Hypertension (HTN) is an established risk factor for neurodegeneration and dementia, supported by epidemiological and neuroimaging studies, although there is large variation in individual outcomes. We developed a machine learning (ML)-based model, termed SPARE-HTN, to quantify the spatial pattern of HTN-related neurodegeneration observable in individual structural magnetic resonance images (sMRI). SPARE-HTN demonstrates superior sensitivity compared to the most widely-used measure of HTN-related brain changes, correlates with cognitive performance, and detects early changes in mid-life years. This study investigated the predictive capacity of SPARE-HTN for incident HTN and its mediating role in the relationship between HTN and cognition. SPARE-HTN, derived from N  = 37,098 cognitively unimpaired individuals from diverse cohorts, was evaluated in N  = 968 (59% female, mean age 63.0 ± 11 years) individuals with longitudinal clinical data from six studies (Table 1). Baseline SPARE-HTN values were compared across participants categorized by longitudinal HTN status: persistently normotensive, persistently hypertensive, or incident HTN. The risk of incident HTN was evaluated among baseline normotensive participants using Cox regression model across the baseline SPARE-HTN quartiles, adjusted for age and sex. The average causal mediated effect (ACME) of SPARE-HTN and total white matter hyperintensity (WMH) volume on the relationship between hypertension and cognitive test scores were assessed using mediation models with 5000 simulated permutations. Table 1 presents the participant characteristics at baseline, stratified by longitudinal HTN status. SPARE-HTN was significantly elevated (Figure 1A) in participants who were normotensive at baseline but developed HTN within 3-7 years (+0.44, p  = 0.01), but not in those who developed HTN in 8-12 years ( p  = 0.65). Normotensive participants with elevated baseline SPARE-HTN scores exhibited significantly higher Cox proportional hazard ratios for HTN incidence (Figure 1B). Mediation analysis demonstrated that SPARE-HTN mediated up to 26% of the effect of HTN on cognitive measures, whilst the conventional WMH volumes showed little mediation effect (Figure 2). Our ML-based sMRI marker predicted incident HTN prior to formal clinical diagnosis, suggesting the presence of subclinical cerebrovascular changes possibly associated with blood pressure variations. These markers, particularly relevant in midlife, offer potential for informing dementia prevention trials by enabling individualized risk stratification and potentially more sensitive measurement of therapeutic efficacy.

Background:Cardiovascular health factors are associated with cognitive decline and risk of dementia, including Alzheimer disease (AD); however, this has been mostly studied in late life. We investigated whether vascular and lifestyle factors are associated with AD plasma and imaging biomarkers in midlife. Methods:We investigated 1,406 participants from the Coronary Artery Risk Development in Young Adults (CARDIA) study with information on vascular and lifestyle factors framed from the American Heart Association (AHA) "life's essential 8" (LE8) guidelines for cardiovascular health at early midlife (mean age 45.0 ± SD 3.6) and AD biomarkers in late midlife (mean age 60 ± SD 3.5). LE8 was calculated and categorized into poor (0-49), intermediate (50-79), and ideal (80-100) cardiovascular health, based on 8 components including smoking, diet, body mass index (BMI), sleep, fasting glucose, blood pressure, cholesterol, and physical activity. We assessed the AD plasma biomarkers phosphorylated tau 217 (ptau-217) and amyloid beta 42/40 ratio (Aβ42/40) and the Spatial Pattern of Abnormality for Recognition of Early AD (SPARE-AD), an algorithm that characterizes AD-like brain atrophy on brain MRI. We used linear regression to examine the association between LE8 and log transformed and standardized AD biomarker measures adjusting for age, sex, race, education, and kidney function. Results:Compared to ideal LE8, intermediate (67.9% of participants) and poor (12.6%) LE8 was associated with lower Αβ42/40 (adjusted mean difference: -2.37, 95% CI: -2.38 to -2.36 and -2.38, 95% CI: -2.40 to -2.36, respectively). There was no association between the LE8 group and ptau-217 level. Moreover, compared to ideal LE8 participants, those with poor LE8 had higher SPARE-AD atrophy pattern (adjusted mean difference: -0.71, 95% CI: -0.81 to -0.62). Conclusion:These findings indicate that poor cardiovascular health in midlife, as defined by the AHA LE8, is linked to less favorable early AD biomarker profiles, particularly reflecting greater amyloid burden and structural brain changes.

BACKGROUND:Alzheimer's disease neuropathology, characterised by amyloid β (Aβ) and phosphorylated-tau (p-tau) protein accumulation, has primarily been assessed with biomarkers in clinical samples of older adults. Less is known about plasma biomarkers of Alzheimer's disease neuropathology and their associations with cognitive outcomes in midlife in diverse community-based samples. Our goal was to address these gaps. METHODS:In this cohort study, we analysed participants who were retained in the US Coronary Artery Risk Development in Young Adults (CARDIA) Study with available plasma biomarkers at year 35 (2020-22). We excluded participants without cognitive measures and individuals with probable dementia. Cognition in five domains was measured with standardised tests at years 30 and 35; accelerated cognitive decline in each domain was defined as a 5-year decline at least 1·5 SD greater than the cohort mean change. Plasma Aβ42, Aβ40, and p-tau217 concentrations were assayed with the use of the Fujirebio Lumipulse G1200 analyser and used to calculate the p-tau217-to-Aβ42 ratio (p-tau217/Aβ42) and Aβ42-to-Aβ40 ratio (Aβ42/40). Alzheimer's disease neuropathology status (ie, negative, intermediate, or positive) was defined based on amyloid PET-validated cutpoints for each biomarker (p-tau217/Aβ42, p-tau217, and Aβ42/40). Associations of Alzheimer's disease neuropathology with cognition (Z scores) and accelerated decline were evaluated with the use of multivariable linear and logistic regression. FINDINGS:From the 2248 CARDIA participants who completed the year 35 visit, we randomly selected 1500 participants for plasma biomarker measurement. We excluded three participants with poor biomarker assay quality, 143 without cognitive measures, and four with probable dementia resulting in a final cohort of 1350. The mean participant age was 61 years (SD 3·6, range 53·0-69·0); 779 (58%) participants were women, 571 (42%) were men, 613 (45%) were Black, and 737 (55%) were White. Alzheimer's disease neuropathology positivity was present in 86 (6%) participants based on p-tau217/Aβ42, 196 (15%) based on Aβ42/40, and 48 (4%) based on p-tau217, and was associated with worse performance on processing speed (standardised cognitive difference comparing Alzheimer's disease neuropathology positive to negative for Aβ42/40, p-tau217, and p-tau217/Aβ42 -0·54 to -0·25; p values 0·0001 to 0·0048) and executive function (-0·42 to -0·19; p values 0·0070 to 0·049). Alzheimer's disease neuropathology positivity was also associated with increased odds of accelerated decline on verbal memory (Aβ42/40: odds ratio 4·31, 95% CI 1·71-10·9, p-tau217/Aβ42: 2·44, 1·16-5·13) and processing speed (p-tau217: 3·98, 1·71-9·3; p-tau217/Aβ42: 3·35, 1·77-6·35) compared with Alzheimer's disease neuropathology negativity. There was no association for global cognition or fluency. Although not consistent, some effect modification was observed, with stronger associations among women and Black participants and individuals with APOE ∈4. INTERPRETATION:Alzheimer's disease neuropathology is relatively uncommon in midlife but associated with worse cognitive performance and accelerated decline and might have stronger association among some groups. Early Alzheimer's disease neuropathology detection with the use of plasma biomarkers might enable timely prevention and intervention in midlife adults including risk reduction and pharmacological therapies. FUNDING:National Heart, Lung, and Blood Institute (75N92023D00002, 75N92023D00003, 75N92023D00004, 75N92023D00005, and 75N92023D00006), National Institute on Aging (R01AG063887, R01AG091431, R35AG071916, and K99AG083211), and the Alzheimer's Association (AARFD-23-1150636).

Background:Social determinants of health (SDOH) are increasingly recognized as contributors to Alzheimer disease (AD) risk, yet the impact of multidimensional social disadvantage early AD-related pathophysiology remains poorly understood. Methods:We studied 1,466 participants from the Coronary Artery Risk Development in Young Adults (CARDIA) cohort with SDOH assessed in early midlife (mean age 40 ± 3.6 years) and plasma AD biomarkers measured 20 years later. A comprehensive SDOH index was constructed from 12 indicators spanning five domains (economic stability, education, neighborhood and physical environment, community and social context, and health care access). We examined associations between SDOH quartile and log-transformed, standardized plasma phosphorylated tau 217 (p-tau217), neurofilament light chain (NfL), and amyloid-β42/40 (Aβ42/40) using linear regression adjusted for age, sex, race, and estimated glomerular filtration rate. Linear trends across SDOH quartile were also evaluated. Results:Participants in the most disadvantaged SDOH quartile had higher p-tau217, higher NfL and lower Aβ42/40 level compared with those in the least disadvantaged quartile (p-tau 217: β = 0.12, 95% CI 0.03-0.21, p = 0.008; NfL: β = 0.20, 95% CI 0.05-0.35, p = 0.009; Aβ42/40: β = -0.15, 95% CI -0.30-0.00, p=0.05). There was also a significant trend across quartile (p-tau 217: p for trend = 0.012; NfL: p for trend =0.001). Analyses of individual SDOH domains indicated that lower economic stability, poorer health care access, and lower education were associated with higher NfL, and poorer health care access was associated with higher p-tau217. Conclusions:Greater SDOH disadvantage in early midlife was associated with higher levels of plasma AD biomarkers reflecting AD pathology and neurodegeneration decades later. These findings suggest that social disadvantage during midlife may contribute to early AD-related biological changes and highlight potentially modifiable social factors relevant for dementia prevention.

BACKGROUND:Social determinants of health (SDOH) are increasingly recognized as important drivers of cognitive outcomes. However, most existing evidence focuses on individual SDOH components and older populations. OBJECTIVES:To develop a comprehensive SDOH index and examine its association with subsequent changes in cognitive function and structural brain measures in midlife. DESIGN:Prospective cohort study with repeated measures of cognition and brain imaging. SETTING:Community-based cohort from the Coronary Artery Risk Development in Young Adults (CARDIA) study. PARTICIPANTS:A total of 3488 participants with SDOH data in early midlife (mean age 40.0 ± 3.6 years); 645 participants had repeated brain magnetic resonance imaging (MRI) data. MEASUREMENTS:A weighted aggregate SDOH index was constructed from 12 items across 5 domains: economic stability, community and social context, education, neighborhood and built environment, and health care access. Cognitive function was assessed using the Digit Symbol Substitution Test (DSST), Stroop Test, and Rey Auditory Verbal Learning Test (RAVLT). Brain MRI outcomes included white matter hyperintensities (WMHs) and total gray matter (GM) volume. Mixed linear regression models examined associations between SDOH quartiles and longitudinal cognitive and MRI outcomes, adjusting for demographics, vascular risk factors, depression, and intracranial volume (for MRI). RESULTS:At baseline, participants in the most disadvantaged SDOH quartile performed worse across all cognitive tests compared with the least disadvantaged quartile (p < 0.001). Over time, the most disadvantaged quartile showed steeper decline in DSST performance (adj. mean change: -0.72, 95% CI: -0.93 to -0.52 vs. -0.55, 95% CI: -0.76 to -0.34, p = 0.013), greater WMH accumulation (ratio: 1.07, 95% CI: 1.05 to 1.09 vs. 1.04, 95% CI: 1.03 to 1.05, p = 0.007), and steeper decline in total GM volume (-2.02 cm³, 95% CI: -2.39 to -1.65 vs. -1.46 cm³, 95% CI: -1.71 to -1.20, p = 0.011) per 5-year interval compared to the least disadvantaged quartile. CONCLUSIONS:Greater social disadvantage in midlife is associated with worse baseline cognition and accelerated decline in cognitive function and brain integrity. These findings highlight the importance of SDOH as key determinants of brain health in midlife and suggest that strategies to mitigate social disadvantage may help preserve cognitive and brain health.

Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we introduce GenFAR, a modular deep learning framework that learns general, clinically informed features from brain MRIs. We trained this modular architecture on 49,246 individuals across 11 cohorts, using 17 diverse classification and regression tasks spanning cognition, clinical, diagnosis, demographics, and biomarkers. This yields aggregated, focused feature sets that capture rich, clinically- and biologically-relevant brain representations. We developed a sequential learning approach where tasks progressively build on previously learned representations. Through an analysis of 5,000 task sequences, we identified an optimal sequence length of six tasks and introduced a Donor Score metric to quantify each task's contribution to downstream performance. This analysis revealed five consistently strong donor tasks (Age, AD/MCI, MMSE, Hypertension, Hyperlipidemia) that formed the base of our sequential model. We demonstrated the utility of our learned representation, in various tasks beyond those included in the training set, to serve as the foundation for specialized secondary predictors. We further showed that using the learned feature representation can substantially increase the sample efficiency of secondary deep learning training tasks and models, as well as improve their accuracy.

Background:The benefits of improved systolic blood pressure (SBP) control on stroke, coronary heart disease, and heart failure are well-established, yet its effect on overall cerebral small vessel disease (SVD) burden remains uncharacterized. We examined the association between intensive SBP control and change in SVD burden. Methods:We conducted a post-hoc analysis of the Systolic Blood Pressure Intervention Trial (SPRINT), a multicenter randomized clinical trial. Of 1267 hypertensive individuals aged ≥50 years without diabetes or prior stroke screened for the brain MRI substudy, 663 and 442 participants completed brain MRI that met quality control criteria and had complete data on SVD indicators at baseline and at a median of 3.9 (interquartile range, 3.6-4.1) years after randomization, respectively. From November 2010 to March 2013, participants were randomly assigned to an intensive SBP target of <120 mmHg (n = 348) or a standard target of <140 mmHg (n = 315). Post-hoc outcome was change in a global SVD factor, longitudinally validated using confirmatory factor analysis and designed to capture overall SVD-related vascular brain injury by integrating three complementary imaging endophenotypes: periventricular white matter hyperintensities, white matter free water, and basal ganglia perivascular spaces. This trial is registered with ClinicalTrials.gov (NCT01206062). Findings:Mean [SD] baseline age was 68.1 (8.6) years; 263 [40%] participants were women. Compared with standard SBP treatment, intensive treatment was associated with significantly less SVD progression (standardized mean difference [Cohen's d] = -0.40 [95% CI, -0.62 to -0.17]). We also observed gradually more favorable SVD burden changes with greater attained SBP reductions, demonstrating a clear dose-response relationship: 21.2% (95% CI, 7.4%-35%), 26.3% (13.1%-39.5%), and 39.4% (24.2%-54.5%) less progression relative to baseline SVD burden for SBP reductions of 0-10 mmHg, 10-20 mmHg, and ≥20 mmHg, respectively. Interpretation:Among hypertensive adults, targeting an SBP of <120 mmHg, compared with <140 mmHg, was associated with less progression of SVD burden. Even modest SBP reductions of ≤10 mmHg conferred measurable brain benefits, with larger reductions providing incrementally greater protection against SVD progression. Funding:National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute on Aging, and National Institute of Neurological Disorders and Stroke.

Background:Lacunes are 3-15 mm cavities originating from small perforating artery disease and are a hallmark of cerebral small vessel disease (cSVD). Prior prevalence studies relied on manual rating, which is prone to inter-rater variability and cannot quantify volume. We applied deep learning to quantify lacunes in a diverse community-based cohort. Methods:In this cross-sectional analysis of 1,038 Multi-Ethnic Study of Atherosclerosis (MESA) participants, with longitudinal cognitive follow-up, we quantified lacunes, confirmed them with a trained rater, and classified them as deep or lobar. Regression models examined associations of lacunes with cardiovascular risk factors, other small vessel disease lesions, brain atrophy, and cognition. Structural equation modeling evaluated whether deep lacune burden mediated associations of age or Framingham All-Cardiovascular Disease (CVD) Risk Score with atrophy and cognition. Results:Overall, 182 participants (17.5%) had at least one lacune (deep: 9.2%; lobar: 9.6%). Hispanic participants had lower lacune burden than White participants. Age, Framingham All-CVD Risk Score, PREVENT 10-year Total CVD Risk Score, and hypertension were associated with overall and deep lacunes, while lobar lacunes showed no associations. Deep lacunes were associated with white matter hyperintensities, enlarged perivascular spaces, and cerebral microbleeds, as well as SPARE-BA, SPARE-AD, and cortical and hippocampal atrophy. Deep lacunes were cross-sectionally associated with decreased global cognition and language/semantic performance, and longitudinally with accelerated executive function decline independent of count. Deep lacune burden significantly mediated associations of age and Framingham All-CVD Risk Score with SPARE-AD, SPARE-BA, and global cognition. Conclusions:In this multi-ethnic community-based cohort, deep lacune burden demonstrated stronger associations with vascular risk, other cerebral small vessel disease markers, brain atrophy, and longitudinal executive function decline than lobar burden. By providing a continuous measure of lesion volume, automated quantification captured information beyond lacune count.

The location of proposed brain MRI markers of small vessel disease (SVD) might reflect their pathogenesis and may translate into differential associations with cognition. We derived regional MRI markers of SVD and studied: (i) associations with cognitive performance, (ii) patterns most likely to reflect underlying SVD, (iii) mediating effects on the relationships of age and cardiovascular disease (CVD) risk with cognition. In 891 participants from The Multi-Ethnic Study of Atherosclerosis, we segmented enlarged perivascular spaces (ePVS), white matter hyperintensities (WMH) and microbleeds (MBs) using deep learning-based algorithms, and calculated white matter (WM) microstructural integrity measures of fractional anisotropy (FA), trace (TR) and free water (FW) using automated DTI-processing pipelines. Measures of global and domain-specific cognitive performance were derived from a comprehensive cognitive evaluation based on the UDS v3 neuropsychological battery. Mean (SD) age was 73.6 (7.9) years; 474 (53%) participants were women. In generalized linear models adjusted for demographics, vascular risk factors, and APOE ε4 carriership, higher basal ganglia ePVS count was associated with worse global, language, and attention cognitive performance (Table 1). Higher periventricular WMH volume was associated with worse global, delayed memory, language, phonemic, and attention performance. Higher WM FA was associated with better global, delayed memory, language, and attention performance. Higher WM TR was associated with worse global, delayed memory, language, phonemic, and attention performance. Exploratory factor analysis revealed that basal ganglia ePVS (standardized loading=0.51), thalamus ePVS (0.43), periventricular WMH (0.85), subcortical WMH (0.65), and WM FA (-0.73) and TR (0.84) loaded onto the same factor, likely reflecting underlying SVD. Structural equation models demonstrated that SVD mediated the effect of age on cognition (β[95%CI]= -0.071[-0.088,-0.053]) through the pathways: Age→SVD→Cognition (-0.044[-0.063,-0.026]) and Age→SVD→Brain Atrophy→Cognition (-0.006[-0.012,-0.002]) – Figure 1, and the effect of CVD risk on cognition (-0.028[-0.044,-0.012]) through the pathways: CVD Risk→SVD→Cognition (-0.021[-0.031,-0.013]) and CVD Risk→SVD→Brain Atrophy→Cognition (-0.007[-0.012,-0.003]) – Figure 2. The location of the proposed MRI markers of SVD likely reflects distinct etiopathogenic substrates and should be considered when examining associations with cognitive or other health-related outcomes. SVD mediates the relationships of age and CVD risk with cognition via both atrophy-related and unrelated pathways.

Colloids present a challenge for nuclear decommissioning and disposal due to their potential to mobilise radionuclides. Waste retrieval and decommissioning of storage ponds for spent nuclear fuel and silos for radioactive waste at the Sellafield nuclear facility, UK, are high priorities. The particulates characterised here originate from facilities > 60 years old and provide a unique opportunity to investigate the long-term fate of radionuclides in an aquatic, engineered storage environment. Radioactive effluents were obtained from a legacy pond and characterised using ultrafiltration, transmission electron microscopy (TEM) and actinide L3 edge X-ray absorption spectroscopy (XAS). TEM analysis showed discrete UO2-like nanoparticles, 5-10 nm in size, often co-associated with Mg-Al- and Fe-(oxyhydr)oxide colloidal phases. Uranium XAS indicated a mix of uranium oxidation states with EXAFS suggesting U(IV)-oxide nanoparticles and sorbed U(VI). Pu XANES identified Pu(IV) as the dominant oxidation state. Both U and Pu associates with large, Mg/Al- and Fe-(oxyhydr)oxide agglomerates highlights the potential for pseudo-colloid formation, explaining the basis of current particle filtration / abatement of technology. This study, which examines novel samples from a complex, highly radioactive facility using advanced techniques, provides a new understanding of radionuclide speciation and mobility in these environments and informs radioactive effluent treatment and disposal.

Obstructive sleep apnea is associated with cognitive impairment; however, the underlying mechanisms remain incompletely understood. Obstructive sleep apnea is characterized by periods of interrupted ventilation (ventilatory burden), leading to hypoxemia (hypoxic burden) and/or arousal (arousal burden) from sleep. Although hypoxemia is considered a key mechanism underlying white matter injury, its measurement has been limited. In our primary analysis, we assessed the association of hypoxic burden, a quantitative measure of hypoxemia, with white matter hyperintensity volume, a marker of small vessel disease, and compared it with that of ventilatory burden and arousal burden (quantitative measures of ventilatory deficit and arousals). Data from participants in the Multi-Ethnic Study of Atherosclerosis with full polysomnograms and brain magnetic resonance imaging were analyzed. Hypoxic burden was defined as the total area under the oxygen desaturation curve per hour of sleep, ventilatory burden was defined as the event-specific area under the ventilation signal, and arousal burden was defined as the normalized cumulative duration of all arousals. The primary outcome was white matter hyperintensity volume, with other magnetic resonance imaging measures considered secondary outcomes. The analysis included polysomnograms from 587 participants (age: 65.5 ± 8.2 years). In the fully adjusted model, each 1 standard deviation increase in hypoxic burden was associated with a 0.09 standard deviation increase in white matter hyperintensity volume (P = .023), after adjusting for demographics, study site, and comorbidities. In contrast, ventilatory burden, arousal burden, and conventional obstructive sleep apnea measures were not associated with outcomes. Hypoxic burden was associated with white matter hyperintensity volume in a racially/ethnically diverse cohort of older individuals with a high prevalence of obstructive sleep apnea . Hajipour M, Hu W-H, Esmaeili N, et al. Sleep apnea physiological burdens and markers of white matter injury: the Multi-Ethnic Study of Atherosclerosis. J Clin Sleep Med. 2025;21(3):457–466.

BackgroundCognitive processing speed is integral to everyday activities and can be improved with training in persons with mild cognitive impairment (MCI). However, whether this training maintains everyday abilities is not known.ObjectiveWe aimed to determine whether everyday functions key to independence could be preserved with two years of processing speed training.MethodsIn a randomized controlled trial, we objectively evaluated a processing speed training protocol compared to a control training protocol, in 103 persons with MCI (n = 90) or very mild dementia (n = 13) due to Alzheimer's disease (AD). Each protocol involved serial assessments, laboratory training, and home training over a two-year period. We accounted for APOE ε4 carrier status and MRI-based neurodegeneration conducted at baseline. Outcomes were longitudinal changes in performance-based Instrumental Activities of Daily Living (IADLs), community mobility, and on-road driving. We used linear mixed models to evaluate changes in these outcomes over time.ResultsChanges in IADL function, driving, and community mobility did not differ by training assignment. Greater baseline neurodegeneration predicted larger declines in all functional outcomes (p values < 0.001).ConclusionsIn persons with MCI or very mild dementia, processing speed training was no more effective for maintaining everyday functions than training involving common computer activities and games that do not target processing speed. Greater baseline neurodegeneration predicted worse performance over time on all measures of function.

Introduction Hypertension (HTN) is a well-established modifiable risk factor for neurodegeneration and dementia. However, traditional neuroimaging markers, such as white matter hyperintensity volume (WMH), often lack the sensitivity required for early detection and individualized risk stratification of HTN-related brain changes and their cognitive consequences. Understanding individual susceptibility and the underlying mechanisms remains a critical challenge for effective intervention. Methods To address this gap, we developed SPARE-HTN (Spatial Patterns of Abnormalities for Recognition of Hypertension), a machine learning-based marker from structural magnetic resonance images. This model was trained on a large, diverse set of studies from the iSTAGING dataset, leveraging regional gray matter volumes from T1- weighted images and lobar WMH volumes from T2-weighted FLAIR images to quantify HTN- related neurodegeneration at an individual level. We performed a genome-wide association study (GWAS) on a subset of hypertensive participants from the UK Biobank to identify genetic variants underlying SPARE-HTN. Longitudinal clinical data from multiple studies were analyzed to evaluate SPARE-HTN's predictive capacity for incident HTN and its mediating role in cognitive decline. Results SPARE-HTN demonstrated superior sensitivity in detecting HTN-related brain changes compared to total WMH, effectively differentiating even subclinical stages of HTN (e.g., Cohen's d=0.2 vs 0.04 for Normal-Stage 1 HTN; d=0.71 vs d=0.38 Normal-Stage 2 HTN). Longitudinal analyses revealed that SPARE-HTN was significantly elevated in participants who were normotensive at baseline but developed HTN within 3-7 years (+0.44, p=0.01). Furthermore, SPARE-HTN mediated up to 26% of the effect of HTN on cognitive measures, significantly more than conventional WMH volumes. GWAS identified significant genomic risk loci on Chromosomes 2 and 17 associated with SPARE-HTN, encompassing genes previously linked to cardiovascular conditions and WM damage.Demographic associations showed higher SPARE-HTN scores in men, Asian and Black individuals, and those with lower education levels. Conclusions SPARE-HTN promises individualized MRI estimation of subtle HTN-related brain changes, predicting incident hypertension and significantly mediating HTN’s impact on cognition. These findings, supported by genetic and longitudinal data, highlight SPARE-HTN's potential for enhanced individualized risk stratification and for serving as a more sensitive outcome measure in clinical trials targeting dementia prevention through treatment of modifiable vascular risk factors.

White matter hyperintensities (WMH) may be indicative of age-related cerebrovascular diseases and contribute to cognitive and functional decline. Normal appearing WM (NAWM) adjacent to WMH, termed "penumbra," is known to be vulnerable to future WMH pathology. WM integrity can be evaluated using multiple magnetic resonance imaging (MRI) modalities. We aimed to identify MRI features predictive of WMH growth and to compare the implications of these features based on spatial proximity to existing WMH versus signal features in baseline NAWM. We used baseline and 5-year follow-up MRI scans in 485 middle-aged participants form the Coronary Artery Risk Development in Young Adults (CARDIA). Multimodal MRI at baseline, including fluid attenuated inversion recovery (FLAIR), diffusion tensor imaging (DTI), and cerebral blood flow (CBF), was measured within WM ROIs including baseline WMH and regions that later developed into new WMH, within and external to the baseline penumbra. Overall, we found that 80% of new WMH appeared within the baseline penumbra. We also found lower fractional anisotropy (FA) and CBF and higher FLAIR and median diffusivity (MD) in NAWM at baseline in regions with subsequent WMH growth compared to those without WMH growth. For NAWM regions defined by signal features, subthreshold FA and suprathreshold MD and FLAIR abnormality at baseline were the most robust predictors of WMH growth. Baseline systolic blood pressure had significant associations with baseline abnormalities in NAWM and subsequently with cognitive decline, particularly for FA and MD measures. The findings support the use of DTI as the predictor of WMH growth, which is correlated with subtle, adverse WM alterations and cognitive function years before developing to WMH. The results may contribute to future clinical trials aimed at preserving WM integrity.

While brain morphology is well-established as a key factor influencing overall brain function, little is known about how brain structural properties are associated with oscillatory activity, particularly during sleep. In this study, we analyzed whole-night sleep EEG and brain structural MRI data from a subset of 621 individuals in the Multi-Ethnic Study of Atherosclerosis to explore the relationship between brain structure and sleep EEG properties. Sleep EEG data were preprocessed and analyzed using the open-source software Luna (https://zzz.bwh.harvard.edu/luna/). We found that larger total white matter (WM) volume was associated with higher absolute broad-band power, regardless of sleep stage (the strongest effects in the beta band during R, bst = 0.45, p = 3x10-6), likely reflecting WM contribution to enhanced synchronization across cortical regions and reduced activation attenuation via long-range myelinated fibers. Additionally, both WM fractional anisotropy and thalamus volume showed negative association with relative slow power and positive association with delta power during non-rapid eye movement sleep (strongest effect with corpus callosum FA, bst = –0.22, p = 2x10-6 for slow and bst = 0.15, p = 1x10-4 for delta band). This was mirrored in the duration of slow oscillations (SOs), both overall and when divided into slow-switching and fast-switching types, with their ratio additionally linked to total WM volume. Furthermore, we observed strong but largely independent effects of age and sex on sleep EEG and structural MRI metrics, suggesting that sleep EEG captures aging processes and sex-specific features that extend beyond the macro-scale brain morphology changes examined here. Overall, these findings deepen our understanding of how structural brain properties influence sleep-related oscillatory activity. This research was supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). Brain MRIs were acquired by grant R01 HL127659 and sleep EEG were collected by grant 5R01AG070867. The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions.

BACKGROUND:Cerebrovascular reactivity reflects changes in cerebral blood flow in response to an acute stimulus and is reflective of the brain's ability to match blood flow to demand. Functional MRI with a breath-hold task can be used to elicit this vasoactive response, but data validity hinges on subject compliance. Determining breath-hold compliance often requires external monitoring equipment. PURPOSE:To develop a non-invasive and data-driven quality filter for breath-hold compliance using only measurements of head motion during imaging. STUDY TYPE:Prospective cohort. PARTICIPANTS:Longitudinal data from healthy middle-aged subjects enrolled in the Coronary Artery Risk Development in Young Adults Brain MRI Study, N = 1141, 47.1% female. FIELD STRENGTH/SEQUENCE:3.0 Tesla gradient-echo MRI. ASSESSMENT:Manual labelling of respiratory belt monitored data was used to determine breath hold compliance during MRI scan. A model to estimate the probability of non-compliance with the breath hold task was developed using measures of head motion. The model's ability to identify scans in which the participant was not performing the breath hold were summarized using performance metrics including sensitivity, specificity, recall, and F1 score. The model was applied to additional unmarked data to assess effects on population measures of CVR. STATISTICAL TESTS:Sensitivity analysis revealed exclusion of non-compliant scans using the developed model did not affect median cerebrovascular reactivity (Median [q1, q3] = 1.32 [0.96, 1.71]) compared to using manual review of respiratory belt data (1.33 [1.02, 1.74]) while reducing interquartile range. RESULTS:The final model based on a multi-layer perceptron machine learning classifier estimated non-compliance with an accuracy of 76.9% and an F1 score of 69.5%, indicating a moderate balance between precision and recall for the identification of scans in which the participant was not compliant. DATA CONCLUSION:The developed model provides the probability of non-compliance with a breath-hold task, which could later be used as a quality filter or included in statistical analyses. LEVEL OF EVIDENCE: 1:TECHNICAL EFFICACY: Stage 3.

OBJECTIVES:The safety of intensive blood pressure lowering in patients with preexisting cerebral small vessel disease (CSVD) remains unclear. METHODS:We used data from 759 participants in Systolic Blood Pressure Intervention Trial (SPRINT) who completed a baseline MRI, and categorized participants by the median abnormal white matter hyperintensity volume (WMHv, <3.2 cm 3 versus ≥3.2 cm 3 ). We estimated the association of the baseline WMHv with cardiovascular outcomes and adverse events using Cox proportional hazards models adjusted for treatment assignment, age, sex, MRI scanner, and intracranial volume. We used stratified analysis to determine the effect of intensive versus standard treatment by the baseline WMHv. RESULTS:The mean age of the participants was 68 ± 9 years and 39% were female. In adjusted models, adults with WMHv above the median had an increased risk of the primary cardiovascular composite outcome [hazard ratio (HR) 2.59, 95% confidence interval (CI) 1.39, 4.81], all-cause mortality (HR 2.06, 95% CI 0.97, 4.37), and mild cognitive impairment or probable dementia (HR 1.76, 95% CI 0.99, 3.13). While the effects of intensive versus standard blood pressure treatment were similar for most outcomes by WMHv, intensive treatment was associated with a higher risk for mild cognitive impairment or probable dementia among adults with a WMHv above the median (HR 2.36, 95% CI 1.20, 4.66), but not among adults with a WMHv below the median (p-value for interaction = 0.09). CONCLUSIONS:In this posthoc analysis of SPRINT, adults with a higher WMHv were at a higher risk for adverse cardiovascular and cognitive outcomes. Among these adults, intensive blood pressure treatment reduced cardiovascular events, while its effects on the risk of cognitive impairment or dementia in this subgroup merit further study.

BACKGROUND AND OBJECTIVES:In neuropathologic studies, iron accumulation in gray matter (GM) is associated with aging and specific neurological diseases, but less is known about its correlates in community-based populations. METHODS:In the Multi-Ethnic Study of Atherosclerosis, brain MRI was conducted in 2018-2019. To estimate iron content, we derived the median quantitative susceptibility mapping (QSM) signal from four regions: the basal ganglia and cortical GM of the frontal, temporal, and parietal lobes. We examined cross-sectional associations with demographic and clinical characteristics, cognitive test performance, gait speed, and brain MRI measures of atrophy and small vessel disease. RESULTS:We studied 943 participants (14 % Chinese, 25 % Black, 20 % Hispanic, 41 % White; mean age 74 years; 48 % men). In multivariable models, higher left basal ganglia QSM signal was associated with older age (7.2 ppb per 10 years; 95 %CI 4.6,9.9), smoking (7.1; 3.4,10.9), and diabetes (7.4; 2.5,12.3). Lower QSM signal was associated with Black race (-15.3; -20.6,-10, relative to White) and higher high-density lipoprotein cholesterol (-3.4 per 20 mg/dL; -5.8,-0.9). In cortical GM, QSM signal was associated with greater waist circumference, lifetime alcohol use, and log-transformed white matter hyperintensity (WMH) volume (0.08-0.12 SD units per SD, all p ≤ 0.002), but not with cognitive test performance or gait speed. DISCUSSION:In cross-sectional analyses in a community-based cohort, older age, White race, smoking, diabetes, and greater WMH volume were associated with higher QSM signal in basal ganglia and/or cortical GM. Longitudinal studies are needed to further explore GM QSM signal in relation to cognition and gait in older individuals.

INTRODUCTION:We aimed to examine the global impact of brain small vessel disease (SVD) on cognitive performance. METHODS:In 892 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we derived perivascular spaces (PVS), white matter hyperintensities (WMH), microbleeds (MB), and white matter fractional anisotropy (FA) and trace (TR). Cognitive function was assessed with a comprehensive neuropsychological battery. RESULTS:A composite SVD measure was constructed as a linear combination of basal ganglia PVS, thalamus PVS, periventricular WMH, subcortical WMH, and white matter FA and TR, and exhibited associations with worse global and domain-specific cognitive performance. Additionally, SVD mediated the effect of age and cardiovascular disease risk on global cognitive function, both directly and through smaller gray matter (GM) volume. DISCUSSION:Integrating multiple individual SVD endophenotypes may more accurately reflect the neurobiology of SVD and capture its global impact on cognition. SVD mediates the effects of age and cardiovascular disease risk on cognition through both atrophy-related and non-atrophy-related pathways. HIGHLIGHTS:Associations between individual magnetic resonance imaging (MRI) markers of brain small vessel disease and cognitive outcomes might not fully capture the global impact of small vessel disease on cognition. We modeled small vessel disease as a latent construct, integrating multiple MRI endophenotypes in strategic brain regions. The small vessel disease construct was associated with worse global and domain-specific cognitive performance. The small vessel disease construct exhibited mediating effects in the relationships of aging and cardiovascular disease risk with cognition through pathways that both involve and are independent of brain atrophy. Integrating information from multiple relevant imaging endophenotypes could open new avenues in small vessel disease research, broadening our understanding of its risk factors and clinical correlates.

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45-85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45-64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies.