The cerebellum is classically associated with motor control. However, accumulating evidence suggests its functions may extend to cognitive processes including navigation (Petrosini et al., 1998; Burguière et al., 2005; Rondi-Reig and Burguière, 2005; Buckner, 2013; Koziol et al., 2014; Stoodley et al., 2017). Indeed, anatomical and functional connectivity has been described between cerebellum and cortical areas that are engaged in cognitive tasks (Kim et al., 1994; Kelly and Strick, 2003; Ramnani, 2006; Watson et al., 2009; Watson et al., 2014; Stoodley and Schmahmann, 2010). Furthermore, the cerebellum has recently been found to form functional networks with subcortical structures associated with higher order functions, such as the basal ganglia (Kelly and Strick, 2004; Hoshi et al., 2005; Bostan et al., 2010; Chen et al., 2014; see Bostan and Strick, 2018 for review), ventral tegmental area (Rogers et al., 2011; Carta et al., 2019) and hippocampus (Rochefort et al., 2013; Iglói et al., 2015; Yu and Krook-Magnuson, 2015; Babayan et al., 2017).

In the hippocampus, spontaneous local field potential (LFP) activity (Iwata and Snider, 1959; Babb et al., 1974; Snider and Maiti, 1975; Krook-Magnuson et al., 2014) and place cell properties (Rochefort et al., 2011; Lefort et al., 2019) are profoundly modulated following cerebellar manipulation (Rondi-Reig et al., 2014 for review). A recent study has also described, at the single cell and blood-oxygen-level-dependent signal level, sustained activation in the dorsal hippocampus during optogenetic enhancement of cerebellar nuclei output in head-fixed mice (Choe et al., 2018). These data point toward the existence of an anatomical projection from the cerebellum to the hippocampus. However, the direct or indirect nature of the connection between the two structures remains unclear. The suggestion of a direct connection between these two structures has been claimed by a recent tractography study in humans (Arrigo et al., 2014) and the presence of short-latency evoked field potentials (2–4 ms) in cat and rat hippocampi after electrical stimulation of the cerebellar vermal and paravermal regions (Whiteside and Snider, 1953; Harper and Heath, 1973; Snider and Maiti, 1976; Heath et al., 1978; Newman and Reza, 1979). However, secondary hippocampal field responses have also been described, at a latency of 12–15 ms following cerebellar stimulation, suggesting the existence of an indirect pathway (Whiteside and Snider, 1953).

Taken together, even if these studies provide compelling physiological evidence of cerebellar influences on the hippocampus, they do not provide direct evidence of neuroanatomical connectivity between the two regions. Given the known complex, modular functional and anatomical organization of the cerebellum (Apps and Hawkes, 2009) this represents a major gap in our understanding of the network architecture linking the two structures. In addition, these studies provide no measure of dynamic physiological communication or associated synchronization between the two structures, which is thought to be essential for maintaining distributed network functions (e.g. Fries, 2005).

Therefore, this study addresses two fundamental, unanswered questions: which regions of the cerebellum are anatomically connected to the hippocampus and what are the spatio-temporal dynamics of synchronized cerebello-hippocampal activity during behavior? To address these unresolved questions, we used rabies virus as a retrograde transneuronal tracer to determine the extent and topographic organization of cerebellar input to the hippocampus. Based upon the anatomical tracing results, we then studied the levels of synchronization between LFPs recorded simultaneously from the cerebellum and dorsal hippocampus in freely moving mice as a proxy for potential cross-structure interactions. We reveal that specific cerebellar modules are anatomically connected to the hippocampus and that these inter-connected regions dynamically synchronized their activity during behavior.

To study the topographical organization of ascending, cerebello-hippocampal projections, we unilaterally injected rabies virus (RABV), together with fluorescent cholera toxin β-subunit (CTb), into the left hippocampus. The extent of the injection site, identified by the presence of fluorescent CTb, included both CA1 (stratum lacunosum-moleculare) and DG (molecular layer, granule cell layer and hilus, Figure 1—figure supplement 1).

Cerebellar projections to the hippocampus are precisely topographically organized

We characterized the presence of retrograde transneuronally RABV-infected neurons after survival times of 30, 48, 58 and 66 hr (Suzuki et al., 2012; Aoki et al., 2017; Coulon et al., 2011). At 30 hr post-infection, staining was found predominantly in the medial septum diagonal band of Broca (MSDB), the lateral and medial entorhinal cortices, the perirhinal cortex and the supramammillary nucleus of the hypothalamus (SUM). Sparse staining was also found in a subset of mice, in the raphe nucleus and in other hypothalamic and thalamic regions (Figure 1—figure supplement 2). All these structures correspond to first-order relays as CTb staining was also systematically associated with each of the sites containing rabies infected neurons (Figure 1—figure supplement 3). Importantly, no RABV or CTb labeling was found in the cerebellum at this time-course ruling out the existence of a direct cerebello-hippocampal DG pathway in mice. Interestingly, among these putative first relays, the septum, the hypothalamus and the raphe nucleus receive direct projections from the deep cerebellar nuclei in cat (Paul et al., 1973), rat (Teune et al., 2000) and Macaca (Haines et al., 1990). In accordance with this, some RABV-infected neurons were observed in the deep cerebellar and vestibular nuclei 48 hr post-hippocampal infection and some labeled cells were found in the cerebellar cortex at 58 hr p.i. (Figure 1A, inset, Figure 1—figure supplement 4) suggesting the existence of single-relay pathways (i.e. di-synaptic connections) between the cerebellum and hippocampus.

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Summary table of RABV labeling in the cerebellum 58 hr post hippocampal infection.

Quantification of the number of rabies-positives cells in the ipsi- (i.) and contra-lateral (c.) side of the cerebellar cortex, the deep cerebellar and vestibular nuclei. Nu, nucleus; Lob, lobule; pm, paramedian lobe; cop, copula pyramidis.

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At 48 hr post-infection, RABV labeling was also found in mammillary bodies, amygdala and several midbrain and pontine regions such as the periaqueductal gray (PAG), the nucleus incertus and the laterodorsal tegmental nucleus (LtDG). These regions, which are known to receive direct projections from the DCN (Teune et al., 2000) were neither stained with CTb nor RABV-positive at 30 hr post-infection and therefore represent a putative additional pathway involving two relays between the cerebellum and the hippocampus (Figure 1—figure supplement 4).

58 hr post-infection, abundant and strong staining was found in the fastigial and dentate nuclei, with almost no staining in the interpositus (Figure 1B–F). Within the fastigial and dentate, RABV-labeled cells were found to be topographically restricted to caudal and central regions, respectively (Figure 1G).

Following 66 hr of incubation, the number of strongly labeled cells increased in the DCN and vestibular nuclei (Figure 1A); however, the topographical distribution remained unchanged (Figure 1—figure supplement 5). At the level of the cerebellar cortex, longitudinal clusters of RABV+ Purkinje cells (PCs) were found bilaterally across highly restricted central and flocculo-nodular regions (Figure 1L). In the central cerebellum (i.e. lobules VI, VII and associated hemispheres), clusters were particularly concentrated in lobule VI and Crus I (Figure 1H,I,M). In the flocculo-nodular cerebellum, RABV-labeled cells were found in the dorsal and ventral paraflocculus (Figure 1J and M and Figure 2B). Within the vermis we identified a single cluster of RABV+ Purkinje cells that extended across both lobule VIa and lobule VIb-c (Figure 2). In contrast, within Crus I, RABV-labeled Purkinje cells were arranged in two spatially isolated clusters, one located rostro-laterally and the other caudo-medially (Figure 2).

Figure 2

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The topographical arrangement of RABV-labeled PCs in longitudinal clusters is in keeping with the well-described modular organization of the cerebellum (e.g. Apps and Hawkes, 2009). Mapping of molecular marker expression patterns, such as zebrin II banding, provides a reliable basis from which modules can be defined and recognized in the cerebellar cortex of rodents. Thus, to further assign the observed PC clusters to previously described cerebellar zones, we used a double immunohistochemical approach to stain for both RABV and aldolase C (zebrin II) in one animal (case S18) after 66 hr of infection (Figure 2B) (Brochu et al., 1990; Sugihara and Shinoda, 2004). Lobule VI, Crus I and paraflocculus are mostly zebrin-positive regions (Sugihara and Quy, 2007), and we found that RABV-labeled Purkinje cells co-localized with zebrin II in all the observed clusters (Figure 2B). In the vermis, lobule VIa RABV-labeled PCs were mostly located in the a+ band. The few RABV-labeled cells found in lobule VII were confined to the 2+ band. Thus, together, these labeled cells belong to the a+//2+ pair that constitutes part of the cerebellar A module (Figure 2C) (Sugihara, 2011). In Crus I, the rostrolateral cluster of RABV-labeled PCs was aligned with the anterior 6+ zebrin band corresponding to module D2. The caudomedial cluster was in continuation with the posterior 5+ zebrin band suggesting that it is part of the paravermal module C2 (Figure 2C). In the paraflocculus, the assignment of the RABV-labeled cells to specific modules was not addressed given the complex morphology of this region. However, the presence of RABV-labeled cells both in the dorsal and ventral paraflocculus suggests the involvement of more than one module (Figure 2B–C) (Voogd and Barmack, 2006).

Cerebellar modules are also defined by their outputs through the deep cerebellar and vestibular nuclei (Apps and Hawkes, 2009; Ruigrok, 2011). The presence of RABV-labeled cells in the fastigial nucleus is consistent with the involvement of module A. Similarly, the D2 module is routed through the dentate nuclei in which we found robust RABV labeling. We also found RABV+ cells in the nucleus interpositus posterior, which provides the output of module C2. Finally, RABV labeling was observed in the vestibular nuclei, which may represent the output of RABV+ Purkinje cell clusters observed in the ventral paraflocculus.

Together, our neuroanatomical tracing data indicate that cerebellar projections to the hippocampus emanate from three distinct cerebellar modules. It also suggests the existence of multiple, convergent pathways from the DCN to the hippocampus.

Cerebello-hippocampal physiological interactions in a familiar home-cage environment

In order to question the potential functional relevance of cerebello-hippocampal anatomical connectivity, we implanted mice (n = 21) with arrays of bipolar LFP recording electrodes in bilateral dorsal hippocampus (HPC) and unilaterally in two highly RABV-labeled regions of the central cerebellum, lobule VI (midline) and Crus I (left hemisphere). For comparison, we also simultaneously recorded LFP from cerebellar regions with minimal RABV labeling (lobule II or lobule III; Figure 1M; Figure 3A and B). Data were excluded from further analysis in cases where postmortem histological inspection revealed that electrode positions were off-target (Figure 3—figure supplement 1).

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Assessment of cerebello-hippocampal interactions during active movement in the homecage.

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The spectral profile of cerebellar and hippocampal LFP activity was first assessed during active movement in a familiar home-cage environment (epochs with speed >3 cm/s; see Materials and methods). Within the HPC, a dominant 6–12 Hz theta oscillation was similarly observed in both hemispheres (Figure 3—figure supplement 2B; left HPC: mean 6–12 Hz z-score power = 4.17 ± 0.20, n = 17; right HPC: mean 6–12 Hz z-score power = 4.52 ± 0.16, n = 19; unpaired t test, t₃₄ = 0.007, p=0.1731), thus, when LFPs from both hippocampi where available we averaged the spectral power from left and right hemispheres (Figure 3C, mean 6–12 Hz z-score power = 4.35 ± 0.14). In the cerebellar recordings, low-frequency oscillations in the delta (2–4 Hz) and high-frequency activity in the mid-high gamma (50–140 Hz) range were prevalent. A clear peak in the 6–12 Hz band was not detected in the power spectrum of any of these recordings, which had similar levels of 6–12 Hz z-score power (Figure 3C; Crus I: 1.57 ± 0.10, n = 12 mice; lobule II/III: 1.57 ± 0.13, n = 13 mice; lobule VI: 1.47 ± 0.07, n = 16 mice; one-way ANOVA, F _{(2, 38)}=0.34, p=0.7131).

In order to explore any potential state-dependent organization of theta oscillations in our recordings, we computed the distributions of instantaneous power in this frequency range (Figure 3D). If the power of theta oscillations was organized in differential states, we would expect to observe a multimodal distribution; however, we found unimodal power distributions in both the hippocampal and cerebellar recordings. The former was negatively skewed suggesting that the peak observed in the hippocampal power spectra is representative of sustained theta activity in the analyzed epochs. In contrast, the cerebellar theta distribution profile was positively skewed suggesting that activity in this frequency range, although of lower power than in the hippocampus, is also sustained within the cerebellar cortical regions we recorded from.

Previous studies have described correlations between locomotion speed and purkinje cell discharge in cerebellar vermal lobules V and VI (Sauerbrei et al., 2015; Muzzu et al., 2018). Furthemore, cerebellar nuclei - prefrontal cortex theta coherence has also been found to increase during active locomotion compared to rest (Watson et al., 2014). Therefore, we next asked if running speed could be modulating theta power in the cerebellar cortex. To do so, we computed the correlation between instantaneous speed and instantaneous theta power in each cerebellar region; however, none were significantly correlated (Figure 3E; Crus I, n = 11, mean Spearman rho = - 0.02 ± 0.04, bootstrap confidence level = [- 0.09 0.08]; lobule II/III, mean Spearman rho = - 0.07 ± 0.03, bootstrap confidence level = [- 0.08 0.08]; lobule VI, mean Spearman rho = - 0.04 ± 0.03, bootstrap confidence level = [- 0.08 0.08]).

As an indicator of cross-structure interaction (Fries, 2005), we next calculated coherence between LFP recorded from the different cerebellar subregions and left or right HPC. We found no statistically significant influence of hippocampal laterality on the measured cerebello-hippocampal coherence (Figure 3—figure supplement 2; Crus I-HPC left, n = 11, Crus I-HPC right, n = 11, Mann-Whitney test, U = 59, p=0.9487; lobule II/III-HPC left, n = 11, lobule II/III-HPC right, n = 12, Mann-Whitney test, U = 63, p=0.8801; lobule VI-HPC left, n = 15, lobule VI – HPC right, n = 14, Mann-Whitney test, U = 105, p>0.99). Therefore, for further analysis, when both hippocampal recording electrodes were on target we first calculated coherence with the cerebellar LFP for each hemisphere then averaged the two. Thus, for each mouse we obtained one coherence value per cerebello-hippocampal recording combination. In cases in which only one of the hippocampal recording electrodes was on target, we excluded the off target recording in our calculations of cerebello-hippocampal coherence.

A clear peak in coherence was observed for all cerebello-hippocampal combinations in the theta frequency range (6–12 Hz, Figure 3F; Crus I-HPC, n = 12; lobule II/III-HPC, n = 13, lobule VI-HPC, n = 16; frequencies between 48 and 52 Hz have been excluded due to notch filtering to remove electrical contamination of the LFP signals; see Materials and methods). We found that significant variations in coherence level were restricted to those within the theta frequency (frequency band (theta, 6–12 Hz; beta, 13–29 Hz; low gamma, 30–48 Hz) x cerebello-hippocampal combination two way repeated measures ANOVA, frequency band effect F_2,76 = 22.42, p<0.0001; combination effect F_2,38 = 2.843, p=0.0707; interaction effect, F_4,76 = 3.825, p=0.0069; post-hoc multiple comparisons with FDR correction revealed significant differences between combinations only in the theta band). In addition, as theta coherence has already been reported as a potential mechanism for long-range network interactions between the hippocampus and the cerebellum (Hoffmann and Berry, 2009; Wikgren et al., 2010) and theta oscillations are known to play an important role in intra-hippocampal network organization, in particular for spatial navigation (see Buzsáki and Moser, 2013 for overview), we focused our analysis on activity within this frequency range. As for the instantaneous LFP power, we next asked if theta coherence between the different cerebello-hippocampal LFP combinations was organized in a state-dependent manner. To address this question, we computed the distribution of instantaneous coherence within this bandwidth. In line with the analysis of LFP power distributions, we did not observe any multi-modality and all combinations followed gaussian distributions (Figure 3G). Correlation analysis between instantaneous speed and instantaneous theta coherence failed to show significant relationships for any cerebello-hippocampal combination (Figure 3H; Crus I-HPC, mean Spearman rho = 0.06 ± 0.03, bootstrap confidence level = [−0.08 0.08]; lobule II/III-HPC, mean Spearman rho = - 0.02 ± 0.02, bootstrap confidence level = [- 0.08 0.08]; lobule VI-HPC, mean Spearman rho = 0.01 ± 0.03, bootstrap confidence level = [−0.09 0.08]). Significant differences across recording combinations were observed within the theta bandwidth (Figure 3I; Crus I-HPC, median [25–75 interquartile range (IQR)] coherence = 0.471 [0.461–0.480]; lobule II/III-HPC, median [IQR] coherence = 0.467 [0.462–0.471]; lobule VI-HPC, median [IQR] coherence = 0.481 [0.471–0.486]; Kruskal-Wallis with FDR correction, Kruskal-Wallis statistic = 6.75, p=0.0342) and post-hoc analysis revealed that LFP oscillations were significantly more synchronized between hippocampus and lobule VI than with lobule II/III (corrected p = 0.0246). Within lobule VI, theta coherence was significantly correlated to the mediolateral position of the recording electrode, which was consistent with the mediolateral location of greatest RABV-labeled PCs (Figure 3J; Spearman rho = 0.572, p=0.0225).

Next, to ascertain if local cerebellar spiking activity is coordinated or modulated by hippocampal theta oscillations we recorded single, photo-identified Purkinje cells from lobule VI of head-fixed L7-ChR2 mice (selectively expressing channelrhodopsin in Purkinje cells; n = 6) simultaneously with hippocampal LFP during periods of active movement. This allowed us to calculate the degree of phase-locking between the cerebellar spikes and hippocampal LFP, which circumvents volume conduction issues associated with LFP-LFP correlations (e.g. Vinck et al., 2011; Nolte et al., 2004; Sirota et al., 2008; Stam et al., 2007). We recorded a total of 22 units, of which 16 (from four mice) were classified as Purkinje cells based upon their responsivity to blue light illumination (see Figure 3—figure supplement 3). Of these 16 Purkinje cells, 31% (5 units) were significantly phase locked to the hippocampal 6–12 Hz oscillation (Figure 3—figure supplement 4) during periods of active movement (see Materials and methods). Of the six non-photo responsive units, 1 (16.7%) was significantly phase locked (Figure 3—figure supplement 4D,E). The mean vector angle of the significantly phase-locked units was 231 ± 18°.

Cerebello-hippocampal interactions during the learning of a goal-directed behavior

To further characterize the dynamics of cerebello-hippocampal interactions, we quantified cerebello-hippocampal theta coherence during a goal-directed task. A subset of mice (n = 8) were trained to traverse a linear track to get a reward (medial forebrain bundle stimulation, see Materials and methods) at a fixed position (Figure 4A).

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Cerebello-hippocampal interactions during goal-directed behavior.

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Across training, mice improved their performance as shown by the optimization of their path (Figure 4A), significant increase in the mean number of rewards obtained per day of training (Figure 4B; mean number of rewards obtained on 1 ^st day = 17 ± 4, mean number of rewards obtained on 7th day = 68 ± 11; repeated measures Friedman test, Friedman statistic = 37.91, p < 0.0001) and the significant increase in their mean speed (Figure 4B; mean speed on 1st day = 5.74 ± 0.54 cm/s, mean speed on 7th day = 13.94 ± 1.63 cm/s; repeated measures Friedman test, Friedman statistic = 36.32, p < 0.0001). Thus, we next explored the dynamics of cerebello-hippocampal theta coherence across this learning period. We confirmed the absence of a laterality effect on the power spectra calculated at the beginning (Figure 4—figure supplement 1A; Session 1; HPC left, n = 6, median [IQR] 6–12 Hz z-score power = 5.842 [5.683 6.384]; HPC right, n = 7, median [IQR] 6–12 Hz z-score power = 6.261 [4.919 6.587]; Mann-Whitney test, U = 18, p = 0.7308) or end (Figure 4—figure supplement 1B; Session 20; HPC left, median [IQR] 6–12 Hz z-score power = 5.850 [5.583 6.002]; HPC right: median [IQR] 6–12 Hz z-score power = 5.898 [5.511 6.110]; Mann-Whitney test, U = 20, p = 0.9452) of training. Consequently, we averaged the spectral power from left and right hemispheres when both were available. Similarly, no differences on coherence between left and right hippocampi and the different cerebellar recordings were observed at the beginning (Figure 4—figure supplement 1C,E; Session 1; Crus I-HPC left, n = 3, median [IQR] 6–12 Hz coherence = 0.4789 [0.4557 0.5326]; Crus I-HPC right, n = 3, median [IQR] 6–12 Hz coherence = 0.4802 [0.4687 0.5238]; Mann-Whitney test, U = 4, p > 0.99; lobule II/III-HPC left, n = 4, median [IQR] 6–12 Hz coherence = 0.4574 [0.4513 0.4709]; lobule II/III-HPC right, n = 5, median [IQR] 6–12 Hz coherence = 0.4633 [0.4549 0.4663]; Mann-Whitney test, U = 8, p = 0.7302; lobule VI-HPC left, n = 6, median [IQR] 6–12 Hz coherence = 0.4764 [0.4653 0.4904]; lobule VI-HPC right, n = 5, median [IQR] 6–12 Hz coherence = 0.4684 [0.4558 0.4857]; Mann-Whitney test, U = 10, p = 0.4286) or end (Figure 4—figure supplement 1D,F; Session 20; Crus I-HPC left, median [IQR] 6–12 Hz coherence = 0.5357 [0.4747 0.5574]; Crus I-HPC right, median [IQR] 6–12 Hz coherence = 0.5137 [0.4616 0.5500]; Mann-Whitney test, U = 3, p = 0.7; lobule II/III-HPC left, median [IQR] 6–12 Hz coherence = 0.4596 [0.4466 0.4769]; lobule II/III-HPC right, median [IQR] 6–12 Hz coherence = 0.4595 [0.4422 0.4736]; Mann-Whitney test, U = 10, p = 0.7302; lobule VI-HPC left, median [IQR] 6–12 Hz coherence = 0.4702 [0.4662 0.5124]; lobule VI-HPC right, median [IQR] 6–12 Hz coherence = 0.4665 [0.4610 0.5066]; Mann-Whitney test, U = 12, p = 0.6623) of training so the averaged coherence between cerebellum and both hippocampal hemispheres was computed when possible.

We first examined overall, mean cerebello-hippocampal theta coherence as learning progressed. We observed significant changes over training (Figure 4C; Crus I-HPC, n = 4; lobule II/III-HPC, n = 6; lobule VI-HPC, n = 6; day of training x cerebello-hippocampal combination two-way repeated measures ANOVA with FDR correction, day effect F_6,84 = 3.873, p = 0.0018) and post-hoc analysis revealed that only Crus I-HPC coherence significantly increased when comparing with values observed on the first day of training (p < 0.01 for days 6 and 7). We next examined detailed power spectra and coherence dynamics at the level of individual sessions from first day of training (session 1), when animals exhibited an exploratory behavioral profile, and the last day of training (session 20), when animals performed efficient goal-directed behavior (Figure 4). This allowed us to investigate the spatial dynamics of both the cerebellar and hippocampal LFP profiles alongside coherence as mice traversed the linear track to reach the reward.

During session 1, mice approached the reward point with a sustained and low speed (Figure 4Di, top), which is in agreement with the exploratory behavioral profile illustrated by the distributed occupancy of their trajectories on the track (Figure 4A bottom). The hippocampal spectrogram was dominated by sustained activity in the theta band across the whole track. In contrast, clear activity in this frequency band was not apparent in the cerebellar recordings (Figure 4Di) and the LFP power profile was maintained at low levels across the track. This homogeneous pattern was consistent with the unimodal distributions of instantaneous hippocampal and cerebellar theta power (Figure 4—figure supplement 2A). Similarly, the coherograms did not reveal clear coherence in any of the cerebello-hippocampal combinations (Figure 4Dii). However, we found a significant interaction between the distance from reward and the theta coherence of different cerebello-hippocampal combinations (Figure 4Diii; distance from reward point x combination two-way repeated measures ANOVA; combination effect, F_2,13 = 2.33, p = 0.1365; distance effect, F_84,1092 = 1.043, p = 0.3772; interaction effect, F_168,1092 = 1.332, p = 0.0053). Post-hoc, FDR corrected, multiple comparisons revealed that significantly higher theta coherence was present between hippocampus and Crus I compared to lobule II/III at certain positions on the track prior to the reward point location (Crus I-HPC vs lobule II/III-HPC, p < 0.05 from −29 to −21 cm from reward; lobule VI-HPC vs lobule II/III-HPC, p < 0.05 from −25 to −22 cm from reward). As for the homecage recordings, instantaneous theta coherence for all cerebello-hippocampal combinations also followed gaussian, unimodal distributions during session one in the linear track (Figure 4—figure supplement 2E).

On the last day of training, during session 20, mice displayed goal-directed behavior on the track. This goal-directed profile was illustrated in the efficient running trajectories (Figure 4A bottom) and by the acceleration-plateau-deceleration speed profile observed along the track (Figure 4Ei top). As in session 1, the hippocampal spectrogram was dominated by sustained theta activity. On the other hand, cerebellar LFP power profiles were notably different from session one with the appearance of sustained activity in the theta and delta (2–4 Hz) frequency bands, particularly in lobule VI and lobule II/III (Figure 4Ei bottom). These differences were not related to transient bouts of theta activity as the instantaneous theta power probability distributions continued showing unimodal profiles (Figure 4—figure supplement 2C). Coherogram analysis also revealed changes in session 20, which were mainly reflected in sustained Crus I-HPC theta coherence spanning multiple positions on the track as animals actively approached the reward (Figure 4Eii). This pattern was not as apparent in the other cerebello-hippocampal combinations (Figure 4Eiii; distance from reward x cerebello-hippocampal combination two-way repeated measures ANOVA, distance from reward effect F_84,1092 = 1.682, p = 0.0002; combinations effect F_2,13 = 6.145, p = 0.0132; interaction effect F_168,1092 = 1.271, p = 0.0163) and post-hoc FDR corrected multiple comparisons revealed significantly higher sustained theta coherence between hippocampus and Crus I than with lobule II/III from −60 to −20 cm or with lobule VI from - 59 to −36 cm prior to the reward point. Lobule VI-HPC coherence was also higher than with lobule II/III at some positions on the track (−44 to −31 cm from reward) although it was not as sustained and prominent as Crus I-HPC coherence. Importantly, we also reproduced these findings when the imaginary part of coherency was computed, which is robust against contamination by volume conduction (Figure 4—figure supplement 3).

In order to better explore the differences between sessions 1 and 20 we then pooled power spectra (Figure 4F and G top panels) and coherence (Figure 4F and G bottom panels) between −60 and −20 cm from reward point (i.e. the range of distances where differences across recording combinations were most apparent in the coherograms). The appearance of a theta peak in the power spectra from all cerebellar recordings in session 20 compared with session one can be clearly seen (Figure 4G) as well as the increase in coherence limited to this frequency band (Session 20: frequency band (theta, beta, low gamma) x cerebello-hippocampal combination two way repeated measures ANOVA, frequency band effect F_2,26 = 13.42, p < 0.0001; combination effect F_2,13 = 6.545, p = 0.0108; interaction effect, F_4,26 = 5.242, p = 0.0031; post-hoc multiple comparisons with FDR correction revealed significant differences between combination only in the theta band). We found that in session 20 theta coherence between hippocampus and Crus I was significantly higher than that obtained with lobule II/III (Figure 4H right; Crus I-HPC, n = 4, median [IQR] coherence = 0.525 [0.480–0.549]; lobule II/III-HPC, n = 6, median [IQR] coherence = 0.461 [0.452–0.468]; lobule VI-HPC, n = 6, median [IQR] coherence = 0.470 [0.465–0.503]; Kruskal-Wallis with FDR correction, Kruskal-Wallis statistic = 7.989, p=0.0103; Crus I-HPC vs lobule VI-HPC, corrected p = 0.0110) while no significant difference was found in session 1 (Figure 4H left; Crus I-HPC, n = 4, median [IQR] coherence = 0.480 [0.466–0.516]; lobule II/III-HPC, n = 6, median [IQR] coherence = 0.462 [0.457–0.466]; lobule VI-HPC, n = 6, median [IQR] coherence = 0.476 [0.463–0.494]; Kruskal-Wallis with FDR correction, Kruskal-Wallis statistic = 4.779, p = 0.0887).

Given that the speed profiles of mice changed significantly between sessions 1 and 20 on the linear track (Figure 4B,D,G) and seemed to mirror the observed modulation in theta power and coherence, we next correlated instantaneous cerebellar theta power (Figure 4—figure supplement 2B,D) and instantaneous theta coherence (Figure 4—figure supplement 2F,H) with instantaneous speed. In contrast to home-cage recordings, for all recorded cerebellar LFPs, theta power was significantly positively correlated with speed during both session 1 (Figure 4—figure supplement 2B; Crus I, median [IQR] Spearman rho = 0.213 [0.209 0.258], bootstrap confidence level = 0.081; lobule II/III, median [IQR] Spearman rho = 0.239 [0.065 0.343], bootstrap confidence level = 0.082; lobule VI, median [IQR] Spearman rho = 0.235 [0.090 0.284], bootstrap confidence level = 0.083) and session 20 (Figure 4—figure supplement 2D, Crus I, median [IQR] Spearman rho = 0.235 [0.056 0.465], bootstrap confidence level = 0.081; lobule II/III, median [IQR] Spearman rho = 0.310 [-0.010 0.490], bootstrap confidence level = 0.081; lobule VI, median [IQR] Spearman rho = 0.194 [0.105 0.550], bootstrap confidence level = 0.085). Cerebello-hippocampal theta coherence was not correlated with instantaneous speed during session 1 (Figure 4—figure supplement 2F; Crus I-HPC, median [IQR] Spearman rho = 0.024 [-0.033 0.237], bootstrap confidence level = 0.082; lobule II/III-hippocampus, median [IQR] Spearman rho = 0.0278 [-0.105 0.050], bootstrap confidence level = 0.075; lobule VI-HPC, median [IQR] Spearman rho = −0.001 [-0.052 0.045], bootstrap confidence level = −0.090). However, during session 20, lobule II/III-HPC theta coherence was anticorrelated with speed (Figure 4—figure supplement 2H; lobule II/III-HPC, median [IQR] Spearman rho = −0.086 [-0.182 0.012], bootstrap confidence level = −0.080) while lobule VI-HPC was weakly but significantly correlated with it (lobule VI-HPC, median [IQR] Spearman rho = 0.091 [-0.140 0.173], bootstrap confidence level = 0.090). Interestingly, the Crus I-HPC recording combination, which presented higher and sustained levels of theta coherence during this task, was not significantly correlated with instantaneous speed (Crus I-HPC, median [IQR] Spearman rho = 0.020 [-0.051 0.094], bootstrap confidence level = 0.083). Together, these findings suggest that the observed Crus I - HPC theta coherence dynamics during goal-directed behavior in the linear track cannot be explained by changes in running speed.

We also observed similar cerebello-hippocampal coherence dynamics in mice navigating for rewards in a virtual reality based linear track (Figure 4—figure supplement 4A–C, n = 6). A marked spatial re-organization of cerebello-hippocampal theta coherence was apparent in those mice that showed behavioral modulation across training (as evidenced by increases in the number of rewards across day of training and speed modulation on a run by run basis during approach to the reward location; Figure 4—figure supplement 4G–I). In contrast, mice that failed to show behavioral modulation across training, and displayed rather homogenous speed profiles, did not present such coherence dynamics (Figure 4—figure supplement 4D–F).

To examine whether the observed changes in coherence across learning of the linear track were specifically related to performance of the goal-directed task itself, we next conducted pairwise analysis of cerebello-hippocampal theta coherence levels across the following conditions: home-cage prior to any linear track training (HC pre, considered as baseline), the 1st and 20th linear track trials, and home-cage following the end of training in the linear track task (HC post) (see Figure 5).

Figure 5

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Figure 5—source data 1

Hippocampal-Crus I theta coherence is dynamic.

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From the three cerebello-hippocampal recording configurations, only Crus I-HPC theta coherence varied significantly across task conditions (Figure 5, n = 4, repeated measures Friedman test with FDR correction, Friedman statistic = 11.1, p = 0.0009). At the outset of linear track learning (session 1), HPC-Crus I coherence values did not significantly differ from home-cage (HC pre, median [IQR] = 0.471 [0.461 0.495]; session 1, median [IQR] = 0.480 [0.466 0.516], corrected p = 0.1196). However, during late stage linear track learning, the level of coherence was significantly higher than in home-cage recordings (HC pre, median [IQR] = 0.471 [0.461 0.495]; session 20, median [IQR] = 0.525 [0.480 0.550], corrected p = 0.0021) and when mice were returned to the home-cage environment following completion of linear track training (HC post) the level of HPC-Crus I coherence dropped back to pre-training levels (HC pre, median [IQR] = 0.471 [0.461 0.495]; HC post, median [IQR] = 0.493 [0.471 0.505], corrected p = 0.0580).

Taken together, our findings reveal previously undescribed cerebellar inputs to the hippocampus and offer novel physiological insights into a long-range neural network linking disparate brain regions initially assumed to support divergent behavioral functions, namely spatial navigation (hippocampus) and motor control (cerebellum). Projections from topographically restricted regions of cerebellar cortex discretely route through restricted parts of their associated nuclei en-route to the hippocampus through multiple, convergent pathways, involving one or two relays, from the DCN. Interestingly, the possible single-relay pathways we described points toward involvement of the medial septum and the supramammillary nucleus, two structures crucial for theta generation. Congruently, our physiological data suggest that these connected cerebellar regions may dynamically interact with the hippocampus during behavior, via theta (6–12 Hz) LFP coherence. Our findings thus offer an anatomical and physiological framework for cerebello-hippocampal interactions that could support cerebellar contributions to hippocampal processes (Burguière et al., 2005), including spatial map maintenance (Rochefort et al., 2011; Rondi-Reig et al., 2014; Lefort et al., 2019).

Whilst previous studies provide compelling physiological evidence of cerebellar influences on the hippocampus (Cooke and Snider, 1955; Iwata and Snider, 1959; Babb et al., 1974; Snider and Maiti, 1975; Krook-Magnuson et al., 2014; Choe et al., 2018), they do not provide the spatial resolution afforded by neuroanatomical tracing. Indeed, to the best of our knowledge, anatomical tracing studies have failed to report a mono-synaptic ascending cerebello-hippocampal projection. This is consistent with our rabies virus tracing study, in which incubation periods of 48–58 hr were required before cell labeling was seen in the cerebellar nuclei. Such a timescale is indicative of a multi-synaptic pathway (Kelly and Strick, 2000; Ugolini, 2010; Suzuki et al., 2012; Jwair et al., 2017). Single relay pathways can be envisioned through the septum, the hypothalamus (potentially including the supramammillary nucleus (SUM)) and the raphe nucleus. Other pathways including two relays through either the lateral and medial entorhinal cortex and/or the perirhinal cortex are also possible. Interestingly, among the different regions labeled at 48 hr post-infection, several midbrain and pontine regions such as the PAG, the nucleus incertus and the LtDG are known to receive direct projections from the DCN and could therefore represent putative first-order relays between cerebellum and hippocampus.

Our anatomical results highlight three main inputs to the hippocampus emanating from the cerebellum. The first input we reveal originates from the vestibulo-cerebellum, specifically from the dorsal and ventral paraflocculus, which is likely routed via the vestibular and dentate nuclei (Voogd and Barmack, 2006). This anatomical connection between the vestibulo-cerebellum and the hippocampus reinforces the already well-described influence of the vestibular system on hippocampal-dependent functions (Stackman et al., 2002; Goddard et al., 2008; Zheng et al., 2009).

In addition to the classically described vestibular pathway, our data reveal that the central cerebellum also provides inputs to the hippocampus from vermal lobule VI, routed through caudal fastigial nucleus, and from Crus I, routed through the dentate. Using a combination of RABV expression and zebrin II staining, we identified three specific cerebellar modules involved in these inputs: (1) the A module in lobule VI, (2) the hemispheric Crus I D2 module and (3) the Crus I paravermal C2 module. Of the latter two modules, C2 is likely less prominently anatomically connected with the hippocampus since the number of RABV+ cells in the nucleus interpositus posterior, its output nucleus (Apps and Hawkes, 2009), was minor compared with the other cerebellar nuclei. The convergence of inputs from disparate cerebellar zones (flocculo-nodular and central zones) and modules from vermal (A), paravermal (C2) and hemispheric (D2) regions in to the hippocampus suggest that its optimal function requires the integration of multiple aspects of sensory-motor processing carried out at these distinct cerebellar locations.

According to Voogd and Barmack (2006), based upon evidence from a wide range of species, the oculomotor cerebellum can be most broadly described to include lobule V, VI and VII. Given the highly conserved structure-function relationships of the vermal cerebellum (Sillitoe et al., 2005), it seem likely that the mouse A module could also be considered part of the oculomotor cerebellum. In rat, it receives climbing fibers from the caudal medial accessory olive, and sends mainly ascending projections through the caudal portion of fastigial nucleus (Apps, 1990; Apps and Hawkes, 2009). The oculomotor vermis receives multiple sensory inputs which include visual, proprioceptive, vibrissae, vestibular and auditory inputs conveyed by both climbing and mossy fibers (Voogd and Barmack, 2006). The D2 module receives its climbing fiber input from the dorsal cap of the principal olive and projects out of the cerebellum through the rostromedial dentate nucleus (Herrero et al., 2006). It receives mossy fiber inputs carrying somatosensory, motor (Mihailoff et al., 1981), and visual (Edge et al., 2003) information; along with inputs from the prefrontal cortex (Kelly and Strick, 2003). Climbing fiber inputs to this module relay information from the parvocellular red nucleus, which receives projections from premotor, motor, supplementary motor and posterior parietal areas. The majority of these cortical areas also receive projections from the D2 module after a thalamic relay in the ventro-lateral nucleus (Kelly and Strick, 2003; Glickstein et al., 2011).

Complementary to these anatomical results, our electrophysiological findings reveal coherent activity between the hippocampus and those cerebellar lobules that are anatomically connected with it (lobule VI and Crus I). This synchronization was restricted to the 6–12 Hz frequency range in the awake, behaving animal and showed dynamic profiles that were lobule dependent. Oscillations can align neuronal activity within and across brain regions, suggesting a facilitation of cross-structure interactions (e.g. Singer, 1999; Fries, 2005). Cerebellar circuits support oscillations across a range of frequencies (for review see De Zeeuw et al., 2008; Cheron et al., 2016). Of particular relevance to the current study are reports of oscillations within the theta frequency (~4–12 Hz), which have been described in the cerebellar input layers at the Golgi (Dugué et al., 2009) and granule cell (Hartmann and Bower, 1998; D'Angelo et al., 2001) level, and also in the cerebellar output nuclei (Wang et al., 2014; Watson et al., 2014).

Neuronal coherence has been described across the cerebro-cerebellar system at a variety of low frequencies (O'Connor et al., 2002; Courtemanche and Lamarre, 2005; Soteropoulos and Baker, 2006; Rowland et al., 2010; Frederick et al., 2014; Watson et al., 2014; Chen et al., 2016) and oscillations within the theta range are thought to support inter-region communication across a wide variety of brain regions (Colgin, 2013). Our finding that cerebello-hippocampal coherence is limited to the 6–12 Hz bandwidth is in keeping with previous studies on cerebro-cerebellar communication in which neuronal synchronization has been observed between the cerebellum and prefrontal cortex (Watson et al., 2014; Chen et al., 2016), primary motor cortex (Soteropoulos and Baker, 2006; Rowland et al., 2010), supplementary motor area (Rowland et al., 2010) and sensory cortex (Rowland et al., 2010). Furthermore, LFPs recorded in the hippocampus and cerebellar cortex are synchronized within the theta bandwidth during trace eye-blink conditioning in rabbits (Hoffmann and Berry, 2009; Wikgren et al., 2010). Human brain imaging studies have also described co-activation of blood oxygen level dependent signals in both cerebellar and hippocampal regions during navigation (Iglói et al., 2015) and spatio-temporal prediction tasks (Onuki et al., 2015), thus highlighting putative neuronal interactions between the two structures. Regarding studies in mice, a recent study has demonstrated the existence of statistically significant co-activation of the dorsal hippocampus and cerebellar lobules IV-V, lobule VI and Crus I after the acquisition of a sequence-based navigation task (Babayan et al., 2017).

Multiple lines of evidence suggest that the coherence described here is unlikely to have resulted from volume conduction: 1) Rather than using a common reference electrode, our recordings were bipolar, with each recording electrode being locally and independently referenced (Kajikawa and Schroeder, 2011). 2) If volume conduction of theta oscillations was emanating from a hippocampal source then it could be assumed that cerebellar regions in closer proximity to the hippocampus would show higher levels of coherence (Figure 3—figure supplement 2). However, we found that coherence values were not related to the relative distance between the hippocampus and cerebellar recording site. 3) By recording simultaneously from hippocampus and multiple cerebellar regions, we have been able to demonstrate that the observed coherence is non-homogenous among the different cerebellar lobules in contrast to what one would expect if theta was volume conducted from a common location. 4) We calculated the imaginary part of coherency, which is not influenced by volume conduction (Nolte et al., 2004), in the linear track paradigm and found results that were remarkably similar to those obtained using standard coherence analysis. 5) We showed that Purkinje cell spikes in lobule VI of the cerebellum can phase lock to hippocampal theta oscillations.

Importantly, we have shown for the first time that theta rhythms in the hippocampus preferentially synchronize with those in discrete regions of the cerebellum and that the degree of this coupling changes depending upon the behavioral context. Lobule VI-HPC coherence was dominant during active movement in the home-cage and remained stable during learning of the real world linear track task. On the other hand, Crus I - HPC coherence was highly dynamic, showing a significant increase over learning of the real world linear track task and becoming dominant after the acquisition of a goal-directed behavior. Furthermore, we reveal that high Crus I-HPC coherence was sustained across the linear track when the animal performed goal-directed behavior but not during the early training, exploratoration phase. Such sustained coherence may be related to the ability of the cerebellum, and particular Crus I, to link internal and external sensory context with specific action toward the goal. In line with this hypothesis, a recent study has suggested that Purkinje cells in the Crus I region and neurons in its output, the dentate nucleus, exhibit firing rate modulation in anticipation of expected reward in a VR task (Chabrol et al., 2018). Interestingly, they show that the activity of Purkinje cells in lateral Crus I was modulated by running and/or visual flow speed. This is consistent with our examples showing that Crus I-HPC coherence remained present when animals performed goal-modulated behavior in VR, although multiple streams of sensory input, such as vestibular, whisker and olfactory, become irrelevant and even confounding in the head-restricted virtual environment task.

We next consider our results within the well characterized, modular understanding of cerebellar function. Within lobule VI, the A module receives multi-modal sensory information, mainly arising from collicular and vestibular centers (Voogd and Barmack, 2006). The superior colliculus plays a role in visual processing and generation of orienting behaviors (Basso and May, 2017), which might be relevant for the establishment and maintenance of the hippocampal spatial map, and thus may be required constantly during active movement, independent of the specific behavioral task. The persistent and similar levels of lobule VI-HPC coherence during active movement in the homecage and linear track task, in both real world and virtual reality environment tasks is in agreement with such a hypothesis.

In monkeys and humans, Crus I is anatomically and functionally associated with prefrontal cortex (Kelly and Strick, 2003; Iglói et al., 2015). In mouse Crus I, the D2 module receives convergent sensory and motor information (Proville et al., 2014). Furthermore, this module has been found to contain internal models, a neural representation of one’s body and the external world based on memory of previous experiences, that are used for visuo-motor coordination (Cerminara et al., 2009). Similarly, the C2 module has been found to also participate in visuo-motor processing related to limb coordination during goal-directed reaching (Cerminara and Apps, 2011). Both modules might be particularly important during the acquisition of a goal-directed behavior such as our real-world linear track task in which animals needed to reach non-cued reward zones. Our finding that Crus I-HPC coherence increases during task learning fits with this hypothesis.

In summary, our results suggest the existence of anatomically discrete hippocampal-cerebellar network interactions with a prominent involvement of Crus I during goal-directed behavior. Both anatomical and electrophysiological data point toward involvement of the theta generating pathway in cerebellum-hippocampus interactions. The topographical dynamic weighting of these interactions may be tailored to the prevailing sensory context and behavioral demands.

Anatomical tracing studies were performed under protocol N°00895.01, in agreement with the Ministère de l’Enseignement Supérieur et de la Recherche. RABV injections were performed by vaccinated personnel in a biosafety containment level two laboratory.

All behavioral experiments were performed in accordance with the official European guidelines for the care and use of laboratory animals (86/609/EEC) and in accordance with the Policies of the French Committee of Ethics (Decrees n° 87–848 and n° 2001–464). The animal housing facility of the laboratory where experiments were made is fully accredited by the French Direction of Veterinary Services (B-75-05-24, 18 May 2010). Surgeries and experiments were authorized by the French Direction of Veterinary Services (authorization number: 75–752).

A total of 44 adult, male mice were used for this study. Seventeen adult male C57BL6-J mice were used for the anatomical tracing study, (Charles River, France) and 21 for the electrophysiology study (Janvier, France). Six adult male CD-L7ChR2 mice were used for the dual hippocampal LFP and cerebellar unit-recording study (in-house colony derived from Jackson labs stock, USA).

Mice received food and water ad libitum, were housed individually (08: 00–20: 00 light cycle) following surgery and given a minimum of 5 days post-surgery recovery before experiments commenced.

The individual values are plotted in all the graphs and the mean and SEM are also plotted or indicated at the legends. The Matlab codes used for the spectral analysis are part of a fully available, well documented toolbox (Chronux toolbox) and referenced in the text. The parameters used for these codes is indicated in the materials and methods section.

1. 1. Aoki S
   2. Coulon P
   3. Ruigrok TJH

   (2017) Multizonal cerebellar influence over sensorimotor areas of the rat cerebral cortex

   Cerebral Cortex 29:598–614.

   https://doi.org/10.1093/cercor/bhx343

   • Google Scholar
2. 1. Apps R

   (1990) Columnar organisation of the inferior olive projection to the posterior lobe of the rat cerebellum

   The Journal of Comparative Neurology 302:236–254.

   https://doi.org/10.1002/cne.903020205

   • PubMed
   • Google Scholar
3. 1. Apps R
   2. Hawkes R

   (2009) Cerebellar cortical organization: a one-map hypothesis

   Nature Reviews Neuroscience 10:670–681.

   https://doi.org/10.1038/nrn2698

   • PubMed
   • Google Scholar
4. 1. Arrigo A
   2. Mormina E
   3. Anastasi GP
   4. Gaeta M
   5. Calamuneri A
   6. Quartarone A
   7. De Salvo S
   8. Bruschetta D
   9. Rizzo G
   10. Trimarchi F
   11. Milardi D

   (2014) Constrained spherical deconvolution analysis of the limbic network in human, with emphasis on a direct cerebello-limbic pathway

   Frontiers in Human Neuroscience 8:987.

   https://doi.org/10.3389/fnhum.2014.00987

   • PubMed
   • Google Scholar
5. 1. Babayan BM
   2. Watilliaux A
   3. Viejo G
   4. Paradis AL
   5. Girard B
   6. Rondi-Reig L

   (2017) A hippocampo-cerebellar centred network for the learning and execution of sequence-based navigation

   Scientific Reports 7:17812.

   https://doi.org/10.1038/s41598-017-18004-7

   • PubMed
   • Google Scholar
6. 1. Babb TL
   2. Mitchell AG
   3. Crandall PH

   (1974) Fastigiobulbar and dentatothalamic influences on hippocampal cobalt epilepsy in the cat

   Electroencephalography and Clinical Neurophysiology 36:141–154.

   https://doi.org/10.1016/0013-4694(74)90151-5

   • PubMed
   • Google Scholar
7. 1. Basso MA
   2. May PJ

   (2017) Circuits for action and cognition: a view from the superior colliculus

   Annual Review of Vision Science 3:197–226.

   https://doi.org/10.1146/annurev-vision-102016-061234

   • PubMed
   • Google Scholar
8. 1. Bokil H
   2. Andrews P
   3. Kulkarni JE
   4. Mehta S
   5. Mitra PP

   (2010) Chronux: a platform for analyzing neural signals

   Journal of Neuroscience Methods 192:146–151.

   https://doi.org/10.1016/j.jneumeth.2010.06.020

   • PubMed
   • Google Scholar
9. 1. Bostan AC
   2. Dum RP
   3. Strick PL

   (2010) The basal ganglia communicate with the cerebellum

   PNAS 107:8452–8456.

   https://doi.org/10.1073/pnas.1000496107

   • PubMed
   • Google Scholar
10. 1. Bostan AC
    2. Strick PL

    (2018) The basal ganglia and the cerebellum: nodes in an integrated network

    Nature Reviews Neuroscience 19:338–350.

    https://doi.org/10.1038/s41583-018-0002-7

    • PubMed
    • Google Scholar
11. 1. Brochu G
    2. Maler L
    3. Hawkes R

    (1990) Zebrin II: a polypeptide antigen expressed selectively by purkinje cells reveals compartments in rat and fish cerebellum

    The Journal of Comparative Neurology 291:538–552.

    https://doi.org/10.1002/cne.902910405

    • PubMed
    • Google Scholar
12. 1. Buckner RL

    (2013) The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging

    Neuron 80:807–815.

    https://doi.org/10.1016/j.neuron.2013.10.044

    • PubMed
    • Google Scholar
13. 1. Burguière E
    2. Arleo A
    3. Hojjati M
    4. Elgersma Y
    5. De Zeeuw CI
    6. Berthoz A
    7. Rondi-Reig L

    (2005) Spatial navigation impairment in mice lacking cerebellar LTD: a motor adaptation deficit?

    Nature Neuroscience 8:1292–1294.

    https://doi.org/10.1038/nn1532

    • PubMed
    • Google Scholar
14. 1. Buzsáki G
    2. Moser EI

    (2013) Memory, navigation and theta rhythm in the hippocampal-entorhinal system

    Nature Neuroscience 16:130–138.

    https://doi.org/10.1038/nn.3304

    • PubMed
    • Google Scholar
15. 1. Carlezon WA
    2. Chartoff EH

    (2007) Intracranial self-stimulation (ICSS) in rodents to study the neurobiology of motivation

    Nature Protocols 2:2987–2995.

    https://doi.org/10.1038/nprot.2007.441

    • PubMed
    • Google Scholar
16. 1. Carta I
    2. Chen CH
    3. Schott AL
    4. Dorizan S
    5. Khodakhah K

    (2019) Cerebellar modulation of the reward circuitry and social behavior

    Science 363:eaav0581.

    https://doi.org/10.1126/science.aav0581

    • PubMed
    • Google Scholar
17. 1. Cerminara NL
    2. Apps R
    3. Marple-Horvat DE

    (2009) An internal model of a moving visual target in the lateral cerebellum

    The Journal of Physiology 587:429–442.

    https://doi.org/10.1113/jphysiol.2008.163337

    • PubMed
    • Google Scholar
18. 1. Cerminara NL
    2. Apps R

    (2011) Behavioural significance of cerebellar modules

    The Cerebellum 10:484–494.

    https://doi.org/10.1007/s12311-010-0209-2

    • PubMed
    • Google Scholar
19. Preprint

    1. Chabrol F
    2. Blot A
    3. Mrsic-Flogel TD

    (2018) Cerebellar contribution to preparatory activity in motor neocortex

    bioRxiv.

    https://doi.org/10.1101/335703

    • Google Scholar
20. 1. Chaumont J
    2. Guyon N
    3. Valera AM
    4. Dugué GP
    5. Popa D
    6. Marcaggi P
    7. Gautheron V
    8. Reibel-Foisset S
    9. Dieudonné S
    10. Stephan A
    11. Barrot M
    12. Cassel JC
    13. Dupont JL
    14. Doussau F
    15. Poulain B
    16. Selimi F
    17. Léna C
    18. Isope P

    (2013) Clusters of cerebellar purkinje cells control their afferent climbing fiber discharge

    PNAS 110:16223–16228.

    https://doi.org/10.1073/pnas.1302310110

    • PubMed
    • Google Scholar
21. 1. Chen CH
    2. Fremont R
    3. Arteaga-Bracho EE
    4. Khodakhah K

    (2014) Short latency cerebellar modulation of the basal ganglia

    Nature Neuroscience 17:1767–1775.

    https://doi.org/10.1038/nn.3868

    • PubMed
    • Google Scholar
22. 1. Chen H
    2. Wang YJ
    3. Yang L
    4. Sui JF
    5. Hu ZA
    6. Hu B

    (2016) Theta synchronization between medial prefrontal cortex and cerebellum is associated with adaptive performance of associative learning behavior

    Scientific Reports 6:20960.

    https://doi.org/10.1038/srep20960

    • PubMed
    • Google Scholar
23. 1. Cheron G
    2. Márquez-Ruiz J
    3. Dan B

    (2016) Oscillations, timing, plasticity, and learning in the cerebellum

    The Cerebellum 15:122–138.

    https://doi.org/10.1007/s12311-015-0665-9

    • PubMed
    • Google Scholar
24. 1. Choe KY
    2. Sanchez CF
    3. Harris NG
    4. Otis TS
    5. Mathews PJ

    (2018) Optogenetic fMRI and electrophysiological identification of region-specific connectivity between the cerebellar cortex and forebrain

    NeuroImage 173:370–383.

    https://doi.org/10.1016/j.neuroimage.2018.02.047

    • PubMed
    • Google Scholar
25. 1. Colgin LL

    (2013) Mechanisms and functions of theta rhythms

    Annual Review of Neuroscience 36:295–312.

    https://doi.org/10.1146/annurev-neuro-062012-170330

    • PubMed
    • Google Scholar
26. 1. Cooke PM
    2. Snider RS

    (1955) Some cerebellar influences on Electrically-Induced cerebral seizures

    Epilepsia C4:19–28.

    https://doi.org/10.1111/j.1528-1157.1955.tb03170.x

    • Google Scholar
27. 1. Coulon P
    2. Bras H
    3. Vinay L

    (2011) Characterization of last-order premotor interneurons by transneuronal tracing with Rabies virus in the neonatal mouse spinal cord

    The Journal of Comparative Neurology 519:3470–3487.

    https://doi.org/10.1002/cne.22717

    • PubMed
    • Google Scholar
28. 1. Courtemanche R
    2. Lamarre Y

    (2005) Local field potential oscillations in primate cerebellar cortex: synchronization with cerebral cortex during active and passive expectancy

    Journal of Neurophysiology 93:2039–2052.

    https://doi.org/10.1152/jn.00080.2004

    • PubMed
    • Google Scholar
29. 1. D'Angelo E
    2. Nieus T
    3. Maffei A
    4. Armano S
    5. Rossi P
    6. Taglietti V
    7. Fontana A
    8. Naldi G

    (2001) Theta-frequency bursting and resonance in cerebellar granule cells: experimental evidence and modeling of a slow k+-dependent mechanism

    The Journal of Neuroscience 21:759–770.

    https://doi.org/10.1523/JNEUROSCI.21-03-00759.2001

    • PubMed
    • Google Scholar
30. 1. de Lavilléon G
    2. Lacroix MM
    3. Rondi-Reig L
    4. Benchenane K

    (2015) Explicit memory creation during sleep demonstrates a causal role of place cells in navigation

    Nature Neuroscience 18:493–495.

    https://doi.org/10.1038/nn.3970

    • PubMed
    • Google Scholar
31. 1. De Zeeuw CI
    2. Hoebeek FE
    3. Schonewille M

    (2008) Causes and consequences of oscillations in the cerebellar cortex

    Neuron 58:655–658.

    https://doi.org/10.1016/j.neuron.2008.05.019

    • PubMed
    • Google Scholar
32. 1. Dugué GP
    2. Brunel N
    3. Hakim V
    4. Schwartz E
    5. Chat M
    6. Lévesque M
    7. Courtemanche R
    8. Léna C
    9. Dieudonné S

    (2009) Electrical coupling mediates tunable low-frequency oscillations and resonance in the cerebellar golgi cell network

    Neuron 61:126–139.

    https://doi.org/10.1016/j.neuron.2008.11.028

    • PubMed
    • Google Scholar
33. 1. Edge AL
    2. Marple-Horvat DE
    3. Apps R

    (2003) Lateral cerebellum: functional localization within crus I and correspondence to cortical zones

    European Journal of Neuroscience 18:1468–1485.

    https://doi.org/10.1046/j.1460-9568.2003.02873.x

    • PubMed
    • Google Scholar
34. Book

    1. Franklin K
    2. Paxinos G

    (2007)

    The Mouse Brain in Stereotaxic Coordinates (Third Edition)

    Elsevier.

    • Google Scholar
35. 1. Frederick A
    2. Bourget-Murray J
    3. Chapman CA
    4. Amir S
    5. Courtemanche R

    (2014) Diurnal influences on electrophysiological oscillations and coupling in the dorsal striatum and cerebellar cortex of the anesthetized rat

    Frontiers in Systems Neuroscience 8:145.

    https://doi.org/10.3389/fnsys.2014.00145

    • PubMed
    • Google Scholar
36. 1. Fries P

    (2005) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence

    Trends in Cognitive Sciences 9:474–480.

    https://doi.org/10.1016/j.tics.2005.08.011

    • PubMed
    • Google Scholar
37. 1. Glickstein M
    2. Sultan F
    3. Voogd J

    (2011) Functional localization in the cerebellum

    Cortex 47:59–80.

    https://doi.org/10.1016/j.cortex.2009.09.001

    • PubMed
    • Google Scholar
38. 1. Goddard M
    2. Zheng Y
    3. Darlington CL
    4. Smith PF

    (2008) Locomotor and exploratory behavior in the rat following bilateral vestibular deafferentation

    Behavioral Neuroscience 122:448–459.

    https://doi.org/10.1037/0735-7044.122.2.448

    • PubMed
    • Google Scholar
39. 1. Haines DE
    2. May PJ
    3. Dietrichs E

    (1990) Neuronal connections between the cerebellar nuclei and hypothalamus in macaca fascicularis: cerebello-visceral circuits

    The Journal of Comparative Neurology 299:106–122.

    https://doi.org/10.1002/cne.902990108

    • PubMed
    • Google Scholar
40. 1. Harper JW
    2. Heath RG

    (1973) Anatomic connections of the fastigial nucleus to the rostral forebrain in the cat

    Experimental Neurology 39:285–292.

    https://doi.org/10.1016/0014-4886(73)90231-8

    • PubMed
    • Google Scholar
41. 1. Hartmann MJ
    2. Bower JM

    (1998) Oscillatory activity in the cerebellar hemispheres of unrestrained rats

    Journal of Neurophysiology 80:1598–1604.

    https://doi.org/10.1152/jn.1998.80.3.1598

    • PubMed
    • Google Scholar
42. 1. Heath RG
    2. Dempesy CW
    3. Fontana CJ
    4. Myers WA

    (1978)

    Cerebellar stimulation: effects on septal region, Hippocampus, and amygdala of cats and rats

    Biological Psychiatry 13:501–529.

    • PubMed
    • Google Scholar
43. 1. Herrero L
    2. Yu M
    3. Walker F
    4. Armstrong DM
    5. Apps R

    (2006) Olivo-cortico-nuclear localizations within crus I of the cerebellum

    The Journal of Comparative Neurology 497:287–308.

    https://doi.org/10.1002/cne.20976

    • PubMed
    • Google Scholar
44. 1. Hoffmann LC
    2. Berry SD

    (2009) Cerebellar theta oscillations are synchronized during hippocampal theta-contingent trace conditioning

    PNAS 106:21371–21376.

    https://doi.org/10.1073/pnas.0908403106

    • PubMed
    • Google Scholar
45. 1. Hoshi E
    2. Tremblay L
    3. Féger J
    4. Carras PL
    5. Strick PL

    (2005) The cerebellum communicates with the basal ganglia

    Nature Neuroscience 8:1491–1493.

    https://doi.org/10.1038/nn1544

    • PubMed
    • Google Scholar
46. 1. Iglói K
    2. Doeller CF
    3. Paradis AL
    4. Benchenane K
    5. Berthoz A
    6. Burgess N
    7. Rondi-Reig L

    (2015) Interaction between hippocampus and cerebellum crus I in Sequence-Based but not Place-Based navigation

    Cerebral Cortex 25:4146–4154.

    https://doi.org/10.1093/cercor/bhu132

    • PubMed
    • Google Scholar
47. 1. Iwata K
    2. Snider RS

    (1959) Cerebello-hippocampal influences on the electroencephalogram

    Electroencephalography and Clinical Neurophysiology 11:439–446.

    https://doi.org/10.1016/0013-4694(59)90043-4

    • PubMed
    • Google Scholar
48. 1. Jones MW
    2. Wilson MA

    (2005) Theta rhythms coordinate hippocampal-prefrontal interactions in a spatial memory task

    PLOS Biology 3:e402.

    https://doi.org/10.1371/journal.pbio.0030402

    • PubMed
    • Google Scholar
49. 1. Jwair S
    2. Coulon P
    3. Ruigrok TJ

    (2017) Disynaptic subthalamic input to the posterior cerebellum in rat

    Frontiers in Neuroanatomy 11:13.

    https://doi.org/10.3389/fnana.2017.00013

    • PubMed
    • Google Scholar
50. 1. Kajikawa Y
    2. Schroeder CE

    (2011) How local is the local field potential?

    Neuron 72:847–858.

    https://doi.org/10.1016/j.neuron.2011.09.029

    • PubMed
    • Google Scholar
51. 1. Kelly RM
    2. Strick PL

    (2000) Rabies as a transneuronal tracer of circuits in the central nervous system

    Journal of Neuroscience Methods 103:63–71.

    https://doi.org/10.1016/S0165-0270(00)00296-X

    • PubMed
    • Google Scholar
52. 1. Kelly RM
    2. Strick PL

    (2003) Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate

    The Journal of Neuroscience 23:8432–8444.

    https://doi.org/10.1523/JNEUROSCI.23-23-08432.2003

    • PubMed
    • Google Scholar
53. 1. Kelly RM
    2. Strick PL

    (2004) Macro-architecture of basal ganglia loops with the cerebral cortex: use of Rabies virus to reveal multisynaptic circuits

    Progress in Brain Research 143:449–459.

    https://doi.org/10.1016/s0079-6123(03)43042-2

    • PubMed
    • Google Scholar
54. 1. Kim SG
    2. Uğurbil K
    3. Strick PL

    (1994) Activation of a cerebellar output nucleus during cognitive processing

    Science 265:949–951.

    https://doi.org/10.1126/science.8052851

    • PubMed
    • Google Scholar
55. 1. Koziol LF
    2. Budding D
    3. Andreasen N
    4. D'Arrigo S
    5. Bulgheroni S
    6. Imamizu H
    7. Ito M
    8. Manto M
    9. Marvel C
    10. Parker K
    11. Pezzulo G
    12. Ramnani N
    13. Riva D
    14. Schmahmann J
    15. Vandervert L
    16. Yamazaki T

    (2014) Consensus paper: the cerebellum's role in movement and cognition

    The Cerebellum 13:151–177.

    https://doi.org/10.1007/s12311-013-0511-x

    • PubMed
    • Google Scholar
56. 1. Krook-Magnuson E
    2. Szabo GG
    3. Armstrong C
    4. Oijala M
    5. Soltesz I

    (2014) Cerebellar directed optogenetic intervention inhibits spontaneous hippocampal seizures in a mouse model of temporal lobe epilepsy

    eNeuro 1:.

    https://doi.org/10.1523/ENEURO.0005-14.2014

    • PubMed
    • Google Scholar
57. 1. Lasztóczi B
    2. Klausberger T

    (2016) Hippocampal place cells couple to three different gamma oscillations during place field traversal

    Neuron 91:34–40.

    https://doi.org/10.1016/j.neuron.2016.05.036

    • PubMed
    • Google Scholar
58. 1. Lefort JM
    2. Vincent J
    3. Tallot L
    4. Jarlier F
    5. De Zeeuw CI
    6. Rondi-Reig L
    7. Rochefort C

    (2019) Impaired cerebellar purkinje cell potentiation generates unstable spatial map orientation and inaccurate navigation

    Nature Communications 10:.

    https://doi.org/10.1038/s41467-019-09958-5

    • PubMed
    • Google Scholar
59. 1. Mihailoff GA
    2. Burne RA
    3. Azizi SA
    4. Norell G
    5. Woodward DJ

    (1981) The pontocerebellar system in the rat: an HRP study. II. hemispheral components

    The Journal of Comparative Neurology 197:559–577.

    https://doi.org/10.1002/cne.901970403

    • PubMed
    • Google Scholar
60. 1. Muzzu T
    2. Mitolo S
    3. Gava GP
    4. Schultz SR

    (2018) Encoding of locomotion kinematics in the mouse cerebellum

    PLOS ONE 13:e0203900.

    https://doi.org/10.1371/journal.pone.0203900

    • PubMed
    • Google Scholar
61. 1. Newman PP
    2. Reza H

    (1979) Functional relationships between the hippocampus and the cerebellum: an electrophysiological study of the cat

    The Journal of Physiology 287:405–426.

    https://doi.org/10.1113/jphysiol.1979.sp012667

    • PubMed
    • Google Scholar
62. 1. Nolte G
    2. Bai O
    3. Wheaton L
    4. Mari Z
    5. Vorbach S
    6. Hallett M

    (2004) Identifying true brain interaction from EEG data using the imaginary part of coherency

    Clinical Neurophysiology 115:2292–2307.

    https://doi.org/10.1016/j.clinph.2004.04.029

    • PubMed
    • Google Scholar
63. 1. O'Connor SM
    2. Berg RW
    3. Kleinfeld D

    (2002) Coherent electrical activity between vibrissa sensory areas of cerebellum and neocortex is enhanced during free whisking

    Journal of Neurophysiology 87:2137–2148.

    https://doi.org/10.1152/jn.00229.2001

    • PubMed
    • Google Scholar
64. 1. Onuki Y
    2. Van Someren EJ
    3. De Zeeuw CI
    4. Van der Werf YD

    (2015) Hippocampal-cerebellar interaction during spatio-temporal prediction

    Cerebral Cortex 25:313–321.

    https://doi.org/10.1093/cercor/bht221

    • PubMed
    • Google Scholar
65. 1. Paul SM
    2. Heath RG
    3. Ellison JP

    (1973) Histochemical demonstration of a direct pathway from the fastigial nucleus to the septal region

    Experimental Neurology 40:798–805.

    https://doi.org/10.1016/0014-4886(73)90113-1

    • PubMed
    • Google Scholar
66. 1. Petrosini L
    2. Leggio MG
    3. Molinari M

    (1998) The cerebellum in the spatial problem solving: a co-star or a guest star?

    Progress in Neurobiology 56:191–210.

    https://doi.org/10.1016/S0301-0082(98)00036-7

    • PubMed
    • Google Scholar
67. 1. Proville RD
    2. Spolidoro M
    3. Guyon N
    4. Dugué GP
    5. Selimi F
    6. Isope P
    7. Popa D
    8. Léna C

    (2014) Cerebellum involvement in cortical sensorimotor circuits for the control of voluntary movements

    Nature Neuroscience 17:1233–1239.

    https://doi.org/10.1038/nn.3773

    • PubMed
    • Google Scholar
68. 1. Ramnani N

    (2006) The primate cortico-cerebellar system: anatomy and function

    Nature Reviews Neuroscience 7:511–522.

    https://doi.org/10.1038/nrn1953

    • PubMed
    • Google Scholar
69. 1. Raux H
    2. Iseni F
    3. Lafay F
    4. Blondel D

    (1997) Mapping of monoclonal antibody epitopes of the Rabies virus P protein

    The Journal of General Virology 78 ( Pt 1:119–124.

    https://doi.org/10.1099/0022-1317-78-1-119

    • PubMed
    • Google Scholar
70. 1. Rochefort C
    2. Arabo A
    3. André M
    4. Poucet B
    5. Save E
    6. Rondi-Reig L

    (2011) Cerebellum shapes hippocampal spatial code

    Science 334:385–389.

    https://doi.org/10.1126/science.1207403

    • PubMed
    • Google Scholar
71. 1. Rochefort C
    2. Lefort JM
    3. Rondi-Reig L

    (2013) The cerebellum: a new key structure in the navigation system

    Frontiers in Neural Circuits 7:35.

    https://doi.org/10.3389/fncir.2013.00035

    • PubMed
    • Google Scholar
72. 1. Rogers TD
    2. Dickson PE
    3. Heck DH
    4. Goldowitz D
    5. Mittleman G
    6. Blaha CD

    (2011) Connecting the dots of the cerebro-cerebellar role in cognitive function: neuronal pathways for cerebellar modulation of dopamine release in the prefrontal cortex

    Synapse 65:1204–1212.

    https://doi.org/10.1002/syn.20960

    • PubMed
    • Google Scholar
73. 1. Rondi-Reig L
    2. Paradis AL
    3. Lefort JM
    4. Babayan BM
    5. Tobin C

    (2014) How the cerebellum may monitor sensory information for spatial representation

    Frontiers in Systems Neuroscience 8:205.

    https://doi.org/10.3389/fnsys.2014.00205

    • PubMed
    • Google Scholar
74. 1. Rondi-Reig L
    2. Burguière E

    (2005) Is the cerebellum ready for navigation?

    Progress in Brain Research 148:.

    https://doi.org/10.1016/S0079-6123(04)48017-0

    • PubMed
    • Google Scholar
75. 1. Rowland NC
    2. Goldberg JA
    3. Jaeger D

    (2010) Cortico-cerebellar coherence and causal connectivity during slow-wave activity

    Neuroscience 166:698–711.

    https://doi.org/10.1016/j.neuroscience.2009.12.048

    • PubMed
    • Google Scholar
76. 1. Ruigrok TJ

    (2011) Ins and outs of cerebellar modules

    The Cerebellum 10:464–474.

    https://doi.org/10.1007/s12311-010-0164-y

    • PubMed
    • Google Scholar
77. 1. Sauerbrei BA
    2. Lubenov EV
    3. Siapas AG

    (2015) Structured variability in purkinje cell activity during locomotion

    Neuron 87:840–852.

    https://doi.org/10.1016/j.neuron.2015.08.003

    • PubMed
    • Google Scholar
78. 1. Sillitoe RV
    2. Marzban H
    3. Larouche M
    4. Zahedi S
    5. Affanni J
    6. Hawkes R

    (2005) Conservation of the architecture of the anterior lobe vermis of the cerebellum across mammalian species

    Progress in Brain Research 148:.

    https://doi.org/10.1016/S0079-6123(04)48022-4

    • PubMed
    • Google Scholar
79. 1. Singer W

    (1999) Neuronal synchrony: a versatile code for the definition of relations?

    Neuron 24:49–65.

    https://doi.org/10.1016/S0896-6273(00)80821-1

    • PubMed
    • Google Scholar
80. 1. Sirota A
    2. Montgomery S
    3. Fujisawa S
    4. Isomura Y
    5. Zugaro M
    6. Buzsáki G

    (2008) Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm

    Neuron 60:683–697.

    https://doi.org/10.1016/j.neuron.2008.09.014

    • PubMed
    • Google Scholar
81. 1. Snider RS
    2. Maiti A

    (1975) Septal afterdischarges and their modification by the cerebellum

    Experimental Neurology 49:529–539.

    https://doi.org/10.1016/0014-4886(75)90106-5

    • PubMed
    • Google Scholar
82. 1. Snider RS
    2. Maiti A

    (1976) Cerebellar contributions to the papez circuit

    Journal of Neuroscience Research 2:133–146.

    https://doi.org/10.1002/jnr.490020204

    • PubMed
    • Google Scholar
83. 1. Soteropoulos DS
    2. Baker SN

    (2006) Cortico-cerebellar coherence during a precision grip task in the monkey

    Journal of Neurophysiology 95:1194–1206.

    https://doi.org/10.1152/jn.00935.2005

    • PubMed
    • Google Scholar
84. 1. Stackman RW
    2. Clark AS
    3. Taube JS

    (2002) Hippocampal spatial representations require vestibular input

    Hippocampus 12:291–303.

    https://doi.org/10.1002/hipo.1112

    • PubMed
    • Google Scholar
85. 1. Stam CJ
    2. Nolte G
    3. Daffertshofer A

    (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources

    Human Brain Mapping 28:1178–1193.

    https://doi.org/10.1002/hbm.20346

    • PubMed
    • Google Scholar
86. 1. Stoodley CJ
    2. D'Mello AM
    3. Ellegood J
    4. Jakkamsetti V
    5. Liu P
    6. Nebel MB
    7. Gibson JM
    8. Kelly E
    9. Meng F
    10. Cano CA
    11. Pascual JM
    12. Mostofsky SH
    13. Lerch JP
    14. Tsai PT

    (2017) Altered cerebellar connectivity in autism and cerebellar-mediated rescue of autism-related behaviors in mice

    Nature Neuroscience 20:1744–1751.

    https://doi.org/10.1038/s41593-017-0004-1

    • PubMed
    • Google Scholar
87. 1. Stoodley CJ
    2. Schmahmann JD

    (2010) Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing

    Cortex 46:831–844.

    https://doi.org/10.1016/j.cortex.2009.11.008

    • PubMed
    • Google Scholar
88. 1. Sugihara I

    (2011) Compartmentalization of the deep cerebellar nuclei based on afferent projections and aldolase C expression

    The Cerebellum 10:449–463.

    https://doi.org/10.1007/s12311-010-0226-1

    • PubMed
    • Google Scholar
89. 1. Sugihara I
    2. Quy PN

    (2007) Identification of aldolase C compartments in the mouse cerebellar cortex by olivocerebellar labeling

    The Journal of Comparative Neurology 500:1076–1092.

    https://doi.org/10.1002/cne.21219

    • PubMed
    • Google Scholar
90. 1. Sugihara I
    2. Shinoda Y

    (2004) Molecular, topographic, and functional organization of the cerebellar cortex: a study with combined aldolase C and olivocerebellar labeling

    Journal of Neuroscience 24:8771–8785.

    https://doi.org/10.1523/JNEUROSCI.1961-04.2004

    • PubMed
    • Google Scholar
91. 1. Suzuki L
    2. Coulon P
    3. Sabel-Goedknegt EH
    4. Ruigrok TJ

    (2012) Organization of cerebral projections to identified cerebellar zones in the posterior cerebellum of the rat

    Journal of Neuroscience 32:10854–10869.

    https://doi.org/10.1523/JNEUROSCI.0857-12.2012

    • PubMed
    • Google Scholar
92. 1. Teune TM
    2. van der Burg J
    3. van der Moer J
    4. Voogd J
    5. Ruigrok TJ

    (2000) Topography of cerebellar nuclear projections to the brain stem in the rat

    Progress in Brain Research 124:.

    https://doi.org/10.1016/S0079-6123(00)24014-4

    • PubMed
    • Google Scholar
93. 1. Ugolini G

    (2010) Advances in viral transneuronal tracing

    Journal of Neuroscience Methods 194:2–20.

    https://doi.org/10.1016/j.jneumeth.2009.12.001

    • PubMed
    • Google Scholar
94. 1. Vinck M
    2. Oostenveld R
    3. van Wingerden M
    4. Battaglia F
    5. Pennartz CM

    (2011) An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias

    NeuroImage 55:1548–1565.

    https://doi.org/10.1016/j.neuroimage.2011.01.055

    • PubMed
    • Google Scholar
95. 1. Voogd J
    2. Barmack NH

    (2006) Oculomotor cerebellum

    Progress in Brain Research 151:.

    https://doi.org/10.1016/S0079-6123(05)51008-2

    • PubMed
    • Google Scholar
96. 1. Wang YJ
    2. Chen H
    3. Hu C
    4. Ke XF
    5. Yang L
    6. Xiong Y
    7. Hu B

    (2014) Baseline theta activities in medial prefrontal cortex and deep cerebellar nuclei are associated with the extinction of trace conditioned eyeblink responses in guinea pigs

    Behavioural Brain Research 275:72–83.

    https://doi.org/10.1016/j.bbr.2014.08.059

    • PubMed
    • Google Scholar
97. 1. Watson TC
    2. Jones MW
    3. Apps R

    (2009) Electrophysiological mapping of novel prefrontal - cerebellar pathways

    Frontiers in Integrative Neuroscience 3:18.

    https://doi.org/10.3389/neuro.07.018.2009

    • PubMed
    • Google Scholar
98. 1. Watson TC
    2. Becker N
    3. Apps R
    4. Jones MW

    (2014) Back to front: cerebellar connections and interactions with the prefrontal cortex

    Frontiers in Systems Neuroscience 8:4.

    https://doi.org/10.3389/fnsys.2014.00004

    • PubMed
    • Google Scholar
99. 1. Whiteside JA
    2. Snider RS

    (1953) Relation of cerebellum to upper brain stem

    Journal of Neurophysiology 16:397–413.

    https://doi.org/10.1152/jn.1953.16.4.397

    • PubMed
    • Google Scholar
100. 1. Wikgren J
     2. Nokia MS
     3. Penttonen M

     (2010) Hippocampo-cerebellar theta band phase synchrony in rabbits

     Neuroscience 165:1538–1545.

     https://doi.org/10.1016/j.neuroscience.2009.11.044

     • PubMed
     • Google Scholar
101. 1. Yu W
     2. Krook-Magnuson E

     (2015) Cognitive collaborations: bidirectional functional connectivity between the cerebellum and the hippocampus

     Frontiers in Systems Neuroscience 9:177.

     https://doi.org/10.3389/fnsys.2015.00177

     • PubMed
     • Google Scholar
102. 1. Zheng Y
     2. Goddard M
     3. Darlington CL
     4. Smith PF

     (2009) Long-term deficits on a foraging task after bilateral vestibular deafferentation in rats

     Hippocampus 19:480–486.

     https://doi.org/10.1002/hipo.20533

     • PubMed
     • Google Scholar

Author details

1. Thomas Charles Watson

   Neuroscience Paris Seine, Cerebellum, Navigation and Memory Team, CNRS UMR 8246, INSERM, UMR-S 1130, Sorbonne Universités, University Pierre and Marie Curie, Paris, France

   Present address

   1. Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
   2. SimonsInitiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom
   3. Patrick Wild Centre for Autism Research, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Formal analysis, Supervision, Validation, Investigation, Methodology, Writing—original draft

   Contributed equally with

   Pauline Obiang and Arturo Torres-Herraez

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5146-6981

2. Pauline Obiang

   Neuroscience Paris Seine, Cerebellum, Navigation and Memory Team, CNRS UMR 8246, INSERM, UMR-S 1130, Sorbonne Universités, University Pierre and Marie Curie, Paris, France

   Contribution

   Validation, Investigation, Methodology, Writing—review and editing

   Contributed equally with

   Thomas Charles Watson and Arturo Torres-Herraez

   Competing interests

   No competing interests declared

3. Arturo Torres-Herraez

   Neuroscience Paris Seine, Cerebellum, Navigation and Memory Team, CNRS UMR 8246, INSERM, UMR-S 1130, Sorbonne Universités, University Pierre and Marie Curie, Paris, France

   Contribution

   Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft

   Contributed equally with

   Thomas Charles Watson and Pauline Obiang

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6333-5623

4. Aurélie Watilliaux

   Neuroscience Paris Seine, Cerebellum, Navigation and Memory Team, CNRS UMR 8246, INSERM, UMR-S 1130, Sorbonne Universités, University Pierre and Marie Curie, Paris, France

   Contribution

   Formal analysis, Validation, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

5. Patrice Coulon

   Institut de Neurosciences de la Timone, CNRS and Aix Marseille Université, Marseille, France

   Contribution

   Resources, Supervision, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2996-405X

6. Christelle Rochefort

   Neuroscience Paris Seine, Cerebellum, Navigation and Memory Team, CNRS UMR 8246, INSERM, UMR-S 1130, Sorbonne Universités, University Pierre and Marie Curie, Paris, France

   Contribution

   Validation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

7. Laure Rondi-Reig

   Neuroscience Paris Seine, Cerebellum, Navigation and Memory Team, CNRS UMR 8246, INSERM, UMR-S 1130, Sorbonne Universités, University Pierre and Marie Curie, Paris, France

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing—original draft, Project administration

   For correspondence

   laure.rondi-reig@upmc.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1006-0501

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