusion, we have shown here an interaction between hippocampus and cerebellum in the performance of a novel task requiring spatio-temporal prediction, or learning-dependent prediction timing. Future experiments and analyses will need to reveal the nature of the lateralization (e.g., using tasks requiring one or both hands, comparing the activations of spatial, temporal, and spatio-temporal prediction task, conducting patient studies with selective lesions to either hippocampal or cerebellar subfields) and the direction of information flow between hippocampus and cerebellum.

Supplementary Material

Supplementary material can be found at: http://www.cercor.oxfordjournals.org/.

Funding

This work was supported by the Programmes for Excellence “Brain & Cognition: an Integrated Approach” number 433-09-245. E.J.W.V.S. is funded by VICI Grant 453.07.001 of the Netherlands Organization of Scientific Research (NWO). C.I.D.Z. is funded by the Dutch Organization for Medical Sciences (ZonMw), Life Sciences (A.L.W.), Senter (NeuroBasic), and the ERC-advanced, CEREBNET and C7 programs of the European Community.

Notes

We are grateful to Dr Atsuko Takashima for her comments of the methods of analysis, and group members in Department of Sleep and Cognition and Department of Cerebellar and Cognition, Netherlands Institute for Neuroscience, for comments of the task design and results. Conflict of Interest: None declared.

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