The cerebellum in Alzheimer's disease: evaluating its role in cognitive decline

Alzheimer's disease cerebrospinal fluid biomarker levels and APOE genetic status are associated with hippocampal-cerebellar functional connectivity

Recent research suggests that hippocampal-cerebellar (Hp-CB) functional connectivity may be altered early in the course of Alzheimer's disease (AD), given the early accumulation of AD pathology in the hippocampi and emerging evidence of cerebellar changes in early AD. This study analyzed the role of AD genetic risk (via APOE ε4 carrier status) and cerebrospinal fluid (CSF) biomarkers of AD pathology (ratio of phosphorylated tau (p-tau181) to amyloid beta (Aβ42/Aβ40)) on the relationship between age and functional Hp-CB resting state fMRI connectivity in 161 cognitively unimpaired older adults (M age =67.3; SD =9.0; 37 % APOE ε4 +). In multiple regression analyses with Hp-CB connectivity as the outcome, there were significant interactions between age and APOE ε4 status, and between age and CSF AD biomarkers. Older age was associated with greater Hp-CB connectivity in APOE ε4 non-carriers and participants with less abnormal CSF AD biomarkers. In contrast, Hp-CB connectivity was marginally lower with older age in ε4 carriers and those with more abnormal AD biomarkers. Furthermore, greater Hp-CB connectivity was associated with better episodic memory performance across all groups. These findings suggest that age-related increases in Hp-CB connectivity among APOE ε4 non-carriers and those with low AD biomarker levels reflect age-related changes that are largely unrelated to AD, while age-related decreases in Hp-CB connectivity in APOE ε4 carriers may reflect AD-related alterations. These findings also highlight the importance of cerebellar contributions to cognitive performance among older adults and suggest that Hp-CB connectivity may be altered in preclinical AD.

The cerebellum in frontotemporal dementia: From neglected bystander to potential neuromodulatory target. A narrative review

BACKGROUND

Though cortical changes in frontotemporal dementia (FTD) are well-documented, the cerebellum's role, closely linked to these areas, remains unclear.

OBJECTIVES

To provide evidence on cerebellar involvement in FTD across clinical, genetic, imaging, neuropathological, and neurophysiological perspectives. Additionally, we sought evidence supporting the application of cerebellar non-invasive brain stimulation (NIBS) in FTD for both diagnostic and therapeutic purposes.

METHODS

We performed a literature review using MEDLINE (via PubMed), Scopus, and Web of Science databases.

RESULTS

We emphasized the involvement of specific cerebellar regions which differentiate each FTD subtypes and may account for some of the characteristic symptoms. Furthermore, we highlighted peculiarities in FTD genetic alterations. Finally, we outlined neurophysiological evidence supporting a role for the cerebellum in FTD pathogenesis.

CONCLUSION

The cerebellum is critically involved in the FTD spectrum. Moreover, it can be speculated that cerebellar modulation, as already shown in other neurodegenerative disorders, could restore the interneuronal intracortical circuits typically impaired in FTD patients, providing clinical improvements and fundamental outcome measures in clinical trials.

Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes

Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for cognition and neurological disease. In addition, interactions between SC and FC within distributed association regions are related to alterations in cognition or neurological diseases, considering the inherent linkage between neural function and structure. However, there is a scarcity of existing learning-based methods that leverage both modality-specific characteristics and high-order interactions between the two modalities for regression or classification. Hence, this study proposes an interpretable modality-specific and interactive graph convolutional network (MS-Inter-GCN) that incorporates modality-specific information, reflecting the unique neural mechanism for each modality, and structure-function interactions, capturing the underlying foundation provided by white-matter fiber tracts for high-level brain function. In MS-Inter-GCN, we generate modality-specific task-relevant embeddings separately from both FC and SC using a graph convolutional encoder-decoder module. Subsequently, we learn the interactive weights between corresponding regions of FC and SC, reflecting the coupling strength, by employing an interactive module on the embeddings of both modalities. A novel graph structure is constructed, which uses modality-specific task-relevant embeddings and inserts the interactive weights as edges connecting corresponding regions of two modalities, and then is used for the regression or classification task. Finally, a post-hoc explainable technology - GNNExplainer- is used to identify salient regions and connections of each modality as well as salient interactions between FC and SC associated with tasks. We apply the proposed framework to fluid cognition prediction, Parkinson's disease (PD), Alzheimer's disease (AD), and schizophrenia (SZ) classification. Experimental results demonstrate that our method outperforms the other ten state-of-the-art methods on multi-modal brain features on all tasks. The GNNExplainer identifies salient structural and functional regions and connections for fluid cognition, PD, AD, and SZ. It confirms that strong structure-function coupling within the executive and control networks, combined with weak coupling within the motor network, is associated with fluid cognition. Moreover, structure-function decoupling in specific brain regions serves as a marker for different diseases: decoupling of the prefrontal, superior parietal, and superior occipital cortices is a marker of PD; decoupling of the middle frontal and lateral parietal cortices, temporal pole, and subcortical regions is indicative of AD; and decoupling of the prefrontal, parietal, and temporal cortices, as well as the cerebellum, contributes to SZ.

Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage

BACKGROUND

Mild cognitive impairment (MCI) and preclinical MCI (e.g., subjective cognitive decline, SCD) are considered risk states of dementia, such as Alzheimer's Disease (AD). However, it is challenging to accurately predict conversion from normal cognition (NC) to MCI, which is important for early detection and intervention. Since neuropathological changes may have occurred in the brain many years before clinical AD, we sought to detect the subtle brain changes in the pre-MCI stage using a deep-learning method based on structural Magnetic Resonance Imaging (MRI).

OBJECTIVES

To discover early structural neuroimaging changes that differentiate between stable and progressive cognitive status, and to establish a predictive model for MCI conversion.

DESIGN, SETTING AND PARTICIPANTS

We first created a unique deep-learning framework for pre-AD conversion prediction through the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) database (n = 845). Then, we tested the model on ADNI-2 (n = 321, followed 3 years) and our private study (n = 109), the China Longitudinal Aging Study (CLAS), to validate the rationality for pre-MCI conversion prediction. The CLAS is a 7-year community-based cohort study in Shanghai. Our framework consisted of two steps: 1) a single-ROI-based network (SRNet) for identifying informative regions in the brain, and 2) a multi-ROI-based network (MRNet) for pre-AD conversion prediction. We then utilized these "ROI-based deep learning" neural networks to create a composite score using advanced algorithm-building. We coined this score as the Progressive Index (PI), which serves as a metric for assessing the propensity of AD conversion. Ultimately, we employed the PI to gauge its predictive capability for MCI conversion in both ADNI-2 and CLAS datasets.

MEASUREMENTS

We primarily utilized baseline T1-weighted MRI scans to identify the most discriminative brain regions and subsequently developed the PI in both training and validation datasets. We compared the PI across different cognitive groups and conducted logistic regression models along with their AUCs, adjusting for education level, gender, neuropsychological test scores, and the presence of comorbid conditions.

RESULTS

We trained the SRNet and MRNet using 845 subjects from ADNI-1 with baseline MRI data, in which AD and progressive MCI (converting to AD within 3 years) patients were considered as positive samples, while NC and stable MCI (remaining stable for 3 years) subjects were considered as negative samples. The convolutional neural networks identified the top 10 regions of interest (ROIs) for distinguishing progressive from stable cases. These key brain regions included the hippocampus, amygdala, temporal lobe, insula, and anterior cerebellum. A total of 321 subjects from ADNI-2, including 209 NC (18 progressive NC (pNC), 113 stable NC (sNC), and 78 remaining NC (rNC)) and 112 SCD (11 pSCD, 5 sSCD, and 96 rSCD), as well as 109 subjects from CLAS, including 17 sNC, 16 pNC, 52 sSCD and 24 pSCD participated in the test set, separately. We found that the PI score effectively sorted all subjects by their stages (stable vs progressive). Furthermore, the PI score demonstrated excellent discrimination between the two outcomes in the CLAS data(p<0.001), even after controlling for age, gender, education level, depression symptoms, anxiety symptoms, somatic diseases, and baseline MoCA score. Better performance for prediction progression to MCI in CLAS was obtained when the PI score was combined with clinical measures (AUC=0.812; 95 %CI: 0.725-0.900).

CONCLUSIONS

This study effectively predicted the progression to MCI among order individuals at normal cognition state by deep learning algorithm with MRI scans. Exploring the key brain alterations during the very early stages, specifically the transition from NC to MCI, based on deep learning methods holds significant potential for further research and contributes to a deeper understanding of disease mechanisms.

Cerebellar Crus II Regulates Recognition and Spatial Memory in Mice

The cerebellar Crus II is implicated in the late stages of Alzheimer's disease (AD), yet its specific roles in memory regulation and therapeutic potential remain unclear. Using in vivo fiber photometry, we observed robust activation of Crus II neurons in healthy mice during recognition memory tasks. Acute chemogenetic inhibition of Crus II neurons impaired recognition and spatial memory in mice. Polysynaptic circuit tracing revealed that Crus II neurons modulate neural activity in the contralateral prelimbic cortex (PrL) via the Crus II-cerebellar lateral nucleus (LN)-ventromedial thalamus/zona incerta (VM/ZI)-PrL pathway. In 5 × FAD mice, β-amyloid (Aβ) plaque deposition in Crus II exhibited age-dependent progression, occurring later and less severely compared to the prefrontal cortex. Chronic activation of Crus II neurons ameliorated recognition and spatial memory deficits in 5 × FAD mice. These findings highlight the cerebellar Crus II as a modulator of cognitive function and a potential therapeutic target for AD.

Cerebellar-hippocampal volume associations with behavioral outcomes following tDCS modulation

Here, we explore the relationship between transcranial direct current stimulation (tDCS) and brain-behavior interactions. We propose that tDCS perturbation allows for the investigation of relationships between brain volume and behavior. We focused on the hippocampus (HPC) and cerebellum (CB) regions that are implicated in our understanding of memory and motor skill acquisition. Seventy-four young adults (mean age: 22 ± 0.42 years, mean education: 14.7 ± 0.25 years) were randomly assigned to receive either anodal, cathodal, or sham stimulation. Following stimulation, participants completed computerized tasks assessing working memory and sequence learning in a magnetic resonance imaging (MRI) environment. We investigated the statistical interaction between CB and HPC volumes. Our findings showed that individuals with larger cerebellar volumes had shorter reaction times (RT) on a high-load working memory task in the sham stimulation group. In contrast, the anodal stimulation group exhibited faster RTs during the low-load working memory condition. These RT differences were associated with the cortical volumetric interaction between CB-HPC. Literature suggests that anodal stimulation down-regulates the CB and here, those with larger volumes perform more quickly, suggesting the potential need for additional cognitive resources to compensate for cerebellar downregulation or perturbation. This new insight suggests that tDCS can aid in revealing structure-function relationships, due to greater performance variability, especially in young adults. It may also reveal new targets of interest in the study of aging or in diseases where there is also greater behavioral variability.

Risk of Alzheimer's disease in Down syndrome: Insights gained by multi-omics

Individuals with Down syndrome (DS) are highly susceptible to Alzheimer's disease (AD). The integration of genomics, transcriptomics, epigenomics, proteomics, and metabolomics enables unprecedented understanding of DS-AD, offering a detailed picture of this complex issue. The vast -omics data also present challenges that reflect the complexity of genetic information flow. These studies nonetheless reveal critical mechanisms behind AD risk, including unique observations in DS that differ from those seen in the general population and familial dominant AD. In addition, the correlations between the AD polygenic risk score and proteins related to female infertility and autoimmune thyroiditis corroborate clinical observations. Metabolomic data reveal disrupted metabolic networks, offering prospects for a dynamic score to create specialized nutritional interventions. By adopting a multidimensional perspective with integrated reductionism, the evolving landscape presents an opportunity to identify promising directions for developing precision strategies to mitigate the impact of AD in the DS population. HIGHLIGHTS: Individuals with Down syndrome (DS) are highly susceptible to Alzheimer's disease (AD). DS-AD is characterized by its polygenic nature, extending beyond chromosome 21 with significant contributions from various chromosomes. DS-AD also presents unique features that differ from those observed in the general population and familial dominant AD. Our review consolidates key findings from genomics, transcriptomics, epigenomics, proteomics, and metabolomics, providing a comprehensive view of the molecular mechanisms underlying DS-AD. We highlight promising research directions to further elucidate the pathogenesis of DS-AD.

Ignoring the cerebellum is hindering progress in neuroscience

Traditionally considered a motor structure, the cerebellum has been shown to play a key role in several cognitive functions. However, for decades, the cerebellum has been largely overlooked and even deliberately excluded from 'whole-brain' neuroimaging studies. Here, we propose that the continued exclusion of the cerebellum has limited our understanding of whole-brain function. We describe reasons - both warranted and unwarranted - behind its historical exclusion from the neuroimaging literature, review literature describing the importance of the cerebellum and its unique role in brain function, and outline the potential unintended negative consequences of exclusion of the cerebellum for our comprehensive understanding of brain function and clinical disorders.

Abnormal cerebellar activity and connectivity alterations of the cerebellar-limbic system in post-stroke cognitive impairment: a study based on resting state functional magnetic resonance imaging

Background

Stroke is an important cause of cognitive impairment. Post-stroke cognitive impairment (PSCI) is a prevalent psychiatric disorder following stroke. However, the effects of PSCI on the cerebellum remain mostly unknown.

Methods

A total of 31 PSCI patients and 31 patients without cognitive impairment after stroke were included in this study. The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were administered to all participants. Analyses of ALFF, fALFF, and ReHo were employed to investigate alterations in brain neuronal activity, while limbic connectivity analysis was utilized to reflect changes within the abnormal connections within brain regions.

Results

We found that ALFF values were increased in Cerebelum_7b_R, Cerebelum_Crus1_L. fALFF values were increased in Vermis_3. The ReHo values were increased in Cerebelum_8_R, Cerebelum_Crus2_R, Cerebelum_Crus1_L. The functional connection between Frontal_Mid_Orb_L and Cerebelum_Crus2_R brain regions was decreased. The functional connection between Hippocampus_L and Cerebelum_Crus2_R brain regions was decreased. The functional connection between Vermis_3 and Frontal_Med_Orb_L brain regions was decreased.

Conclusion

The severity of cognitive impairment may influence the extent of functional connectivity disruption between the cerebellum and the limbic system. Furthermore, atypical alterations in neuronal activity within cerebellar regions are associated with cognitive decline.

DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI

This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution (1 mm3) or using mono-modal data, the proposed method improves cerebellum lobule segmentation through the use of a multimodal and ultra-high resolution (0.125 mm3) training dataset. To develop the method, first, a database of semi-automatically labelled cerebellum lobules was created to train the proposed method with ultra-high resolution T1 and T2 MR images. Then, an ensemble of deep networks has been designed and developed, allowing the proposed method to excel in the complex cerebellum lobule segmentation task, improving precision while being memory efficient. Notably, our approach deviates from the traditional U-Net model by exploring alternative architectures. We have also integrated deep learning with classical machine learning methods incorporating a priori knowledge from multi-atlas segmentation which improved precision and robustness. Finally, a new online pipeline, named DeepCERES, has been developed to make available the proposed method to the scientific community requiring as input only a single T1 MR image at standard resolution.

Differences in Finger Dexterity in Patients With Mild and Moderate Alzheimer's Disease-A Study of Cognitive Function by Disease Severity

AIM

This study aimed to estimate the relationship between finger function and cognitive function in patients with Alzheimer's disease (AD).

METHODS

Patients diagnosed with AD at the Outpatient Center for Comprehensive Care and Research on Memory Disorder of the National Center for Geriatrics and Gerontology underwent a 15-s bimanual alternating tapping task to measure finger movements. After finger movement measurements, we classified the severity of AD into mild and moderate and compared the finger movements. The Mann-Whitney U test and effect size were used to compare parameter values between the two groups (mild and moderate AD), and the calculated p values were corrected using the Bonferroni method. The Spearman rank correlation coefficient was calculated to determine the association between finger parameters and cognitive function (Mini-Mental Examination [MMSE]).

RESULTS

Data from 163 patients with AD were analyzed. When comparing finger parameters between the mild AD (64 individuals) and moderate AD (99 individuals) groups, the moderate AD group demonstrated fewer taps (p = 0.005; r = 0.22) and a longer interval between taps with the thumb and index finger (p = 0.007; r = 0.21) than the mild AD group. The correlation between the MMSE score and finger function was weakly positive for the number of taps and weakly negative for the average of tapping interval.

CONCLUSIONS

These parameters reflect the decline in finger function associated with the advanced stages of dementia and may help assess the severity of AD. Additionally, these findings may have clinical utility in assessing the severity of AD, potentially enhancing diagnostic accuracy for differentiating stages of AD.

Exome sequencing identifies novel genes associated with cerebellar volume and microstructure

Proteins encoded by exons are critical for cellular functions, and mutations in these genes often result in significant phenotypic effects. The cerebellum is linked to various heritable human disease phenotypes, yet genome-wide association studies have struggled to capture the effects of rare variants on cerebellar traits. This study conducts a large-scale exome association analysis using data from approximately 35,000 UK Biobank participants, examining seven cerebellar traits, including total cerebellar volume and white matter microstructure. We identify 90 genes associated with cerebellar traits, 60 of which were previously unreported in genome-wide association studies. Notable findings include the discovery of genes like PRKRA and TTK, as well as RASGRP3, linked to cerebellar volume and white matter microstructure. Gene enrichment analysis reveals associations with non-coding RNA processing, cognitive function, neurodegenerative diseases, and mental disorders, suggesting shared biological mechanisms between cerebellar phenotypes and neuropsychiatric diseases.

The Human Cerebello-Hippocampal Circuit Across Adulthood

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.

Hyperconnectivity and Connectome Gradient Dysfunction of Cerebello-Thalamo-Cortical Circuitry in Alzheimer's Disease Spectrum Disorders

Cerebellar functional connectivity changes have been reported in Alzheimer's disease (AD), but a comprehensive framework integrating these findings is lacking. This retrospective study investigates the cerebello-thalamo-cortical (CTC) circuit in AD, using functional gradient analysis to elucidate deficits and potential biomarkers. We analyzed data from 246 participants enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI-3; NCT02854033), including 58 with AD, 103 with mild cognitive impairment (MCI), and 85 cognitively normal (CN) controls, matched for age and sex. All individuals underwent comprehensive neuropsychological assessments (MMSE, MoCA, ADAS-Cog) and MRI scans. We extracted mean time series for 270 brain regions (an extended Power atlas) and computed pairwise functional connectivity, focusing on CTC circuitry. Thalamic and cerebellar connectivity gradients were derived using voxel-wise correlation matrices and the BrainSpace toolbox, defining thalamic and cerebellar masks from the Melbourne subcortical atlas and AAL atlas, respectively. ANCOVA with post hoc analyses, controlling for age and sex, was conducted to assess abnormal CTC connectivity across AD, MCI, and CN groups. LASSO regression identified edges within the CTC circuitry that significantly differed between AD and CN, MCI and CN, AD and MCI, as well as was used to construct Logistic classification model. Pearson correlations were performed to examine relationships between mean CTC connectivity, individual edges, and cognitive scores (MMSE, MoCA, ADAS-Cog). To explore the hierarchical organization of the thalamus and cerebellum, global gradient distributions were compared across groups using two-sample Kolmogorov-Smirnov tests. Additionally, ANCOVA was applied to compare subfield- and functional-level gradients of the thalamus and cerebellum among AD, MCI, and CN. False discovery rate (FDR) corrections were used, setting the statistical significance threshold was set at P < 0.05. AD and MCI individuals exhibited increased CTC connectivity compared to CN (all P < 0.05). Average CTC connectivity did not correlate with cognitive scores (P > 0.05), but specific CTC edges were correlated. LASSO regression identified 20 discriminative edges, achieving high accuracy in AD-CN classification (AUC = 0.92 training, AUC = 0.80 test). Thalamic and cerebellar gradient distributions differed significantly across groups (all P < 0.05), with specific regions showing distinct gradient scores. Five cerebellar functional networks exhibited decreased gradient scores. Significant CTC hyperconnectivity in AD and MCI compared with CN suggests early thalamic and cerebellar dysregulation. Classification analyses effectively distinguished AD vs. CN but were moderate for MCI vs. CN and limited for MCI vs. AD. Gradient analyses revealed global- and subfield-level disruptions in AD, emphasizing the role of thalamic and cerebellar interactions in cognitive decline and offering potential diagnostic markers and therapeutic targets.

Anti-herpetic tau preserves neurons via the cGAS-STING-TBK1 pathway in Alzheimer's disease

Alzheimer's disease (AD) diagnosis relies on the presence of extracellular β-amyloid (Aβ) and intracellular hyperphosphorylated tau (p-tau). Emerging evidence suggests a potential link between AD pathologies and infectious agents, with herpes simplex virus 1 (HSV-1) being a leading candidate. Our investigation, using metagenomics, mass spectrometry, western blotting, and decrowding expansion pathology, detects HSV-1-associated proteins in human brain samples. Expression of the herpesvirus protein ICP27 increases with AD severity and strongly colocalizes with p-tau but not with Aβ. Modeling in human brain organoids shows that HSV-1 infection elevates tau phosphorylation. Notably, p-tau reduces ICP27 expression and markedly decreases post-infection neuronal death from 64% to 7%. This modeling prompts investigation into the cGAS-STING-TBK1 pathway products, nuclear factor (NF)-κB and IRF-3, which colocalizes with ICP27 and p-tau in AD. Furthermore, experimental activation of STING enhances tau phosphorylation, while TBK1 inhibition prevents it. Together, these findings suggest that tau phosphorylation acts as an innate immune response in AD, driven by cGAS-STING.

White matter correlates of cognition: A diffusion magnetic resonance imaging study

BACKGROUND

Our comprehension of the interplay of cognition and the brain remains constrained. While functional imaging studies have identified cognitive brain regions, structural correlates of cognitive functions remain underexplored. Advanced methods like Diffusion Magnetic Resonance Imaging (DMRI) facilitate the exploration of brain connectivity and White Matter (WM) tract microstructure. Therefore, we conducted connectometry method on DMRI data, to reveal WM tracts associated with cognition.

METHODS

125 healthy participants from the National Institute of Mental Health Intramural Healthy Volunteer Dataset were recruited. Multiple regression analyses were conducted between DMRI-derived Quantitative Anisotropy (QA) values within WM tracts and scores of participants in Flanker Inhibitory Control and Attention Test (attention), Dimensional Change Card Sort (executive function), Picture Sequence Memory Test (episodic memory), and List Sorting Working Memory Test (working memory) tasks from National Institute of Health toolbox. The significance level was set at False Discovery Rate (FDR)<0.05.

RESULTS

We identified significant positive correlations between the QA of WM tracts within the left cerebellum and bilateral fornix with attention, executive functioning, and episodic memory (FDR=0.018, 0.0002, and 0.0002, respectively), and a negative correlation between QA of WM tracts within bilateral cerebellum with attention (FDR=0.028). Working memory demonstrated positive correlations with QA of left inferior longitudinal and left inferior fronto-occipital fasciculi (FDR=0.0009), while it showed a negative correlation with QA of right cerebellar tracts (FDR=0.0005).

CONCLUSION

Our results underscore the intricate link between cognitive performance and WM integrity in frontal, temporal, and cerebellar regions, offering insights into early detection and targeted interventions for cognitive disorders.

Associations of Mild Behavioral Impairment Domains with Brain Volumes: Cross-sectional Analysis of Atherosclerosis Risk in Community (ARIC) Study

BACKGROUND

Mild behavioral impairment (MBI) has been associated with global brain atrophy, but the regional neural correlates of MBI symptoms are less clear, particularly among community-dwelling older individuals without dementia.

OBJECTIVE

Our objective was to examine the associations of MBI domains with gray matter (GM) volumes in a large population-based sample of older adults without dementia.

METHODS

We performed a cross-sectional study of 1445 community-dwelling older adults in the Atherosclerosis Risk in Communities Study who underwent detailed neurocognitive assessment and brain magnetic resonance imaging in 2011-2013. MBI domains were defined using an established algorithm that maps data collected from informants on the Neuropsychiatric Inventory Questionnaire to the 5 MBI domains of decreased motivation, affective dysregulation, impulse dyscontrol, social inappropriateness, and abnormal perception/thought content. We performed voxel-based morphometry analyses to investigate associations of any MBI domain symptoms with GM volumes. We additionally performed region-of-interest analyses using adjusted linear regression models to examine associations between individual MBI domains with a priori-hypothesized regional GM volumes.

RESULTS

Overall, the mean age of participants was 76.5 years; 59% were female, 21% were of Black race, and 26% had symptoms in at least one MBI domain. Participants with normal cognition comprised 60% of the population, and 40% had mild cognitive impairment. Compared to individuals without any MBI domain symptoms, voxel-based morphometry analyses showed that participants with symptoms in at least one MBI domain had consistently lower GM volumes in the cerebellum and bilateral temporal lobes, particularly involving the hippocampus. In adjusted region-of-interest models, affective dysregulation domain symptoms were associated with lower GM volume in the inferior temporal lobe (β = -0.34; 95% confidence interval = -0.64, -0.04), and impulse dyscontrol domain symptoms were associated with lower GM volume in the parahippocampal gyrus (β = -0.06; 95% confidence interval = -0.11, 0.00).

CONCLUSIONS

In this community-dwelling population of older adults without dementia, MBI symptoms were associated with lower GM volumes in regions commonly implicated in early Alzheimer's disease pathology. These findings lend support to the notion that MBI symptoms may be useful in identifying individuals at risk for future dementia.

Unstable functional brain states and reduced cerebro-cerebellar modularity in elderly individuals with subjective cognitive decline

The preclinical stage of Alzheimer's Disease (AD) holds great potential for intervention, therefore, it is crucial to elucidate the neural mechanisms underlying the progression of subjective cognitive decline (SCD). Previous studies have predominantly focused on the neural changes in the cerebrum associated with SCD, but have relatively neglected the cerebellum, and its functional relationship with the cerebrum. In the current study, we employed dynamic functional connectivity and large-scale brain network approaches to investigate the pathological characteristics of dynamic brain states and cerebro-cerebellar collaboration between SCD (n = 32) and the healthy elderly (n = 29) using resting-state fMRI. Two-way repeated measures ANOVA and permutation t-tests revealed significant group differences, with individuals with SCD exhibiting shorter state duration and more frequent transitions between states compared to the healthy elderly individuals. Additionally, individuals with SCD showed lower levels of intracerebellar functional connectivity, but higher levels of cerebellar-cerebral functional integration. Furthermore, the hub nodes of the functional networks in SCD shifted between the cerebellum and cerebrum across different brain states. These findings indicate that SCD exhibits greater state instability but may compensate for the negative effects of early disease by integrating cerebellar and cerebral networks, thereby maintaining cognitive performance. This study enhances our theoretical understanding of cerebellar-cerebral relationship changes in the early stages of AD and provides evidence for early interventions targeting the cerebellum.

Sex-Specific Entorhinal Cortex Functional Connectivity in Cognitively Normal Older Adults with Amyloid-β Pathology

Sex and apolipoprotein E (APOE) genotype have been shown to influence the risk and progression of Alzheimer's disease (AD). However, the impact of these factors on the functional connectivity of the entorhinal cortex (ERC) in clinically unpaired older adults (CUOA) with amyloid-β (Aβ +) pathology remains unclear. A total of 1022 cognitively normal older adults with Aβ + (603 females and 586 APOE ε4 +) from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study were included in this study. The 2 × 2 (gender, 2 APOE genotypes) analysis of covariance was performed to compare the demographic information, cognitive performance, and volumetric MRI data among these groups. Voxel-wise comparisons of bilateral ERC functional connectivity (FC) were conducted, and partial correlation analyses were used to explore the associations between cognitive performance and ERC-FC strength. We found that the APOE genotype influenced ERC functional connectivity mainly in the sensorimotor network (SMN). Males exhibited higher ERC-FC in the salience network (SN), while females displayed higher ERC-FC in the default mode network (DMN), executive control network (ECN), and reward network. The interplay of sex and APOE genotype on ERC-FC was observed in the SMN and cerebellar lobe. The ERC-FC was associated with executive function and memory performance in individuals with CUOA-Aβ + . Our findings provide evidence of sex-specific ERC functional connectivity compensation mechanism in cognitively normal older adults with Aβ + pathology. This study may contribute to a better understanding of the mechanisms underlying the early stages of AD and may help develop personalized interventions in preclinical AD.

Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease

INTRODUCTION

Pathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum-derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear.

METHODS

Radiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 1319) and an in-house dataset (n = 308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6-year follow-up.

RESULTS

The cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both cohorts. Key predictors included textural features in the right III and left I and II lobules, and network properties in Vermis I and II, which were associated with cognitive decline in AD.

DISCUSSION

Cerebellum-derived radiomic-network modeling shows promise as a tool for early identification and prediction of disease progression during the preclinical stage of AD.

HIGHLIGHTS

Altered cerebellar radiomic features and topological networks were identified in the subjects with mild cognitive impairment (MCI). The cerebellar radiomic-network integrated models outperformed hippocampal models in distinguishing MCI from normal cognition. The cerebellar radiomic model effectively predicts MCI risk and can stratify individuals into distinct risk categories. Specific cerebellar radiomic features are associated with cognitive impairment across various stages of amyloid beta and tau pathology.

Berberine chloride loaded nano-PEGylated liposomes attenuates imidacloprid-induced neurotoxicity by inhibiting NLRP3/Caspase-1/GSDMD-mediated pyroptosis

Concerns have been expressed about imidacloprid (IMI), one of the most often used pesticides, and its potential neurotoxicity to non-target organisms. Chronic neuroinflammation is central to the pathology of several neurodegenerative disorders. Hence, exploring the molecular mechanism by which IMI would trigger neuroinflammation is particularly important. This study examined the neurotoxic effects of oral administration of IMI (45 mg/kg/day for 30 days) and the potential neuroprotective effect of berberine (Ber) chloride loaded nano-PEGylated liposomes (Ber-Lip) (10 mg/kg, intravenously every other day for 30 days) using laboratory rat. The histopathological changes, anti-oxidant and oxidative stress markers (GSH, SOD, and MDA), proinflammatory cytokines (IL1β and TNF-α), microglia phenotype markers (CD86 and iNOS for M1; CD163 for M2), the canonical pyroptotic pathway markers (NLRP3, caspase-1, GSDMD, and IL-18) and Alzheimer's disease markers (Neprilysin and beta amyloid [Aβ] deposits) were assessed. Oral administration of IMI resulted in apparent cerebellar histopathological alterations, oxidative stress, predominance of M1 microglia phenotype, significantly upregulated NLRP3, caspase-1, GSDMD, IL-18 and Aβ deposits and significantly decreased Neprilysin expression. Berberine reduced the IMI-induced aberrations in the measured parameters and improved the IMI-induced histopathological and ultrastructure alterations brought on by IMI. This study highlights the IMI neurotoxic effect and its potential contribution to the development of Alzheimer's disease and displayed the neuroprotective effect of Ber-Lip.

An experimental framework for conjoint measures of olfaction, navigation, and motion as pre-clinical biomarkers of Alzheimer's disease

Elucidating Alzheimer's disease (AD) prodromal symptoms can resolve the outstanding challenge of early diagnosis. Based on intrinsically related substrates of olfaction and spatial navigation, we propose a novel experimental framework for their conjoint study. Artificial intelligence-driven multimodal study combining self-collected olfactory and motion data with available big clinical datasets can potentially promote high-precision early clinical screenings to facilitate timely interventions targeting neurodegenerative progression.

Brain network fingerprints of Alzheimer's disease risk factors in mouse models with humanized APOE alleles

Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic and modifiable risk factors influence disease susceptibility are under intense investigation, with APOE being the major genetic risk factor for late onset AD. Yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear. To model multiple risk factors, including APOE genotype, age, sex, diet, and immunity we used a cross sectional design, leveraging mice expressing human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. We used network topological and GraphClass analyses of brain connectomes derived from accelerated diffusion-weighted MRI to assess the global and local impact of risk factors, in the absence of AD pathology. Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles. Sparse Canonical Correlation Analysis (CCA) including spatial memory as a risk factor resulted in a network comprising 80 edges, showing significant overlap with risk-associated networks from GraphClass. The largest overlaps were observed with networks impacted by diet (47 edges), immunity (39 edges), APOE3 vs 4 (26 edges), sex (23 edges), and age (19 edges), the resulting networks supporting the use of sensory cues in spatial memory retrieval. These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.

Cerebellum in neurodegenerative diseases: Advances, challenges, and prospects

Neurodegenerative diseases (NDs) are a group of neurological disorders characterized by the progressive dysfunction of neurons and glial cells, leading to their structural and functional degradation in the central and/or peripheral nervous system. Historically, research on NDs has primarily focused on the brain, brain stem, or spinal cord associated with disease-related symptoms, often overlooking the role of the cerebellum. However, an increasing body of clinical and biological evidence suggests a significant connection between the cerebellum and NDs. In several NDs, cerebellar pathology and biochemical changes may start in the early disease stages. This article provides a comprehensive update on the involvement of the cerebellum in the clinical features and pathogenesis of multiple NDs, suggesting that the cerebellum is involved in the onset and progression of NDs through various mechanisms, including specific neurodegeneration, neuroinflammation, abnormal mitochondrial function, and altered metabolism. Additionally, this review highlights the significant therapeutic potential of cerebellum-related treatments for NDs.

Tissue nonspecific alkaline phosphatase deficiency impairs Purkinje cell development and survival in a mouse model of infantile hypophosphatasia

Loss-of-function mutations in the tissue-nonspecific alkaline phosphatase (TNAP) gene can result in hypophosphatasia (HPP), an inherited multi-systemic metabolic disorder that is well-known for skeletal and dental hypomineralization. However, emerging evidence shows that both adult and pediatric patients with HPP suffer from cognitive deficits, higher measures of depression and anxiety, and impaired sensorimotor skills. The cerebellum plays an important role in sensorimotor coordination, cognition, and emotion. To date, the impact of TNAP mutation on the cerebellar circuitry development and function remains poorly understood. The main objective of this study was to investigate the roles of TNAP in cerebellar development and function, with a particular focus on Purkinje cells, in a mouse model of infantile HPP. Male and female wild type (WT) and TNAP knockout (KO) mice underwent behavioral testing on postnatal day 13-14 and were euthanized after completion of behavioral tests. Cerebellar tissues were harvested for gene expression and immunohistochemistry analyses. We found that TNAP mutation resulted in significantly reduced body weight, shorter body length, and impaired sensorimotor functions in both male and female KO mice. These developmental and behavioral deficits were accompanied by abnormal Purkinje cell morphology and dysregulation of genes that regulates synaptic transmission, cellular growth, proliferation, and death. In conclusion, inactivation of TNAP via gene deletion causes developmental delays, sensorimotor impairment, and Purkinje cell maldevelopment. These results shed light on a new perspective of cerebellar dysfunction in HPP.

Exploring the relationship among Alzheimer's disease, aging and cognitive scores through neuroimaging-based approach

Alzheimer's disease (AD) is a fatal neurodegenerative disorder, with the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR) serving significant roles in monitoring its progression. We hypothesize that while cognitive assessment scores can detect AD-related brain changes, the targeted brain regions may differ. Additionally, given AD's strong association with aging, we propose that specific brain regions are influenced by both AD pathology and aging, exhibiting strong correlations with both. To test these hypotheses, we developed a 3D convolutional network with a mixed-attention mechanism to recognize AD subjects from structural magnetic resonance imaging (sMRI) data and utilize 3D convolutional methods to pinpoint brain regions significantly correlated with the AD, MMSE, CDR and age. All models were trained and internally validated on 417 samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI), and the classification model was externally validated on 382 samples from the Australian Imaging and Lifestyle flagship (AIBL). This approach provided robust support for using MMSE and CDR in assessing AD progression and visually illustrated the relationship between aging and AD. The analysis revealed correlations among the four identification tasks (AD, MMSE, CDR and age) and highlighted asymmetric brain lesions in both AD and aging. Notably, we found that AD can accelerate aging to some extent, and a significant correlation exists between the rate of aging and cognitive assessment scores. This offers new insights into the relationship between AD and aging.

Mediation Analysis with Multiple Exposures and Multiple Mediators

A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework. It assumes that there exist orthogonal components that demonstrate parallel mediation mechanisms on the outcome, and thus is named principal component mediation analysis (PCMA). Likelihood-based estimators are introduced for simultaneous estimation of the component projections and effect parameters. The asymptotic distribution of the estimators is derived for low-dimensional data. A bootstrap procedure is introduced for inference. Simulation studies illustrate the superior performance of the proposed approach. Applied to a proteomics-imaging dataset from the Alzheimer's disease neuroimaging initiative (ADNI), the proposed framework identifies protein deposition - brain atrophy - memory deficit mechanisms consistent with existing knowledge and suggests potential AD pathology by integrating data collected from different modalities.

Chronic Sleep Deprivation Altered the Expression of Memory-Related Genes and Caused Cognitive Memory Dysfunction in Mice

Lack of sleep, whether acute or chronic, is quite common and negatively affects an individual's memory and cognitive function. The question of whether chronic sleep deprivation (CSD) causes cognitive impairment to arise and progress is not well studied. To investigate the effects of CSD on memory and cognition, this study began by establishing a CSD mouse model. Behavioral experiments on animals revealed that CSD induced cognitive behavioral abnormalities reminiscent of Alzheimer's disease. Western blot experiments further demonstrated a considerable increase in amyloid-β (Aβ) expression in the mouse brain following CSD. Meanwhile, the hub gene Prkcg was searched for in the cerebellum using RNA-seq and bioinformatics analysis. PKCγ (Prkcg) expression was significantly reduced, as demonstrated by RT-qPCR and Western blot validations. Additionally, CSD was associated with downregulated CREB expression, decreased expression of the endothelin-converting enzyme (ECE1), and increased phosphorylation of ERK1/2 downstream of PKCγ. These findings suggested that CSD down-regulated PKCγ expression, decreased ECE1 expression, impaired Aβ degradation, and affected the PKCγ/ERK/CREB pathway and the synthesis of memory-related proteins. Overall, this study highlighted how CSD modulated PKCγ-related metabolism, impacting Aβ clearance and the production of memory-related proteins. Such insights are crucial for understanding and preventing sporadic Alzheimer's disease (sAD) associated with CSD.

Identification of immune traits associated with neurodevelopmental disorders by two-sample Mendelian randomization analysis

BACKGROUND

One of the main causes of health-related issues in children is neurodevelopmental disorders (NDDs), which include attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and Tourette syndrome (TS). Nonetheless, there is relatively little prior research looking at the link between immunological inflammation and NDDs. Our work uses a two-sample Mendelian Randomization (MR) approach to provide a thorough evaluation of the causal effects of immune traits on ADHD, ASD, and TS.

METHODS

As exposures, 731 immunological traits' genetic associations were chosen, and the outcomes were genome-wide association data for ADHD, ASD, and TS. The inverse-variance weighted (IVW), weighted median (WM), and MR-Egger methods were used to conduct MR analysis. The results' robustness, heterogeneity, and horizontal pleiotropy were confirmed using extensive sensitivity analysis.

RESULTS

With single-nucleotide polymorphisms serving as instruments and false discovery rate (FDR) correction applied, the study found that significantly higher expression of CD62L on CD62L+ myeloid DC (IVW, OR: 0.926, 95% CI 0.896~0.958, P = 9.42 × 10-6, FDR = 0.007) and suggestively higher absolute cell count (AC) of CD28 + DN (CD4-CD8-) (IVW, OR: 0.852, 95% CI = 0.780 ∼ 0.932, P-value = 4.65 × 10-4, FDR = 0.170) was associated with a lower risk of ADHD. There was no pleiotropy, and the causal relationships were strong according to sensitivity, leave-one-out, and MR-Steiger directionality tests. For ASD and TS, no harmful or protective immune traits were observed.

CONCLUSIONS

The results of the study lend credence to the theory that deficiency in CD62L on CD62L+ myeloid DC and CD28 + DN (CD4-CD8) AC may contribute to the onset of ADHD.

MRI radiomics combined with machine learning for diagnosing mild cognitive impairment: a focus on the cerebellar gray and white matter

Objective

Mild Cognitive Impairment (MCI) is a recognized precursor to Alzheimer's Disease (AD), presenting a significant risk of progression. Early detection and intervention in MCI can potentially slow disease advancement, offering substantial clinical benefits. This study employed radiomics and machine learning methodologies to distinguish between MCI and Normal Cognition (NC) groups.

Methods

The study included 172 MCI patients and 183 healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, all of whom had 3D-T1 weighted MRI structural images. The cerebellar gray and white matter were segmented automatically using volBrain software, and radiomic features were extracted and screened through Pyradiomics. The screened features were then input into various machine learning models, including Random Forest (RF), Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), K Nearest Neighbors (KNN), Extra Trees, Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). Each model was optimized for penalty parameters through 5-fold cross-validation to construct radiomic models. The DeLong test was used to evaluate the performance of different models.

Results

The LightGBM model, which utilizes a combination of cerebellar gray and white matter features (comprising eight gray matter and eight white matter features), emerges as the most effective model for radiomics feature analysis. The model demonstrates an Area Under the Curve (AUC) of 0.863 for the training set and 0.776 for the test set.

Conclusion

Radiomic features based on the cerebellar gray and white matter, combined with machine learning, can objectively diagnose MCI, which provides significant clinical value for assisted diagnosis.

Cerebello-Hippocampal Interactions in the Human Brain: A New Pathway for Insights Into Aging

The cerebellum is recognized as being important for optimal behavioral performance across task domains, including motor function, cognition, and affect. Decades of work have highlighted cerebello-thalamo-cortical circuits, from both structural and functional perspectives. However, these circuits of interest have been primarily (though not exclusively) focused on targets in the cerebral cortex. In addition to these cortical connections, the circuit linking the cerebellum and hippocampus is of particular interest. Recently, there has been an increased interest in this circuit, thanks in large part to novel findings in the animal literature demonstrating that neuronal firing in the cerebellum impacts that in the hippocampus. Work in the human brain has provided evidence for interactions between the cerebellum and hippocampus, though primarily this has been in the context of spatial navigation. Given the role of both regions in cognition and aging, and emerging evidence indicating that the cerebellum is impacted in age-related neurodegenerative disease such as Alzheimer's, I propose that further attention to this circuit is warranted. Here, I provide an overview of cerebello-hippocampal interactions in animal models and from human imaging and outline the possible utility of further investigations to improve our understanding of aging and age-related cognitive decline.

Oxidative Stress Markers and Na,K-ATPase Enzyme Kinetics Are Altered in the Cerebellum of Zucker Diabetic Fatty fa/fa Rats: A Comparison with Lean fa/+ and Wistar Rats

Type 2 diabetes mellitus has been referred to as being closely related to oxidative stress, which may affect brain functions and brain glucose metabolism due to its high metabolic activity and lipid-rich content. Na,K-ATPase is an essential enzyme maintaining intracellular homeostasis, with properties that can sensitively mirror various pathophysiological conditions such as diabetes. The goal of this study was to determine oxidative stress markers as well as Na,K-ATPase activities in the cerebellum of Zucker diabetic fatty (ZDF) rats depending on diabetes severity. The following groups of male rats were used: Wistar, ZDF Lean (fa/+), and ZDF (fa/fa) rats, arbitrarily divided according to glycemia into ZDF obese (ZO, less severe diabetes) and ZDF diabetic (ZOD, advanced diabetes) groups. In addition to basic biometry and biochemistry, oxidative stress markers were assessed in plasma and cerebellar tissues. The Na, K-ATPase enzyme activity was measured at varying ATP substrate concentrations. The results indicate significant differences in basic biometric and biochemical parameters within all the studied groups. Furthermore, oxidative damage was greater in the cerebellum of both ZDF (fa/fa) groups compared with the controls. Interestingly, Na,K-ATPase enzyme activity was highest to lowest in the following order: ZOD > ZO > Wistar > ZDF lean rats. In conclusion, an increase in systemic oxidative stress resulting from diabetic conditions has a significant impact on the cerebellar tissue independently of diabetes severity. The increased cerebellar Na,K-ATPase activity may reflect compensatory mechanisms in aged ZDF (fa/fa) animals, rather than indicating cerebellar neurodegeneration: a phenomenon that warrants further investigation.

Differential network knockoff filter with application to brain connectivity analysis

The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.

Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concern

The timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state‐of‐the‐art review followed by a comprehensive evaluation of machine learning models within the randomized neural networks (RNNs) and hyperplane‐based classifiers (HbCs) family to investigate SMC diagnosis thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset, 111 individuals with SMC and 111 healthy older adults are analyzed based on T1W magnetic resonance imaging (MRI) scans, extracting rich features. This analysis is based on baseline structural MRI (sMRI) scans, extracting rich features from gray matter (GM), white matter (WM), Jacobian determinant (JD), and cortical thickness (CT) measurements. In RNNs, deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best classifiers in terms of performance metrics in the identification of SMC. In HbCs, Kernelized pinball general twin support vector machine (Pin‐GTSVM‐K) excels in CT and WM features, whereas Linear Pin‐GTSVM (Pin‐GTSVM‐L) and Linear intuitionistic fuzzy TSVM (IFTSVM‐L) performs well in the JD and GM features sets, respectively. This comprehensive evaluation emphasizes the critical role of feature selection, feature  based‐interpretability and model choice in attaining an effective classifier for SMC diagnosis. The inclusion of statistical analyses further reinforces the credibility of the results, affirming the rigor of this analysis. The performance measures exhibit the suitability of this framework in aiding researchers with the automated and accurate assessment of SMC. The source codes of the algorithms and datasets used in this study are available at https://github.com/mtanveer1/SMC.

This article is categorized under:

An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.

Cerebellar white and gray matter abnormalities in temporal lobe epilepsy: a voxel-based morphometry study

Background

Previous structural neuroimaging studies linked cerebellar deficits to temporal lobe epilepsy (TLE). The functions of various cerebellar regions are increasingly being valued, and their changes in TLE patients warrant further in-depth investigation. In this study, we used the Spatially Unbiased Infratentorial (SUIT) toolbox with a new template to evaluate the cerebellar structural abnormalities in patients with TLE, and further explored the relationship between the changes of different cerebellar regions and cognition.

Methods

Thirty-two patients with TLE were compared with 39 healthy controls (HC) matched according to age, gender, handedness, and education level. All participants underwent a high-resolution T1-weighted MRI scan on a 3.0 Tesla scanner. We used a voxel-based morphometry (VBM) approach utilizing the SUIT toolbox to provide an optimized and fine-grained exploration of cerebellar structural alterations associated with TLE.

Results

Compared with HC, TLE patients showed a significant reduction in the volume of gray matter in the Left lobule VI and white matter in the Right Crus II. In the TLE patient group, we conducted partial correlation analysis between the volumes of different cerebellar regions and cognitive rating scale scores, such as MMSE and MoCA. The volume of the Left lobule VI (GM) exhibited a positive correlation with the MMSE score, but no significant correlation was found with the MoCA score. On the other hand, there was no significant correlation observed between the volume of the Right Crus II (WM) and the two cognitive scale scores mentioned above. Furthermore, it was observed that the MMSE was more effective than the MoCA in identifying epilepsy patients with cognitive impairment.

Conclusion

This study supported previous research indicating that temporal lobe epilepsy (TLE) is linked to structural changes in the cerebellum, specifically affecting the volume of both gray and white matter. These findings offer valuable insights into the neurobiology of TLE and hold potential to inform the development of enhanced diagnostic methods and more effective treatment approaches.

APOE, Immune Factors, Sex, and Diet Interact to Shape Brain Networks in Mouse Models of Aging

Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic, fixed and modifiable risk factors influence susceptibility to AD are under intense investigation, yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear. To model multiple risk factors including APOE genotype, age, sex, diet, and immunity we leveraged mice expressing the human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. Employing graph analyses of brain connectomes derived from accelerated diffusion-weighted MRI, we assessed the global and local impact of risk factors in the absence of AD pathology. Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.

Significance Statement

Current interventions for Alzheimer's disease (AD) do not provide a cure, and are delivered years after neuropathological onset. Addressing the impact of risk factors on brain networks holds promises for early detection, prevention, and revealing putative therapeutic targets at preclinical stages. We utilized six mouse models to investigate the impact of factors, including APOE genotype, age, sex, immunity, and diet, on brain networks. Large structural connectomes were derived from high resolution compressed sensing diffusion MRI. A highly parallelized graph classification identified subnetworks associated with unique risk factors, revealing their network fingerprints, and a common network composed of 63 connections with shared vulnerability to all risk factors. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles.

Cerebellum in Alzheimer's disease and other neurodegenerative diseases: an emerging research frontier

The cerebellum is crucial for both motor and nonmotor functions. Alzheimer's disease (AD), alongside other dementias such as vascular dementia (VaD), Lewy body dementia (DLB), and frontotemporal dementia (FTD), as well as other neurodegenerative diseases (NDs) like Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and spinocerebellar ataxias (SCA), are characterized by specific and non-specific neurodegenerations in central nervous system. Previously, the cerebellum's significance in these conditions was underestimated. However, advancing research has elevated its profile as a critical node in disease pathology. We comprehensively review the existing evidence to elucidate the relationship between cerebellum and the aforementioned diseases. Our findings reveal a growing body of research unequivocally establishing a link between the cerebellum and AD, other forms of dementia, and other NDs, supported by clinical evidence, pathological and biochemical profiles, structural and functional neuroimaging data, and electrophysiological findings. By contrasting cerebellar observations with those from the cerebral cortex and hippocampus, we highlight the cerebellum's distinct role in the disease processes. Furthermore, we also explore the emerging therapeutic potential of targeting cerebellum for the treatment of these diseases. This review underscores the importance of the cerebellum in these diseases, offering new insights into the disease mechanisms and novel therapeutic strategies.

Telomere length and micronuclei trajectories in APP/PS1 mouse model of Alzheimer's disease: Correlating with cognitive impairment and brain amyloidosis in a sexually dimorphic manner

Although studies have demonstrated that genome instability is accumulated in patients with Alzheimer's disease (AD), the specific types of genome instability linked to AD pathogenesis remain poorly understood. Here, we report the first characterization of the age- and sex-related trajectories of telomere length (TL) and micronuclei in APP/PS1 mice model and wild-type (WT) controls (C57BL/6). TL was measured in brain (prefrontal cortex, cerebellum, pituitary gland, and hippocampus), colon and skin, and MN was measured in bone marrow in 6- to 14-month-old mice. Variation in TL was attributable to tissue type, age, genotype and, to a lesser extent, sex. Compared to WT, APP/PS1 had a significantly shorter baseline TL across all examined tissues. TL was inversely associated with age in both genotypes and TL shortening was accelerated in brain of APP/PS1. Age-related increase of micronuclei was observed in both genotypes but was accelerated in APP/PS1. We integrated TL and micronuclei data with data on cognition performance and brain amyloidosis. TL and micronuclei were linearly correlated with cognition performance or Aβ40 and Aβ42 levels in both genotypes but to a greater extent in APP/PS1. These associations in APP/PS1 mice were dominantly driven by females. Together, our findings provide foundational knowledge to infer the TL and micronuclei trajectories in APP/PS1 mice during disease progression, and strongly support that TL attrition and micronucleation are tightly associated with AD pathogenesis in a female-biased manner.

Neural Underpinnings of Learning in Dementia Populations: A Review of Motor Learning Studies Combined with Neuroimaging

The intent of this review article is to serve as an overview of current research regarding the neural characteristics of motor learning in Alzheimer disease (AD) as well as prodromal phases of AD: at-risk populations, and mild cognitive impairment. This review seeks to provide a cognitive framework to compare various motor tasks. We will highlight the neural characteristics related to cognitive domains that, through imaging, display functional or structural changes because of AD progression. In turn, this motivates the use of motor learning paradigms as possible screening techniques for AD and will build upon our current understanding of learning abilities in AD populations.

Human Cerebellum Gangliosides: A Comprehensive Analysis by Ion Mobility Tandem Mass Spectrometry

The human cerebellum is an ultraspecialized region of the brain responsible for cognitive functions and movement coordination. The fine mechanisms through which the process of aging impacts such functions are not well understood; therefore, a rigorous exploration of this brain region at the molecular level is deemed necessary. Gangliosides, sialylated glycosphingolipids, highly and specifically expressed in the human central nervous system, represent possible molecular markers of cerebellum development and aging. In this context, for a comprehensive determination of development- and age-specific components, we have conducted here a comparative profiling and structural determination of the gangliosides expressed in fetal cerebellum in two intrauterine developmental stages and aged cerebellum by ion mobility separation (IMS) mass spectrometry (MS) and tandem MS (MS/MS). Due to the high sensitivity and efficiency of separation provided by IMS MS, no less than 551 chemically distinct species were identified, which represents 4.5 times more gangliosides than ever discovered in this brain region. The detailed assessment of fetal vs aged cerebellum gangliosidome showed marked discrepancies not only in the general number of the species expressed, but also in their sialylation patterns, the modifications of the glycan core, and the composition of the ceramides. All of these characteristics are potential markers of cerebellum development and aging. The structural analysis by collision-induced dissociation (CID) documented the occurrence of GD1b (d18:1/18:0) isomer in the fetal cerebellum in the second gestational trimester, with all probability of GQ1b (t18:1/18:0) in the near-term fetus and of GQ1b (d18:1/18:0) in aged cerebellum.

Non-invasive neuromodulation of cerebello-hippocampal volume-behavior relationships

The study here explores the link between transcranial direct current stimulation (tDCS) and brain-behavior relationships. We propose that tDCS may indirectly influence the complex relationships between brain volume and behavior. We focused on the dynamics between the hippocampus (HPC) and cerebellum (CB) in cognitive processes, a relationship with significant implications for understanding memory and motor skills. Seventy-four young adults (mean age: 22±0.42 years, mean education: 14.7±0.25 years) were randomly assigned to receive either anodal, cathodal, or sham stimulation. Following stimulation, participants completed computerized tasks assessing working memory and sequence learning in a magnetic resonance imaging (MRI) environment. We investigated the statistical interaction between CB and HPC volumes. Our findings showed that individuals with larger cerebellar volumes had shorter reaction times (RT) on a high-load working memory task in the sham stimulation group. In contrast, the anodal stimulation group exhibited faster RTs during the low-load working memory condition. These RT differences were associated with the cortical volumetric interaction between CB-HPC. Literature suggests that anodal stimulation down-regulates the CB and here, those with larger volumes perform more quickly, suggesting the potential need for additional cognitive resources to compensate for cerebellar downregulation. This new insight suggests that tDCS can aid in revealing structure-function relationships, due to greater performance variability, especially in young adults. It may also reveal new targets of interest in the study of aging or in diseases where there is also greater behavioral variability.

Consensus Paper: Cerebellum and Ageing

Given the key roles of the cerebellum in motor, cognitive, and affective operations and given the decline of brain functions with aging, cerebellar circuitry is attracting the attention of the scientific community. The cerebellum plays a key role in timing aspects of both motor and cognitive operations, including for complex tasks such as spatial navigation. Anatomically, the cerebellum is connected with the basal ganglia via disynaptic loops, and it receives inputs from nearly every region in the cerebral cortex. The current leading hypothesis is that the cerebellum builds internal models and facilitates automatic behaviors through multiple interactions with the cerebral cortex, basal ganglia and spinal cord. The cerebellum undergoes structural and functional changes with aging, being involved in mobility frailty and related cognitive impairment as observed in the physio-cognitive decline syndrome (PCDS) affecting older, functionally-preserved adults who show slowness and/or weakness. Reductions in cerebellar volume accompany aging and are at least correlated with cognitive decline. There is a strongly negative correlation between cerebellar volume and age in cross-sectional studies, often mirrored by a reduced performance in motor tasks. Still, predictive motor timing scores remain stable over various age groups despite marked cerebellar atrophy. The cerebello-frontal network could play a significant role in processing speed and impaired cerebellar function due to aging might be compensated by increasing frontal activity to optimize processing speed in the elderly. For cognitive operations, decreased functional connectivity of the default mode network (DMN) is correlated with lower performances. Neuroimaging studies highlight that the cerebellum might be involved in the cognitive decline occurring in Alzheimer's disease (AD), independently of contributions of the cerebral cortex. Grey matter volume loss in AD is distinct from that seen in normal aging, occurring initially in cerebellar posterior lobe regions, and is associated with neuronal, synaptic and beta-amyloid neuropathology. Regarding depression, structural imaging studies have identified a relationship between depressive symptoms and cerebellar gray matter volume. In particular, major depressive disorder (MDD) and higher depressive symptom burden are associated with smaller gray matter volumes in the total cerebellum as well as the posterior cerebellum, vermis, and posterior Crus I. From the genetic/epigenetic standpoint, prominent DNA methylation changes in the cerebellum with aging are both in the form of hypo- and hyper-methylation, and the presumably increased/decreased expression of certain genes might impact on motor coordination. Training influences motor skills and lifelong practice might contribute to structural maintenance of the cerebellum in old age, reducing loss of grey matter volume and therefore contributing to the maintenance of cerebellar reserve. Non-invasive cerebellar stimulation techniques are increasingly being applied to enhance cerebellar functions related to motor, cognitive, and affective operations. They might enhance cerebellar reserve in the elderly. In conclusion, macroscopic and microscopic changes occur in the cerebellum during the lifespan, with changes in structural and functional connectivity with both the cerebral cortex and basal ganglia. With the aging of the population and the impact of aging on quality of life, the panel of experts considers that there is a huge need to clarify how the effects of aging on the cerebellar circuitry modify specific motor, cognitive, and affective operations both in normal subjects and in brain disorders such as AD or MDD, with the goal of preventing symptoms or improving the motor, cognitive, and affective symptoms.

A multimodal submillimeter MRI atlas of the human cerebellum

The human cerebellum is engaged in a broad array of tasks related to motor coordination, cognition, language, attention, memory, and emotional regulation. A detailed cerebellar atlas can facilitate the investigation of the structural and functional organization of the cerebellum. However, existing cerebellar atlases are typically limited to a single imaging modality with insufficient characterization of tissue properties. Here, we introduce a multifaceted cerebellar atlas based on high-resolution multimodal MRI, facilitating the understanding of the neurodevelopment and neurodegeneration of the cerebellum based on cortical morphology, tissue microstructure, and intra-cerebellar and cerebello-cerebral connectivity.

Digging into the intrinsic capacity concept: Can it be applied to Alzheimer's disease?

Historically, aging research has largely centered on disease pathology rather than promoting healthy aging. The World Health Organization's (WHO) policy framework (2015-2030) underscores the significance of fostering the contributions of older individuals to their families, communities, and economies. The WHO has introduced the concept of intrinsic capacity (IC) as a key metric for healthy aging, encompassing five primary domains: locomotion, vitality, sensory, cognitive, and psychological. Past AD research, constrained by methodological limitations, has focused on single outcome measures, sidelining the complexity of the disease. Our current scientific milieu, however, is primed to adopt the IC concept. This is due to three critical considerations: (I) the decline in IC is linked to neurocognitive disorders, including AD, (II) cognition, a key component of IC, is deeply affected in AD, and (III) the cognitive decline associated with AD involves multiple factors and pathophysiological pathways. Our study explores the application of the IC concept to AD patients, offering a comprehensive model that could revolutionize the disease's diagnosis and prognosis. There is a dearth of information on the biological characteristics of IC, which are a result of complex interactions within biological systems. Employing a systems biology approach, integrating omics technologies, could aid in unraveling these interactions and understanding IC from a holistic viewpoint. This comprehensive analysis of IC could be leveraged in clinical settings, equipping healthcare providers to assess AD patients' health status more effectively and devise personalized therapeutic interventions in accordance with the precision medicine paradigm. We aimed to determine whether the IC concept could be extended from older individuals to patients with AD, thereby presenting a model that could significantly enhance the diagnosis and prognosis of this disease.

Pathology-specific patterns of cerebellar atrophy in neurodegenerative disorders

INTRODUCTION

Associations of cerebellar atrophy with specific neuropathologies in Alzheimer's disease and related dementias (ADRD) have not been systematically analyzed. This study examined cerebellar gray matter volume across major pathological subtypes of ADRD.

METHODS

Cerebellar gray matter volume was examined using voxel-based morphometry in 309 autopsy-proven ADRD cases and 80 healthy controls. ADRD subtypes included AD, mixed Lewy body disease and AD (LBD-AD), and frontotemporal lobar degeneration (FTLD). Clinical function was assessed using the Clinical Dementia Rating (CDR) scale.

RESULTS

Distinct patterns of cerebellar atrophy were observed in all ADRD subtypes. Significant cerebellar gray matter changes appeared in the early stages of most subtypes and the very early stages of AD, LBD-AD, FTLD-TDP type A, and progressive supranuclear palsy. Cortical atrophy positively predicted cerebellar atrophy across all subtypes.

DISCUSSION

Our findings establish pathology-specific profiles of cerebellar atrophy in ADRD and propose cerebellar neuroimaging as a non-invasive biomarker for differential diagnosis and disease monitoring.

HIGHLIGHTS

Cerebellar atrophy was examined in 309 patients with autopsy-proven neurodegeneration. Distinct patterns of cerebellar atrophy are found in all pathological subtypes of Alzheimer's disease and related dementias (ADRD). Cerebellar atrophy is seen in early-stage (Clinical Dementia Rating [CDR] ≤1) AD, Lewy body dementia (LBD), frontotemporal lobar degeneration with tau-positive inclusion (FTLD-tau), and FTLD-transactive response DNA binding protein (FTLD-TDP). Cortical atrophy positively predicts cerebellar atrophy across all neuropathologies.

Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection

Black-box deep learning (DL) models trained for the early detection of Alzheimer's Disease (AD) often lack systematic model interpretation. This work computes the activated brain regions during DL and compares those with classical Machine Learning (ML) explanations. The architectures used for DL were 3D DenseNets, EfficientNets, and Squeeze-and-Excitation (SE) networks. The classical models include Random Forests (RFs), Support Vector Machines (SVMs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), Decision Trees (DTs), and Logistic Regression (LR). For explanations, SHapley Additive exPlanations (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (GradCAM), GradCAM++ and permutation-based feature importance were implemented. During interpretation, correlated features were consolidated into aspects. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The validation includes internal and external validation on the Australian Imaging and Lifestyle flagship study of Ageing (AIBL) and the Open Access Series of Imaging Studies (OASIS). DL and ML models reached similar classification performances. Regarding the brain regions, both types focus on different regions. The ML models focus on the inferior and middle temporal gyri, and the hippocampus, and amygdala regions previously associated with AD. The DL models focus on a wider range of regions including the optical chiasm, the entorhinal cortices, the left and right vessels, and the 4th ventricle which were partially associated with AD. One explanation for the differences is the input features (textures vs. volumes). Both types show reasonable similarity to a ground truth Voxel-Based Morphometry (VBM) analysis. Slightly higher similarities were measured for ML models.

Long-term suboptimal dietary trace element supply does not affect trace element homeostasis in murine cerebellum

The ageing process is associated with alterations of systemic trace element (TE) homeostasis increasing the risk, e.g. neurodegenerative diseases. Here, the impact of long-term modulation of dietary intake of copper, iron, selenium, and zinc was investigated in murine cerebellum. Four- and 40-wk-old mice of both sexes were supplied with different amounts of those TEs for 26 wk. In an adequate supply group, TE concentrations were in accordance with recommendations for laboratory mice while suboptimally supplied animals received only limited amounts of copper, iron, selenium, and zinc. An additional age-adjusted group was fed selenium and zinc in amounts exceeding recommendations. Cerebellar TE concentrations were measured by inductively coupled plasma-tandem mass spectrometry. Furthermore, the expression of genes involved in TE transport, DNA damage response, and DNA repair as well as selected markers of genomic stability [8-oxoguanine, incision efficiency toward 8-oxoguanine, 5-hydroxyuracil, and apurinic/apyrimidinic sites and global DNA (hydroxy)methylation] were analysed. Ageing resulted in a mild increase of iron and copper concentrations in the cerebellum, which was most pronounced in the suboptimally supplied groups. Thus, TE changes in the cerebellum were predominantly driven by age and less by nutritional intervention. Interestingly, deviation from adequate TE supply resulted in higher manganese concentrations of female mice even though the manganese supply itself was not modulated. Parameters of genomic stability were neither affected by age, sex, nor diet. Overall, this study revealed that suboptimal dietary TE supply does not substantially affect TE homeostasis in the murine cerebellum.

Functional Connectivity Differences in Distinct Dentato-Cortical Networks in Alzheimer's Disease and Mild Cognitive Impairment

Recent research has implicated the cerebellum in Alzheimer's disease (AD), and cerebrocerebellar network connectivity is emerging as a possible contributor to symptom severity. The cerebellar dentate nucleus (DN) has parallel motor and non-motor sub-regions that project to motor and frontal regions of the cerebral cortex, respectively. These distinct dentato-cortical networks have been delineated in the non-human primate and human brain. Importantly, cerebellar regions prone to atrophy in AD are functionally connected to atrophied regions of the cerebral cortex, suggesting that dysfunction perhaps occurs at a network level. Investigating functional connectivity (FC) alterations of the DN is a crucial step in understanding the cerebellum in AD and in mild cognitive impairment (MCI). Inclusion of this latter group stands to provide insights into cerebellar contributions prior to diagnosis of AD. The present study investigated FC differences in dorsal (dDN) and ventral (vDN) DN networks in MCI and AD relative to cognitively normal participants (CN) and relationships between FC and behavior. Our results showed patterns indicating both higher and lower functional connectivity in both dDN and vDN in AD compared to CN. However, connectivity in the AD group was lower when compared to MCI. We argue that these findings suggest that the patterns of higher FC in AD may act as a compensatory mechanism. Additionally, we found associations between the individual networks and behavior. There were significant interactions between dDN connectivity and motor symptoms. However, both DN seeds were associated with cognitive task performance. Together, these results indicate that cerebellar DN networks are impacted in AD, and this may impact behavior. In concert with the growing body of literature implicating the cerebellum in AD, our work further underscores the importance of investigations of this region. We speculate that much like in psychiatric diseases such as schizophrenia, cerebellar dysfunction results in negative impacts on thought and the organization therein. Further, this is consistent with recent arguments that the cerebellum provides crucial scaffolding for cognitive function in aging. Together, our findings stand to inform future clinical work in the diagnosis and understanding of this disease.