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Abstract Alzheimer’s disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the “diffuse-AD” (R1) dimension shows widespread brain atrophy, and the “MTL-AD” (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were “druggable genes” for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction—driven by genes different from APOE—which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .

Abstract Background Numerous upper airway anatomy characteristics are risk factors for sleep apnea, which affects 26% of older Americans, and more severe sleep apnea is associated with cognitive impairment. This study explores the pathophysiology and links between upper airway anatomy, sleep, and cognition. Methods Participants in the Multi-Ethnic Study of Atherosclerosis underwent an upper airway MRI, polysomnography to assess sleep measures including the apnea-hypopnea index (AHI) and completed the Cognitive Abilities Screening Instrument (CASI). Two model selection techniques selected from among 67 upper airway measures those that are most strongly associated with CASI score. The associations of selected upper airway measures with AHI, AHI with CASI score, and selected upper airway anatomy measures with CASI score, both alone and after adjustment for AHI, were assessed using linear regression. Results Soft palate volume, maxillary divergence, and upper facial height were significantly positively associated with higher CASI score, indicating better cognition. The coefficients were small, with a 1 standard deviation (SD) increase in these variables being associated with a 0.83, 0.75, and 0.70 point higher CASI score, respectively. Additional adjustment for AHI very slightly attenuated these associations. Larger soft palate volume was significantly associated with higher AHI (15% higher AHI (95% CI 2%,28%) per SD). Higher AHI was marginally associated with higher CASI score (0.43 (95% CI 0.01,0.85) per AHI doubling). Conclusions Three upper airway measures were weakly but significantly associated with higher global cognitive test performance. Sleep apnea did not appear to be the mechanism through which these upper airway and cognition associations were acting. Further research on the selected upper airway measures is recommended.

Key points Although an overall boon to clinical diagnosis, increasingly voluminous, diverse, and fragmented diagnostic data can overwhelm physicians and frustrate patients. Data are gathered and processed within “silos” of the diagnostic disciplines, including radiology and pathology. The EHR does little to intelligently organize and synthesize these disparate data to facilitate diagnosis. ID envisions a process in which data from the entire arsenal of in vivo and in vitro diagnostics, together with clinical data from the EHR, are aggregated and contextualized to enhance diagnosis and direct clinical action

Abstract Objective Expression of glial fibrillary acidic protein (GFAP), a marker of reactive astrocytosis, colocalizes with neuropathology in the brain. Blood levels of GFAP have been associated with cognitive decline and dementia status. However, further examinations at a population‐based level are necessary to broaden generalizability to community settings. Methods Circulating GFAP levels were assayed using a Simoa HD‐1 analyzer in 4338 adults without prevalent dementia from four longitudinal community‐based cohort studies. The associations between GFAP levels with general cognition, total brain volume, and hippocampal volume were evaluated with separate linear regression models in each cohort with adjustment for age, sex, education, race, diabetes, systolic blood pressure, antihypertensive medication, body mass index, apolipoprotein E ε4 status, site, and time between GFAP blood draw and the outcome. Associations with incident all‐cause and Alzheimer's disease dementia were evaluated with adjusted Cox proportional hazard models. Meta‐analysis was performed on the estimates derived from each cohort using random‐effects models. Results Meta‐analyses indicated that higher circulating GFAP associated with lower general cognition (ß = −0.09, [95% confidence interval [CI]: −0.15 to −0.03], p = 0.005), but not with total brain or hippocampal volume (p > 0.05). However, each standard deviation unit increase in log‐transformed GFAP levels was significantly associated with a 2.5‐fold higher risk of incident all‐cause dementia (Hazard Ratio [HR]: 2.47 (95% CI: 1.52–4.01)) and Alzheimer's disease dementia (HR: 2.54 [95% CI: 1.42–4.53]) over up to 15‐years of follow‐up. Interpretation Results support the potential role of circulating GFAP levels for aiding dementia risk prediction and improving clinical trial stratification in community settings.

Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity = 0.82, precision = 0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.

Background Atrial fibrillation (AF) is associated with increased stroke risk and accelerated cognitive decline, but the association of early manifestations of left atrial (LA) impairment with subclinical changes in brain structure is unclear. We investigated whether abnormal LA structure and function, greater supraventricular ectopy, and intermittent AF are associated with small vessel disease on magnetic resonance imaging of the brain. Methods and Results In the Multi‐Ethnic Study of Atherosclerosis, 967 participants completed 14‐day ambulatory electrocardiographic monitoring, speckle tracking echocardiography and, a median 17 months later, magnetic resonance imaging of the brain. We assessed associations of LA volume index and reservoir strain, supraventricular ectopy, and prevalent AF with brain magnetic resonance imaging measures of small vessel disease and atrophy. The mean age of participants was 72 years; 53% were women. In multivariable models, LA enlargement was associated with lower white matter fractional anisotropy and greater prevalence of microbleeds; reduced LA strain, indicating worse LA function, was associated with more microbleeds. More premature atrial contractions were associated with lower total gray matter volume. Compared with no AF, intermittent AF (prevalent AF with

Abstract Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

White matter hyperintensities (WMH) are a common brain-imaging feature of cerebral small vessel disease. Here, the authors carry out a GWAS and followup analyses for WMH-volume, implicating several variants with potential for risk stratification and drug targeting.

Background Cardiovascular risk factors are associated with cognitive decline and dementia. Magnetic resonance imaging provides sensitive measurement of brain morphology and vascular brain injury. However, associations of risk factors with brain magnetic resonance imaging findings have largely been studied in White participants. We investigated associations of race, ethnicity, and cardiovascular risk factors with brain morphology and white matter (WM) injury in a diverse population. Methods and Results In the Multi‐Ethnic Study of Atherosclerosis, measures were made in 2018 to 2019 of total brain volume, gray matter and WM volume, and WM injury, including WM hyperintensity volume and WM fractional anisotropy. We assessed cross‐sectional associations of race and ethnicity and of cardiovascular risk factors with magnetic resonance imaging measures. Magnetic resonance imaging data were complete in 1036 participants; 25% Black, 15% Chinese‐American, 19% Hispanic, and 41% White. Mean (SD) age was 72 (8) years and 53% were women. Although WM injury was greater in Black than in White participants in a minimally adjusted model, additional adjustment for cardiovascular risk factors and socioeconomic status each attenuated this association, rendering it nonsignificant. Overall, greater average WM hyperintensity volume was associated with older age and current smoking (69% greater vs never smoking); lower fractional anisotropy was additionally associated with higher diastolic blood pressure, use of antihypertensive medication, and diabetes. Conclusions We found no statistically significant difference in measures of WM injury by race and ethnicity after adjustment for cardiovascular risk factors and socioeconomic status. In all racial and ethnic groups, older age, current smoking, hypertension, and diabetes were strongly associated with WM injury.

Delineation and quantification of normal and abnormal brain tissues on Magnetic Resonance Images is fundamental to the diagnosis and longitudinal assessment of neurological diseases. Here we sought to develop a convolutional neural network for automated multiclass tissue segmentation of brain MRIs that was robust at typical clinical resolutions and in the presence of a variety of lesions. We trained a 3D U-Net for full brain multiclass tissue segmentation from a prior atlas-based segmentation method on an internal dataset that consisted of 558 clinical T1-weighted brain MRIs (453/52/53; training/validation/test) of patients with one of 50 different diagnostic entities (n = 362) or with a normal brain MRI (n = 196). We then used transfer learning to refine our model on an external dataset that consisted of 7 patients with hand-labeled tissue types. We evaluated the tissue-wise and intra-lesion performance with different loss functions and spatial prior information in the validation set and applied the best performing model to the internal and external test sets. The network achieved an average overall Dice score of 0.87 and volume similarity of 0.97 in the internal test set. Further, the network achieved a median intra-lesion tissue segmentation accuracy of 0.85 inside lesions within white matter and 0.61 inside lesions within gray matter. After transfer learning, the network achieved an average overall Dice score of 0.77 and volume similarity of 0.96 in the external dataset compared to human raters. The network had equivalent or better performance than the original atlas-based method on which it was trained across all metrics and produced segmentations in a hundredth of the time. We anticipate that this pipeline will be a useful tool for clinical decision support and quantitative analysis of clinical brain MRIs in the presence of lesions.

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3–96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

An increase in the volume of the brain lateral ventricles is a sign of normal aging, but can also be associated with neurological and psychiatric disorders. Here, Vojinovic et al. identify seven genetic loci in a GWA study for ventricular volume in 23,500 individuals and find correlation with thalamus volume.

Objective: We examined imaging surrogates of white matter microstructural abnormalities which may precede white matter lesions (WML) and represent a relevant marker of cerebrovascular injury in adults in midlife. Methods: In 698 community-dwelling adults (mean age 50 years ±3.5 SD) from the Coronary Artery Risk Development in Young Adults (CARDIA) Brain MRI sub-study, WML were identified on structural MR and fractional anisotropy (FA), representing WM microstructural integrity, was derived using Diffusion Tensor Imaging. FA and WML maps were overlaid on a parcellated T1-template, based on an expert-delineated brain atlas, which included 42 WM tract ROIs. Analyses occurred in stages: 1) WML were quantified for the different tracts (i.e., frequency, volume, volume relative to tract size); 2) the interdependence of FA in normal appearing WM (NAWM) and WML was examined across tracts; 3) associations of NAWM FA and hypertension status were assessed controlling for WML volume. In the latter analysis, both overall hypertension (i.e. hypertension vs. normotension and prehypertension vs. normotension) and hypertension categorized by antihypertensive treatment status (yes/no) and blood pressure control (e.g., diastolic

The periventricular white matter (PVWM) is supplied by terminal distributions of small vessels and is particularly susceptible to developing white matter lesions (WML) associated with cerebral small vessel disease (CSVD). We obtained group-averaged cerebral blood flow (CBF) maps from Arterial Spin Labeled (ASL) perfusion MRI data obtained in 436 middle-aged (50.4 ± 3.5 years) subjects in the NHLBI CARDIA study and in 61 elderly (73.3 ± 6.9 years) cognitively normal subjects recruited from the Penn Alzheimer's Disease Center (ADC) and found that the lowest perfused brain voxels are located within the PVWM. We constructed a white matter periventricular small vessel (PSV) region of interest (ROI) by empirically thresholding the group averaged CARDIA CBF map at CBF

BackgroundWe investigated whether cardiac parameters in young adulthood are associated with indicators of brain health in midlife. Methods and ResultsThis study includes 648 participants from the CARDIA (Coronary Artery Risk Development in Young Adults) study (52% women, 38% black). We studied associations of cardiac parameters assessed by echocardiography (left ventricular ejection fraction, left atrial volume, and left ventricular mass) in young adulthood (mean age: 30 years) with brain measures obtained by magnetic resonance imaging (total brain, gray and white matter volume, white matter integrity, abnormal white matter) in midlife (mean age: 50 years). In 406 individuals with complete measurements, higher left atrial volume was associated with lower white matter fractional anisotropy, independent of traditional cardiovascular risk factors (β=−0.002; P

BackgroundThe classic view of blood pressure (BP) reactivity to psychological stress in relation to cardiovascular risks assumes that excess reactivity is worse and lower reactivity is better. Evidence addressing how stress‐induced BP reactivity in young adults is associated with midlife cognitive function is sparse. Methods and ResultsWe assessed BP reactivity during a star tracing task and a video game in adults aged 20 to 32 years. Twenty‐three years later, cognitive function was assessed with use of the Digit Symbol Substitution Test (a psychomotor speed test), the Rey Auditory Verbal Learning Test (a verbal memory test), and the modified Stroop test (an executive function test). At the time of follow‐up, participants (n=3021) had a mean age of 50.2 years; 56% were women, and 44% were black. In linear regression models adjusted for demographic and clinical characteristics including baseline and follow‐up resting BP, lower systolic BP (SBP) reactivity during the star tracing and video game was associated with worse Digit Symbol Substitution Test scores (β [SE]: 0.11 [0.02] and 0.05 [0.02], respectively) and worse performance on the Stroop test (β [SE]: −0.06 [0.02] and −0.05 [0.02]; all P

Clinical and epidemiological studies of older persons have implicated clusterin in Alzheimer's disease (AD) pathogenesis. In the context of identifying early biomarkers of risk, we examined associations of plasma clusterin and characteristics of AD in middle-aged individuals from the community.Subjects were 639 cognitively normal individuals (mean age 50 ± 3.5) from the Coronary Artery Risk Development in Young Adults (CARDIA) Brain MRI sub-study. Clusterin was quantified using ELISA (mean 255± 31 ng/ml). Associations were assessed between clusterin and volumes of brain regions known to atrophy in early AD, including entorhinal cortex (ECV), hippocampus (HV), and medial temporal lobe (MTLV) volumes (cm3). Total brain volume (TBV) and volumes of structures affected in later AD were examined for comparison.In multivariable models, higher clusterin had a negative non-linear association with ECV (combined left and right hemispheres), and this association was influenced by the highest clusterin levels. Compared to mean clusterin, 1 and 2 standard deviation (SD) level increases in clusterin were associated with -2.1% (95% CI: -3.3,-0.9) and -7.3% (95% CI: -11.3,-3.3) lower ECV, respectively. Similar relationships were observed between clusterin and HV, although the relationship was stronger for left-side HV than the right-side. However, the association was not significant after adjusting for covariates. Negative non-linear associations between clusterin and MTLV were strongest for the left side: compared to mean clusterin, 1 and 2 SD level increases in clusterin were associated with -0.9% (95% CI: -1.9, 0.1) and -3.7% (95% CI: -7.1, -0.3) lower MTLV. There were no significant associations between clusterin and brain structures affected in later AD.In middle-aged adults unselected for AD, plasma clusterin was associated with lower volume of the entorhinal cortex, an area that atrophies early in AD. Clusterin could be informative as part of a multi-component preclinical marker for AD.

BACKGROUNDS:The Women's Health Initiative Memory Study Magnetic Resonance Imaging (WHIMS-MRI) provides an opportunity to evaluate how menopausal hormone therapy (HT) affects the structure of older women's brains. Our earlier work based on region of interest (ROI) analysis demonstrated potential structural changes underlying adverse effects of HT on cognition. However, the ROI-based analysis is limited in statistical power and precision, and cannot provide fine-grained mapping of whole-brain changes. METHODS:We aimed to identify local structural differences between HT and placebo groups from WHIMS-MRI in a whole-brain refined level, by using a novel method, named Optimally-Discriminative Voxel-Based Analysis (ODVBA). ODVBA is a recently proposed imaging pattern analysis approach for group comparisons utilizing a spatially adaptive analysis scheme to accurately locate areas of group differences, thereby providing superior sensitivity and specificity to detect the structural brain changes over conventional methods. RESULTS:Women assigned to HT treatments had significant Gray Matter (GM) losses compared to the placebo groups in the anterior cingulate and the adjacent medial frontal gyrus, and the orbitofrontal cortex, which persisted after multiple comparison corrections. There were no regions where HT was significantly associated with larger volumes compared to placebo, although a trend of marginal significance was found in the posterior cingulate cortical area. The CEE-Alone and CEE+MPA groups, although compared with different placebo controls, demonstrated similar effects according to the spatial patterns of structural changes. CONCLUSIONS:HT had adverse effects on GM volumes and risk for cognitive impairment and dementia in older women. These findings advanced our understanding of the neurobiological underpinnings of HT effects.

ObjectiveTo identify early changes in brain structure and function that are associated with cardiovascular risk factors (CVRF).DesignCross-sectional brain Magnetic Resonance I (MRI) study.SettingCommunity based cohort in three U.S. sites.ParticipantsA Caucasian and African-American sub-sample (n= 680; mean age 50.3 yrs) attending the 25 year follow-up exam of the Coronary Artery Risk Development in Young Adults Study.Primary and secondary outcomes3T brain MR images processed for quantitative estimates of: total brain (TBV) and abnormal white matter (AWM) volume; white matter fractional anisotropy (WM-FA); and gray matter cerebral blood flow (GM-CBF). Total intracranial volume is TBV plus cerebral spinal fluid (TICV). A Global Cognitive Function (GCF) score was derived from tests of speed, memory and executive function.ResultsAdjusting for TICV and demographic factors, current smoking was significantly associated with lower GM-CBF and TBV, and more AWM (all ConclusionIn middle age adults, CVRF are associated with brain health, reflected in MRI measures of structure and perfusion, and cognitive functioning. These findings suggest markers of mid-life cardiovascular and brain health should be considered as indication for early intervention and future risk of late-life cerebrovascular disease and dementia.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Sleep apnea, affecting an estimated 1 in 4 American adults, has been reported to be associated with both brain structural abnormality and impaired cognitive function. Obstructive sleep apnea is known to be affected by upper airway anatomy. To better understand the contribution of upper airway anatomy to pathways linking sleep apnea with impaired cognitive function, we investigated the association of upper airway anatomy with structural brain abnormalities. Based in the Multi-Ethnic Study of Atherosclerosis, a longitudinal cohort study of community-dwelling adults, a comprehensive sleep study and an MRI of the upper airway and brain were performed on 578 participants. Machine learning models were used to select from 74 upper airway measures those measures most associated with selected regional brain volumes and white matter hyperintensity volume. Linear regression assessed associations between the selected upper airway measures, sleep measures, and brain structure. Maxillary divergence was positively associated with hippocampus volume, and mandible length was negatively associated with total white and gray matter volume. Both coefficients were small (coefficients per standard deviation 0.063 mL, p = 0.04, and - 7.0 mL, p < 0.001 respectively), and not affected by adjustment for sleep study measures. Self-reported snoring >2 times per week was associated with larger hippocampus volume (coefficient 0.164 mL, p = 0.007), and higher percentage of time in the N3 sleep stage was associated with larger total white and gray matter volume (4.8 mL, p = 0.004). Despite associations of two upper airway anatomy measures with brain volume, the evidence did not suggest that these upper airway and brain structure associations were acting primarily through the pathway of sleep disturbance., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background Cardiovascular risk factors are associated with cognitive decline and dementia. Magnetic resonance imaging provides sensitive measurement of brain morphology and vascular brain injury. However, associations of risk factors with brain magnetic resonance imaging findings have largely been studied in White participants. We investigated associations of race, ethnicity, and cardiovascular risk factors with brain morphology and white matter (WM) injury in a diverse population. Methods and Results In the Multi-Ethnic Study of Atherosclerosis, measures were made in 2018 to 2019 of total brain volume, gray matter and WM volume, and WM injury, including WM hyperintensity volume and WM fractional anisotropy. We assessed cross-sectional associations of race and ethnicity and of cardiovascular risk factors with magnetic resonance imaging measures. Magnetic resonance imaging data were complete in 1036 participants; 25% Black, 15% Chinese-American, 19% Hispanic, and 41% White. Mean (SD) age was 72 (8) years and 53% were women. Although WM injury was greater in Black than in White participants in a minimally adjusted model, additional adjustment for cardiovascular risk factors and socioeconomic status each attenuated this association, rendering it nonsignificant. Overall, greater average WM hyperintensity volume was associated with older age and current smoking (69% greater vs never smoking); lower fractional anisotropy was additionally associated with higher diastolic blood pressure, use of antihypertensive medication, and diabetes. Conclusions We found no statistically significant difference in measures of WM injury by race and ethnicity after adjustment for cardiovascular risk factors and socioeconomic status. In all racial and ethnic groups, older age, current smoking, hypertension, and diabetes were strongly associated with WM injury.

Delineation and quantification of normal and abnormal brain tissues on Magnetic Resonance Images is fundamental to the diagnosis and longitudinal assessment of neurological diseases. Here we sought to develop a convolutional neural network for automated multiclass tissue segmentation of brain MRIs that was robust at typical clinical resolutions and in the presence of a variety of lesions. We trained a 3D U-Net for full brain multiclass tissue segmentation from a prior atlas-based segmentation method on an internal dataset that consisted of 558 clinical T1-weighted brain MRIs (453/52/53; training/validation/test) of patients with one of 50 different diagnostic entities (n = 362) or with a normal brain MRI (n = 196). We then used transfer learning to refine our model on an external dataset that consisted of 7 patients with hand-labeled tissue types. We evaluated the tissue-wise and intra-lesion performance with different loss functions and spatial prior information in the validation set and applied the best performing model to the internal and external test sets. The network achieved an average overall Dice score of 0.87 and volume similarity of 0.97 in the internal test set. Further, the network achieved a median intra-lesion tissue segmentation accuracy of 0.85 inside lesions within white matter and 0.61 inside lesions within gray matter. After transfer learning, the network achieved an average overall Dice score of 0.77 and volume similarity of 0.96 in the external dataset compared to human raters. The network had equivalent or better performance than the original atlas-based method on which it was trained across all metrics and produced segmentations in a hundredth of the time. We anticipate that this pipeline will be a useful tool for clinical decision support and quantitative analysis of clinical brain MRIs in the presence of lesions., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: We examined imaging surrogates of white matter microstructural abnormalities which may precede white matter lesions (WML) and represent a relevant marker of cerebrovascular injury in adults in midlife., Methods: In 698 community-dwelling adults (mean age 50 years ±3.5 SD) from the Coronary Artery Risk Development in Young Adults (CARDIA) Brain MRI sub-study, WML were identified on structural MR and fractional anisotropy (FA), representing WM microstructural integrity, was derived using Diffusion Tensor Imaging. FA and WML maps were overlaid on a parcellated T1-template, based on an expert-delineated brain atlas, which included 42 WM tract ROIs. Analyses occurred in stages: 1) WML were quantified for the different tracts (i.e., frequency, volume, volume relative to tract size); 2) the interdependence of FA in normal appearing WM (NAWM) and WML was examined across tracts; 3) associations of NAWM FA and hypertension status were assessed controlling for WML volume. In the latter analysis, both overall hypertension (i.e. hypertension vs. normotension and prehypertension vs. normotension) and hypertension categorized by antihypertensive treatment status (yes/no) and blood pressure control (e.g., diastolic <90 mmHg, systolic <140 mmHg), were assessed., Results: WML were widely distributed across different WM tracts, however, WML volume was small. Mean NAWM FA was lower in participants with vs. participants without WML in given tracts. Hypertension was significantly associated with lower mean NAWM FA globally across tracts, both before and after adjustment for WML volume. Moreover, the magnitude of this association differed by treatment status and the level of control of the hypertension., Conclusions: In middle-aged adults, NAWM FA could represent a relevant marker of cerebrovascular injury when WML are minimally present.

Objectives: To determine whether long-term behavioral intervention targeting weight loss through increased physical activity and reduced caloric intake would alter cerebral blood flow (CBF) in individuals with type 2 diabetes mellitus., Design: Postrandomization assessment of CBF., Setting: Action for Health in Diabetes multicenter randomized controlled clinical trial., Participants: Individuals with type 2 diabetes mellitus who were overweight or obese and aged 45 to 76 (N = 310)., Interventions: A multidomain intensive lifestyle intervention (ILI) to induce weight loss and increase physical activity for 8 to 11 years or diabetes support and education (DSE), a control condition., Measurements: Participants underwent cognitive assessment and standardized brain magnetic resonance imaging (MRI) (3.0 Tesla) to assess CBF an average of 10.4 years after randomization., Results: Weight changes from baseline to time of MRI averaged -6.2% for ILI and -2.8% for DSE (P < .001), and increases in self-reported moderate or intense physical activity averaged 444.3 kcal/wk for ILI and 114.8 kcal/wk for DSE (P = .03). Overall mean CBF was 6% greater for ILI than DSE (P = .04), with the largest mean differences between ILI and DSE in the limbic region (3.39 mL/100 g per minute, 95% confidence interval (CI) = 0.07-6.70 mL/100 g per minute) and occipital lobes (3.52 mL/100 g per minute, 95% CI = 0.20-6.84 mL/100 g per minute). In ILI, greater CBF was associated with greater decreases in weight and greater increases in physical activity. The relationship between CBF and scores on a composite measure of cognitive function varied between intervention groups (P = .02)., Conclusions: Long-term weight loss intervention in overweight and obese adults with type 2 diabetes mellitus is associated with greater CBF., (© 2017, Copyright the Authors Journal compilation © 2017, The American Geriatrics Society.)