Vascular risk factors (VRF) exert deleterious effects on the vasculature throughout the body, including cerebrovasculature. These effects are characterized by structural and functional alterations in the cerebrovasculature, which in turn, may impact white matter (WM) microstructure. We sought to determine the relative contributions of VRF to alterations in WM tract microstructure in a large, multi-study cohort using diffusion tensor imaging (DTI). We collated datasets from the Vanderbilt Memory and Aging Project (VMAP) and several Alzheimer's Disease (AD) Sequencing Project-Phenotype Harmonization Consortium (ADSP-PHC) cohorts, including diffusion magnetic resonance imaging and VRF ( n  = 1,304, 72.3±9.5 years, 53% female). ADSP-PHC cohorts included in this study were the AD Neuroimaging Initiative (ADNI), the National Alzheimer's Coordinating Center (NACC), the Religious Orders Study/Rush Memory and Aging Project/Minority Aging Research Study (ROS/MAP/MARS), and the Wisconsin Registry of Alzheimer's Prevention (WRAP). To assess the contributions of VRF to WM microstructure, we quantified the cross-sectional association between VRF and white matter microstructure. Specifically, VRF were used to predict differences in free-water (FW) and fractional anisotropy (FA FWcorr ) across 10 AD-relevant WM tracts. VRF variables studied included hypertension, diabetes, heart disease, body mass index (BMI), and Atherosclerotic Cardiovascular Disease Framingham Risk Score (FRS). Covariates included age, sex, education, race, apolipoprotein E -ɛ4 status, and cognitive status. Models were corrected for multiple comparisons using the FDR approach. Hypertension and heart disease were significantly associated with FA FWcorr in all tracts, while FRS was a significant predictor for FA FWcorr in 9/10 tracts (top associations shown in Figure 1). Diabetes and BMI were not significant predictors for FA FWcor in any of the tracts. Hypertension was a significant predictor for FW in all tracts, and FRS was a significant predictor only in the cingulum. Diabetes, BMI, and heart disease were not significant predictors for FW in any of the tracts. This study demonstrates that VRF, especially hypertension, heart disease, and FRS, differentially impact WM tract microstructure, providing insight into their unique contributions to WM microstructure in a large multi-cohort of older adults. These findings underscore the critical importance of managing vascular risk factors to preserve white matter integrity to potentially prevent cognitive decline.

We investigate whether common circle of Willis (CoW) variants relate to cerebral blood flow (CBF) characteristics among aging adults. Vanderbilt Memory and Aging Project participants free of clinical stroke ( n = 390, 71 ± 8 years, 55% male) completed magnetic resonance angiography to assess cerebral artery variants and pseudocontinuous arterial spin labeling to quantify resting CBF and spatial coefficient of variation (sCoV), a marker of perfusion heterogeneity or stability. Linear regression models related categorical variants to regional CBF and sCoV, adjusting for age, sex, race/ethnicity, education, cognitive status, Framingham Stroke Risk Profile, and APOE -ε4 status. Absence of at least one posterior communicating artery (PcoA) related to lower occipital lobe CBF ( p = 0.001) and higher global ( p = 0.003), temporal ( p = 0.001), and occipital lobe sCoV ( p = 0.01). Bilateral PcoA absence related to lower global ( p = 0.01), parietal ( p = 0.01), and occipital lobe CBF ( p = 0.0001) and higher global ( p < 0.0001), temporal ( p < 0.0001), and occipital lobe sCoV ( p = 0.0003). Hypoplastic and fetal-type variants did not relate to outcomes ( p > 0.22). Findings demonstrate that missing PcoAs influence both perfusion magnitude and efficiency, suggesting that common CoW variants may produce regionally coherent hemodynamic effects in heart-healthy aging adults and represent markers of early cerebrovascular vulnerability.

White matter (WM) abnormalities are prevalent in aging and neurodegenerative cognitive decline. This study compares single-shell free-water (FW) imaging and multi-shell NODDI Isotropic Volume Fraction (ISOVF) in assessing WM microstructure and their associations with cognitive decline. FW and ISOVF were quantified across 48 white matter tracts in cognitively unimpaired (CU) and impaired (MCI and AD dementia) individuals from ADNI and VMAP. Multi-shell dMRI data (b=1000, 2000 s/mm2) were collated from 139 ADNI-3 participants (age=75.9 ± 6.7; 56.8% female; 92.1% NHW) and 437 VMAP participants (age=65.8 ± 9.3; 57.0% female; 78.0% NHW). Memory and executive function composites were harmonized using ComBat. Covariates included age, sex, years of education, race, clinical status, and ApoE-ε4 positivity. Analyses included linear correlations between FW and ISOVF, group comparisons, and age modeling via general linear models. Linear regression examined associations between tract-specific metrics and cognitive composites, with bootstrapped comparisons testing differences in R2adj values between FW and ISOVF models. FW and ISOVF showed strong correlations across key white matter tracts (Figure 1), particularly in transcallosal (TC) pathways, with the highest correlations in the calcarine sulcus TC (r=0.784), lingual gyrus TC (r=0.780), and ILF (r=0.775). Group-wise analyses revealed significant CU vs. MCI differences in all 48 tracts, most notably in the ILF (FW: p  = 1.05×10-15) and temporoparietal SLF (ISOVF: p  = 4.36×10-14). Age effects were significant across all 96 measures, with FW explaining marginally greater variance than ISOVF. The highest R2adj values were in the ILF for FW (54.82%) and ISOVF (47.73%). FW in the lateral (42.93%) and anterior (47.84%) orbital gyri TC best explained memory variance, while FW in the ILF (51.98%) and ISOVF in the inferior parietal lobule TC (51.59%) best explained executive function. Bootstrapped comparisons identified significant R2adj differences between FW and ISOVF in 10 tracts for memory and 14 tracts for executive function, including the ILF, SLF, UF, and superior temporal gyrus TC. Both bi-tensor FW and NODDI ISOVF showed robust associations with age and cognitive performance, varying in magnitude and tract specificity. FW and ISOVF provided largely comparable insights, highlighting their complementary value for understanding white matter health in neurodegenerative research.

Background Von Willebrand factor (VWF) and ADAMTS13 (a disintegrin and metalloproteinase with thrombospondin type 1 motif, 13) are linked to dementia risk, and limited evidence suggests apolipoprotein E (APOE)‐ε4 alters VWF release. This study assessed whether baseline VWF and ADAMTS13 levels predict neurodegeneration and cognitive decline and evaluated effect modification by APOE‐ε4 carriership. Methods Vanderbilt Memory and Aging Project cohort participants (n=332, 73±7 years, 59% male) completed serial blood draw, neuropsychological assessment, and brain magnetic resonance imaging over 6.4 years (range 1.4–9.7 years). Baseline plasma VWF and ADAMTS13 levels were quantified using mass spectrometry and Olink. Fully adjusted linear mixed‐effects models related protein×time and protein×APOE‐ε4×time interaction terms to longitudinal brain magnetic resonance imaging and neuropsychological outcomes. Results Lower baseline ADAMTS13 predicted faster declines in language (β=0.11, P=0.01), information processing speed (β=0.27, P=0.001), executive function (β=0.01, P=0.03), episodic memory (β=0.01, P=0.03), and visuospatial ability (β=0.11, P=0.001) and faster increases in global (β=−0.29, P=0.01) and frontal (β=−0.17, P=0.01) white matter hyperintensity volumes. Associations between ADAMTS13 and faster rates of cognitive decline and white matter injury were driven by APOE‐ε4 carriers. Models relating VWF to longitudinal outcomes were null. APOE‐ε4 interacted with VWF on longitudinal gray matter volumetric outcomes, such that faster rates of global gray matter atrophy were observed with higher baseline VWF levels among APOE‐ε4 noncarriers only (β=−1530.5, P<0.001). Conclusions ADAMTS13 shows promise as a potential plasma biomarker for brain aging outcomes, but additional research is warranted to understand the performance of VWF in the presence versus absence of an APOE‐ε4 allele.

INTRODUCTION:Vascular risk factors (VRFs) contribute to white matter microstructural degeneration, but their tract-specific contributions are unclear. We assessed the independent associations of four major VRFs with white matter microstructure in a large, multi-cohort study. METHODS:Diffusion magnetic resonance imaging data from five harmonized cohorts were free water (FW) corrected (n = 2961, 73.00 ± 9.27 years, 59.34% female). We associated body mass index (BMI) and presence of hypertension, diabetes, and heart disease with FW and FW-corrected diffusion metrics (fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity) in 48 tracts. RESULTS:Hypertension and heart disease showed the most robust and widespread independent associations with white matter microstructure. In contrast, diabetes associations were attenuated in sensitivity analyses, while BMI associations were inconsistent across metrics. DISCUSSION:Hypertension and heart disease are associated most strongly with white matter microstructure, suggesting that tighter management of these conditions may more reliably yield benefits in preserving white matter health.

Nonlinear gradients alter the diffusion encoding in brain diffusion tensor imaging (DTI), leading to spatially varying diffusion weighting which bias quantitative measures if uncorrected. Although the overall effects of gradient nonlinearity correction in brain studies are typically minimal and often fall below the detection limits of traditional imaging resolutions and sensitivities, their cumulative impact on clinical outcomes requires further study. This study investigates the significance and effects of correcting gradient nonlinearity in DW-MRI, focusing on the microstructural and macrostructural changes in white matter (WM) and gray matter (GM) across a clinical cohort. Our primary aim is to clarify whether the observed nonlinearity significantly alters the interpretation of aging in clinical settings, particularly in studies comparing healthy individuals to those with neurological conditions. We assess the extent of nonlinear fields impact on individual scans, interscanner observations, and a tract-based analysis. Using data from the Vanderbilt Memory & Aging Project (n = 948 imaging sessions, 933 on Scanner B and 15 on Scanner A acquired with single-shell diffusion tensor imaging protocol), we find 1%, 3.3%, and 5-degree changes in microstructure measures, fractional anisotropy (FA), mean diffusivity (MD), and primary eigen vector (V1) respectively, affecting at least 20% of the brain. Across sessions, head positioning sampled typical clinical variability, with head offsets of approximately 0-10 mm and rotations of 0-10° relative to magnet isocenter. Subcortical regions in the superior regions, occipital lobules, and parietal lobules exhibit relatively higher impacts. Macrostructural measures show changes up to 12% after nonlinear field correction. GNL effects are 5% and 0.33% of FA and MD changes between mild cognitive impairment and controls. A simple power analysis indicates that these subtle effects of gradient nonlinearity correction can become statistically detectable in larger multi-site studies exceeding ~1000 subjects, suggesting that GNL should be considered and, where possible, corrected or at least quantified in such settings.

Brain charts, or normative models of quantitative neuroimaging measures, can identify trajectories of brain development and abnormalities in groups and individuals by leveraging large populations. Recent work has extended these brain charts to model microstructural and macrostructural features of white matter. Assessments of variance for these brain charts are necessary to determine whether the models being used for these data are stable. We implement an analytic approach to characterize variability of the parameters in previously released brain charts created using the generalized additive models for location, scale, and shape (GAMLSS) framework. Additionally, we empirically validate the accuracy of each analytic model through a comparison to a bootstrapping approach from 0.2 to 90 years of age. We find that across all models, the analytic coefficient of variation (COV) remains below 5% for ages greater than 0.25 years, with the maximum empirical observed COV reaching 7% at 0.2 years of age. Further, the empirical assessment shows high agreement with the analytic assessment, with COV estimates averaged across the lifespan for all models having a Pearson correlation coefficient of 0.776 and a mean difference of 4 x 10-4. Both methods exhibit volume and surface area as the features with the largest average COV for the majority of tracts. However, the analytic assessment yields axial diffusivity as the feature most frequently having the smallest COV, whereas the corresponding feature for the empirical assessment is average length. These results suggest that the analytic approach overestimates model stability for WM brain charts when the COV is low and that the validation method is suitable for assessing whether GAMLSS models are unstable.

Studies examining differences in cerebral blood flow (CBF) across the cognitive aging spectrum [cognitively unimpaired (CU), mild cognitive impairment (MCI), dementia] often normalize or adjust regional CBF values using a reference region. Commonly used reference regions include CBF in the putamen or precentral gyrus, though it remains unclear whether normalization is necessary or how reference region choice affects findings. We investigated whether associations between cognitive status and CBF vary by reference region use. Vanderbilt Memory and Aging Project participants ( n = 441, 74% CU, 19% MCI, 7% dementia, 72±10 years old, 49% female) underwent 3T magnetic resonance imaging and neuropsychological evaluation. Pseudo-continuous arterial spin labeling assessed CBF in total cerebral grey matter and cortical lobar gray matter (frontal, parietal, temporal, occipital) regions of interest (ROI) and in reference regions (putamen, precentral gyrus). Ordinary least squares regressions related cognitive status to CBF in each ROI, adjusting for age, sex, race/ethnicity, education, Framingham Stroke Risk Profile, apolipoprotein E- ε4 status, and, when appropriate, reference region CBF. ANOVA and pairwise comparisons (MCI–CU, dementia–MCI) followed. Without reference region adjustment, CBF differed by cognitive status ( p -values<0.02) in all ROIs, though CU and MCI did not differ in pairwise comparisons ( p -values>0.10). Relative to individuals with MCI, individuals with dementia had significantly lower CBF in total, frontal, temporal, and, particularly, parietal (β=-7.9 mL/100g/min, p = 0.005) gray matter. Compared to adjusting for precentral gyrus CBF, adjusting for putamen CBF resulted in more associations between cognitive status and CBF. After adjusting for putamen CBF, CBF was lower in MCI relative to CU across all ROIs ( p -values<0.01), but differences between MCI and dementia were attenuated compared to models without a reference region (e.g., parietal lobe β=-4.8 mL/100g/min, p = 0.01). Adjusting for CBF in a reference region, particularly the putamen, may enhance the detectability of CBF differences early along the cognitive aging spectrum. However, including a reference region may reduce sensitivity to detect CBF differences among individuals already exhibiting cognitive impairment, potentially because reference regions become hypoperfused. Future research should determine whether adjusting for CBF in a reference region enhances the detection of AD biomarker positivity.

INTRODUCTION:Limbic white matter (WM) abnormalities are prevalent in aging and Alzheimer's disease (AD), but genetic drivers are unclear. METHODS:In 2614 older adults (mean age ± SD: 73.7 ± 9.8 years; 26% cognitively impaired) from seven harmonized cohorts enriched for cognitive impairment, we quantified free-water-corrected diffusion MRI (dMRI) metrics in seven limbic tracts. We estimated single nucleotide polymorphism (SNP) heritability, performed cohort genome-wide association studies (GWASs) with meta-analysis, evaluated shared genetic architecture and enriched pathways, and assessed AD relevance using brain RNA-seq data. RESULTS:Limbic WM is heritable (h2 = 0.26-0.60; pFDR < 0.05). Meta-GWAS identified six loci (p < 5 × 10- 8), including a signal implicating CDH19, an oligodendrocyte-enriched cell-adhesion gene. Additional loci were near the KC6, SENP5, RORA, FAM107B, and MIR548A1 genes. In brain tissue, RORA, FAM107B, and KC6 expression was associated with cognition and AD neuropathology. Results converged on insulin and immune biology and shared genetic architecture with lipid and cardiovascular traits. DISCUSSION:Limbic WM microstructure is genetically influenced and links oligodendrocyte and vascular-inflammatory biology to AD-relevant outcomes.

INTRODUCTION:Prior studies showed inconsistent links between dietary fat and Alzheimer's disease (AD) risk. We examined whether dietary fat affected brain atrophy markers differentially based on risk factors like female sex, apolipoprotein ε4 (APOE ε4) status, and cognitive status. METHODS:Participants from the Vanderbilt Memory and Aging Project, classified as cognitively unimpaired (CU) or with mild cognitive impairment (MCI), were included (n = 758). Linear mixed-effects regression models examined associations between total fat intake (Tfat, times/day) and percentage of energy from fat (Pfat, %) and longitudinal gray matter volumes, as well as interactions. RESULTS:Over 4.6 ± 3.1 years, Pfat interacted with cognitive status on longitudinal temporal lobe (p = 0.009) and inferior lateral ventricle volume (p = 0.002). Higher Pfat was associated with faster reduction in temporal lobe volume in CU participants (β = 47.2, p = 0.007) but slower enlargement of the inferior lateral ventricle among participants with MCI (β = -22.5, p = 0.006). DISCUSSION:Different mechanisms may underlie the fat-neurodegeneration relationship across cognitive statuses.

BACKGROUND AND OBJECTIVES:Cerebral small vessel disease (SVD) is the most common vascular contributor to dementia. SVD markers often coexist, contributing to difficulty assessing their independent contributions to cognitive domains. MRI-visible perivascular spaces (PVSs) are an emerging SVD marker visualized by MRI. We previously showed that basal ganglia PVSs cross-sectionally contribute to worse cognition, independent of other SVD markers. To further characterize the clinical relevance of PVS, we studied its role as a unique SVD marker of longitudinal cognitive decline. METHODS:Participants without stroke or dementia were included in the Vanderbilt Memory and Aging Project, a longitudinal observational cohort study based in Nashville, TN. Participants completed 3T MRI at study entry to measure SVD burden (PVS volume fraction, white matter hyperintensities volume, lacune counts, and cerebral microbleeds counts). PVS volumes were segmented using a deep learning algorithm. Participants underwent comprehensive serial neuropsychological testing over an 11-year follow-up period (mean follow-up = 4.9 ± 3.1 years). Each SVD marker was related to longitudinal neuropsychological performances using a linear mixed-effects model adjusting for age, sex, race/ethnicity, education, baseline cognitive status, apolipoprotein E-ε4 presence, Framingham Stroke Risk Profile, and intracranial volume. Head-to-head comparisons simultaneously tested multiple statistically significant SVD markers. RESULTS:Among participants (n = 750, age 68 ± 9 years, 52% female), higher basal ganglia PVS burden at baseline was associated with worse longitudinal performances in Boston Naming Test (β = -29.63; 95% CI -56.66 to -2.60), Animal Naming (β = -33.09; 95% CI -65.66 to -0.51), Wechsler Adult Intelligence Scale IV Coding (β = -86.36; 95% CI -150.9 to -21.82), executive function composite (β = -9.51; 95% CI -14.33 to -4.68), Hooper Visual Organization Test (β = -26.06; 95% CI -50.53 to -1.59), and episodic memory composite (β = -7.05; 95% CI -11.9 to -2.21). In head-to-head comparisons, basal ganglia PVS remained independent associations with executive function composite (β = -7.47; 95% CI -12.84 to -2.10) and Hooper Visual Organization Test (β = -22.11; 95% CI -42.38 to -1.85). DISCUSSION:Basal ganglia PVS burden independently contributes to worse longitudinal executive function and visuospatial skills independent of other SVD markers, highlighting PVS as an emerging marker of domain-specific cognitive decline in aging. Although causation cannot be established, findings further support PVS as a vascular contributor to deep brain structure damage underlying cognitive decline over time.

INTRODUCTION:White matter (WM) microstructure is essential for brain function but deteriorates with age and in neurodegenerative conditions such as Alzheimer's disease (AD). Diffusion MRI, enhanced by advanced bi-tensor models accounting for free water (FW), enables in vivo quantification of WM microstructural differences. METHODS:To evaluate how AD genetic risk factors affect limbic WM microstructure - crucial for memory and early impacted in disease - we conducted linear regression analyses in a cohort of 2,614 non-Hispanic White aging adults (aged 50.12 to 100.85 years). The study evaluated 36 AD risk variants across 26 genes, the association between AD polygenic scores (PGSs) and WM metrics, and interactions with cognitive status. RESULTS:AD PGSs, variants in TMEM106B, PTK2B, WNT3, and apolipoprotein E (APOE), and interactions involving MS4A6A were significantly linked to WM microstructure. DISCUSSION:These findings implicate AD-related genetic factors related to neurodevelopment (WNT3), lipid metabolism (APOE), and inflammation (TMEM106B, PTK2B, MS4A6A) that contribute to alternations in WM microstructure in older adults. HIGHLIGHTS:AD risk variants in TMEM106B, PTK2B, WNT3, and APOE genes showed distinct associations with limbic FW-corrected WM microstructure metrics. Interaction effects were observed between MS4A6A variants and cognitive status. PGS for AD was associated with higher FW content in the limbic system.

Recent research emphasizes the significance of white matter tracts and the free-water (FW) component in understanding cognitive decline. The goal of this study is to conduct a large-scale assessment on the role of white matter microstructure on longitudinal cognitive decline. This study used a cohort collated from seven longitudinal cohorts of aging (ADNI, BIOCARD, BLSA, NACC, ROS/MAP/MARS, VMAP, and WRAP). In total, this dataset included 2,220 participants aged 50+ who had both diffusion MRI and harmonized composites of memory performance and executive function. This dataset included a total of 4,918 imaging sessions with corresponding cognitive data (mean number of visits per participant: 1.69 ± 1.67, interval range: 1-10 years). Diffusion MRI was preprocessed using the PreQual pipeline and FW correction was used to create FW and FW-corrected intracellular metrics. Conventional and FW-corrected measures were harmonized using the Longitudinal ComBat package. Linear mixed effects regression was used for longitudinal analysis, in which we covaried for age, age squared, education, sex, race/ethnicity, diagnosis at baseline, APOE-ε4 status, and APOE-ε2 status. All models were corrected for multiple comparisons using the FDR approach. For longitudinal memory performance, we found global associations with conventional diffusion MRI metrics, in which abnormalities were associated with lower memory performance. Following FW correction, we found that the FW metric itself was strongly associated with memory performance, in which higher FW was associated with lower memory performance and exacerbated decline. Interestingly, following FW-correction the intracellular contributions were largely mitigated. As illustrated in Figure 1A, the most significant effects were found in the limbic tracts, with the most significant associations found for cingulum bundle FW (p=5.80x10 -45 ). Figure 1B illustrates the association between cingulum FW and longitudinal memory performance. Findings for longitudinal executive function performance are shown in Figure 2. To date, this is the largest study combining FW-corrected diffusion MRI data and harmonized cognitive composites to understand cognitive trajectories in aging. Future studies evaluating how white matter microstructure may be incorporated into models of AD may further our knowledge into the neurodegenerative cascade of AD.

Purpose:Connectome network metrics are commonly regarded as fundamental properties of the brain, and their alterations have been implicated in the development of Alzheimer's disease, multiple sclerosis, and traumatic brain injury. However, these metrics are actually estimated properties through a multistage propagation from local voxel diffusion estimations, regional tractography, and region of interest mapping. These estimation processes are significantly influenced by choices specific to imaging protocols and software, producing site-wise effects. Approach:Recent advances in disentanglement techniques offer opportunities to learn representational spaces that separate factors that cause domain shifts from intrinsic biological factors. Although these techniques have been applied in unsupervised brain anomaly detection and image-level features, their application to the unique manifold structures of connectome adjacency matrices remains unexplored. Here, we explore the conditional variational autoencoder structure for generating site-invariant representations of the connectome, allowing the harmonization of brain network measures. Results:Focusing on the context of aging, we conducted a study involving 823 patients across two sites. This approach effectively segregates site-specific influences from biological features, aligns network measures across different domains (Cohen's D < 0.2 and Mann-Whitney U - test < 0.05 ), and maintains associations with age ( 2.71 × 10 - 02 ± 2.86 × 10 - 03 error in years) and sex ( 0.92 ± 0.02 accuracy). Conclusions:Our findings demonstrate that using latent representations significantly harmonizes network measures and provides robust metrics for multi-site brain network analysis.

Cerebral arterial dilatation, signifying outward vascular remodeling, is linked to a higher risk of Alzheimer’s disease and a higher burden of white matter hyperintensities (WMH). Arterial dilatation may disrupt cerebral hemodynamics and lead to delayed blood arrival to the brain, which is itself linked to an increased burden of WMH. We examined if arterial dilatation was associated with blood arrival timing and if blood arrival timing mediated the effect of arterial dilatation on WMH burden. Vanderbilt Memory and Aging Project participants free of clinical dementia or stroke at enrollment (n=249, 73±8 years, 85% cognitively unimpaired, 41% female) underwent multimodal 3T brain magnetic resonance imaging. Internal carotid artery (ICA) and basilar artery (BA) arterial inner diameters were quantified by an expert rater using vessel wall imaging. An established marker of blood arrival timing, the arterial spin labeling spatial coefficient of variation (ASL-sCoV), was quantified using pseudo-continuous ASL. Higher ASL-sCoV indicates slower blood arrival. WMH burden was quantified using semi-automated methods. Linear regressions related ICA and BA inner diameters to ASL-sCoV and log-transformed WMH burden in anterior and posterior arterial territories, individually. Models were adjusted for age, sex, race/ethnicity, cognitive status, Framingham Stroke Risk Profile, intracranial volume, and apolipoprotein E-ε4 status. We also assessed if ASL-sCoV mediated associations between arterial diameters and WMH burden. Larger ICA inner diameter was associated with higher anterior ASL-sCoV (p=1.7x10 -8 ) and higher anterior WMH burden (p=9.6x10 -5 ). Anterior ASL-sCoV partially mediated the association between ICA inner diameter and anterior WMH burden (proportion mediated=15%, p=0.04). Larger BA inner diameter was associated with higher posterior ASL-sCoV (p=1.3x10 -8 ) but not with higher posterior WMH burden (p=0.06). Our study illustrates novel links between cerebral vascular structure, cerebrovascular hemodynamics, and vascular brain pathology. Because most of the effect of arterial dilatation on WMH burden was not mediated through blood arrival timing, future work will consider other potential mediators, such as arterial stiffness.

Poor sleep has emerged as a potentially modifiable risk factor for Alzheimer’s disease (AD) and related dementias (ADRD). Few previous studies have incorporated objectively-measured sleep and structural neuroimaging to understand if poor sleep relates to changes in brain structure. In a cohort of older adults, we investigated actigraphy-measured sleep health and associations with well-established magnetic resonance imaging (MRI) markers of AD-specific neurodegeneration. Participants from the Vanderbilt Memory and Aging Project (n=390, Mean age: 69.5± 9.3 year; 47.4% women) wore ActiGraph GT9X accelerometers on their wrist daily for 10 days. Data were processed to estimate sleep regularity, wake after sleep onset (WASO), average awakening length, sleep fragmentation index, sleep timing, and sleep duration. 3T brain MRI was used to quantify hippocampal volume and to estimate the McEvoy AD signature (calculated according to published guidelines). We used cross-sectional multivariate linear regression to assess the associations between individual sleep measures and neuroimaging markers. Models adjusted for age, sex, race, education, APOE-ε4 status, cognitive status, sleep medication use, body mass, index, physical activity, depressive symptoms, and cardiovascular risk. For sleep timing and duration, quadratic terms were added in regression models. Models examining the total hippocampal volume also adjusted for intracranial volume. In the fully adjusted models, we found abnormal sleep duration (β = -2.35, 95% CI = -4.58, -0.11) and greater sleep irregularity (β = -0.34, 95% CI = -0.62, -0.07) were associated with lower AD signature values. Additionally, greater sleep irregularity (β = -262.36; 95% CI = -451.71, -73.00), greater WASO (β = -2.76; 95% CI = -5.30, -0.21) and greater average awakening length (β = -74.20; 95% CI = -128.47, -19.94) were associated with lower hippocampal volumes. Our results suggest that several measures of poor sleep, including greater sleep irregularity, wake time, and sleep fragmentation, are cross-sectionally linked to smaller hippocampal volume and smaller brain volumes in regions most affected by AD neuropathology. Longitudinal studies are needed to clarify if poor sleep contributes to brain atrophy over time.

Hippocampal volume is an important in vivo imaging marker for Alzheimer's disease (AD) risk and progression. Reported sex differences show accelerated hippocampal atrophy in females compared to males as AD-related pathology increases. However, genome-wide association studies (GWAS) of hippocampal volume have been mainly conducted in mid-life participants with no AD pathology and have not been systemically examined for sex-specific genetic effects. We investigated the sex-specific genetic architecture of hippocampal volume in eight aging and AD cohorts. This study included 5,523 non-Hispanic White participants (N Males =2,549; N Females =2,974; mean baseline age=72 yrs; mean number of visits=2.3; 32.8% cognitively impaired). Hippocampal volume and estimated total intracranial volume (eTIV) were segmented from T 1 -weighted MRIs using Hippodeep. Total hippocampal volume (Left+Right) and eTIV were harmonized using neuroCombat in R. Longitudinal slopes for hippocampal volume were calculated with linear mixed-effects models. GWAS were performed, at baseline and longitudinally, by cohort in all participants, sex-stratified, and sex-interaction models. Models covaried for baseline age, sex (in all participants), eTIV, and the first five genetic principal components. Longitudinal models also covaried for diagnosis conversion over time. Results were meta-analyzed across cohorts. We identified three genome-wide significant genetic loci associated with hippocampal volume. Specifically, a chromosome 6 locus (index SNP rs62434269; MAF=0.33) near ARID1B was associated at baseline in all participants (β=0.10, p  = 3.54x10 -8 ) (Figure 1). Further, we found a locus on chromosome 8 (rs34173062; MAF=0.09), which is a previously reported AD risk variant and eQTL for SHARPIN (β=-0.22, p  = 1.68E-09), with significant effects in males (β Males =-0.32, p Males =2.14x10 -9 ) but not females (β Females =-0.11, p Females =0.03; p sex-interaction =0.004) (Figure 2A-B). A chromosome 14 locus (rs75592630; MAF=0.05) near AKAP6 , a gene previously associated with cognition, was associated with hippocampal volume change over time in females (β Females =-0.28, p Females =1.19x10 -9 ) but not males (β Males =-0.01, p Males =0.79; p sex-interaction =0.003) (Figure 2C-D). We extend findings of a previously reported AD risk variant near SHARPIN to hippocampal volume and provide evidence of novel sex-specific genetic effects. While replication is warranted, our results suggest the importance of genetic predictors and sex differences on imaging biomarkers.

Limbic white matter (WM) abnormalities are strongly elevated along the Alzheimer's Disease (AD) diagnostic continuum, but the underlying biological mechanisms remain unclear. This study aims to conduct a large‐scale genetic analysis of WM microstructure in older adults. WM was assessed in seven limbic tracts, including the cingulum, fornix, inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and transcallosal tracts of the inferior, middle, and superior temporal gyri (ITG, MTG, STG) using advanced diffusion MRI metrics corrected for free‐water (FW) (fractional anisotropy [FA FWcorr ], axial diffusivity [AxD FWcorr ], mean diffusivity [MD FWcorr ], radial diffusivity [RD FWcorr ]). Genetic associations with WM microstructure were investigated using harmonized data from seven aging cohorts, comprising 2,614 non‐Hispanic white older adults (mean age = 73.66 ± 9.76; 42.65% male), through SNP‐heritability estimation, genome‐wide association studies (GWAS), and post‐GWAS analyses (genetic correlation, gene‐level, and pathway analysis). Bulk RNA‐seq brain data were used to evaluate the relationship between expression of genes identified in the GWAS with cognition and AD pathologies. WM microstructure is heritable, with 16 of 35 metrics exhibiting estimates between 0.26 and 0.60 (p FDR <0.05). Genome‐wide associations ( p <5×10 −8 ) were observed for fornix AxD FWcorr (chr3, rs78407651), ILF FA FWcorr (chr15, rs8026709) and AxD FWcorr (chr15, rs8026709), STG RD FWcorr (chr10, rs11542181), and cingulum RD FWcorr (chr6, rs56017587). A locus with 38 genome‐wide significant SNPs (chr18, rs12959877) was associated with FA FWcorr and RD FWcorr (Figure 1). These SNPs are eQTLs for CDH19 , a gene highly expressed in oligodendrocytes with a role in cell adhesion. Among the genes identified in the GWAS, RORA , FAM107 and KC6 expression in brain tissues was linked to cognitive decline and AD pathologies (p FDR <.05). Gene‐level analysis highlighted SERPINA12 (z=4.60, p FDR =.03), a gene implicated in type 2 diabetes and atherosclerosis. Pathway analysis revealed associations with insulin, immune response, and neurotrophic signaling. Genetic correlations were identified with lipid profiles, cardiovascular traits, and neuropsychiatric conditions (p FDR <.05). This study identified genetic factors related to cognition, vascular health, and inflammation as contributors to WM microstructure changes in aging and AD. These findings open avenues for future research on AD's molecular mechanisms and therapeutic targets for improving vascular and metabolic health in aging populations.

Fixel-Based Analysis (FBA) offers a novel framework to disentangle fiber-specific white matter (WM) degeneration, particularly within complex crossing-fiber architecture where conventional diffusion tensor imaging (DTI) falls short. In this study, we leveraged single-shell diffusion MRI data from 297 older adults enrolled in the Vanderbilt Memory & Aging Project to quantify fiber density (FD), cross-section (FC), and their composite (FDC) across primary (N1), secondary (N2), and tertiary (N3) fiber populations. By stratifying white matter into anatomically defined crossing-fiber convergence groups, we examined how fiber dominance and architectural complexity modulate associations with age, cognitive status, and longitudinal cognitive decline. Results revealed that FD and FDC in non-dominant (N2/N3) fibers were the strongest predictors of both baseline and longitudinal cognitive trajectories, particularly in memory and executive domains. These associations persisted across fiber convergence strata, suggesting that fiber population identity, rather than anatomical complexity, may confer greater vulnerability to age- and disease-related degeneration. Our findings position non-dominant fiber metrics as sensitive candidate biomarkers for early white matter disruption in Alzheimers disease and support the application of multi-fiber modeling to enhance detection of preclinical neurodegeneration. ### Competing Interest Statement Timothy J. Hohman, PhD is a member of the scientific advisory board for Vivid Genomics and serves on the editorial board for Alzheimers & Dementia and Alzheimers & Dementia: Translational Research and Clinical Intervention. ### Funding Statement This study was supported by several funding sources, including K01EB032898 (K.G.S.), K01AG073584 (D.B.A.), U24AG074855 (T.J.H.), 75N95D22P00141 (T.J.H.), and R01AG059716 (T.J.H.). The research was support in part by the Intramural Research Program of the National Institutes of Health, National Institute on Aging. Study data were obtained from the Vanderbilt Memory and Aging Project (VMAP). VMAP data were collected by Vanderbilt Memory and Alzheimers Center Investigators at Vanderbilt University Medical Center. This work was supported by NIA grants R01AG034962 (PI: A.L.J.), R01AG056534 (PI: A.L.J.), K24AG046373 (PI: A.L.J.), and Alzheimers Association IIRG-08-88733 (PI: A.L.J.). Data collection and analysis was supported by UL1TR000445, UL1TR002243 (Vanderbilt Clinical Translational Science Award), and S10OD023680 (Vanderbilt High-Performance Computer Cluster for Biomedical Research). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of Vanderbilt University Medical Center gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data from the VMAP cohort can be accessed freely following data use approval (www.vmacdata.org). The source code for the analysis conducted in this study is available on request.