Alzheimer’s disease currently has no cure and is usually detected too late for interventions to be effective. In this study we have focused on cognitively normal subjects to study the impact of risk factors on their long-range brain connections. To detect vulnerable connections, we devised a multiscale, hierarchical method for spatial clustering of the whole brain tractogram and examined the impact of age and APOE allelic variation on cognitive abilities and bundle properties including texture e.g., mean fractional anisotropy, variability, and geometric properties including streamline length, volume, shape, as well as asymmetry. We found that the third level subdivision in the bundle hierarchy provided the most sensitive ability to detect age and genotype differences associated with risk factors. Our results indicate that frontal bundles were a major age predictor, while the occipital cortex and cerebellar connections were important risk predictors that were heavily genotype dependent, and showed accelerated decline in fractional anisotropy, shape similarity, and increased asymmetry. Cognitive metrics related to olfactory memory were mapped to bundles, providing possible early markers of neurodegeneration. In addition, physiological metrics associated with cardiovascular disease risk were associated with changes in white matter tracts. Our novel method for a data driven analysis of sensitive changes in tractography may differentiate populations at risk for AD and isolate specific vulnerable networks.

Alzheimer's disease (AD) is multifactorial, thus multivariate analyses help untangle its effects. We employed multiple contrast MRI to reveal age-related brain changes in populations at risk for AD, due to APOE4 carriage. We assessed volume and microstructure changes using diffusion weighted imaging, and quantitative magnetic susceptibility maps (QSM) reflective primarily of cerebral iron metabolism. Our study included 48 non APOE4 carriers, and 42 APOE4 carriers, with age ranging from 20.2 to 83; males and females. Imaging was done at 3T using diffusion weighted imaging (DWI) with TE 60.6 ms, TR=14725 ms, 21 diffusion directions (b=1000 s/mm2), reconstructed at 1 mm isotropic resolution using MUSE. A T1-weighted FSPGR sequence was used for morphometry and co-registration, TE=3.2 ms, TR=2263.7 ms, α=8°, prep time 900 ms, recovery time 700 ms, 1 mm isotropic resolution. QSM were calculated using STISuite based on fGRE images using TEeff=24.3 ms, TR 100 ms, α=15°, 8 echoes, reconstructed at 1x1x1.5 mm. Images were mapped into a common space before univariate and multivariate voxel-based analyses (VBA). Univariate VBA (Figure 1) showed accentuated age effects in APOE4 carriers in areas involved in visual information processing (pericalcarine and lingual cortex, precuneus); as well as in cognitive and motor control processes (superior frontal cortex). FA supported the findings in visual processing areas, and added regions in the left temporal lobe (entorhinal cortex, middle and inferior-temporal cortices). FA also revealed a role for the anterior cingulate, insula, left cerebellum; pars triangularis and pars orbitalis, involved in language processing. QSM analyses supported the findings in visual, cingulate, and cerebellar cortices, and revealed a role for the hippocampus, amygdala, and basal ganglia (caudate, putamen, pallidum). Multivariate analyses (Figure 2) supported a role for the precuneus, cuneus, and lingual cortex, the inferior temporal and cingulate cortices, and the caudate. The canonical vectors (Figure 3) for the top significant clusters revealed an important role for QSM. Our results suggest accelerated changes in multiple domains besides memory in aging APOE4 carriers. Multivariate analyses supported an important role for age associated changes in cerebral iron metabolism, and oxidative stress, suggesting increased vulnerability to toxic insults.

Olfactory impairment is a hallmark of early Alzheimer's disease (AD), but the underlying mechanisms connecting sensory decline to genetic and environmental risk factors remain unclear. Our integrative analysis combines ethologically relevant olfactory behavior assays, high-resolution diffusion MRI connectomics, and blood transcriptomics in a large cohort of humanized APOE mice stratified by APOE genotype (APOE2, APOE3, APOE4), age, sex, high-fat diet, and immune background (HN). Behaviorally, APOE4 mice exhibited accelerated deficits in odor salience, novelty detection, and memory, especially when exposed to a high-fat diet, whereas APOE2 mice showed resilience (ANOVA: APOE x HN, F(2,1669)=77.25, p<0.001, eta squared effect size = 0.08). Notably, age and diet exerted compounding effects, with older and HFD-fed mice displaying reduced odor-guided exploration (diet x age: F(1,1669)=16.04, p<0.001, eta squared effect size = 0.01). Memory analyses revealed robust genotype- and age-dependent impairments: at 24- and 48-hour delays, recognition indices were significantly lower in APOE4 mice compared to APOE2 (long-term memory: APOE x HN, F(2,395)=5.6, p=0.004). Elastic Net-regularized multi-set canonical correlation analysis (MCCA) linked behavior to brain network substrates, revealing subnetworks whose connectivity explained up to 24 percent of behavioral variance (sum of canonical correlations: 1.27, 95% CI [1.18, 1.85], p<0.0001). High-weighted connections between the ventral orbital cortex, somatosensory cortex, and cerebellar-brainstem pathways were identified as critical nodes for risk or compensation. Integrative blood transcriptomics revealed eigengene modules strongly correlated with imaging changes in olfactory-memory circuits (for example, eigengene 2 vs. subiculum diffusivity: r = -0.5, p < 1e-30, explaining up to 24 percent of variance). Gene ontology analysis pinpointed shared pathways in synaptic signaling, translation, and metabolic regulation across brain and blood. Notably, glutamatergic and synaptic pathways were enriched among genes linking peripheral and central compartments. Collectively, these results demonstrate that olfactory behavior, quantitatively shaped by genotype, age, diet, and immune status, serves as a sensitive and translatable early biomarker of Alzheimer's disease risk. Our systems-level approach identifies specific brain networks and peripheral molecular signatures underlying sensory-cognitive vulnerability, providing a robust framework for early detection and targeted intervention in AD.

Alzheimer's disease (AD) lacks effective cures and is typically detected after substantial pathological changes have occurred, making intervention challenging. Alzheimer's disease (AD) intervention requires early detection of risk factors and understanding their complex interactions before substantial pathological changes manifest. Current research often examines individual risk factors in isolation, limiting our understanding of their combined effects. We present a novel multivariate analytical framework to simultaneously assess multiple AD risk factors using mouse models expressing human ApoE alleles. Our methodological innovation lies in combining high-resolution magnetic resonance diffusion imaging with a comprehensive multifactorial analysis that integrates genotype, age, sex, diet, and immunity as interacting variables. This approach enables the simultaneous examination of regional brain volume and fractional anisotropy changes across multiple risk factors, providing a more holistic view than traditional univariate analyses. Our proposed method effectively identified how these factors converge on specific brain regions - with genotype influencing the caudate putamen, pons, cingulate cortex, and cerebellum; sex affecting the amygdala and piriform cortex; and immune status impacting association cortices and cerebellar nuclei. Importantly, our integrated approach revealed factor interactions that would remain undetected in single-variable studies, particularly in the amygdala, thalamus, and pons. While many findings align with previous research, our multidimensional framework offers a methodological advancement for studying AD risk factors by modeling their combined effects rather than isolated impacts. This approach creates a template for future studies to investigate mechanisms underlying coordinated changes in brain structure through network analyses of gene expression, metabolism, and structural pathways involved in neurodegeneration.

While we do not yet have the means to detect early Alzheimer’s disease (AD), studying subjects at risk conferred by the presence of the APOE4 allele, can provide useful information before clinical onset. We show that using symmetric bilinear regression with L1 penalty (SBL) of individual (DTI, fMRI) and fused connectomes, we can identify vulnerable regions changing in association with hallmark AD biomarkers measured in cerebrospinal fluid: amyloid beta Aβ42/40, phosphorylated tau (PTAU), and neurofibrillary light (NfL) as a proxy for neurodegeneration. We use structural connectomes derived from diffusion-weighted MRI (DTI) and functional connectomes (fMRI) from 57 subjects, 45 normal controls and 12 cognitively impaired to predict CSF Aβ42/40, PTAU, and NfL to reflect neurodegeneration. To fuse connectomes, we first ranked them and then used the SNFtool. We run SBL for the three types of connectomes (DTI, fMRI, and fusion) and the three biomarkers. We compared the models using a k-fold cross-validations scheme and standardized root mean square errors (SRMSE) by dividing RMSE by the standard deviation of the biomarker outcome. Linear modeling approaches showed that APOE is a significant factor with respect to NfL biomarker measures and that the interaction between APOE and age, APOE and sex, sex and age, were significant for PTAU (Figure 1). The SBL results showed that the best predictions were for NfL, PTAU and Aβ ratio based on DTI connectomes (Table 1.) The common sub-network across biomarkers via DTI connectomes included the right inferior parietal, inferior temporal, superior parietal and caudal middle frontal, postcentral; and left superior frontal, right precentral, right superior frontal. The common connections across biomarkers via fMRI included the right putamen, left frontal pole and left accumbens, and right transversetemporal gyrus (Figure 2). DTI stood out as the best performer in predicting key biomarkers for AD (NfL, PTAU, Ab ratio). This research enhances our understanding of vulnerable brain networks, including in subjects at risk. Future work will explore strategies for data fusion to preserve common and unique contributions from multiple data sources.

ABSTRACT Objective Possible pleiotropic effects of apolipoprotein E4 (APOE E4) in individuals with congenital brain malformations are relatively unknown. Our goal was to determine if neurodegeneration‐linked brain region volumes differ significantly between E4 carriers and noncarriers in young adults with spina bifida (SB). Methods Eleven individuals ( > 18 years), genotyped for APOE, underwent neuroimaging and neurocognitive evaluation. Primary analysis: Magnetic resonance imaging (MRI) data from 10 a priori neurodegeneration‐risk regions of interest were compared between E4 carriers and noncarriers, adjusting for age, sex, and total intracranial volume (FDR‐adjusted p < 0.05). Secondary analyses: Age‐adjusted neurocognitive standard scores were compared between groups (p < 0.05). Post hoc analyses of NeuroQuant‐derived regional brain volumes were examined for combined group differences in young adults with SB. Results Comparison of a priori risk region volumes revealed significantly lower left amygdala volumes (FDR‐adjusted p = 0.04) in young adult E4 carriers (n = 4) relative to noncarriers (n = 7). Neurocognitive data were not significantly different between the groups. A possible trend was detected for enlarged parietal volumes in E4 carriers (p = 0.07), while volumetric extremes ( > 95% or < 5%) were detected for the anterior cingulate (100% of cases; p = 0.001), frontal cortices (90% of cases), hippocampus (80% of cases), and entorhinal cortices (70% of cases). Interpretation Early left amygdala volumetric reduction was found in E4 carriers; combined group volume comparisons revealed frontal and temporal lobe differences in young adults with SB relative to age‐ and sex‐matched volumetric estimates. This pilot investigation does not appear to support E4 conferring a pleiotropic benefit in young adults with SB but rather supports further investigation of MRI volumetrics as a possible biomarker for this population.

Exercise is a promising strategy for preventing or delaying Alzheimer's disease (AD), yet its mechanisms remain unclear. We investigated how exercise influences brain structure, function, and behavior in a familial AD model. Mice underwent voluntary, voluntary plus enforced exercise, or remained sedentary. Neuroimaging included in vivo manganese-enhanced MRI (MEMRI). perfusion, and ex vivo diffusion MRI to assess morphometry, activity, cerebral blood flow (CBF), microstructural integrity and connectivity.Both exercise regimens induced structural and functional brain adaptations while reducing anhedonia. Voluntary exercise increased cortical and limbic volumes, particularly in the hippocampus, cingulate, and entorhinal cortex, supporting cognitive and emotional regulation. Adding enforced exercise influenced subcortical and sensory regions, including visual, motor and associative areas, supporting sensory-motor integration. MEMRI revealed increased activity in sensorimotor, limbic, and associative cortices, with voluntary exercise enhancing limbic and associative regions, and enforced exercise strengthening sensorimotor and subcortical circuits.White matter integrity improved in memory-associate pathways such as the corpus callosum, cingulum, and hippocampal commissure. Synaptic remodeling was observed in the cingulate cortex, anterior thalamic nuclei, and amygdala. Voluntary exercise enhanced CBF in the motor cortex and hippocampus, while enforced exercise limited these increases.Connectivity analyses revealed exercise-responsive networks spanning the cingulate cortex, entorhinal cortex, anterior thalamic nuclei, and basolateral amygdala, and associated tracts. Graph analyses linked running distance with increased thalamic, brainstem, and cerebellar connectivity, associating exercise intensity with plasticity.These findings highlight the ability of chronic exercise to modulate neuroimaging biomarkers through distinct but complementary pathways, reinforcing its potential as a neuroprotective intervention for AD.

Background/Objectives: Olfactory impairment has been proposed as an early marker for Alzheimer’s disease (AD), yet the mechanisms linking sensory decline to genetic and environmental risk factors remain unclear. We aimed to identify early biomarkers and brain network alterations associated with AD risk by multimodal analyses in humanized APOE mice. Methods: We evaluated olfactory behavior, diffusion MRI connectomics, and brain and blood transcriptomics in mice stratified by APOE2, APOE3, and APOE4 genotypes, age, sex, high-fat diet, and immune background (HN). Behavioral assays assessed odor salience, novelty detection, and memory. Elastic Net-regularized multi-set canonical correlation analysis (MCCA) was used to link behavior to brain connectivity. Blood transcriptomics and gene ontology analyses identified peripheral molecular correlates. Results: APOE4 mice exhibited accelerated deficits in odor-guided behavior and memory, especially under high-fat diet, while APOE2 mice were more resilient (ANOVA: APOE x HN, F(2, 1669) = 77.25, p < 0.001, eta squared = 0.08). Age and diet compounded behavioral impairments (diet x age: F(1, 1669) = 16.04, p < 0.001). Long-term memory was particularly reduced in APOE4 mice (APOE x HN, F(2,395) = 5.6, p = 0.004). MCCA identified subnetworks explaining up to 24% of behavioral variance (sum of canonical correlations: 1.27, 95% CI [1.18, 1.85], p < 0.0001), with key connections involving the ventral orbital and somatosensory cortices. Blood eigengene modules correlated with imaging changes (e.g., subiculum diffusivity: r = −0.5, p < 1 × 10−30), and enriched synaptic pathways were identified across brain and blood. Conclusions: Olfactory behavior, shaped by genetic and environmental factors, may serve as a sensitive, translatable biomarker of AD risk. Integrative systems-level approaches reveal brain and blood signatures of early sensory–cognitive vulnerability, supporting new avenues for early detection and intervention in AD.

Background Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer’s disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT. Methods We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex. Results Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD. Conclusions This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, is influenced by an intricate mix of risk factors including age, genetics, and environmental variables. In our study, we employed mouse models with human APOE alleles and nitric oxide synthase 2, and adjusted environmental factors like diet, to replicate controlled genetic risk and innate immune response associated with AD in human subjects. We utilized a Feature Attention Graph Neural Network (FAGNN), integrating brain structural connectomes, genetic traits, environmental factors, and behavioral data, to estimate brain age. Our method demonstrated improved accuracy in age prediction over other methods and highlighted age-associated brain connections. The most impactful connections included the cingulum, striatum, corpus callosum, and hippocampus. We further investigated these findings through fractional anisotropy in different age groups of mice, and our results underlined the significance of white matter degradation in aging. Our results underscore the effectiveness of integrative graph neural networks in predicting brain age and delineating important neural connections associated with brain aging.

Brain region segmentation and morphometry in mouse models of Alzheimer's Disease (AD) risk allow us to understand how various factors affect the brain. Photon-Counting Detector (PCD) micro-CT can provide faster brain imaging than MRI and superior contrast and spatial resolution to Energy-Integrating Detector (EID) micro-CT. This paper demonstrates a PCD micro-CT based approach for mouse brain imaging, segmentation, and morphometry. We extracted and stained the brains of 26 mice from three genotypes (APOE22HN, APOE33HN, APOE44HN). We scanned these brains with PCD and EID micro-CT, performed hybrid (PCD and EID) iterative reconstruction, and used the Small Animal Multivariate Brain Analysis (SAMBA) tool to segment the brains via registration to our new PCD CT mouse brain atlas. We used the outputs of SAMBA to run region-based and voxel-based comparisons by genotype and sex. Together, PCD and EID scanning take approximately five hours and produce images with a voxel size of 22 μm, which is faster than prior MRI protocols that produce images with a voxel size above 40 μm. PCD images from hybrid iterative reconstruction have minimal artifacts and higher spatial resolution and contrast than EID images. Qualitative and quantitative analyses confirmed that our PCD atlas is similar to the prior MRI atlas and that it successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant difference in relative size in 26 brain regions. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus. This study successfully establishes a PCD micro-CT approach for mouse brain analysis that can be used for future AD research.

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.

APOE allelic variation is critical in brain aging and Alzheimer’s disease (AD). The APOE2 allele associated with cognitive resilience and neuroprotection against AD remains understudied. We employed a multipronged approach to characterize the transition from middle to old age in mice with APOE2 allele, using behavioral assessments, image-derived morphometry and diffusion metrics, structural connectomics, and blood transcriptomics. We used sparse multiple canonical correlation analyses (SMCCA) for integrative modeling, and graph neural network predictions. Our results revealed brain sub-networks associated with biological traits, cognitive markers, and gene expression. The cingulate cortex emerged as a critical region, demonstrating age-associated atrophy and diffusion changes, with higher fractional anisotropy in males and middle-aged subjects. Somatosensory and olfactory regions were consistently highlighted, indicating age-related atrophy and sex differences. The hippocampus exhibited significant volumetric changes with age, with differences between males and females in CA3 and CA1 regions. SMCCA underscored changes in the cingulate cortex, somatosensory cortex, olfactory regions, and hippocampus in relation to cognition and blood-based gene expression. Our integrative modeling in aging APOE2 carriers revealed a central role for changes in gene pathways involved in localization and the negative regulation of cellular processes. Our results support an important role of the immune system and response to stress. This integrative approach offers novel insights into the complex interplay among brain connectivity, aging, and sex. Our study provides a foundation for understanding the impact of APOE2 allele on brain aging, the potential for detecting associated changes in blood markers, and revealing novel therapeutic intervention targets.

Age-related macular degeneration (AMD) has recently been linked to cognitive impairment. We hypothesized that AMD modifies the brain aging trajectory, and we conducted a longitudinal diffusion MRI study on 40 participants (20 with AMD and 20 controls) to reveal the location, extent, and dynamics of AMD-related brain changes. Voxel-based analyses at the first visit identified reduced volume in AMD participants in the cuneate gyrus, associated with vision, and the temporal and bilateral cingulate gyrus, linked to higher cognition and memory. The second visit occurred 2 years after the first and revealed that AMD participants had reduced cingulate and superior frontal gyrus volumes, as well as lower fractional anisotropy (FA) for the bilateral occipital lobe, including the visual and the superior frontal cortex. We detected faster rates of volume and FA reduction in AMD participants in the left temporal cortex. We identified inter-lingual and lingual–cerebellar connections as important differentiators in AMD participants. Bundle analyses revealed that the lingual gyrus had a lower streamline length in the AMD participants at the first visit, indicating a connection between retinal and brain health. FA differences in select inter-lingual and lingual cerebellar bundles at the second visit showed downstream effects of vision loss. Our analyses revealed widespread changes in AMD participants, beyond brain networks directly involved in vision processing.

Alzheimer's disease (AD), a widely studied neurodegenerative disorder, poses significant research challenges due to its high prevalence and complex etiology. Age, a critical risk factor for AD, is typically assessed by comparing physiological and estimated brain ages. This study utilizes mouse models expressing human alleles of APOE and human nitric oxide synthase 2 (hNOS2), replicating genetic risks for AD alongside a human-like immune response. We developed a multivariate model that incorporates brain structural connectomes, APOE genotypes, demographic traits (age and sex), environmental factors such as diet, and behavioral data to estimate brain age. Our methodology employs a Feature Attention Graph Neural Network (FAGNN) to integrate these diverse datasets. Behavioral data are processed using a 2D convolutional neural network (CNN), demographic traits via a 1D CNN, and brain connectomes through a graph neural network equipped with a quadrant attention module that accentuates critical neural connections. The FAGNN model demonstrated a mean absolute error in age prediction of 31.85 days and a root mean squared error of 41.84 days, significantly outperforming simpler models. Our analysis further focused on the brain age delta, which assesses accelerated or delayed aging by comparing brain age, predicted by FAGNN, to the chronological age. A high-fat diet and the presence of the humanNOS2gene were identified as significant accelerators of brain aging in the old age group. Key neural connections identified by FAGNN, such as those between the cingulum, corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex, were found to be significant in the aging process. Validation using diffusion MRI-based metrics, including fractional anisotropy and return-to-origin probability measures across these connections, revealed significant age-related differences. These findings suggest that white matter degradation in the connections highlighted by FAGNN plays a key role in aging. Our findings suggest that the complex interplay of APOE genotype with sex, immunity, and environmental factors modulates brain aging and enhance our understanding of AD risk 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 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.

Brain networks and covariates for mouse models of aging. Includes APOE22/33/44 with and without HN, age, sex, and diet. Files: - connectomes.rda: a tensor of symmetric adjacency matrices corresponding to brain networks. - mice.rda: a dataframe containing mouse covariates. - mouse_anatomy.csv: a table containing scientific names for each brain region in connectomes.rda.

The selective vulnerability of brain networks in individuals at risk for Alzheimer’s disease (AD) may help differentiate pathological from normal aging at asymptomatic stages, allowing the implementation of more effective interventions. We used a sample of 72 people across the age span, enriched for the APOE4 genotype to reveal vulnerable networks associated with a composite AD risk factor including age, genotype, and sex. Sparse canonical correlation analysis (CCA) revealed a high weight associated with genotype, and subgraphs involving the cuneus, temporal, cingulate cortices, and cerebellum. Adding cognitive metrics to the risk factor revealed the highest cumulative degree of connectivity for the pericalcarine cortex, insula, banks of the superior sulcus, and the cerebellum. To enable scaling up our approach, we extended tensor network principal component analysis, introducing CCA components. We developed sparse regression predictive models with errors of 17% for genotype, 24% for family risk factor for AD, and 5 years for age. Age prediction in groups including cognitively impaired subjects revealed regions not found using only normal subjects, i.e. middle and transverse temporal, paracentral and superior banks of temporal sulcus, as well as the amygdala and parahippocampal gyrus. These modeling approaches represent stepping stones towards single subject prediction.

Brain connectomes provide untapped potential for identifying individuals at risk for Alzheimer’s disease (AD), and can help provide novel targets based on selective circuit vulnerability. Age, APOE4 genotype, and female sex are thought to contribute to the selective vulnerability of brain networks in Alzheimer’s disease, in a manner that differentiates pathological versus normal aging. These brain networks may predict pathology otherwise hard to detect, decades before overt disease manifestation and cognitive decline. Uncovering network based biomarkers at prodromal, asymptomatic stages may offer new windows of opportunity for interventions, either therapeutic or preventive. We used a sample of 72 people across the age span to model the relationship between Alzheimer’s disease risk and vulnerable brain networks. Sparse Canonical Correlation analysis (SCCA) revealed relationships between brain subgraphs and AD risk, with bootstrap based confidence intervals. When constructing a composite AD risk factor based on sex, age, genotype, the highest weight was associated with genotype. Next, we mapped networks associated with auditory, visual, and olfactory memory, and identified networks extending beyond the main nodes known to be involved in these functions. The inclusion of cognitive metrics in a composite risk factor pointed to vulnerable networks, and associated with the specific memory tests. These regions with the highest cumulative degree of connectivity in our studies were the pericalcarine, insula, banks of the superior sulcus and cerebellum. To help scale up our approach, we extended Tensor Network Principal Component Analysis (TNPCA) to evaluate AD risk related subgraphs, introducing CCA components and sparsity. When constructing a composite AD risk factor based on sex, age, and genotype, and family risk factor the most significant risk was associated with age. Our sparse regression based predictive models revealed vulnerable networks associated with known risk factors. The prediction error was 17% for genotype, 24% for family risk factor, and 5 years for age. Age prediction in groups including MCI and AD subjects involved several regions that were not prominent for age prediction otherwise. These regions included the middle and transverse temporal, paracentral and superior banks of temporal sulcus, as well as the amygdala and parahippocampal gyrus. The joint estimation of AD risk and connectome based mappings involved the cuneus, temporal, and cingulate cortices known to be associated with AD, and add new candidates, such as the cerebellum, whose role in AD is to be understood. Our predictive modeling approaches for AD risk factors represent a stepping stone towards single subject prediction, based on distances from normative graphs.