{"paper_id":"69f7c7d5-2a97-4049-bb3b-7e5bec747c43","body_text":"1 \nMapping hippocampal-cerebellar functional \nconnectivity across the adult lifespan \n Kavishini Apasamy¹,  Narender Ramnani¹*,  Carl J. Hodgetts¹* \n1. Department of Psychology, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK \n* Joint senior authors \nFor correspondence: carl.hodgetts@rhul.ac.uk \n \nAlthough the hippocampus and cerebellum are traditionally considered to support distinct \nmemory systems, evidence from nonhuman species indicates a close bidirectional \nrelationship during learning and navigational behaviour, with the hippocampus projecting  \nto – and receiving input from – several cerebellar regions. However, little is known about the \nnature and topography of hippocampal -cerebellar connectivity in the human brain. To \naddress this gap, we applied seed -based functional connectivity analyses to resting-state \nfMRI data from 479 cognitively normal participants, aged 18 –88 years.  We identified \nsignificant functional correlations between the hippocampus and widespread areas of \ncerebellar cortex, particularly lobules HIV, HV, HVI, HVIIA (Crus I and II ), HIX, and HX. \nContrasting the left and right hippocampus, we found significant correlations with the \ncontralateral Crus II.  We also compared longitudinal subdivisions of the hippocampus, \nrevealing that anterior hippocampus demonstrated stronger connectiv ity with right Crus II, \nwhereas posterior hippocampus was strongly connected to vermal parts of lobule V. Finally, \nwe found that functional correlations between several hippocampal seeds (left, right, and \nanterior) and lobules HVI and HV decreased signific antly with age.  These results provide \nnovel insights into hippocampal -cerebellar functional organisation and the influence of \nageing on this system. Further studies are required to establish the role of this connection \nin learning and memory, as well as its potential vulnerability to neurodegeneration. \nKeywords: cerebellum, hippocampus, ageing, memory, functional connectivity \n \nIntroduction \nThe ability to represent and navigate spatial environments is thought to be supported by an extended \nposteromedial navigation system in the brain that includes the hippocampus, as well as entorhinal, \nretrosplenial and parahippocampal cortices (Hodgetts et al., 2017; Murray et al., 2018; Ritchey et al., \n2015, see also Ekstrom and Ranganath, 2017). Critically, this brain system is also thought to undergo \npronounced structural and functional alterations across the adult lifespan (Lester et al., 2017), acting \nas a ‘hotspot’ for age -associated neurodegenerative processes, such as tau and amyloid -beta \naccumulation (Berron et al., 2021; Lace et al., 2009). In this context, it is important to better understand \nthe organisation of the brain’s navigation system, parti cularly by considering the influence of other \ncortical and subcortical areas.  \nThe cerebellum, for instance, is classically associated with motor processing (Glickstein, 2007; \nGlickstein, 1993; Jimsheleishvili and Dididze, 2021) but has been shown to be strongly connected with \nhigher-order cognitive areas, such as prefrontal cortex ( Kelly and Strick, 2003; Middleton and Strick, \n2001; O’Reilly et al., 2009). In line with this, evidence from nonhuman species suggests that the \ncerebellum has functional and structural interactions with the hippocampus (Rochefort et al., 2013; \nRondi-Reig et al., 2022). For example, electrophysiological studies in transgenic mice have shown that \ndisrupting cerebellar cells alters hippocampal place cell firing and impairs navigational performance \n(Lefort et al., 2015; Rochefort et al., 2011). Similarly, optogenetic excitation or inhibition of midline and \nlateral cerebellar neurons has been shown to reduce the duration of left hippocampal seizure activity \n(Krook-Magnuson et al., 2014), and combined optogenetic stimulation -fMRI revealed that cerebellar \nstimulation (between lobule V and VI) increased dorsal hippocampal blood -oxygen level dependent \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n2 \n(BOLD) signal (Choe et al., 2018). Taken together, these studies establish an argument that the \ncerebellum modulates the physiology of the hippocampus, as well as influencing hippocampal -\ndependent behaviour (e.g., spatial learning and memory).  \nWhile early electrophysiological evidence in primates and cats suggested a direct structural \nmonosynaptic connection between these structures (as evidenced by short observed latencies between \ncerebellar fastigial nucleus stimulation and evoked hippocampal responses; Heath and Harper, 1974; \nNewman and Reza, 1979), recent anatomical tracing work in rodents indicates connectivity through \nmultisynaptic pathways (Watson et al., 2019). Specifically, left hippocampus has been shown to receive \ninput from distribute d areas of cerebellar cortex, including bilateral and vermal lobule VI, lobule VIIA \n(Crus I) and paraflocculus via at least two relay stations (Watson et al., 2019). This study also observed \nphase-locked theta coherence between cerebellar Purkinje cells in  lobule VI, VIIA (Crus I) and the \ndorsal hippocampus during exploratory behaviour. Notably, evidence in macaque monkeys suggests \nthat lobule VI and VIIA sends its output to the cerebellar fastigial nuclei (Coffman et al., 2011) – the \nsame area found by the  above electrophysiological studies to evoke responses in the hippocampus. \nThis suggests that topographically distinct areas of the cerebellum send their input to the hippocampus \nvia distinct pathways. \nDespite strong evidence of connectivity in nonhuman species, this is lacking in the human brain. Such \nevidence has the potential to extend and inform current neurobiological accounts of human memory \nand navigation, incorporating the cerebellum as a key str ucture, and may have implications for \nunderstanding memory decline in ageing and neurodegeneration. \nStudies of healthy ageing in humans, for example, indicate that the cerebellum and hippocampus are \nboth vulnerable to age -related structural changes (Cui et al., 2020; Du et al., 2006; Ramanoël et al., \n2023) and exhibit comparable grey matter loss (Bernard  and Seidler, 2014; Gellersen et al., 2021; \nWoodruff et al., 2010). Notably, age-related reductions in cerebellar volume have been linked to poorer \nperformance on hippocampal-dependent navigation tasks (Daugherty and Raz, 2017), suggesting that \nstructural changes may disrupt hippocampal-cerebellar communication. Indeed, recent work in humans \nhas found that age-related cerebellar atrophy is mainly localised to lobule HVI and lobule HVIIA (Crus \nI; Ramanoël et al., 2023): two regions shown to connect with the hippocampus in nonhuman animal \nstudies (Watson et al., 2019) and co -activate with the hippocampus during navigational learning in \nyoung human adults (Iglói et al., 2015). Despite this evidence, there remains a significant gap in \nunderstanding hippocampal -cerebellar connectivity in humans, and how it may differ across the \nlifespan. \nHere, we aimed to examine these questions using resting-state fMRI data from the Cambridge Centre \nfor Ageing and Neuroscience (CamCAN) dataset (Taylor et al., 2017). We first sought to map patterns \nof bilateral hippocampal connectivity (independent of age) within the cerebellum. We hypothesised that \nthe hippocampus would show a strong functional correlation with several areas of the cerebellum, \nincluding lobule VI and HVIIA (Crus I), as predicted by prior nonhuman animal tracing work (e.g., \nWatson et al., 2019) and electrophysiological evidence showing that stimulating fastigial nucleus (which \nreceives input from these areas) leads to evoked responses in the hippocampus (e.g., Coffman et al., \n2011). We also contrasted the connectivity of the left hippocampus  against the right, drawing on \nevidence that hippocampally -dependent tasks might be lateralised in the cerebellum (e.g., Riva and \nGiorgi, 2000). It is also possible that a distinctive pattern of connectivity exists for each hemisphere – a \nquestion that rem ains unresolved as previous anatomical studies have primarily focused on left \nhippocampal connectivity (e.g., Krook-Magnuson et al., 2014; Watson et al., 2019). The hippocampus \nis also thought to display functional gradients along its longitudinal (anterio r-posterior) axis, arising, in \npart, from variation in connectional anatomy (Aggleton, 2012; Poppenk et al., 2013; Strange et al., \n2014). For instance, the anterior hippocampus preferentially connects to the amygdala and prefrontal \ncortex (Aggleton et al., 2010; Aggleton, 1986), whereas posterior hippocampus preferentially connects \nwith the parahippocampal cortex (Aggleton, 2012). As regions within these anterior and posterior \nhippocampal networks might differentially connect with cerebellar cortex (see e.g ., prefrontal \nconnections with lobule HVIIA; Ramnani, 2006), we also predicted connectivity differences when \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n3 \ncontrasting anterior and posterior hippocampal seed regions. Finally, the large age range within the \nCamCAN dataset (18-87 years old) enabled us to examine how increasing age influences the degree \nand pattern of hippocampal -cerebellar functional connectivi ty. We predicted that the strongest age -\nrelated decreases in functional connectivity will be observed in lobules HVI and HVIIA, consistent with \ntheir vulnerability to age -related atrophy (see e.g., Gellersen et al., 2021; Ramanoël et al., 2023) and \nsuggested involvement in spatial cognition (Iglói et al., 2015).  \n \nMethodology \nParticipants \nWe used structural and functional MRI data from the Cambridge Centre of Ageing and Neuroscience \n(CamCAN) study (Taylor et al., 2017). This dataset contained 653 participants (323 males, 330 females, \n18-87 years old, mean = 54.3, SD=18.6). About fifty males and females were collected from each decile \n(deciles: 18-27 years, 28-37 years, 38-47 years, 48-57 years, 58-67 years, 68-77 years, 78-87 years; \nfor more information about participants see Shafto et al., 2014). Participants in the dataset were \ncognitively healthy, assessed via a score above 25 on the mini -mental state examination (Folstein et \nal.,1975), and did not have any neurological or psychiatric conditions. Written informed consent for the \ndatabase was obtained in accordance with the Cambridgeshire Research Ethics Committee, and these \nsecondary data analyses were conducted in accordance with Royal Holloway, University of London \nEthics Committee processes.  \nNeuroimaging data acquisition \nAll CamCAN MRI data was acquired using a 3T Siemens TIM Trio Scanner at the Medical Research \nCouncil (UK) Cognition and Brain Science Unit using a 32 -channel head coil. The data used in this \nstudy forms part of a larger scanning protocol (see https://camcan-archive.mrc-\ncbu.cam.ac.uk/dataaccess/ or Taylor et al., 2017 for more detail). High -resolution structural images \nwere obtained using a T1-weighted magnetisation-prepared rapid sequence (MPRAGE; TE= 2.99 ms; \nTR= 2250ms; TI = 900 ms; voxel size = 1x1x1 mm; field -of-view = 256x240x192 mm; flip a ngle = 9°). \nResting-state fMRI data was acquired using a T2* -weighted gradient echo planar image (EPI) \nsequence. Rest (resting in the scanner with eyes closed) consisted of 261 volumes and lasted 8 minutes \nand 40 seconds (32 slices; TE = 30 ms, TR = 1970ms, voxel size: 3x3x4.44 mm; field-of-view: 192x192 \nmm).  \nData preprocessing and denoising \nInitially, functional and structural MRI data from a random sample of approximately 25% of cases were \nvisually inspected for image quality and motion artefacts by KA (with support from CH). Functional MRI \nimages were pre -processed and denoised using the standard pipeline in the CONN toolbox \n(RRID:SCR_009550; version 22.a; Nieto -Castanon and Whitfield -Gabrieli, 2022) and SPM12 (RRID: \nSCR_007037; version 12.7771; Penny et al., 2011) running on Matlab 2022b (The Mathworks Inc, \nNatick, Massachusetts, USA). Functional MRI data were first realigned and unwarped using SPM12 \n(Andersson et al., 2001). For this, all scans were co-registered to the first volume using a least-squares \napproach and a 6 -parameter rigid body transformation (Friston et al., 1995). These were then \nresampled using b -spline interpolation to correct for motion and magnetic susceptibly interactio ns. \nTemporal misalignment between slices was corrected using the SPM12 slice -timing correction \nprocedure (Henson et al., 1999; Sladky et al., 2011), which involved sinc temporal interpolation to \nresample each BOLD timeseries slice to a common mid-acquisition time. Potential outlier scans (based \non motion and global signal fluctuation) were identified using conservative (95 th percentile) outlier \nparameters in ART (Artifact Detection Tools; Whitfield-Gabrieli et al., 2011). Specifically, volumes with \nframewise displacement above 0.5 mm, or global BOLD signal changes above 3 SDs, were flagged as \noutliers (Nieto-Castanon, 2022; Power et al., 2014). A reference mean BOLD image was then computed \nfor each subject by averaging all scans, excluding outliers. Followin g this, functional and structural \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n4 \nimages were separately normalised into standard MNI space and segmented into grey matter, white \nmatter and CSF ‘tissue’ types using the SPM12 unified segmentation and normalization algorithm \n(Ashburner and Friston, 2005; Ashburner, 2007). Functional and structural images were then resampled \nto 2 mm and 1 mm isotropic voxels, respectively, following a direct normalization procedure (Calhoun \net al., 2017; Nieto -Castanon, 2022) with the default IXI -549 tissue probability map template. Finally, \nfunctional data was smoothed using a Gaussian kernel of 5 mm full-width half-maximum (FWHM; e.g., \nDombrovski et al., 2020).  \nFollowing preprocessing, the fMRI data were next denoised using the default pipeline in the CONN \ntoolbox (Nieto-Castanon, 2020). This involved regressing out noise components using an anatomical \ncomponent-based noise correction procedure (aCompCor), which included noise components from \nwhite matter (5 noise components), CSF (5 noise components), motion parameters (3 translational and \n3 rotational and their first order derivatives; Friston et al.,1996), outliers volumes derived from scrubbing \n(38 factors) (Power et al., 2014), effect of rest and its first order derivatives (2 factors; default setting \nwhich removes residual trends/instabilities only at the beginning of the timeseries). These were followed \nby a bandpass frequency filtering of the BOLD timeseries (Hallquist et al., 2013) between 0.01 Hz and \n0.09 Hz (e.g., Stefanov et al., 2020), which filters low frequencies (e.g., physiological noise). From the \nnumber of noise terms included in this denoising strategy, the effective degrees of freedom of the BOLD \nsignal after denoising were estimated to range from 62.1 to 74.1 (average 70.5) across all subjects \n(Nieto-Castanon, 2022; see Morfini et al., 2023 for a full description of quality control measures, \nincluding degrees of freedom).  \nFollowing quality assurance, 479 participants (242 males, 237 females, 18 -87 years old, mean= 50.7, \nSD= 18.2) were entered into the seed-based connectivity analysis. The remaining cases were rejected \nfor reasons such as having more than 10% of invalid scans, motion above 0.5 mm, etc. \nAnatomical methods \nRegions of interests (ROIs) for the hippocampus were defined using probabilistic, anatomical atlases. \nHippocampal seed ROIs were created by combining the hippocampal ROI from the Harvard -Oxford \nsubcortical atlas and the subiculum ROI from the Jülich histological atlas (Amunts et al., 2005) ensuring \nthat the hippocampal ROI was extended medially incorporating the subicular complex (see Hodgetts et \nal., 2017). The Harvard-Oxford atlas ROI was thresholded at 50% and the Jülich atlas ROI thresholded \nat 75% to ensure both ROIs were constrained to grey matter and did not extend into adjacent regions. \nUsing this method, left and right hippocampal ROIs were defined (Figure 1a). For the long-axis analysis, \nthe hippocampal ROIs were split into anterior and posterior zones arbitrarily at the uncal apex (Hodgetts \net al., 2017; Poppenk et al., 2013), corresponding to MNI slice y = -21 (see Figure 1b for segmentation \nof the left hippocampus).  \nFunctional correlations within the cerebellum were visualised and interrogated using the Spatially \nUnbiased Infratentorial Template (SUIT) (Diedrichsen et al., 2009). As well as containing a detailed \nprobabilistic atlas of the cerebellum, SUIT also contains a cerebellar flatmap, allowing for visualisation \nof functional connectivity maps within and across cerebellar lobules/subregions. The nomenclature of \nLarsell and Jansen (1972) was used to characterise cerebellar cortical results. \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n5 \n \nFigure 1. Hippocampal regions-of-interest. (a) The left hippocampal ROI made from combining the \nhippocampus from the Harvard-Oxford atlas and the subicular complex from the Jüelich Histological atlas; (b) \nThe left and right hippocampal ROI split into anterior and posterior hippocampus using the uncal apex for a \nlandmark-based segmentation.  \n \nMRI analysis \nFollowing denoising (see above), a first -level seed-based connectivity analysis was conducted using \nthe CONN toolbox. Here, the BOLD timeseries for each hippocampal seed (left hippocampus, right \nhippocampus, anterior hippocampus, posterior hippocampus), as well as confound regressors (e.g., \nwhite matter and CSF) were entered as first -level covariates into general linear models (GLMs). \nFunctional connectivity between the seeds and every other voxel in the brain was represented by the \nFisher-transformed bivariate correlation coefficient ( r-to-Z) from a weighted GLM. At the first level, we \nconducted eight analyses whereby four of them related to single seed and four of them to between -\nseed contrasts. Here we considered the issue of shared variance, and this was minimised by the use \nof separate GLMs for each seed which avoids multicollinearity between seeds in the same model. For \nthe single seed analyses, the left, right, anterior, and posterior hippocampus design matrices contained \na single column representing all subjects, allowing the investigation of seed -to-voxel connectivity for \neach ROI in isolation.  \nTo examine hemispheric and long-axis differences in hippocampal connectivity with the cerebellum, we \nspecified between-seed contrasts (referred to as between-source contrasts in CONN). Here, the design \nmatrices included two columns representing each seed i n each contrast. Contrasts compared the \nconnectivity of left and right hippocampus (contrast vectors: 1 -1 and -1 1). Others compared the \nconnectivity of anterior and posterior hippocampus (contrast vector: 1 -1 and -1 1). For single seed \nanalyses, beta images were carried to the second -level analysis for one-sample t-tests. For between-\nseed analyses, contrast images generated in the first -level analysis were carried over to the second -\nlevel analysis for one -sample t-test. To explore age -related variation in hippocampal -cerebellar \nfunctional correlations, we also re-specified these GLMs with age as an additional subject -level effect, \nfirstly as a regressor-of-no-interest (contrast vector: 1 0), and then as a regressor -of-interest (contrast \nvector: 0 1) into a bivariate regression analysis. For these contrasts, age was mean -centred and to \nensure that we captured anticorrelations, we applied a contrast vector of 0 -1 to the demeaned age \ndata.  \nThe unthresholded group-level t-statistic maps (as well as between-source contrast maps) from CONN \nwere interrogated in SPM. These were thresholded using a family -wise error correction p-FWE <0.05 \nbased on Random Field Theory (Worsley et al., 1996). The thresholded images were then loaded into \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n6 \nSUIT to localise (and visualise) significant clusters within cerebellar subregions. This produced \ncerebellar flatmaps showing suprathreshold connectivity strength within and across cerebellar lobules. \n \nResults \nThe hippocampus is functionally connected to widespread areas of the cerebellar \ncortex. \nInitially, we used seed-based connectivity to examine patterns of cerebellar connectivity from left and \nright hippocampal seed ROIs. As predicted by anatomical studies, both the left and right hippocampus \nshowed bilateral connections to widespread areas of  the cerebellum, including the vermis and lateral \nhemispheres. Both hemispheres showed strongest connections with the border of lobule HIV and HV, \nHIX, HX, and lobule HVIIA (medial parts of Crus II and laterally within the horizontal fissure at the \njunction of Crus I and Crus II; Figure 2a-b). \n \n \nFigure 2. Cerebellar regions showing significant functional connectivity with left and right hippocampus. \nThresholded SPM {T} maps (p<0.05) overlaid on the cerebellar flatmap using SUIT (Diedrichsen et al., 2006) and \non coronal and mid-sagittal sections of the standard MNI T1 2mm template. Contrasts for main effects of LEFT and \nRIGHT hippocampal seeds (a) and (b), respectively; contrasts for LEFT > RIGHT and RIGHT > LEFT hippocampal \nseeds, (c) and (d), respectively. \n \nTo examine hemispheric differences, cerebellar functional correlations with left and right hippocampus \nwere contrasted directly. The left hippocampus, compared to the right hippocampus, showed \npreferential connections with a contralateral region of lobule HVIIA (Crus I and peak in Crus II; number \nof voxels = 3063; p<.001), as well as lobule HIX (number of voxels = 181, p<.001; Figure 2c). Similarly, \nthe right hippocampal seed also showed a strong preferential functional correlation with the contralateral \n(i.e., left) region of lobule HVIIA (peak in Crus II; number of voxels = 178, p<.001; Figure 2d). Peak \ncluster statistics for these hemispheric contrasts are shown in Table 1.  \n \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n7 \nLong-axis subdivisions of the hippocampus show both overlapping and distinct \npatterns of cerebellar connectivity. \nNext, we examined hippocampal -cerebellar functional connectivity along the longitudinal axis of the \nhippocampus. The anterior and posterior hippocampus showed connectivity to similar regions as those \nconnected to the left and right hippocampus, including t he border of lobule HIV and HV, vermal parts \nof lobule V, lobule HVIIA (bilaterally at the fissure separating Crus I and II), lobule HIX and HX (Figure \n3a-b).  \n \n \nFigure 3. Cerebellar regions showing significant functional connectivity with anterior and posterior \nhippocampal seeds. Thresholded SPM {T} maps (p<0.05) overlaid on coronal, sagittal slices and cerebellar \nflatmaps. Contrasts for main effects of ANTERIOR and POSTERIOR hippocampal seeds (a) and (b), respectively \n(c) represents contrast for ANTERIOR > POSTERIOR hippocampal seed connectivity with the cerebellum. (d) \nrepresents contrast for POSTERIOR > ANTERIOR hippocampal seed connectivity with the cerebellum.  \n \nHowever, direct contrasts revealed that the anterior hippocampus, compared to the posterior \nhippocampus, showed significantly greater functional correlations with lobule HI-HIV (number of voxels \n= 185, p<.001) and bilateral regions of lobule HVIIA, including right Crus II (number of voxels = 2778, \np<.001; Figure 3c). Note, this same region of Crus II also displays significantly greater connectivity with \nleft versus right hippocampus (Figure 2c). Additional clusters were also seen in extreme areas of right \nlobule HIX and HX, and bilateral HVIIB and HVIII. The posterior versus anterior hippocampus contrast \nshowed significantly greater connectivity with widespread regions of the anterior cerebellum. \nSpecifically, we observed a peak in the bilateral extremes of the Crus I region of lobule HVIIA (number \nof voxels = 4761; p<.001), which extended medially into vermal areas of lobule V (see Figure 3d). \nFurther small clusters were found in the lateral extremes of lobule HVIIIA, bilaterally, which continued \nalong the border of HVIIIB and HIX. Peak cluster statistics for these direct long-axis contrasts are shown \nin Table 1. \n \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n8 \nContrasts Cerebellar lobule \nPeak \ncoordinate \n(mm) \nProbabilistic value z t \nLeft > right \nhippocampus \nRight lobule HVIIA (Crus II) \n36 -80 -42 Right Crus II (97%) Inf 10.7 \n28 -84 -32 \nRight Crus I (80%) \nInf 10.14 \nRight Crus II (19%) \n28 -76 -44 Right Crus II (76%) inf 9.23 \nRight lobule HIX \n4 -52 -48 \nRight HIX (93%) \nInf 8.28 \nBrain stem (1.2%) \n6 -48 -40 \nRight HIX (83.2%) \n7.69 7.94 \nBrain stem (16.8%) \nRight > left \nhippocampus \nLeft lobule HVIIA (Crus II) \n-38 -64 -44 \nLeft Crus II (87%) \n6.34 6.48 \nLeft Crus I (3%) \n-36 -70 -50 \nLeft Crus II (63%) \n5.96 6.07 \nLeft VIIB (34%) \n-28 -74 -50 \nLeft VIIB (63%) \n5.66 5.76 \nLeft Crus II (36%) \nRight lobule HI-HIV 12 -34 -20 \nRight HI-HIV (60%) \n6.12 6.24 \nBrain stem (24.4%) \nLeft lobule HVIIA (Crus II) -12 -78 -34 Left Crus II (87%) 5.64 5.74 \nRight lobule HVIIIA 32 -44 -56 \nRight HVIIIA (28%) \n5.49 5.58 \nRight HVIIIB (6%) \nLeft lobule VI -36 -36 -38 \nLeft VI (62%) \n5.39 5.48 \nLeft V (4%) \nLeft lobule VIIB -20 -76 -52 \nLeft VIIB (88%) \n5.21 5.28 \nLeft Crus II (10%) \nLeft lobule VIIB -40 -54 -58 \nLeft VIIB (40%) \n5.19 5.27 \nLeft VIIIA (26%) \nRight lobule HVIIIB 20 -44 -56 \nRight HVIIIB (89%) \n5.09 5.16 \nRight HIX (2%) \nLeft lobule HVIIB -32 -70 -58 \nLeft HVIIB (77%) \n5.03 5.1 \nLeft HVIIIA (6%) \nAnterior > \nposterior \nhippocampus \nRight lobule HI-HIV \n12 -42 -28 \nRight HI-HIV (47%) \nInf 13.07 \nBrain stem (4%) \n20 -36 -30 \nRight HI-HIV (37%) \nInf 12.11 \nBrain stem (15.1%) \n22 -46 -30 \nRight HV (17%) \nInf 10.28 \nRight VI (14%) \n  28 -84 -44 Right Crus II (92%) Inf 12.13 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n9 \nRight lobule HVIIA (Crus II) \n  \n  \n  \n36 -80 -40 \nRight Crus II (93%) \nInf 11.99 \nRight Crus I (6%) \n14 -84 -46 \nRight Crus II (79%) \nInf 10.26 \nRight HVIIB (7%) \n  \nBrain stem \n  \n  \n  \n  \n-8 -42 -28 \nBrain stem (69.7%) \nInf 11.48 \nLeft HI-HIV (19.0%) \n-18 -34 -30 \nBrain stem (48.6%) \nInf 11.23 \nLeft HI-HIV (20%) \n-26 -42 -34 \nLeft HVI (7%) \n7.68 7.93 \nLeft HV (5%) \nRight lobule HIX \n6 -46 -36 \nRight HIX (32%) \nInf 8.96 \nBrain stem (11.3%) \n-2 -46 -34 \nVermal X (39%) \n7.37 7.59 \nBrain stem (2.1%) \n0 -48 -44 \nRight HIX (42%) \n6.43 6.57 \nLeft IX (14%) \nRight lobule VIIIB 12 -40 -60 Right HVIIIB (12%) 5.19 5.26 \nRight lobule HVIIA (Crus I) 46 -58 -34 Right Crus I (93%) 5.05 5.12 \nPosterior > anterior \nhippocampus \nLeft lobule HVIIA (Crus I) \n-44 -42 -34 \nLeft Crus I (49%) \nInf 16.6 \nLeft HVI (15%) \n \n46 -40 -34 \nRight Crus I (22%) \nInf 15.96 \nRight HVI (3%) \n0 -60 -6 \nLeft HV (47%) \nInf 15.8 \nRight HV (42%) \n \nLeft lobule HVIIB \n-30 -68 -48 \nLeft HVIIB (70%) \n7.78 8.04 \nLeft Crus II (17%) \n-40 -60 -42 \nLeft Crus II (49%) \n6.92 7.1 \nLeft Crus I (48%) \nLeft lobule HIX -10 -52 -50 \nLeft HIX (71%) \n6.55 6.7 \nLeft HVIIIB (2%) \nRight lobule HVIIA (Crus I)  50 -62 -26 \nRight Crus I (64%) \n6.33 6.47 \nTemporal Occipital Fusiform (3%) \nRight lobule HVIIA (Crus I) 20 -90 -26 \nRight Crus I (54%) \n5.19 5.27 \nRight Crus II (23%) \n \nTable 1. Significant clusters identified within the cerebellum. Peak coordinates, z- and t-values are reported \nfor all clusters. All statistics are reported at FWE p<0.05. NOTE: Coordinates are reported in MNI 2 mm space. \nNegative X-coordinates indicate the left hemisphere. Inf indicates large values that surpass threshold. \nProbabilistic values are derived from SPM Anatomy Toolbox (Eickhoff et al., 2005). \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n10 \n \n \nFigure 4. Regions in cerebellar cortex showing age -related decreases in functional connectivity with key \nhippocampal seeds. (a) Thresholded SPM {T} maps ( p<0.05) overlaid on cerebellar flatmaps showing ageing \nmain effect for left (a), right (b), anterior (c) and posterior (d) hippocampus to the cerebellum. Darker colours \nindicate decreased functional connectivity with seed ROI with age.  \n \nHippocampal-cerebellar functional connectivity is reduced in ageing \nFinally, we examined the effect of age on hippocampal-cerebellar functional connectivity. We observed \nthat several cerebellar regions showed negative correlations between age and hippocampal-cerebellar \nfunctional connectivity (i.e., lower connectivity in older participants). For the left hippocampal seed, we \nsaw large age -related reductions in the depths of the right primary fissure that separates cerebellar \nlobules HV and HVI (number of voxels = 382; p<.001), which extended into adjacent parts of lobule \nHVIIA (Crus I; see Figure 4a). A similar spatial pattern of connectivity alterations was seen for the right \nhippocampus, but also revealed a strong cluster in the contralateral region of lobule HVI (number of \nvoxels = 71; p<.001 (see Figure 4b). No differences were observed when directly contrasting left and \nright hippocampus.  \nNext, we explored whether these age -related effects within the cerebellum differed across different \nhippocampal long -axis subdivisions. Notably, we found that the anterior hippocampus showed \nwidespread age-related connectivity reductions within the cerebellum, and these were present primarily \nwithin the primary fissure between lobules HV and HVI (number of voxels = 837; p<.001; see Figure \n4c). In contrast, the posterior hippocampus showed minimal age -related changes in functional \nconnectivity, with very sma ll suprathreshold clusters observed in dispersed areas of the cerebellum, \nincluding lobules HV and HVI, with a peak in lobule HIX (number of voxels = 1; p = .026; see Figure \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n11 \n4d). However, no age-dependent connectivity differences were observed when contrasting anterior and \nposterior hippocampus. Cluster statistics for the ageing analyses are shown in Table 2. \n \nSeed ROI Peak cerebellar lobule \nPeak \ncoordinate \n(mm) \nProbabilistic value z t \nLeft hippocampus \nRight lobule HVI \n32 -42 -26 \nRight HVI (61%) \n6.7 6.87 \nRight HV (11%) \n14 -58 -16 \nRight HV (55%) \n6.52 6.67 \nRight HVI (45%) \n22 -50 -22 \nRight HVI (68%) \n6.45 6.59 \nRight HV (31%) \nVermal lobule VI 0 -66 -16 \nVermis VI (32%) \n5.89 6 \nRight HV (26%) \nLeft lobule HV -10 -56 -14 \nLeft HV (95%) \n5.85 5.96 \nLeft HI-HIV (2%) \nVermal lobule VIIIA 6 -64 -30 \nVermis VIIIA (23%) \n5.58 5.67 \nRight HVIIIA (8%) \nLeft lobule HVI -10 -64 -14 \nLeft HVI (83%) \n5.52 5.62 \nLeft HV (16%) \nRight lobule HVIIIA \n14 -66 -44 \nRight HVIIIA (56%) \n5.48 5.57 \nRight HVIIB (15%) \n16 -58 -46 \nRight HVIIIB (46%) \n5.45 5.54 \nRight HIX (13%) \nLeft lobule HVI \n-30 -48 -26 \nLeft HVI (94%) \n5.46 5.55 \nLeft HV (5%) \n-32 -38 -28 \nLeft HVI (46%) \n5.27 5.35 \nLeft HV (23%) \nLeft lobule HVI -22 -56 -20 \nLeft HVI (92%) \n5.44 5.52 \nLeft HV (7%) \nLeft lobule HVIIIA -24 -56 -48 \nLeft HVIIIA (47%) \n5.17 5.25 \nLeft HVIIIB (39%) \nRight lobule HVIIIA 28 -50 -48 \nRight HVIIIA (62%) \n5.11 5.18 \nRight HVIIIB (17%) \nRight lobule HVIIB 6 -72 -42 \nRight HVIIB (51%) \n5.01 5.08 \nRight HVIIIA (19%) \nRight lobule HI-HIV 22 -32 -24 \nRight HI-HIV (30%) \n4.98 5.04 \nParahippocampal gyrus (23%) \nLeft lobule HVI -32 -52 -24 Left HVI (95%) 6.21 6.34 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n12 \nRight \nhippocampus \nRight lobule HV \n12 -60 -16 \nRight HV (56%) \n6.12 6.24 \nRight HVI (42%) \n14 -52 -18 \nRight HV (88%) \n5.19 5.27 \nRight HVI (5%) \nRight lobule HVI \n32 -42 -26 \nRight HVI (61%) \n6.07 6.19 \nRight HV (11%) \n24 -48 -22 \nRight HVI (68%) \n6.01 6.13 \nRight HV (30%) \n22 -30 -26 \nRight HI-HIV (44%) \n5.92 6.04 \nParahippocampal gyrus (16%) \nVermal lobule VI 0 -66 -16 \nVermis VI (32%) \n5.59 5.69 \nRight HV (26%) \nLeft lobule HVI -24 -54 -20 \nLeft HVI (90.1%) \n5.31 5.39 \nLeft HV (6.9%) \nVermal lobule VIIIA 0 -74 -40 \nVermis VIIIA (56%) \n5.08 5.15 \nVermis VIIB (8%) \nLeft lobule HV -10 -54 -14 \nLeft HV (89%) \n4.99 5.05 \nLeft HI-HIV (8%) \nAnterior \nhippocampus \nRight lobule HV  \n12 -60 -16 \nRight HV (56%) \n7.65 7.89 \nRight HVI (42%) \n32 -42 -26 \nRight HVI (61%) \n7.41 7.63 \nRight HV (11%) \n0 -66 -16 \nVermis VI (32%) \n7.02 7.21 \nRight HV (26%) \nLeft lobule HVI \n-22 -56 -20 \nLeft HVI (92%) \n6.06 6.19 \nLeft HV (7%) \n-32 -50 -26 \nLeft HVI (98%) \n6.01 6.13 \nLeft HV (2%) \n-24 -46 -22 \nLeft HVI (49%) \n5.89 6 \nLeft HV (48%) \nLeft lobule HVIIIA -24 -56 -48 \nLeft HVIIIA (47%) \n5.41 5.49 \nLeft HVIIIB (39%) \nLeft lobule HV -18 -42 -18 \nLeft HV (71%) \n5.12 5.19 \nLeft HI-HIV (16%) \nRight lobule HVIIIB 14 -60 -44 \nRight HVIIIB (33%) \n5.09 5.16 \nRight HIX (11%) \nRight lobule HVIIA (Crus I) 48 -62 -26 \nRight Crus I (83%) \n5.07 5.14 \n3% Temporal Occipital Fusiform \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n13 \nLeft lobule HV -22 -36 -22 \nLeft HV (40%) \n5.07 5.14 \nParahippocampal gyrus (24%) \nLeft lobule HVIIA (Crus I) -40 -72 -24 Left Crus I (96%) 5.03 5.1 \nVermal lobule VIIIA 6 -64 -30 \nVermis VIIIA (23%) \n5.02 5.09 \nRight HVIIIA (8%) \nPosterior \nhippocampus \nLeft lobule HIX -2 -54 -56 \nLeft HIX (53%) \n5.1 5.18 \nRight HIX (17%) \nRight lobule HVI 24 -46 -22 \nRight HVI (53%) \n4.99 5.06 \nRight HV (45%) \nLeft lobule HVI -32 -50 -26 \nLeft HVI (98%) \n4.98 5.04 \nLeft HV (2%) \n \nTable 2. Significant clusters displaying significant age-related reductions in functional connectivity in the \ncerebellum. Peak coordinates, z- and t-values are shown for all clusters. All statistics are reported at FWE p<0.05. \nNOTE: Coordinates are reported in MNI 2 mm space. Negative X -coordinates indicate the left hemisphere. \nProbabilistic values are derived from SPM Anatomy Toolbox (Eickhoff et al., 2005). \n \nDiscussion \nWhile prior evidence in nonhuman animals suggest that a close functional interaction exists between \nthe hippocampus and cerebellum, there is a limited understanding of the nature and topography of this \nfunctional connection in humans and how it be might va ry with age. In the current study, we sought to \nclose this gap by applying seed -based functional connectivity analyses to resting -state fMRI data \ncollected in a large -scale lifespan cohort (N = 479 from CamCAN). Our results yielded several key \nfindings. Firstly, the shared bilateral functional connectivity between the left and right hippocampus with \ncerebellum involves four areas of the cerebellar cortex bilaterally: (i) almost the whole of the anterior \nlobe, and in addition, the anterior bank of lobule HVI ; (ii) lobule HVIIA, both the anterior and posterior \nbank in the mid-point of the horizontal fissure; (iii) a separate region in lobule HVIIA in Crus II, adjacent \nto a medial part of the ansoparamedian fissure; and (iv) lobules HIX and HX. We also consider ed \nlaterality-related connectivity differences. When directly contrasting the left and right hippocampus, we \nfound that the left hippocampus showed greater connectivity with contralateral lobules HVIIA (Crus I \nand Crus II) and HIX, whilst the right hippoca mpus showed greater connectivity with the contralateral \nlobule HVIIA (Crus II). \nSecond, we found that the shared functional connectivity of the anterior and posterior hippocampus \nwith the cerebellum encompassed similar areas as the bilateral shared functional connectivity of the left \nand right hippocampus. When examining differential functional connectivity between anterior and \nposterior hippocampus, we found that these individually correlated with distinct parts of the cerebellar \ncortex. The anterior compared to posterior hippocampus contrast revealed largely medial cerebellar \nconnectivity dominated by areas posterior to the horizontal fissure (including the mid -portion of lobule \nHVIIA, extending into more medial parts of HVIIB and HVIII and small areas of lobule HIX and HX). The \nposterior compared to anterior hippocampus contrast revealed connectivity with areas of the cerebellar \ncortex that were largely anterior to the horizontal fissure, including the vermal portion of lobules I to VII, \nand hemispherical portions of lobules HI to HVIIA, including Crus I.  \nFinally, we observed age-related decreases in hippocampal-cerebellar connectivity that were confined \nlargely to areas of the anterior lobe. Similar patterns were observed for left and right hippocampus, \nwhereby decreases were observed in lobule HVI, and ar ound the right primary fissure. Anterior \nhippocampus showed age-related decreases in connectivity with those same areas of the cerebellar \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n14 \ncortex, whereas the posterior hippocampus revealed no appreciable effects in any part of it. \nPrevious electrophysiological studies have suggested a strong functional interaction exists between the \nhippocampus and cerebellum, based on the effects of cerebellar disruption on hippocampal place cell \nfiring (Lefort et al., 201 5; Rochefort et al., 2011) and hippocampal -cerebellar synchronous oscillatory \nactivity (Watson et al., 2019). The current findings support notions that these functional interactions \nexist in the human brain but, importantly, elucidates the topography/spatia l organisation of these \nfunctional interactions in the cerebellum. The findings of hippocampal -cerebellar connectivity within \ncerebellar lobule HIV, HV, HVI and bilateral regions of lobule HVIIA (Crus I and Crus II) aligns with prior \nelectrophysiological findings (e.g., Babayan et al., 2017; Watson et al., 2019; Zeidler et al., 2020) and \nanatomical work in mice (e.g., Bohne et al., 2019; Watson et al., 2019). For instance, hippocampal field \npotentials have been shown to be modulated, most strongly, by stimulation of (vermal) lobule s IV-V \n(Zeidler et al.,2020), and phase -locked theta coherence has been observed between dorsal \nhippocampus and cerebellar lobule VI and VIIA (Crus I; Watson et al., 2019). Likewise, the combination \nof c-Fos imaging and graph theory suggest that two cerebe llar networks – one including lobule IV -V, \nVI, lobule VIIA (Crus I), and other including lobule IX, X – are differentially connected with dorsal \nhippocampal CA1 neurons during spatial exploration (Babayan et al., 2017). Notably, these same \ncerebellar lobules are also functionally correlated with left, right, anterior and posterior hippocampal \nseeds in this study (see also Figure 2a-b, 3a-3b).  \nOur findings also align with previous tracing studies. For example, tracing injections into the dentate \ngyrus of the mouse hippocampus identified polysynaptic connectivity with lobule HIV and lobule HV \n(Bohne et al., 2019). Further, retrograde tracing stud ies also identified inputs to the left hippocampus \nfrom lobule HVI and lobule HVIIA (Crus I) (Watson et al., 2019) – all regions which were also found be \nfunctionally connected with the hippocampus in the current study, some of these even showing \npreferential connections with specific hippocampal regions (e.g., lobule HIV, HV and HVI with posterior \nhippocampus). \nOur findings also provide novel insights into hippocampal -cerebellar connectivity in humans. For \nexample, in contrast to prior work in nonhuman species (discussed above), the strongest connectivity \nin lobule HVIIA was found in Crus II rather than the more commonly reported Crus I (e.g., Watson et \nal., 2019). This was particularly evident when contrasting anterior and posterior hippocampus (a \ncomparison which has not been made in the animal studies). Specifically, while each long -axis \nsubdivision showed similar patterns of cerebellar connectivity when considered in isolation (and indeed \nshowed patterns akin to the hippocampus as a whole), when directly contrasted we found that the \nanterior hippocampus was strongly correlated with Crus II (extending into lobul e HVIIB and HVIII). In \ncomparison, the posterior hippocampus showed the strong functional correlation with lobule HVIIA and \nextending HV (extending into lobule VI). In fMRI studies, Crus II has been shown to be involved in more \nabstract, non -motor function s, such as first - and second -order rule learning (Balsters et al., 2013), \ncreative thinking (Gao et al., 2020), and mentalising (Guell and Schmahmann, 2020; Van Overwalle et \nal., 2020). In contrast, the regions identified in the posterior versus anterior c ontrast (notably lobule V) \nare implicated in sensorimotor processing (e.g., Bushara et al., 2001) and fine -grained digit \nrepresentations (Grodd et al., 2001; van der Zwaag et al., 2013). The differential connectivity of anterior \nand posterior hippocampus to functionally distinct regions of the cerebellum is interesting as it suggests \nthat functional subdivisions within the hippocampal formation – which are considered to emerge partially \nfrom differences in neocortical connectivity (Adnan et al., 2015; Aggle ton, 2012; Dalton et al., 2019) – \nare also reflected within the cerebellum. The cerebellum is thought to have motor and non-motor loops \nwith different cortical areas (Ramnani et al., 2006). Given our data, it is possible that the anterior \nhippocampus participates more strongly in the cerebellar non-motor loop (consistent with its increased \nconnectivity with prefrontal cortex), whilst the posterior hippocampus participates more strongly in the \ncerebellar motor loop. Such a distinction aligns with current vie ws of long -axis specialisation in the \nhippocampus, in which anterior hippocampus provides more abstract or coarse-grained representations \nin spatial and episodic memory, whereas the posterior hippocampus supports more fine-grained spatial \nprocessing (Poppenk et al., 2013; Robin and Moscovitch, 2017). \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n15 \nOur findings also suggest that hippocampal-cerebellar connectivity decreases with age, and this was \nmost evident in areas of cerebellar lobules HV, HVI and their border. This finding dovetails with \nprevious studies that have observed structural and functio nal changes in both the hippocampus and \nthe cerebellum throughout ageing, making it plausible that this reflects, in part, age-related alterations \nin hippocampal-cerebellar interactions. Indeed, the current findings show that the left, right and anterior \nhippocampus show age-related connectivity reductions with areas of cerebellar lobules HV, HVI and \ntheir border. This evidence is in accordance with previous evidence that reported atrophy in lobule HVI \nduring ageing (Cui et al., 2020; Ramanoël et al., 2023) . Reduced grey matter volume in lobule HVI \nwas also negatively associated with performance on a perspective-taking task (Ramanoël et al., 2023) \n– a task which is thought to involve hippocampally -dependent allocentric representations (Labash et \nal., 2020). Further, fMRI work in young adults reported increased but differential activation for place - \nversus sequence -based navigation in hemispheric lobule HVI (Iglói et al., 2015). The age -related \nreduction in hippocampal-lobule HVI connectivity seen here, theref ore, may partly reflect age-related \nchanges in the ability to adopt allocentric or place-based strategies during navigation, as observed in \nother studies (Moffat and Resnick, 2002; Rodgers et al., 2012).   \nIn this context, it is important to note that while our results strongly support a close functional interaction \nbetween hippocampus and cerebellum, they cannot speak directly to how such connectivity supports \nspatial/mnemonic behaviour. In nonhuman species , there is growing evidence that the cerebellum \ndirectly influences hippocampal spatial representations and navigational behaviour (e.g., Rochefort et \nal., 2013). One particular fMRI investigation in humans found that distinct hippocampal -cerebellar \nconnectivity patterns might relate to the application of different strategies during virtual navigation. For \ninstance, it was found that the right hippocampus and contralateral lobule HVIIA (Crus I) co -activated \nduring place -based navigation whilst left hippocam pus and contralateral lobule HVIIA (Crus I) co -\nactivated to support sequence-based navigation (Iglói et al., 2015). These same cerebellar regions were \nalso strongly functionally correlated with the hippocampus in this study. \nOne possible account for these interactions is that the cerebellum supports the updating and re -\norganisation of hippocampally-based spatial representations (e.g., cognitive maps) via its use of forward \nmodels (Ramnani, 2014; Rondi -Reig et al., 2022). This describes a model, created from an internal \ncommand, that is an internal representation of the behaviour modified by sensory feedback (Ito, 2008). \nUpon receiving sensory information that mismatches the internal representation, the cerebellum could \nsupport the re -computation of cognitive maps in the hippocampus when novel information is \nencountered.  \nAn additional finding in the current study was that several hippocampal seeds showed strong functional \ncorrelations with regions of the ‘vestibulocerebellum’ – namely, lobules IX and X. While speculative, this \nfunctional interaction is consistent with prio r work in rodents that has demonstrated the importance of \nvestibular information for the accuracy and stability of hippocampal representations (Sharp et al., 1995), \nand aligns with theoretical models that suggest that the cerebellum supplies the hippocampu s with \ninformation in an appropriate (world-centred) format to be used for allocentric navigation (Rochefort et \nal., 2013).  \nFunctional connectivity approaches also cannot elucidate how the hippocampus and cerebellum are \nstructurally connected. Whilst it was previously speculated that a direct route exists between these \nregions (Heath and Harper, 1974), recent work points towards a more indirect route via a number of \nrelay stations. These include thalamic regions (e.g., laterodorsal a nd ventrolateral thalamus; Bohne et \nal., 2019), which receive input from the cerebellum (Fujita et al., 2020) and project to areas that input \nto the hippocampus, such as the retrosplenial cortex (Van der Werf et al., 2002). Other relay stations \ninclude the septum and supramamillary nucleus, which are thought to be key regions involved in \ngenerating theta, and thus potentially relevant to theta couplin g observed between hippocampus and \ncerebellum during navigation/exploration (Watson et al., 2019) and cerebellar -dependent eye -blink \nconditioning (Hoffmann and Berry, 2009). It may be possible to map structural pathways between \ncerebellar regions/nuclei an d the human hippocampus by leveraging high -resolution diffusion MRI \ntractography, though the range and complexity of fiber populations (e.g., through the midbrain) would \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint \n\n \n \n16 \nrequire advanced modelling approaches and potential validation data in other species. One study, for \nexample, used probabilistic constrained spherical deconvolution tractography (which enables \ncomplex/crossing fiber populations to be better resolved) and i dentified tractography streamlines \nbetween the hippocampus and cerebellar lobules HVIII, IX, X, Crus I, Crus II and the fastigial nucleus \n(Arrigo et al., 2014).  \nHere, through the application of seed -based functional connectivity analyses within a large lifespan \nimaging cohort, we demonstrated that the human hippocampus is strongly functionally correlated with \nwidespread areas of the cerebellar cortex, including lo bule I-IV, V, VI, Crus I, Crus II, IX and X. This \nfinding provides additional important support for the idea that the two key brain structures, which are \nclassically considered to underpin distinct memory systems, may collaborate closely in the human brain. \nFurther, we show that hippocampal-cerebellar connectivity decreases significantly across the lifespan, \nparticular in cerebellar regions that are vulnerable to age -related structural atrophy (lobules HV and \nHVI). It will be important for future studies to examine this interaction during behaviour, which will inform \nthe development of more detailed neurobiological models of (spatial) learning and memory that \nincorporate these distinct regions, as well as examine the vulnerability of this functional link in \nneurodegenerative disorders (e.g., Alzheimer's disease). \n \nAcknowledgements \nThis work was supported a Sarah Parker Remond PhD studentship at Royal Holloway, University of \nLondon, and the Biotechnology and Biological Sciences Research Council (BBSRC) [BB/V010549/1]. \nFor the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) \nlicence to any Author Accepted Manuscript version arising. 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The Journal of Neuroscience, 40(36), 6910–\n6926. https://doi.org/10.1523/JNEUROSCI.0763-20.2020 \n \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 28, 2024. ; https://doi.org/10.1101/2024.10.28.620678doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}