Raghavan, Sheelakumari

BACKGROUND: White matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease but reflect late-stage injury. Diffusion magnetic resonance imaging (MRI) biomarkers have been proposed to capture earlier small vessel disease-related microstructural damage but their temporal progression relative to WMH and risk factors associated with progression remain unexplored. METHODS: We identified 2077 participants from the population-based cohort study of the Mayo Clinic Study of Aging in Olmsted County, Minnesota (aged 50–101 years) collected between 05/2005 and 09/2024 with longitudinal neuroimaging. Using multioutput nonlinear mixed-effects models in those with at least 2 fluid-attenuated inversion recovery-MRI and diffusion MRI scans, we characterized the temporal progression of WMH and 4 diffusion MRI biomarkers: fractional anisotropy of the genu of the corpus callosum, peak width of skeletonized mean diffusivity, free water, and Arteriolosclerosis-score, which were automatically estimated. Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE ε4 status, cardiometabolic conditions). RESULTS: The study population had a mean age of 78 years, 47% were women, 28% were APOE ε4 allele carriers, and 80% were cognitively unimpaired, with an average follow-up of 5.2 years (SD, 4.3 years) for fluid-attenuated inversion recovery-MRI and 4.3 years (SD, 3.9 years) for diffusion MRI. Arteriolosclerosis-score, fractional anisotropy of the genu of the corpus callosum, free water, and peak width of skeletonized mean diffusivity became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH become abnormal (half-width of CI <1 year), respectively. Global markers (Arteriolosclerosis-score, free water, peak width of skeletonized mean diffusivity, and WMH) were correlated, indicating shared substrates of widespread white matter injury. Fractional anisotropy of the genu of the corpus callosum, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Female participants showed earlier WMH, fractional anisotropy of the genu of the corpus callosum, and Arteriolosclerosis-score abnormalities, whereas male participants exhibited earlier peak width of skeletonized mean diffusivity and free water abnormalities. CONCLUSIONS: Diffusion MRI biomarkers were abnormal at least a decade before WMH become abnormal in the population, revealing a prolonged phase of early small vessel disease and highlighting their potential for small vessel disease prevention.

Objective Magnetic resonance imaging (MRI)‐visible enlarged perivascular spaces (PVS) are markers of cerebral small vessel disease (SVD) and aging, processes implicated in both neurodegenerative and cerebrovascular pathologies. However, longitudinal positron emission tomography (PET) studies examining PVS as a mechanism underlying Alzheimer's disease (AD) pathophysiological progression are lacking. Our overall objective was to investigate the associations of baseline PVS burden with white matter hyperintensity (WMH) volume, amyloid‐PET, and tau‐PET, both cross‐sectionally and longitudinally. Methods We examined 2,229 participants across the aging‐AD spectrum from the Mayo Clinic Study of Aging (MCSA) and the Mayo Alzheimer's Disease Research Center (ADRC) (mean age = 68.59 ± 12.70 and 48.94% women). PVS and WMH were quantified using a novel in‐house algorithm. Amyloid on [ 11 C]Pittsburgh Compound B (PiB)‐PET and tau on AV1451‐PET were measured. Cross‐sectional regional and global associations were examined using canonical correlation analysis (CCA). Longitudinal associations were examined using age‐ and sex‐adjusted linear mixed effect (LME) models with natural splines and subject‐wise 10‐fold cross‐validation. Result Three distinct associations were observed: (1) PVS in the basal ganglia (PVS‐BG) was associated with greater WMH volume both cross‐sectionally and longitudinally; (2) PVS‐BG covaried with higher PiB standardized uptake value ratio (SUVR) cross‐sectionally, with longitudinal trajectory associations limited to amyloid‐negative individuals; (3) PVS in the centrum semiovale (PVS‐CSO) was not associated with amyloid or tau, but was associated with occipital WMH, a pattern characteristic of CAA. Interpretation Findings lend support for PVS‐BG as an early indicator of small vessel dysfunction and WMH pathogenesis. Individuals with higher PVS‐BG burden exhibited higher WMH burden and faster WMH progression, suggesting that PVS‐BG may identify individuals at increased risk of progressive SVD. Evidence linking PVS to downstream AD biomarker progression was weaker. Findings support the relevance of PVS‐CSO to CAA, suggesting that they may reflect changes occurring early in the disease process. ANN NEUROL 2026

Abstract Background White matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease (SVD) but reflect late-stage injury. Diffusion MRI based biomarkers have been proposed to capture earlier SVD-related microstructural damage but their temporal progression relative to WMH and the risk factors associated with this progression have not been explored. Methods We analyzed longitudinal neuroimaging data from 2,047 participants from a population-based cohort study (aged 49-101 years, 47% female). Using multi-output nonlinear mixed-effects models, we characterized the temporal progression of WMH and four diffusion MRI based biomarkers: fractional anisotropy of the genu of the corpus callosum (Genu-FA), peak width of skeletonized mean diffusivity (PSMD), free water (FW), and Arteriolosclerosis-score (ARTS). Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE ε4 status, and cardiometabolic conditions). Results ARTS, Genu-FA, FW, and PSMD became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH, respectively. Global markers (ARTS, FW, PSMD, WMH) were correlated, indicating shared substrates of widespread white matter injury. Genu-FA, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Females showed earlier WMH, Genu-FA, and ARTS abnormalities whereas males exhibited earlier PSMD and FW abnormalities. Conclusions Diffusion MRI based biomarkers capture microstructural injury at least a decade before appearance of WMH, revealing a prolonged phase of early SVD and highlighting their potential for SVD prevention.

Abstract Background White matter hyperintensity topography, particularly posterior confluent and subcortical multispot (WMH‐MS), has been implicated as surrogate imaging biomarker of cerebral amyloid angiopathy (CAA). Usefulness of WMH‐MS for prediction of future cerebral microbleeds (CMB's) has significant clinical implications for anti‐amyloid therapy risk assessment. Our primary goal was to identify the prevalence of positive WMH‐MS status and their utility for predicting the incident CMBs in a population‐based sample. Our secondary goal was to evaluate its usefulness after accounting for amyloid (surrogate for CAA) and WMH burden (surrogate of small vessel disease) which are both risk factors for CMBs. Method We identified 651 participants (mean age (SD) 69.8 (10.0) years, 55% male) from the Mayo Clinic Study of Aging, who had 3T baseline whole brain fluid‐attenuated inversion recovery (FLAIR) MRI and at least two T2*‐weighted gradient recalled echo (GRE) sequences (concomitant with or after FLAIR) which were used to ascertain CMBs. Amyloid positron emission tomography (PiB‐PET) scans were available in 87% of the sample. Visual reads on FLAIR scans were performed using a 4‐point Fazekas rating scale of periventricular and deep WMH (positivity: Fazekas ≥ 2) and a 3‐point MS severity visual scale (positivity: nWMH‐MS > 10 circular or ovoid WMH spots, Figure 1) according to established criteria. Using univariate and multivariable logistic regression, we assessed the independent association of WMH‐MS > 10 and Fazekas ≥ 2 for prediction of incident CMB's. Result The frequency of WMH‐MS pattern was greater in those who developed an incident CMB compared to those who did not develop a CMB (20.8% vs. 2.8%, p < 0.001). Older age and male sex were predictive of incident CMBs (Figure 2A). In univariate models, presence of WMH‐MS pattern but not Fazekas was associated with incident CMBs after adjusting for age and sex. The associations remained significant after additional adjustment for APOE4 , hypertension, number of baseline CMBs (Prior CMB), and amyloid effects (Figure 2B). Conclusion Visually identified WMH‐MS patterns on FLAIR‐MRI were predictive of incident CMBs in this population and may be useful markers of CAA. Future studies evaluating its utility as an additional biomarker of amyloid related imaging abnormalities in clinical trials are necessary.

Abstract Background Cognitive resilience to Alzheimer's disease (AD) pathology has been widely studied, but distinguishing resilient individuals from those with typical tau pathologies remains challenging due to the overlap in imaging and plasma biomarker profiles. We hypothesize that our machine learning‐based (ML) tau heterogeneity score, THETA (JNUMED, July 2024), could serve as an effective marker of resilience and provide a better estimation of tau's temporal progression. Method Longitudinal imaging data from the Mayo Clinic Study of Aging (MCSA, n = 1834) and ADRC ( n = 246) were analyzed. Participants were categorized into typical AD and resilient groups using amyloid PiB‐PET (abnormal PiB‐PET≥25 Centiloid units), Flortaucipir (FTP‐PET) (meta‐ROI≥1.29 SUVr, ERC≥1.27 SUVr) and Mini‐Mental State Examination (MMSE). Typical AD was defined as abnormal PiB‐PET and abnormal FTP‐PET for both biomarkers (Figure 1). Resilience to AD pathology was determined by predicted values of MMSE >28 from a linear mixed effects model for cognitively unimpaired participants. In addition, a resistant group was broadly defined as having normal PiB‐PET. To capture the spatial heterogeneity of tau distribution in the brain, regional THETA scores were generated using ML model and visual ratings. Additionally, a progression model, Sampled Iterative Local Approximation (SILA), was fitted using AD and plasma biomarkers ( p ‐tau181 and GFAP) for validation. Result THETA identified regions of reduced tau uptake in the frontal, parietal, temporal, occipital, and limbic regions when compared to the typical AD group (Figure 2A). This was confirmed by the preserved cortical thickness in these same regions (Figure 2D). In addition, SILA showed THETA can distinguish global tau temporal progressions of typical AD, resilient, and resistant groups compared to the other biomarkers (Figure 3). Interestingly, the onset age for GFAP was younger for resilient and typical AD, indicating earlier onset of inflammation in the presence of pathology. Conclusion The significant regions identified by THETA, showing reduced tau and preserved cortical thickness, combined with the clear offset in progression models between resilient and typical AD, suggest that THETA may help uncover pathological tau mechanisms that differentiate these groups. These findings highlight the potential of THETA to provide valuable insights into resilience mechanisms and enhance our understanding of AD.

Abstract Background White matter (WM) plays a dual role in interacting with tau pathology. There is an intricate dynamic relationship with WM acting as highways for tau spreading, while the increasing tau burden ultimately compromises WM health. We aimed at studying the associations between tau accumulation and WM integrity using longitudinal data with focus on sex differences. Method We included 374 participants who had longitudinal multishell diffusion MRI and flortaucipir PET (baseline mean age = 70.9 years, 52% male, 17% MCI/AD dementia) scans from the Mayo ADRC and Mayo Clinic Study of Aging. Rate of tau accumulation was calculated from the entorhinal cortex (ERC) and inferior temporal cortex (IT) SUVRs, with IT‐WM serving as the WM highway between the two regions. IT‐WM was defined by JHU “Eve” WM atlas, and its integrity was assessed using neurite density index (NDI), which reflects the packing density of neurites. Structural equation modeling (SEM) evaluated relationships between tau and WM metrics. Result At baseline, males and females showed no significant differences in tau SUVR or IT‐WM NDI ( p >0.05), though males had higher vascular risk and averaged two years older than females (Table 1). Figure 1a showed that baseline ERC‐tau influenced baseline IT‐tau, as well as both rate of ERC‐tau and baseline IT‐tau influenced rate of IT‐tau. After incorporating IT‐WM NDI as a mediator (Figure 1b), we discovered 1) baseline ERC‐tau influencing rate of IT‐tau accumulation through baseline IT‐WM NDI, suggesting the role of WM health in the rate of tau accumulation; 2) Rate of IT‐tau accumulation negatively affected follow‐up IT‐WM NDI suggesting the deterioration of WM health as tau accumulation progresses. These relationships were stronger in males compared to females. We also observed greater impact of vascular risk on WM health in males and greater rates of IT‐tau accumulation in females. Conclusion Our work sheds light on the mechanistic role of intact WM in tau accumulation (through dense microtubules and synaptic connections) and the deterioration of WM that is associated with higher rates of tau accumulation. We found interesting sex differences that warrant further investigation.

Abstract Background Aging and dementia are generally associated with lower metabolism in gray matter on FDG‐PET and have been widely studied. However, white matter (WM) metabolic changes remain understudied. We investigated the metabolic changes in WM associated with aging and dementia and their relationships with established Alzheimer's Disease (AD) biomarkers and cognitive outcomes. Method We analyzed data from 3,169 participants (72 ± 9 years; 53% male; 65% cognitively unimpaired) enrolled in the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). All participants underwent baseline MRI, amyloid‐PET, and FDG‐PET scans, as well as neuropsychological evaluations, with at least one clinical follow‐up. Voxel‐wise multiple regression analysis identified WM metabolic changes associated with global cognition in cognitively unimpaired MCSA participants ( n = 1,754). Univariate analyses and structural equation models were applied to elucidate the driving factors underlying the WM metabolic changes in MCSA data. Multiple regression and linear mixed‐effects models evaluated the explanatory utility of WM metabolism for baseline global cognition and cognitive decline in both cohorts. In a subset of MCSA participants, we evaluated neurite density index from diffusion MRI as a surrogate of WM health and glial fibrillary acidic protein (GFAP) as a surrogate of neuroinflammation to understand underlying mechanisms. Result We identified an atypical WM signature (AWM‐Signature) in the anterior and superior corona radiata, where glucose metabolism increased as cognitive performance worsened (Figure 1). Hypermetabolism in AWM‐Signature was related to increased age, amyloidosis, vascular disease, and neuroinflammation, as well as decreasing WM neurite density in the same regions (Figure 2). Hypermetabolism in AWM‐Signature regions predicted faster future cognitive decline, even after accounting for demographics, amyloid, and vascular disease metrics. The results observed in the AWM‐Signature regions were consistently in the opposite direction of the associations seen with gray matter and typical WM regions. Conclusion We found unexpected metabolism increases in corona radiata, a hub that carries significant portion of the brain's neural traffic, with aging and disease which likely indicates a compensatory response. These changes may be useful cognitive resilience measures and require further investigation to understand their role in maintaining cognitive function.

Abstract Background Chronic disease conditions lead to brain changes elevating dementia risk. We hypothesized that tissue microstructural changes caused by type 2 diabetes mellitus (T2DM) and hypertension could be effectively captured using an advanced diffusion MRI technique, Neurite Orientation Dispersion and Density Imaging (NODDI). Our goal was to evaluate the usefulness of composite NODDI scores, customized for increased hemoglobin (Hb) A1c and systolic blood pressure as predictors of cognition and compare their performance to other measures typically used to assess dementia risk. Method We collected multi‐shell diffusion MR on 1024 Mayo Clinic Study of Aging participants. NODDI measures (neurite density index (NDI), orientation dispersion index (ODI), and free water fraction (FWF)) in gray matter (GM) and white matter (WM) regions were estimated. To identify brain regions susceptible to both conditions, a multiple linear regression model was fitted for each NODDI metric, using hemoglobin (Hb) A1c (in N = 305 participants) or systolic blood pressure ( N = 906) as predictor, with age, sex, and body mass index (BMI) as covariates. Event‐based modeling was performed with the identified NODDI metrics to investigate the chronological pattern along the course of chronic condition development from this cross‐sectional dataset. Composite metrics were created as a weighted sum of susceptible NODDI metrics. We evaluated the impact of both conditions on cognition using gradient boosting models incorporating composite metrics, additional brain measurements, and demographics. Result Increased HbA1c impacts both GM and WM NODDI relatively evenly, whereas increased systolic blood pressure related NODDI changes were mainly seen in WM (data not shown). With increased HbA1c, the earliest changes were observed in GM ODI, followed by changes in the pons and cortical FWF (Figure 1A). With increased systolic blood pressure, the earliest changes occurred in association fibers and middle cerebellar peduncle, followed by FWF change in lobar and projection WM fibers (Figure 1B). Average feature importance from 100 gradient boosting models for cognition prediction suggests that diabetes‐induced GM changes play a moderately important role in cognitive decline (Figure 2). Conclusion Chronic medical conditions, especially T2DM, result in brain changes that are captured by NODDI and contribute moderately to cognitive performance.

AbstractINTRODUCTIONWe investigated the usefulness of plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) for capturing vascular cognitive impairment (VCI) in the context of amyloidosis.METHODSUsing two independent cohorts (n = 1810), we assessed the relationship of plasma NfL and GFAP with (1) vascular brain indices; (2) diagnostic states using the following definitions: vascular versus not (white matter hyperintensity/total intracranial volume ≥ 1.3%), and cognitively impaired (CI) versus cognitively unimpaired (CU) using Clinical Dementia Rating ([CDR] scale ≥ 0.5); and (3) their upstream predictors using structural equation models (SEMs).RESULTSPlasma NfL and GFAP were associated with vascular brain damage and differed across states (VCI > vascular CU > non‐vascular CI > non‐vascular CU). In a population‐based sample, these biomarkers distinguished vascular CU and VCI from non‐vascular CU groups with greater separation in amyloid negative participants. Pathway analyses showed NfL was primarily influenced by systemic/brain vascular health, whereas amyloid contributed to GFAP variability.DISCUSSIONPlasma biomarkers, particularly NfL, capture vascular brain changes and show promise for VCI identification.Highlights Plasma NfL and glial fibrillary acidic protein (GFAP) were associated with vascular brain indices. Plasma biomarkers differed across diagnostic states (VCI > vascular CU > non‐vascular CI > non‐vascular CU). Plasma markers discriminated vascular from non‐vascular states with greater separation in Aβ− participants. NfL was linked to vascular health, while amyloid influenced GFAP variability in the population‐based sample. Future longitudinal frameworks should consider systemic inflammation markers along with these plasma markers to better understand VCID‐related brain changes and cognitive decline.

Importance Tau positron emission tomography (PET) allows in vivo detection of neurofibrillary tangles, a core neuropathologic feature of Alzheimer disease (AD). Objective To provide estimates of the frequency of tau PET positivity and its associated risk of clinical outcomes. Design, Setting, and Participants Longitudinal study using data pooled from 21 cohorts, comprising a convenience sample of 6514 participants from 13 countries, collected between January 2013 and June 2024. Cognitively unimpaired individuals and patients with a clinical diagnosis of mild cognitive impairment (MCI), AD dementia, or other neurodegenerative disorders were included. Exposures Tau PET with flortaucipir F 18, amyloid-β (Aβ) PET, and clinical examinations. Tau PET scans were visually rated as positive according to a US Food and Drug Administration– and European Medicines Agency–approved method, designed to indicate the presence of advanced neurofibrillary tangle pathology (Braak stages V-VI). Main Outcomes and Measures Frequency of tau PET positivity and absolute risk of clinical progression (eg, progression to MCI or dementia). Results Among the 6514 participants (mean age, 69.5 years; 50.5% female), median follow-up time ranged from 1.5 to 4.0 years. Of 3487 cognitively unimpaired participants, 349 (9.8%) were tau PET positive; the estimated frequency of tau PET positivity was less than 1% in those aged younger than 50 years, and increased from 3% (95% CI, 2%-4%) at 60 years to 19% (95% CI, 16%-24%) at 90 years. Tau PET positivity frequency estimates increased across MCI and AD dementia clinical diagnoses (43% [95% CI, 41%-46%] and 79% [95% CI, 77%-82%] at 75 years, respectively). Most tau PET–positive individuals (92%) were also Aβ PET positive. Cognitively unimpaired participants who were positive for both Aβ PET and tau PET had a higher absolute risk of progression to MCI or dementia over the following 5 years (57% [95% CI, 45%-71%]) compared with both Aβ PET–positive/tau PET–negative (17% [95% CI, 13%-22%]) and Aβ PET–negative/tau PET–negative (6% [95% CI, 5%-8%]) individuals. Among participants with MCI at the time of the tau PET scan, an Aβ PET–positive/tau PET–positive profile was associated with a 5-year absolute risk of progression to dementia of 70% (95% CI, 59%-81%). Conclusions and Relevance In a large convenience sample, a positive tau PET scan occurred at a nonnegligible rate among cognitively unimpaired individuals, and the combination of Aβ PET positivity and tau PET positivity was associated with a high risk of clinical progression in both preclinical and symptomatic stages of AD. These findings underscore the potential of tau PET as a biomarker for staging AD pathology.