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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.)