The Human Cerebello-Hippocampal Circuit Across Adulthood

The Human Cerebello-Hippocampal Circuit Across Adulthood | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results The Human Cerebello-Hippocampal Circuit Across Adulthood View ORCID Profile Tracey H. Hicks , View ORCID Profile Thamires N. C. Magalhães , View ORCID Profile Jessica A. Bernard doi: https://doi.org/10.1101/2025.02.17.638640 Tracey H. Hicks 1 Department of Psychological and Brain Sciences, Texas A&M University, College Station , Texas, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tracey H. Hicks For correspondence: tslonim{at}tamu.edu Thamires N. C. Magalhães 1 Department of Psychological and Brain Sciences, Texas A&M University, College Station , Texas, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thamires N. C. Magalhães Jessica A. Bernard 1 Department of Psychological and Brain Sciences, Texas A&M University, College Station , Texas, United States of America 2 Texas A&M Institute for Neuroscience, Texas A&M University, College Station , Texas, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica A. Bernard Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Direct communication between the hippocampus and cerebellum has been shown via coactivation and synchronized neuronal oscillations in animal models. Further, this novel cerebello-hippocampal circuit may be impacted by sex steroid hormones. The cerebellum and hippocampus are dense with estradiol and progesterone receptors relative to other brain regions. Females experience up to a 90% decrease in ovarian estradiol production after the menopausal transition. Postmenopausal women show lower cerebello-cortical and intracerebellar FC compared to reproductive aged females. Sex hormones are established modulators of both memory function and synaptic organization in the hippocampus in non-human animal studies. However, investigation of the cerebello-hippocampal (CB-HP) circuit has been limited to animal studies and small homogeneous samples of young adults as it relates to spatial navigation. Here, we investigate the CB-HP circuit in 138 adult humans (53% female) from 35-86 years of age, to define its FC patterns, and investigate its associations with behavior, hormone levels, and sex differences therein. We established robust FC patterns between the CB and HP in this sample. We predicted and found negative relationships between age and CB-HP FC. As expected, estradiol levels exhibited positive relationships with CB-HP. We found lower CB-HP FC with higher levels of progesterone. We provide the first characterization of the CB-HP circuit across middle and older adulthood and demonstrate that connectivity is sensitive to sex steroid hormone levels. This work provides the first clear CB-HP circuit mapping in the human brain and serves as a foundation for future work in neurological and psychiatric diseases. Introduction Both the hippocampus and cerebellum exhibit atrophy in aging, play a role in cognition, and are modulated by sex steroid hormones 1 – 16 . Importantly, these regions have shown coactivation, synchronized neuronal oscillations, and bidirectional relationships on a cellular level 17 , suggesting direct communication between these regions. Further support for structural and functional connectivity between these regions has been shown in animal studies. Rabies virus tract tracing in rodents identified three primary pathways linking the cerebellum to the hippocampus 18 , while optogenetic stimulation of the cerebellum impacts hippocampal function and hippocampal-mediated behavior 19 . In humans, the hippocampus and cerebellum coactivate during tasks requiring spatio-temporal prediction of movements in visuomotor integration 20 . While this has clear implications for both spatial and cognitive-motor abilities, it may also be applied to episodic memory 17 . That is, much like spatial representations of places, routes, and maps, our memories are created in time-centered sequences resembling a conceptual path or route marked by life events 21 . As both cognitive and motor abilities decline with age 22 – 24 , interactions between these regions may be especially important. However, investigation of the cerebello-hippocampal (CB-HP) circuit has been limited to animal studies 18 , 19 , 25 and small homogeneous samples of young adults as it relates to visuospatial abilities 20 , 26 , 27 . Evidence suggests that the cerebello-hippocampal circuit may be impacted by sex steroid hormones. Higher levels of estradiol have been linked to greater functional network coherence and greater cortical to subcortical functional connectivity (FC) at rest 28 – 31 , whereas higher progesterone has displayed mixed relationships of higher and lower cortical to subcortical and intracortical FC 29 , 30 , 32 – 35 . Importantly, both the cerebellum and hippocampus are dense with estradiol and progesterone receptors relative to other brain regions 36 . Sex hormones are established modulators of both memory function and synaptic organization in the hippocampus in non-human animal studies 9 . In women, lower levels of sex hormones during the menopausal transition have been associated with altered hippocampal activity during memory tasks 7 , 8 . Further, postmenopausal women show lower cerebello-cortical and intracerebellar FC compared to reproductive aged females 2 . Broadly, aging has been linked to deficits or declines in cognitive performance 22 , 37 , functional brain changes 38 – 42 , and a drop in sex hormones 29 , 43 , 44 . Functionally, aging is associated with reduced network segregation and increased integration of brain networks that have been linked to poorer cognitive performance 38 – 40 , 42 , 45 . Regarding sex hormones, females experience up to a 90% decrease in ovarian estradiol production after the menopausal transition 46 . The significant fluctuations and inevitable drop in sex steroid hormones during the menopausal transition and post - menopause have been associated with cognitive decline in females 47 – 50 . Further, higher levels of endogenous estradiol have been associated with better working memory performance 51 , 52 . Given the broad age-related impacts on brain FC, hormones, and cognition, coupled with the role of both the CB and HP in cognitive performance, exploration of this circuit in the context of these factors stands to be particularly informative for our understanding of aging and preclinical indicators of pathological decline. Here, we first seek to quantify CB-HP interactions across middle and older adulthood. Second, we will investigate the relationship between the CB-HP circuit and both behavioral performance and sex steroid hormone levels. We will also investigate sex differences in this circuit. More specifically, we hypothesized that age would negatively correlate with CB-HP connectivity. Further, we predicted that better behavioral performance would be associated with higher CB-HP FC. We further expected higher CB-HP connectivity with greater 17β-estradiol levels 28 – 31 . As previous findings with higher levels of progesterone and FC have been mixed 29 , 30 , 32 – 35 , we did not have specific predictions for CB-HP connectivity with progesterone levels. Methods 1.1. Study sample Participants (total n =175) were enrolled as part of a larger study on aging. All participants underwent a battery of cognitive and motor tasks and saliva samples were collected for sex-steroid hormone quantification during this assessment (described below). After the behavioral visit the participants returned for a magnetic resonance imaging (MRI) session approximately two weeks later. However, due to unexpected delays related to the Covid-19 pandemic, the time between the two sessions ranged from 0 to 216 days between participants with a mean of 34.08 days and standard deviation of 40.22 days in our sample. Here, we focused on salivary hormone assays, behavioral performance, and relationships with brain imaging data. Exclusion criteria were history of neurological disease, stroke, or formal diagnosis of psychiatric illness (e.g., depression or anxiety), contraindications for the brain imaging environment, diagnosis of mild cognitive impairment or dementia, and use of hormone therapy (HTh) or hormonal contraceptives (intrauterine device (IUD), possible use of continuous birth control (oral), and no history of hysterectomy in the past 10 years. For our analyses here we focused only on those with available neuroimaging, hormone level, and cognitive performance data. Thus, our final sample included 138 participants (74 females ( M = 56.73 years, SD = 12.53) and 64 males ( M = 56.58 years, SD = 12.53); range across both groups, 35-86 years); however, our sample size varies slightly across analyses based on participants with usable hormone data. Regarding ethnicity, our sample was 88.4% Caucasian and 11.6% Hispanic/Latino ( Supplementary Table 1 ). Demographic details are presented for generalizability purposes and we recognize that these are social and political categories given meaning by social, historical, and political forces 53 . Race and ethnicity will not be evaluated separately in this study, as information about socio-economic status is better indicated for causal inferences in neuroimaging 53 and that information was not collected in this study. All study procedures were approved by the Institutional Review Board at Texas A&M University, and written informed consent was obtained from each participant prior to initiating any data collection. 1.2. Behavioral Testing The behavioral testing sections ( section 1.2 . through 1.5.) utilize standardized text to ensure consistency and reproducibility in reporting the research protocol across work associated with this study 54 – 57 . Participants completed a battery of cognitive and motor tasks to quantify attention, processing speed, working memory, delayed recall, executive functioning, grip strength, fine motor abilities, and cognitive-motor integration. A commonly used screening tool that assesses global cognitive functioning, the Montreal Cognitive Assessment (MoCA) was also included 58 . The Wechsler Adult Intelligence Scale, 4th Edition (WAIS-IV) Letter-Number Sequencing subtest 59 broadly assessed working memory. The Wechsler Memory Scale, 4 th Edition (WMS-IV) Symbol Span subtest was used to assess visual-spatial working memory 60 . Episodic memory was assessed via the Shopping List Memory Task 61 which was administered on a computer. 1.2.1. The Stroop Task The Stroop Task 62 was adapted to computer format and was used to gauge executive function. Both congruent and incongruent items were displayed for 2.6 seconds each. There were 10 blocks with untimed (self-paced) breaks in between each block. This study specifically examined the Stroop effect as an outcome. Mathematically, the score was the difference in reaction times between congruent (e.g., the word “blue” in blue font) and incongruent (e.g., the word “blue” in red font) items (incongruent – congruent). A higher score indicated a slower reaction time on incongruent trials, score of 0 indicated performing the same (on average) for both congruent and incongruent trials, and a negative score indicated a faster reaction time on incongruent trials. Those with incomplete data (n = 1; 9/10 blocks incomplete) and outliers with values greater than 3 standard deviations from the mean (n=1) were also excluded from calculations for this task. 1.2.2. Sequence Learning Paradigm An explicit sequence learning paradigm was based on a task created by Bo and colleagues 63 . This task assessed learning of motor behaviors and working memory. The task consisted of 6 random blocks with 18 items each interspersed with 9 sequence blocks with 36 items each. In sequence blocks, participants learned a 12-element sequence shown 1 second per element and repeated this sequence 4 times in each block. In the random blocks, participants learned a different sequence each time. For both blocks, they were shown a sequence of filled squares and asked to repeat it in its entirety. We chose to quantify learning by calculating the slope of task accuracy (labeled as sequence learning accuracy in the results section) from Block 1 to Block 9. Mathematically, the slope of task accuracy value was derived using the slope of a regression line equation to evaluate change across trials: m=(n(∑xy) − (∑x)(∑y))/ (n(∑x 2 ) − (∑x) 2 ). Where ‘∑x’ was the sum of trial numbers, ‘∑x 2 ’ was the sum of squares of trial numbers, ‘∑y’ is the sum of performance accuracies, and ‘∑xy’ is the sum of the corresponding trial numbers and their accuracies. Conceptually, we would expect a positive slope for accuracy if learning/improvement had occurred across trials and a negative or no slope if it had not (i.e., a score above 0 indicated learning, with higher scores indicating a higher learning curve). To statistically evaluate whether learning had occurred, we conducted a one-sample t-test to determine if the Sequence learning slope was significantly different from zero. 1.2.3. Purdue Pegboard Assembly Cognitive-motor abilities were assessed via the Purdue Pegboard Task 64 . The Purdue Pegboard is comprised of four subtests: dominant hand, non-dominant hand, both hands simultaneously, and assembly task. We focused our investigations on the assembly subtest due to the integration of cognitive and motor demands of the task which is particularly relevant to our investigation of the cerebellum and cerebello-hippocampal connectivity. This subtest required participants to “assemble multiple components into a unit, which is made by placing a peg in a hole (dominant hand), placing a washer over the peg (nondominant hand), then a small cylindrical collar (dominant hand), followed by a second washer on top (non-dominant hand)” (Wilkes et al., 2023). This engages fine motor function as well as sequencing skills and bimanual coordination. A higher value for Purdue Pegboard Assembly can be interpreted as better performance on this task. 1.2.4. Shopping List Memory Task The Shopping List Memory Task was administered on a computer. Words of 30 items resembling those commonly seen on a shopping list were presented to the participant one at a time for 14 seconds each with a 0.5 second fixation cross in between words. Memory for the items was assessed approximately 20 minutes later via recognition discrimination for the original list via percentage of items answered correctly. A higher value indicated better performance on this task. Outliers with values greater than 3 standard deviations from the mean were excluded from these task-based analyses (n=2). 1.3. Hormone quantification We followed the methodology described in our recent work 54 for hormonal analyses, and to ensure clarity and replicability, we have shared those methods directly here. Participants were instructed to abstain from alcohol consumption for 24 hours and to avoid eating or drinking for 3 hours prior to the first study session to minimize external influences on hormone levels. Saliva samples were collected exclusively in the morning and afternoon. Participants were also screened for oral disease or injury, use of substances such as nicotine or caffeine, and prescription medications that may impact the saliva pH and compromise samples. Participants rinsed their mouths with water 10 minutes before providing a saliva sample to remove any residue. Samples were then collected in pre-labeled cryovials provided by Salimetrics ( https://salimetrics.com/saliva-collection-training-videos/ ) using the passive drool technique. Participants were asked to supply 1mL of saliva, after which samples were immediately stored in a -80° Celsius bio-freezer for stabilization. Assays were completed by Salimetrics to quantify 17β-estradiol and progesterone levels for each participant. The amount of saliva collected was sufficient to detect 17β-estradiol at a high sensitivity threshold of 0.1pg/mL, along with 5.0 pg/mL and 1.0 pg/mL thresholds for progesterone and testosterone, respectively. 1.4. Imaging acquisition The imaging sections ( section 1.4 through 1.6) utilize standardized text to ensure consistency and reproducibility in the research protocol. These standardized descriptions can also be found in other publications from our laboratory 55 , 66 and align with current best practices in the field, providing a clear and detailed framework for the procedures undertaken. Participants underwent structural and resting-state MRI using a Siemens Magnetom Verio 3.0 Tesla scanner and a 32-channel head coil. For structural MRI, we collected a high-resolution T1-weighted 3D magnetization prepared rapid gradient multi-echo (MPRAGE) scan (repetition time (TR) = 2400 ms; acquisition time = 7 minutes; voxel size = 0.8 mm 3 ) and a high-resolution T2-weighted scan (TR = 3200 ms; acquisition time = 5.5 minutes; voxel size = 0.8 mm 3 ), each with a multiband acceleration factor of 2. For resting-state imaging, we administered four blood -oxygen level dependent (BOLD) functional connectivity (fcMRI) scans with the following parameters: multiband factor of 8, 488 volumes, TR of 720 ms, and 2.5 mm 3 voxels. Each fcMRI scan was 6 minutes in length for a total of 24 minutes of resting-state imaging, and scans were acquired with alternating phase encoding directions (i.e., two anterior to posterior scans and two posteriors to anterior scans). During the fcMRI scans, participants were asked to lie still with their eyes open while fixating on a central cross. In total, the acquisition of images took about 45 minutes, including a 1.5-minute localizer. Scanning protocols were adapted from the multiband sequences developed by the Human Connectome Project (HCP) 67 and the Center for Magnetic Resonance Research at the University of Minnesota to facilitate future data sharing and reproducibility. 1.5. Image processing The images underwent several preprocessing steps to prepare them for further analysis. Initially, they were converted from DICOM to NIFTI format and organized following the Brain Imaging Data Structure (BIDS, version 1.6.0) using the bidskit docker container (version 2021.6.14, https://github.com/jmtyszka/bidskit ). Afterward, a single volume was extracted from two oppositely coded BOLD images to estimate B0 field maps using the split tool from the FMRIB Software Library (FSL) package (Jenkinson et al., 2012). Subsequently, the anatomical and functional images were preprocessed using fMRIPrep (version 20.2.3; for detailed methods, see https://fmriprep.org/ ), which includes automated procedures to align the functional volume with the anatomical image, correct for motion, correct field map distortions, segment the anatomical image into distinct tissues (e.g., gray matter, white matter, cerebrospinal fluid), remove the skull from the anatomical image, normalize the data to a common space, align motion-corrected functional volumes with the normalized anatomical image, and apply spatial smoothing. 1.6. Functional analysis Following preprocessing with fMRIPrep, the subsequent analyses were conducted using the CONN toolbox (version 21a) 68 . This involved additional processing to eliminate noise and artifacts and enhance data quality. Denoising in CONN typically comprises several stages, such as removing motion signals and regressing out confounding signals (e.g., signals from ventricles, white matter, and global signals). Motion information from fMRIPrep was utilized in CONN for this purpose. A 0.008-0.099 Hz bandpass filter was applied to eliminate high-frequency noise. The denoising step is crucial for enhancing the quality of FC data by minimizing artifacts and enhancing the ability to detect genuine FC patterns in the brain. Resting-state FC analyses focused on regions of interest (ROIs) in both the hippocampus and the cerebellum ( Figure 1 ). The hippocampal seed regions were loosely based on coordinates derived from Grady’s meta-analysis on hippocampal function 69 . These coordinates helped determine anatomical hippocampal boundaries and the original coordinates were shifted from the original values to accommodate a new center for 5mm spherical seeds in MNI space. That is, certain coordinates from Grady 69 fell on the outer edges of the hippocampus and to cover 3-dimensional space within the boundaries of the hippocampus, we had to shift certain coordinates to account for a 5mm spherical seed. Cerebellar seeds were determined by mapping dorsal, ventral, rostral, caudal, medial, and lateral boundaries of the right cerebellum in MNI space. Please refer to Supplementary Table 2 for coordinates for all our seeds. Following this step, we used the oro.nifti and spatstat packages in R Studio to create a non-overlapping grid in 3 dimensional space of 5mm spherical seeds covering the right cerebellum 70 , 71 . As our aim for this study was to cover the entirety of the right cerebellum and left hippocampus, seeds were created to cover the 3-dimensional anatomical regions in MNI space. For ease of interpretation, we used the automated anatomical atlas (AAL) in the label4MRI package in R to approximate anatomical regions for each seed 72 which is specified in Supplementary Table 2 . Our seeds are visualized in a simpler, easier to view, rendering in Figure 1 , and we have created a more in-depth representation in Supplementary Figure 1. The investigation was limited to the right cerebellar hemisphere and left hippocampus, to mitigate multiple comparisons and ensure examination of cross lateralized connections. Download figure Open in new tab Figure 1. These figures illustrate several regions of interest (ROIs) used in this study. Seed colors are helpful for differentiating seeds, but do not hold additional meaning. A . Hippocampal seeds are displayed with directional indicators of the anterior and posterior long-axis. B . Cerebellar seeds are displayed with directional indicators for the right cerebellar hemisphere. The caudal region of the cerebellum is better visualized in a more detailed representation of the ROI seeds in Supplementary Figure 1 . CB-HP FC was examined via region of interest (ROI)-to-ROI correlations using CONN toolbox at the group level. Relationships between age and CB-HP FC were evaluated via correlations in the whole sample. Sex differences in CB-HP FC were investigated via an analysis of covariance (ANCOVA), controlling for age. Group-level correlations were also performed to examine CB-HP connectivity patterns in relation to hormone levels (i.e., 17-β estradiol, progesterone, respectively) in the whole sample, controlling for age. To determine brain-behavior performance relationships, the variables Symbol Span, Stroop, Purdue Pegboard Assembly, Shopping List Memory task, Sequence Learning, and Letter-Number Sequencing were separately correlated with CB-HP FC while controlling for age. We employed standard settings for cluster-based inferences using parametric statistics based on random field theory. We used an initial voxel threshold at p<.001 along with a cluster threshold set at p < .05, with a false discovery rate (FDR) correction. To determine effect size specifically with Cohen’s d, we utilized the equation ((2* t-value)/√degrees of freedom) 73 in Excel. T-values and degrees of freedom were extracted from analytic results in CONN toolbox. 1.7. Statistical analyses For statistical analyses of the age, behavior, and hormone level results, we used R (v2024.04.2.764, R Core Team, 2021). We first sought to ascertain relationships between age, education, and cognitive performance (i.e., Symbol Span, Stroop, Purdue Pegboard Assembly, Shopping List Memory task, Sequence Learning, and Letter-Number Sequencing) while controlling for education level via linear regressions. To explore differences in hormone levels (i.e., 17-β estradiol, progesterone) with age, we conducted linear regressions. Results For the following results, please refer to Figure 1 as a reference for directionality regarding both the hippocampus and cerebellum. Age is Associated with CB-HP Connectivity Age was significantly correlated with CB-HP connectivity ( Figure 2 , Supplementary Table 3 ). Increased age correlated significantly with lower CB-HP FC; specifically encompassing the majority of the hippocampus on the anterior to posterior long axis and dorsal, medial, and caudal regions of the cerebellum ( Figure 2 ). However, there were also findings in the opposite direction. Higher CB-HP FC correlated with increased age between the left posterior hippocampus and right dorsal cerebellum only. Download figure Open in new tab Figure 2. Patterns of cortical functional connectivity (FC) between ROIs. A . ROIs are shown in an FC ring where blue represents lower FC between ROIs with increased age; while orange-red displays greater FC between ROIs with increased age. B . ROIs are shown on a subcortical model where blue represents negative FC relationships between ROIs with increased age, while red displays positive FC relationships between ROIs with increased age. Colors in figure B do not correspond to the key in figure A and only indicate positive versus negative relationships in blue and red as described. CB-HP Connectivity and Age Associations with Hormone Levels Sex differences in CB-HP FC were explored via group-level ROI-to-ROI contrasts in males versus females when controlling for age. We did not find significant sex differences in CB-HP FC (pFDR > 0.05). While the primary aim of our study was not intended to examine intracerebellar or intrahippocampal FC as we were interested in the CB-HP circuit, we did find greater intracerebellar FC in males as compared to females when controlling for age (pFDR < 0.05, Supplementary Figure 2). We discovered several robust associations between hormones and CB-HP connectivity across all participants (male and female). Higher 17β-estradiol levels correlated significantly with higher CB-HP FC; more specifically, connectivity along the majority of the hippocampus on the anterior to posterior long axis and both medial and dorsal regions of the cerebellum were associated with higher levels of 17β-estradiol ( Figure 3 , Supplementary Table 4 ). However, there was also one finding in the opposite direction. Lower CB-HP FC correlated with greater 17β-estradiol levels between the left posterior hippocampus and a medial-dorsal region of the cerebellum (lobule VI) only. Regarding progesterone levels, we primarily found that higher progesterone levels were linked to lower CB-HP FC across the majority of both cerebellar and hippocampal ROIs, when controlling for age ( Figure 4 , Supplementary Table 5 ), the opposite of what we found with 17β-estradiol. We also demonstrated patterns of greater intracerebellar and intrahippocampal FC (respectively) with higher progesterone levels when controlling for age ( Supplementary Figure 3). Download figure Open in new tab Figure 3. Associations between 17β-estradiol and CB-HP connectivity. A . ROIs are shown in an FC ring where orange-red displays greater FC between ROIs with higher levels of 17β-estradiol. Blue represents lower FC between ROIs with higher levels of 17β-estradiol. B . ROIs are shown on a subcortical model where red displays positive FC relationships, while blue represents negative FC relationships between ROIs with higher levels of 17β-estradiol. Colors in figure B do not correspond to the key in figure A and only indicate positive versus negative relationships in blue and red as described. Download figure Open in new tab Figure 4. Associations between progesterone and CB-HP connectivity A . ROIs are shown in an FC ring where green displays lower FC between ROIs with higher levels of progesterone. B . ROIs are shown on a subcortical model where red displays positive FC relationships with higher levels of progesterone, while blue represents negative FC relationships with higher levels of progesterone. Colors in figure B do not correspond to the key in figure A and only indicate positive versus negative relationships in blue and red as described. To assess for expected trends in our data, we evaluated linear relationships between hormone levels (i.e., 17β-estradiol and progesterone) and age. As expected, both 17β-estradiol (β = -0.0126 pFDR = 0.001) and progesterone (β = -1.7041 pFDR < 0.0001) displayed negative relationships with age. Age Associations with Behavioral Performance and CB-HP Connectivity Regarding relationships between behavioral measures, age, and education level, linear regressions revealed significant negative relationships between age and Symbol Span, Shopping List Memory, and Letter-Number Sequencing, when controlling for education level (see Supplementary Table 6 for statistics). That is, with increasing age, behavioral performance was significantly lower in Symbol Span, Shopping List Memory, and Letter-Number Sequencing. Relationships between Purdue Pegboard Assembly, Stroop Effect, Sequence Learning and age have been previously reported in this population 56 . There were no main effects of education level across behavioral tasks (pFDR > 0.05, Supplementary Table 6 ). There were no significant associations between CB-HP FC and cognitive variables (i.e., Symbol Span, Stroop, Pegboard Assembly, Shopping List Memory, Sequence Learning, or Letter-Number Sequencing) when controlling for age, pFDR > 0.05. Discussion This study explored the CB-HP circuit in the context of age, hormone levels and sex differences, and behavioral performance across the adult lifespan. We have mapped this novel and important circuit for the first time, in detail, in the adult and aging human brain. Given the importance of these regions individually for behavior and aging, this work provides a critical mapping of the CB-HP circuit that stands to serve as a foundation for future work in age-related neurodegenerative disease. Our results revealed CB-HP functional associations with cross-sectional age, such that as age is higher, connectivity is lower. We also found associations with hormone levels, though the directionality of these relationships differs between 17β-estradiol and progesterone. Our study provides unique insights into the CB-HP circuit with respect to hormonal and behavioral differences across a wide age range of healthy middle aged and older adults. Hormone Level Associations with Age and CB-HP Connectivity As expected, both 17-β estradiol and progesterone exhibited lower levels with increased age across participants. In women, 17-β estradiol levels fluctuate cyclically during the reproductive stage and drop significantly around perimenopause 46 , 74 , 75 . The trajectory of estradiol has shown a much lower rate of decline in aging men 75 , 76 . Progesterone levels tend to have the steepest decline around post-menopause in women and also display a lower rate of decline in aging men 75 . Specific sex differences in hormone levels (17-β estradiol and progesterone) were previously investigated by age group (early middle-age, late middle-age, and older adults) within the same sample as this study 57 and aligned with the aforementioned literature. While not surprising, this provides an important confidence check with respect to the assays and results used here. Consistent with our hypothesis and the literature on estradiol levels related to FC 28 – 31 , we found greater CB-HP connectivity with higher estradiol levels across participants when controlling for age. Previously, a review by Peper et. al (2011) noted that higher levels of both exogenous and endogenous estradiol were associated with increased FC within the cortex and between the cortex and subcortex 28 . In women, there is also evidence of greater coherence of functional networks with higher levels of 17-β estradiol 29 , 30 . However, in a dense sampling of a single reproductive aged woman, higher estradiol was associated with reductions in cerebellar connectivity to other networks 34 . We specifically found greater FC between the majority of the hippocampus along the anterior-posterior long axis and both medial and dorsal regions of the cerebellum. Based on 7-network parcellation of the cerebellum by Yeo and colleagues (2011), the medial-dorsal regions of the cerebellum that showed greater FC to the hippocampus at rest in this study, display overlap with somatomotor, limbic, and ventral attention networks 31 . This finding is consistent with the hippocampus’ key role in limbic network function. While the majority of significant associations demonstrated lower connectivity with higher levels of 17β-estradiol, we did see a positive correlation such that connectivity between the left posterior hippocampus and a medial-dorsal region of the cerebellum were higher instead. This cerebellar region overlapped with the limbic network 31 , which was unexpected. However, the relationship we found between higher 17β-estradiol and greater FC in part of the limbic network could have affective implications that were not explored in this study. Overall, our results showing lower CB-HP FC with higher levels of progesterone add evidence to the literature suggesting lower cerebello-cortical FC with higher endogenous progesterone across aging men and women. Further, we found greater intracerebellar FC with higher levels of progesterone. However, direct comparisons between our results and findings to date are limited to young women in their reproductive stage 30 as well as dense samplings (n=1) of that same population 29 , 32 – 35 . Dense sampling revealed reduced network coherence with higher levels of progesterone across 7 out of 9 functional networks examined 29 which aligns with our findings of reduced CB-HP FC. However, another dense sampling in a reproductive stage woman displayed increased FC between the hippocampus, dorsolateral prefrontal cortex, and sensorimotor cortex with increased progesterone levels 32 . Compounding the mixed results in the literature, a small group of reproductive aged women displayed both positive and negative correlations between intracortical FC and progesterone levels 30 . Thus, there are not clear patterns in the literature to draw direct conclusions about our results. Broadly, our results suggest that progesterone modulates the CB-HP circuit and intracerebellar FC in both sexes and across the adult lifespan. While our behavioral results did not support our hypothesis that the FC relationships seen here are related to preclinical cognitive function, our results may illuminate important indicators of brain function in aging and the impact of the hormonal environment on brain function whether or not there are sex differences. For instance, progesterone has been described as an antagonist for estradiol’s proliferative impact on dendritic spines 77 , suggesting that ratios between estradiol and progesterone could provide valuable insight on brain network organization, characterization, and ultimate associations with behavioral functioning. Our results have particular implications for aging women given their significant declines in sex steroid hormones with aging 78 . Diminished estradiol has also been linked to greater AD risk and pathogenesis in the aging brain 79 – 83 . Our findings of lower CB-HP FC with higher progesterone levels and greater CB-HP FC with higher 17β-estradiol levels suggest that these hormones modulate brain function in a circuit that appears to have relevance in aging and AD risk. Future research could benefit from targeted investigation of this circuit in the context of menopausal transition in women and in age-related neurodegenerative disease. Age is Associated with CB-HP Connectivity In aging and Alzheimer’s Disease (AD), hippocampal structure and function are notably impacted 84 , 85 . Recently, cerebellar integrity has also been implicated in aging and AD via neuroimaging studies and amyloid-ß presence in familial cases 86 – 91 . While the cerebellum may not be a primary driver of pathology in aging, evidence suggests that it plays a significant role in symptomatology through its cognitive and behavioral interactions, particularly noted in cerebellar cognitive affective syndrome 92 A recent review by Bernard (2024) suggests that this cerebellar scaffolding in aging supports optimal cortical function, but as AD progresses, its effectiveness diminishes, pointing to the need for more research on cerebellar-hippocampal interactions 93 . Taken together, the literature implies that the CB-HP circuit plays a role aging and AD. Consistent with our hypothesis, greater age was associated with lower CB-HP FC across most of the nodes we investigated here. However, we did demonstrate one instance where CB-HP FC was higher with older age when looking at the left posterior hippocampus and right dorsal cerebellum only. A comparison of young versus older adults similarly exhibited lower FC between several cerebellar regions and both the hippocampus and parahippocampal gyrus in older adults 3 . In healthy aging adults, Uwisengeyimana and colleagues found alterations in cerebellar to hippocampal FC across five age groups that started at 40 years of age and ended at 90 years which is somewhat consistent with the patterns of both lower and higher CB-HP FC with increased age seen here 94 . To date, human studies of the CB-HP FC have largely been limited to visuospatial task-based examinations in young adults 20 , 26 , 27 . Our approach is the first to evaluate this circuit at rest and across a healthy aging population. Notably, studies with broader explorations that included the hippocampus and cerebellum found FC relationships when comparing healthy older adult populations to those with significant neuropathology (AD) and/or cognitive disfunction (mild cognitive impairment; MCI) 95 – 98 . While these studies did not specifically target the CB-HP circuit, those specifically comparing AD or amnestic MCI patients to healthy controls found lower FC between the hippocampus and cerebellum 97 , 98 , consistent with our hypothesis that CB-HP FC declines with aging and is impacted by neurodegenerative pathology. In conceptualizing the literature here comparing different stages of adulthood, MCI, or AD 95 – 98 , we speculate a quadratic relationship with CB-HP as its related to neuropathology, cognitive function, and functional abilities. Specifically, lower CB-HP FC in younger adulthood, increased compensatory FC 99 in middle-age or at the MCI stage, and declines in CB-HP in the context of normative aging pathology or progressed neuropathology (e.g., AD). Behavioral Associations with Age, Sex differences, Hormones, and CB-HP Connectivity Regarding CB-HP FC associations with behavioral measures, we did not show significant associations across tasks. Task-based cerebellar functional activation has been seen in attention, working memory, language, executive function, and spatial abilities 100 , 101 . Task-based hippocampal functional activation has shown the strongest associations with encoding, memory retrieval, and spatial processing 1 , 102 , 103 . However, our results were not consistent with studies implicating a CB-HP circuit in spatial, cognitive-motor, and episodic memory tasks 17 , 20 , 98 . This may be attributed to our cognitive measures as they were initially intended to examine cerebellar function, and tasks that are easily translatable to those given in clinical cognitive assessments. Measures that are more sensitive to spatio-temporal relationships, visuomotor integration, and more complex measures of episodic memory may be required to appropriately assess the CB-HP circuit as it relates to behavior. Another limitation was that behavioral tasks were administered outside of the scanner. It is possible that cognitive tasks in the domains we examined would engage the CB-HP circuit if they were assessed with fMRI. Future work examining the CB-HP circuit using a task-based paradigm is warranted and may clarify which tasks are most sensitive to changes in this circuit with aging. Conclusion Our results characterized relationships between age, the CB-HP circuit, behavioral performance, sex differences, and sex hormone levels across the adult lifespan. We found largely lower CB-HP FC with increased age and poorer behavioral performance with increased age. Both estradiol and progesterone appear to modulate CB-HP FC, with higher estradiol largely associated with greater FC and progesterone largely associated with lower FC. Given the notable changes in sex steroid hormone levels with menopause in aging females, this is of particular interest and potential importance. Most notable are possible implications for this circuit in age-related neurodegenerative disease such as Alzheimer’s Disease, and sex differences therein. These results replicate and extend findings in the literature supporting a role for the CB-HP circuit in understanding advanced age. Future research can utilize these findings as a foundation for clinical and translational applications, given the robust baseline established by the wide age range and varied set of tasks used in this study. Acknowledgment This work was supported by R01AG065010 to J.A.B. This work was further supported by the Texas Virtual Data Library (ViDaL), a high-performance cluster, funded by the Texas A&M University Research Development fund. In this cluster the imaging analyses for the current work were carried out using the resources provided by the Texas A&M High Performance Research Computing organization. Footnotes Co-author emails: Thamires N. C. Magalhães: thamiresncm{at}tamu.edu Jessica A. Bernard: jessica.bernard{at}tamu.edu References 1. ↵ Bai F , Zhang Z , Watson DR , et al. Abnormal Functional Connectivity of Hippocampus During Episodic Memory Retrieval Processing Network in Amnestic Mild Cognitive Impairment . Biol Psychiatry . 2009 ; 65 ( 11 ): 951 – 958 . doi: 10.1016/j.biopsych.2008.10.017 OpenUrl CrossRef PubMed Web of Science 2. ↵ Ballard HK , Jackson TB , Hicks TH , Bernard JA . The association of reproductive stage with lobular cerebellar network connectivity across female adulthood . Neurobiol Aging . 2022 ; 117 : 139 – 150 . doi: 10.1016/j.neurobiolaging.2022.05.014 OpenUrl CrossRef PubMed 3. ↵ Bernard JA , Peltier SJ , Wiggins JL , et al. Disrupted cortico-cerebellar connectivity in older adults . NeuroImage . 2013 ; 83 : 103 – 119 . doi: 10.1016/j.neuroimage.2013.06.042 OpenUrl CrossRef PubMed 4. Bernard JA , Leopold DR , Calhoun VD , Mittal VA . Regional cerebellar volume and cognitive function from adolescence to late middle age: Cerebellar Volume and Cognition . Hum Brain Mapp . 2015 ; 36 ( 3 ): 1102 – 1120 . doi: 10.1002/hbm.22690 OpenUrl CrossRef PubMed 5. Fitzgerald M , Pritschet L , Santander T , Grafton ST , Jacobs EG . Cerebellar network organization across the human menstrual cycle . Sci Rep . 2020 ; 10 ( 1 ): 20732 . doi: 10.1038/s41598-020-77779-4 OpenUrl CrossRef PubMed 6. Hausman HK , Jackson TB , Goen JRM , Bernard JA . From Synchrony to Asynchrony: Cerebellar–Basal Ganglia Functional Circuits in Young and Older Adults . Cereb Cortex. Published online June 20 , 2019 : bhz121 . doi: 10.1093/cercor/bhz121 OpenUrl CrossRef 7. ↵ Jacobs EG , Weiss B , Makris N , et al. Reorganization of Functional Networks in Verbal Working Memory Circuitry in Early Midlife: The Impact of Sex and Menopausal Status . Cereb Cortex. Published online May 13 , 2016 : bhw127 . doi: 10.1093/cercor/bhw127 OpenUrl CrossRef PubMed 8. ↵ Jacobs EG , Weiss BK , Makris N , et al. Impact of Sex and Menopausal Status on Episodic Memory Circuitry in Early Midlife . J Neurosci . 2016 ; 36 ( 39 ): 10163 – 10173 . doi: 10.1523/JNEUROSCI.0951-16.2016 OpenUrl Abstract / FREE Full Text 9. ↵ Jacobs EG , Goldstein JM . The middle-aged brain: biological sex and sex hormones shape memory circuitry . Curr Opin Behav Sci . 2018 ; 23 : 84 – 91 . doi: 10.1016/j.cobeha.2018.03.009 OpenUrl CrossRef PubMed 10. Lee JY , Lyoo IK , Kim SU , et al. Intellect declines in healthy elderly subjects and cerebellum . Psychiatry Clin Neurosci . 2005 ; 59 ( 1 ): 45 – 51 . doi: 10.1111/j.1440-1819.2005.01330.x OpenUrl CrossRef PubMed 11. Paul R , Grieve SM , Chaudary B , et al. Relative contributions of the cerebellar vermis and prefrontal lobe volumes on cognitive function across the adult lifespan . Neurobiol Aging . 2009 ; 30 ( 3 ): 457 – 465 . doi: 10.1016/j.neurobiolaging.2007.07.017 OpenUrl CrossRef PubMed 12. Petersen RC , Jack CR , Xu YC , et al. Memory and MRI-based hippocampal volumes in aging and AD . Neurology . 2000 ; 54 ( 3 ): 581 – 581 . doi: 10.1212/WNL.54.3.581 OpenUrl CrossRef PubMed 13. Raz N , Gunning-Dixon F , Head D , Williamson A , Acker JD . Age and Sex Differences in the Cerebellum and the Ventral Pons: A Prospective MR Study of Healthy Adults . Published online 2001 : 7 . 14. Raz N , Ghisletta P , Rodrigue KM , Kennedy KM , Lindenberger U . Trajectories of brain aging in middle-aged and older adults: Regional and individual differences . NeuroImage . 2010 ; 51 ( 2 ): 501 – 511 . doi: 10.1016/j.neuroimage.2010.03.020 OpenUrl CrossRef PubMed Web of Science 15. Raz N , Rodrigue KM . Differential aging of the brain: Patterns, cognitive correlates and modifiers . Neurosci Biobehav Rev . 2006 ; 30 ( 6 ): 730 – 748 . doi: 10.1016/j.neubiorev.2006.07.001 OpenUrl CrossRef PubMed Web of Science 16. ↵ Won J , Callow DD , Pena GS , et al. Hippocampal Functional Connectivity and Memory Performance After Exercise Intervention in Older Adults with Mild Cognitive Impairment . J Alzheimers Dis . 2021 ; 82 ( 3 ): 1015 – 1031 . doi: 10.3233/JAD-210051 OpenUrl CrossRef PubMed 17. ↵ Rondi-Reig L , Paradis AL , Fallahnezhad M . A Liaison Brought to Light: Cerebellum-Hippocampus, Partners for Spatial Cognition . The Cerebellum . 2022 ; 21 ( 5 ): 826 – 837 . doi: 10.1007/s12311-022-01422-3 OpenUrl CrossRef PubMed 18. ↵ Watson TC , Obiang P , Torres-Herraez A , et al. Anatomical and physiological foundations of cerebello-hippocampal interaction . eLife . 2019 ; 8 : e41896 . doi: 10.7554/eLife.41896 OpenUrl CrossRef 19. ↵ Zeidler Z , Hoffmann K , Krook-Magnuson E . HippoBellum: Acute Cerebellar Modulation Alters Hippocampal Dynamics and Function . J Neurosci . 2020 ; 40 ( 36 ): 6910 – 6926 . doi: 10.1523/JNEUROSCI.0763-20.2020 OpenUrl Abstract / FREE Full Text 20. ↵ Onuki Y , Van Someren EJW , De Zeeuw CI , Van der Werf YD . Hippocampal– Cerebellar Interaction During Spatio-Temporal Prediction . Cereb Cortex . 2015 ; 25 ( 2 ): 313 – 321 . doi: 10.1093/cercor/bht221 OpenUrl CrossRef PubMed 21. ↵ Redish AD . Beyond the Cognitive Map: From Place Cells to Episodic Memory . MIT Press ; 1999 . 22. ↵ Harada CN , Natelson Love MC , Triebel KL . Normal Cognitive Aging . Clin Geriatr Med. 2013 ; 29 ( 4 ): 737 – 752 . doi: 10.1016/j.cger.2013.07.002 OpenUrl CrossRef PubMed 23. Lezak MD , Howieson DB , Loring DW , Fischer JS . Neuropsychological Assessment . Oxford University Press ; 2004 . 24. ↵ Seidler R , Bernard JA , Burutolu TB , et al. Motor control and aging: Links to age-related brain structural, functional, and biochemical effects . Neurosci Biobehav Rev . 2010 ; 34 ( 5 ): 721 – 733 . doi: 10.1016/j.neubiorev.2009.10.005 OpenUrl CrossRef PubMed Web of Science 25. ↵ Krook-Magnuson E , Szabo GG , Armstrong C , Oijala M , Soltesz I. Cerebellar Directed Optogenetic Intervention Inhibits Spontaneous Hippocampal Seizures in a Mouse Model of Temporal Lobe Epilepsy . eneuro . 2014 ; 1 ( 1 ):ENEURO.0005-14.2014. doi: 10.1523/ENEURO.0005-14.2014 OpenUrl Abstract / FREE Full Text 26. ↵ Hauser MFA , Heba S , Schmidt-Wilcke T , Tegenthoff M , Manahan-Vaughan D . Cerebellar-hippocampal processing in passive perception of visuospatial change: An ego- and allocentric axis? Hum Brain Mapp . 2020 ; 41 ( 5 ): 1153 – 1166 . doi: 10.1002/hbm.24865 OpenUrl CrossRef PubMed 27. ↵ Iglói K , Doeller CF , Paradis AL , et al. Interaction Between Hippocampus and Cerebellum Crus I in Sequence-Based but not Place-Based Navigation . Cereb Cortex . 2015 ; 25 ( 11 ): 4146 – 4154 . doi: 10.1093/cercor/bhu132 OpenUrl CrossRef PubMed 28. ↵ Peper JS , van den Heuvel MP , Mandl RCW , Pol HEH , van Honk J . Sex steroids and connectivity in the human brain: A review of neuroimaging studies . Psychoneuroendocrinology . 2011 ; 36 ( 8 ): 1101 – 1113 . doi: 10.1016/j.psyneuen.2011.05.004 OpenUrl CrossRef PubMed Web of Science 29. ↵ Pritschet L , Santander T , Taylor CM , et al. Functional reorganization of brain networks across the human menstrual cycle . NeuroImage . 2020 ; 220 : 117091 . doi: 10.1016/j.neuroimage.2020.117091 OpenUrl CrossRef PubMed 30. ↵ Syan SK , Minuzzi L , Costescu D , et al. Influence of endogenous estradiol, progesterone, allopregnanolone, and dehydroepiandrosterone sulfate on brain resting state functional connectivity across the menstrual cycle . Fertil Steril . 2017 ; 107 ( 5 ): 1246 – 1255 .e4. doi: 10.1016/j.fertnstert.2017.03.021 OpenUrl CrossRef PubMed 31. ↵ Yeo BTT , Krienen FM , Sepulcre J , et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity . J Neurophysiol . 2011 ; 106 ( 3 ): 1125 – 1165 . doi: 10.1152/jn.00338.2011 OpenUrl CrossRef PubMed Web of Science 32. ↵ Arelin K , Mueller K , Barth C , et al. Progesterone mediates brain functional connectivity changes during the menstrual cycle: a pilot resting state MRI study . Front Neurosci . 2015 ; 9 . doi: 10.3389/fnins.2015.00044 OpenUrl CrossRef PubMed 33. De Filippi E , Uribe C , Avila-Varela DS , et al. The Menstrual Cycle Modulates Whole-Brain Turbulent Dynamics . Front Neurosci . 2021 ; 15 : 753820 . doi: 10.3389/fnins.2021.753820 OpenUrl CrossRef PubMed 34. ↵ Fitzgerald M , Pritschet L , Santander T , Grafton ST , Jacobs EG . Cerebellar network organization across the human menstrual cycle . Sci Rep . 2020 ; 10 ( 1 ): 20732 . doi: 10.1038/s41598-020-77779-4 OpenUrl CrossRef PubMed 35. ↵ Mueller JM , Pritschet L , Santander T , et al. Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle . Netw Neurosci . 2021 ; 5 ( 1 ): 125 – 144 . doi: 10.1162/netn_a_00169 OpenUrl CrossRef 36. ↵ Boyle CP , Raji CA , Erickson KI , et al. Estrogen, brain structure, and cognition in postmenopausal women . Hum Brain Mapp . 2021 ; 42 ( 1 ): 24 – 35 . doi: 10.1002/hbm.25200 OpenUrl CrossRef PubMed 37. ↵ Tucker-Drob EM . Cognitive Aging and Dementia: A Life-Span Perspective . Annu Rev Dev Psychol . 2019 ; 1 ( 1 ): 177 – 196 . doi: 10.1146/annurev-devpsych-121318-085204 OpenUrl CrossRef PubMed 38. ↵ Chan MY , Park DC , Savalia NK , Petersen SE , Wig GS . Decreased segregation of brain systems across the healthy adult lifespan . Proc Natl Acad Sci . 2014 ; 111 ( 46 ). doi: 10.1073/pnas.1415122111 OpenUrl Abstract / FREE Full Text 39. Geerligs L , Renken RJ , Saliasi E , Maurits NM , Lorist MM . A Brain-Wide Study of Age-Related Changes in Functional Connectivity . Cereb Cortex . 2015 ; 25 ( 7 ): 1987 – 1999 . doi: 10.1093/cercor/bhu012 OpenUrl CrossRef PubMed 40. ↵ Goh JOS . Functional Dedifferentiation and Altered Connectivity in Older Adults: Neural Accounts of Cognitive Aging . Published online 2011 : 26 . 41. Kong TS , Gratton C , Low KA , et al. Age-related differences in functional brain network segregation are consistent with a cascade of cerebrovascular, structural, and cognitive effects . Netw Neurosci . 2020 ; 4 ( 1 ): 89 – 114 . doi: 10.1162/netn_a_00110 OpenUrl CrossRef 42. ↵ Setton R , Mwilambwe-Tshilobo L , Girn M , et al. Age differences in the functional architecture of the human brain . Cereb Cortex. Published online March 1 , 2022 :bhac056. doi: 10.1093/cercor/bhac056 OpenUrl CrossRef 43. ↵ Epperson CN , Sammel MD , Freeman EW . Menopause Effects on Verbal Memory: Findings From a Longitudinal Community Cohort . J Clin Endocrinol Metab . 2013 ; 98 ( 9 ): 3829 – 3838 . doi: 10.1210/jc.2013-1808 OpenUrl CrossRef PubMed 44. ↵ Rentz DM , Weiss BK , Jacobs EG , et al. Sex differences in episodic memory in early midlife: impact of reproductive aging . Menopause . 2017 ; 24 ( 4 ): 400 – 408 . doi: 10.1097/GME.0000000000000771 OpenUrl CrossRef PubMed 45. ↵ Koen JD , Srokova S , Rugg MD . Age-related neural dedifferentiation and cognition . Curr Opin Behav Sci . 2020 ; 32 : 7 – 14 . doi: 10.1016/j.cobeha.2020.01.006 OpenUrl CrossRef PubMed 46. ↵ Taylor CM , Pritschet L , Yu S , Jacobs EG . Applying a Women’s Health Lens to the Study of the Aging Brain . Front Hum Neurosci . 2019 ; 13 : 224 . doi: 10.3389/fnhum.2019.00224 OpenUrl CrossRef PubMed 47. ↵ Koebele SV , Bimonte-Nelson HA . Modeling menopause: The utility of rodents in translational behavioral endocrinology research . Maturitas . 2016 ; 87 : 5 – 17 . doi: 10.1016/j.maturitas.2016.01.015 OpenUrl CrossRef PubMed 48. Bove R , Secor E , Chibnik LB , et al. Age at surgical menopause influences cognitive decline and Alzheimer pathology in older women . Neurology . 2014 ; 82 ( 3 ): 222 – 229 . doi: 10.1212/WNL.0000000000000033 OpenUrl Abstract / FREE Full Text 49. Gilsanz P , Lee C , Corrada MM , Kawas CH , Quesenberry CP , Whitmer RA . Reproductive period and risk of dementia in a diverse cohort of health care members . Neurology . 2019 ; 92 ( 17 ): e2005 – e2014 . doi: 10.1212/WNL.0000000000007326 OpenUrl Abstract / FREE Full Text 50. ↵ Rocca WA . Increased risk of cognitive impairment or dementia in women who underwent oophorectomy before menopause . 2007 ; 69 : 11 . 51. ↵ Hampson E , Morley EE . Estradiol concentrations and working memory performance in women of reproductive age . Psychoneuroendocrinology . 2013 ; 38 ( 12 ): 2897 – 2904 . doi: 10.1016/j.psyneuen.2013.07.020 OpenUrl CrossRef PubMed Web of Science 52. ↵ Hampson E. Estrogens, Aging, and Working Memory . Curr Psychiatry Rep . 2018 ; 20 ( 12 ): 109 . doi: 10.1007/s11920-018-0972-1 OpenUrl CrossRef PubMed 53. ↵ Cardenas-Iniguez C , Gonzalez MR . Recommendations for the responsible use and communication of race and ethnicity in neuroimaging research . Nat Neurosci. Published online March 22 , 2024 . doi: 10.1038/s41593-024-01608-4 OpenUrl CrossRef PubMed 54. ↵ Ballard HK , Jackson TB , Hicks TH , et al. Hormone-sleep interactions predict cerebellar connectivity and behavior in aging females . Psychoneuroendocrinology . 2023 ; 150 : 106034 . doi: 10.1016/j.psyneuen.2023.106034 OpenUrl CrossRef 55. ↵ Hicks TH , Magalhães TNC , Ballard HK , Jackson TB , Cox SJ , Bernard JA . Network Segregation in Aging Females and Evaluation of the Impact of Sex Steroid Hormones . Neuroscience ; 2022 . doi: 10.1101/2022.10.12.511918 OpenUrl Abstract / FREE Full Text 56. ↵ Hicks TH , Magalhães TNC , Jackson TB , Ballard HK , Herrejon IA , Bernard JA . Functional and structural cerebellar-behavior relationships in aging . Brain Struct Funct . 2024 ; 230 ( 1 ): 10 . doi: 10.1007/s00429-024-02862-9 OpenUrl CrossRef PubMed 57. ↵ Magalhães TNC , Hicks TH , Jackson TB , Ballard HK , Herrejon IA , Bernard JA . Sex-steroid hormones relate to cerebellar structure and functional connectivity across adulthood . Published online June 25 , 2024 . doi: 10.1101/2024.06.24.600454 OpenUrl Abstract / FREE Full Text 58. ↵ Nasreddine ZS , Phillips NA , Bédirian V , et al. The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment . J Am Geriatr Soc . 2005 ; 53 ( 4 ): 695 – 699 . doi: 10.1111/j.1532-5415.2005.53221.x OpenUrl CrossRef PubMed Web of Science 59. ↵ Benson N , Hulac DM , Kranzler JH . Independent examination of the Wechsler Adult Intelligence Scale—Fourth Edition (WAIS-IV): What does the WAIS-IV measure? Psychol Assess . 2010 ; 22 ( 1 ): 121 – 130 . doi: 10.1037/a0017767 OpenUrl CrossRef PubMed 60. ↵ Lepach AC , Pauls F , Petermann F . Executive Functioning and Visual Working Memory . Appl Neuropsychol Adult . 2015 ; 22 ( 2 ): 100 – 107 . doi: 10.1080/23279095.2013.860603 OpenUrl CrossRef PubMed 61. ↵ Flegal KE , Lustig C . You can go your own way: effectiveness of participant-driven versus experimenter-driven processing strategies in memory training and transfer . Aging Neuropsychol Cogn . 2016 ; 23 ( 4 ): 389 – 417 . doi: 10.1080/13825585.2015.1108386 OpenUrl CrossRef PubMed 62. ↵ Stroop JR . Studies of interference in serial verbal reactions . J Exp Psychol . 1935 ; 18 ( 6 ): 643 . OpenUrl CrossRef Web of Science 63. ↵ Bo J , Borza V , Seidler RD . Age-Related Declines in Visuospatial Working Memory Correlate With Deficits in Explicit Motor Sequence Learning . J Neurophysiol . 2009 ; 102 ( 5 ): 2744 – 2754 . doi: 10.1152/jn.00393.2009 OpenUrl CrossRef PubMed Web of Science 64. ↵ Tiffin J , Asher EJ . The Purdue Pegboard: norms and studies of reliability and validity . J Appl Psychol . 1948 ; 32 ( 3 ): 234 – 247 . doi: 10.1037/h0061266 OpenUrl CrossRef PubMed 65. Wilkes BJ , Tobin ER , Arpin DJ , et al. Distinct cortical and subcortical predictors of Purdue Pegboard decline in Parkinson’s disease and atypical parkinsonism . Npj Park Dis . 2023 ; 9 ( 1 ): 85 . doi: 10.1038/s41531-023-00521-0 OpenUrl CrossRef 66. ↵ Ballard HK , Jackson TB , Miller AC , Hicks TH , Bernard JA . Age-Related Differences in Functional Network Segregation in the Context of Sex and Reproductive Stage . Neuroscience ; 2022 . doi: 10.1101/2022.03.28.486067 OpenUrl Abstract / FREE Full Text 67. ↵ Harms MP , Somerville LH , Ances BM , et al. Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects . NeuroImage . 2018 ; 183 : 972 – 984 . doi: 10.1016/j.neuroimage.2018.09.060 OpenUrl CrossRef PubMed 68. ↵ Whitfield-Gabrieli S , Nieto-Castanon A . Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks . Brain Connect . 2012 ; 2 ( 3 ): 125 – 141 . doi: 10.1089/brain.2012.0073 OpenUrl CrossRef PubMed 69. ↵ Grady CL . Meta-analytic and functional connectivity evidence from functional magnetic resonance imaging for an anterior to posterior gradient of function along the hippocampal axis . Hippocampus . 2020 ; 30 ( 5 ): 456 – 471 . doi: 10.1002/hipo.23164 OpenUrl CrossRef PubMed 70. ↵ Baddeley A , Rubak E , Turner R . Spatial Point Patterns: Methodology and Applications with R . CRC Press, Taylor & Francis Group ; 2016 . 71. ↵ Whitcher B , Schmid V , Thornton A , Muschelli J . oro.nifti: Rigorous - “NIfTI” + “ANALYZE” + ’AFNI’ : Input / Output . Published online March 4, 2010 : 0 . 11 . 4 . doi: 10.32614/CRAN.package.oro.nifti OpenUrl CrossRef 72. ↵ Rolls ET , Huang CC , Lin CP , Feng J , Joliot M . Automated anatomical labelling atlas 3 . NeuroImage . 2020 ; 206 : 116189 . doi: 10.1016/j.neuroimage.2019.116189 OpenUrl CrossRef PubMed 73. ↵ Cooper HM , Hedges LV , eds. The Handbook of Research Synthesis . Russell Sage Foundation ; 1994 . 74. ↵ Mauvais-Jarvis F , Lindsey SH . Metabolic benefits afforded by estradiol and testosterone in both sexes: clinical considerations . J Clin Invest . 2024 ; 134 ( 17 ): e180073 . doi: 10.1172/JCI180073 OpenUrl CrossRef PubMed 75. ↵ Fiacco S , Walther A , Ehlert U . Steroid secretion in healthy aging . Psychoneuroendocrinology . 2019 ; 105 : 64 – 78 . doi: 10.1016/j.psyneuen.2018.09.035 OpenUrl CrossRef PubMed 76. ↵ Decaroli MC , Rochira V . Aging and sex hormones in males . Virulence . 2017 ; 8 ( 5 ): 545 – 570 . doi: 10.1080/21505594.2016.1259053 OpenUrl CrossRef PubMed 77. ↵ Brinton RD , Thompson RF , Foy MR , et al. Progesterone receptors: Form and function in brain . Front Neuroendocrinol . 2008 ; 29 ( 2 ): 313 – 339 . doi: 10.1016/j.yfrne.2008.02.001 OpenUrl CrossRef PubMed Web of Science 78. ↵ Pike CJ . Sex and the development of Alzheimer’s disease: Sex Differences in AD . J Neurosci Res . 2017 ; 95 ( 1-2 ): 671 – 680 . doi: 10.1002/jnr.23827 OpenUrl CrossRef PubMed 79. ↵ Buckner RL. Molecular, Structural, and Functional Characterization of Alzheimer’s Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory . J Neurosci . 2005 ; 25 ( 34 ): 7709 – 7717 . doi: 10.1523/JNEUROSCI.2177-05.2005 OpenUrl Abstract / FREE Full Text 80. Harlow SD , Gass M , Hall JE , et al. Executive Summary of the Stages of Reproductive Aging Workshop + 10: Addressing the Unfinished Agenda of Staging Reproductive Aging . J Clin Endocrinol Metab . 2012 ; 97 ( 4 ): 1159 – 1168 . doi: 10.1210/jc.2011-3362 OpenUrl CrossRef PubMed Web of Science 81. Hedges VL , Ebner TJ , Meisel RL , Mermelstein PG . The cerebellum as a target for estrogen action . Front Neuroendocrinol . 2012 ; 33 ( 4 ): 403 – 411 . doi: 10.1016/j.yfrne.2012.08.005 OpenUrl CrossRef PubMed 82. Morrison JH , Brinton RD , Schmidt PJ , Gore AC. Estrogen , Menopause, and the Aging Brain: How Basic Neuroscience Can Inform Hormone Therapy in Women . J Neurosci . 2006 ; 26 ( 41 ): 10332 – 10348 . doi: 10.1523/JNEUROSCI.3369-06.2006 OpenUrl FREE Full Text 83. ↵ Vest RS , Pike CJ . Gender, sex steroid hormones, and Alzheimer’s disease . Horm Behav . 2013 ; 63 ( 2 ): 301 – 307 . doi: 10.1016/j.yhbeh.2012.04.006 OpenUrl CrossRef Web of Science 84. ↵ Bettio LEB , Rajendran L , Gil-Mohapel J . The effects of aging in the hippocampus and cognitive decline . Neurosci Biobehav Rev . 2017 ; 79 : 66 – 86 . doi: 10.1016/j.neubiorev.2017.04.030 OpenUrl CrossRef PubMed 85. ↵ Driscoll I . The Aging Hippocampus: Cognitive, Biochemical and Structural Findings . Cereb Cortex . 2003 ; 13 ( 12 ): 1344 – 1351 . doi: 10.1093/cercor/bhg081 OpenUrl CrossRef PubMed Web of Science 86. ↵ Arleo A , Bareš M , Bernard JA , et al. Consensus Paper: Cerebellum and Ageing . The Cerebellum . 2023 ; 23 ( 2 ): 802 – 832 . doi: 10.1007/s12311-023-01577-7 OpenUrl CrossRef 87. Gellersen HM , Guell X , Sami S . Differential vulnerability of the cerebellum in healthy ageing and Alzheimer’s disease . NeuroImage Clin . 2021 ; 30 : 102605 . doi: 10.1016/j.nicl.2021.102605 OpenUrl CrossRef PubMed 88. Heckmann JM . Novel presenilin 1 mutation with profound neurofibrillary pathology in an indigenous Southern African family with early-onset Alzheimer’s disease . Brain . 2004 ; 127 ( 1 ): 133 – 142 . doi: 10.1093/brain/awh009 OpenUrl CrossRef PubMed Web of Science 89. Kim H , Cheong E , Jo S , et al. The cerebellum could serve as a potential imaging biomarker of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment . Eur J Neurol . 2021 ; 28 ( 5 ): 1520 – 1527 . doi: 10.1111/ene.14770 OpenUrl CrossRef PubMed 90. Sepulveda-Falla D , Barrera-Ocampo A , Hagel C , et al. Familial Alzheimer’s disease–associated presenilin-1 alters cerebellar activity and calcium homeostasis . J Clin Invest . 2014 ; 124 ( 4 ): 1552 – 1567 . doi: 10.1172/JCI66407 OpenUrl CrossRef PubMed Web of Science 91. ↵ Xia M , Gao C , Wang H , et al. Novel PSEN1 (P284S) Mutation Causes Alzheimer’s Disease with CerebellarAmyloid β-Protein Deposition . Curr Alzheimer Res . 2022 ; 19 ( 7 ): 523 – 529 . doi: 10.2174/1567205019666220718151357 OpenUrl CrossRef PubMed 92. ↵ Jacobs HIL , Hopkins DA , Mayrhofer HC , et al. The cerebellum in Alzheimer’s disease: evaluating its role in cognitive decline . Brain . 2018 ; 141 ( 1 ): 37 – 47 . doi: 10.1093/brain/awx194 OpenUrl CrossRef PubMed 93. ↵ Bernard JA . Cerebello-Hippocampal Interactions in the Human Brain: A New Pathway for Insights Into Aging . The Cerebellum. Published online March 4 , 2024 . doi: 10.1007/s12311-024-01670-5 OpenUrl CrossRef 94. ↵ Uwisengeyimana J de D , Nguchu BA , Wang Y , et al. Cognitive function and cerebellar morphometric changes relate to abnormal intra-cerebellar and cerebro-cerebellum functional connectivity in old adults . Exp Gerontol . 2020 ; 140 : 111060 . doi: 10.1016/j.exger.2020.111060 OpenUrl CrossRef PubMed 95. ↵ Delli Pizzi S , Punzi M , Sensi SL . Functional signature of conversion of patients with mild cognitive impairment . Neurobiol Aging . 2019 ; 74 : 21 – 37 . doi: 10.1016/j.neurobiolaging.2018.10.004 OpenUrl CrossRef PubMed 96. Qi Z , An Y , Zhang M , Li HJ , Lu J . Altered Cerebro-Cerebellar Limbic Network in AD Spectrum: A Resting-State fMRI Study . Front Neural Circuits . 2019 ; 13 : 72 . doi: 10.3389/fncir.2019.00072 OpenUrl CrossRef PubMed 97. ↵ Xue J , Guo H , Gao Y , et al. Altered Directed Functional Connectivity of the Hippocampus in Mild Cognitive Impairment and Alzheimer’s Disease: A Resting-State fMRI Study . Front Aging Neurosci . 2019 ; 11 : 326 . doi: 10.3389/fnagi.2019.00326 OpenUrl CrossRef PubMed 98. ↵ Bai F , Xie C , Watson DR , et al. Aberrant Hippocampal Subregion Networks Associated with the Classifications of aMCI Subjects: A Longitudinal Resting-State Study. He Y, ed . PLoS ONE . 2011 ; 6 ( 12 ): e29288 . doi: 10.1371/journal.pone.0029288 OpenUrl CrossRef PubMed 99. ↵ Reuter-Lorenz PA , Cappell KA . Neurocognitive Aging and the Compensation Hypothesis . Curr Dir Psychol Sci . 2008 ; 17 ( 3 ): 177 – 182 . doi: 10.1111/j.1467-8721.2008.00570.x OpenUrl CrossRef Web of Science 100. ↵ King M , Hernandez-Castillo CR , Poldrack RA , Ivry RB , Diedrichsen J . Functional boundaries in the human cerebellum revealed by a multi-domain task battery . Nat Neurosci . 2019 ; 22 ( 8 ): 1371 – 1378 . doi: 10.1038/s41593-019-0436-x OpenUrl CrossRef PubMed 101. ↵ Stoodley C , Schmahmann J . Functional topography in the human cerebellum: A meta-analysis of neuroimaging studies . NeuroImage . 2009 ; 44 ( 2 ): 489 – 501 . doi: 10.1016/j.neuroimage.2008.08.039 OpenUrl CrossRef PubMed Web of Science 102. ↵ Beason-Held LL , Shafer AT , Goh JO , et al. Hippocampal activation and connectivity in the aging brain . Brain Imaging Behav . 2021 ; 15 ( 2 ): 711 – 726 . doi: 10.1007/s11682-020-00279-6 OpenUrl CrossRef PubMed 103. ↵ Kircher T , Weis S , Leube D , et al. Anterior hippocampus orchestrates successful encoding and retrieval of non-relational memory: an event-related fMRI study . Eur Arch Psychiatry Clin Neurosci . 2008 ; 258 ( 6 ): 363 – 372 . doi: 10.1007/s00406-008-0805-z OpenUrl CrossRef PubMed Web of Science View the discussion thread. 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