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BACKGROUND AND PURPOSE: The Systolic Blood Pressure Intervention (SPRINT) randomized trial demonstrated that intensive blood pressure management resulted in slower progression of cerebral white matter hyperintensities, compared with standard therapy. We assessed longitudinal changes in brain functional connectivity to determine whether intensive treatment results in less decline in functional connectivity and how changes in brain functional connectivity relate to changes in brain structure. MATERIALS AND METHODS: Five hundred forty-eight participants completed longitudinal brain MR imaging, including resting-state fMRI, during a median follow-up of 3.84 years. Functional brain networks were identified using independent component analysis, and a mean connectivity score was calculated for each network. Longitudinal changes in mean connectivity score were compared between treatment groups using a 2-sample t test, followed by a voxelwise t test. In the full cohort, adjusted linear regression analysis was performed between changes in the mean connectivity score and changes in structural MR imaging metrics. RESULTS: Four hundred six participants had longitudinal imaging that passed quality control. The auditory-salience-language network demonstrated a significantly larger decline in the mean connectivity score in the standard treatment group relative to the intensive treatment group (P = .014), with regions of significant difference between treatment groups in the cingulate and right temporal/insular regions. There was no treatment group difference in other networks. Longitudinal changes in mean connectivity score of the default mode network but not the auditory-salience-language network demonstrated a significant correlation with longitudinal changes in white matter hyperintensities (P = .013). CONCLUSIONS: Intensive treatment was associated with preservation of functional connectivity of the auditory-salience-language network, while mean network connectivity in other networks was not significantly different between intensive and standard therapy. A longitudinal increase in the white matter hyperintensity burden is associated with a decline in mean connectivity of the default mode network.

The detection and monitoring of brain lesions caused by multiple sclerosis is commonly performed with the use of magnetic resonance imaging. Analysis of a large number of images is a time-consuming challenge to the neuroradiologist, that can be accelerated with the assistance of computer-detection software. In 98 baseline and follow-up brain magnetic resonance studies from 88 patients with a diagnosis of multiple sclerosis, we employed locally developed lesion-detection software to assess temporal change in the load of brain lesions and compared its results to routine clinical reports. Analyzing the differences between the follow-up study and the baseline study, the software displays the results in the form of a scrollable axial volume, with the changed lesions highlighted in different colors and superimposed on the baseline reference scan. Although disagreements between the software and the clinical readers in the detection of changed lesions were observed only in 12 (12.2%) cases, the difference reached statistical significance (p=0.04). The mean interpretation time with assistance of the software was 2.7±2.2 minutes. We conclude that the performance of the software-assisted interpretation in the analysis of change over time in multiple sclerosis brain lesions is different from the performance of clinical readers, with a possibly shorter assessment time. The software detected more changes from baseline than clinical readers, suggesting a higher sensitivity, which will have to be confirmed on further analysis. [ABSTRACT FROM AUTHOR]

Background and purpose: Dementia in Parkinson's disease (PD) is common and disabling. Identification of modifiable risk factors for it is essential. Vascular risk factors (VRFs) may be associated with cognitive decline in early PD. Biomarkers that serve as surrogates of the long‐term effect of VRFs on PD are needed. To that end, we aimed to quantitate white matter hyperintensities (WMH) in early PD, measure associations with VRFs and examine relationships between WMH and longitudinal cognition. Methods: Participants in the Parkinson's Progression Markers Initiative study (141 patients with PD, 63 healthy controls) with adequate baseline structural brain magnetic resonance imaging data were included. Hypertension and diabetes history, and body mass index were combined to create a vascular risk score. WMH were quantitated via automated methods. Cognition was assessed annually with a comprehensive test battery. Results: In the PD group, vascular risk score was associated with WMH for total brain (β = 0.210; P = 0.021), total white matter (β = 0.214; P = 0.013), frontal (β = 0.220; P = 0.002) and temporal (β = 0.212; P = 0.002) regions. Annual rate of change in global cognition was greater in those with higher vascular risk score (β = −0.040; P = 0.007) and greater WMH (β = −0.029; P = 0.049). Higher temporal WMH burden was associated with great decline over time in verbal memory (β = −0.034; P = 0.031). Conclusions: In early PD, modifiable VRFs are associated with WMH on brain magnetic resonance imaging. Temporal WMH burden predicts decline in verbal memory. WMH may serve as a surrogate marker for the effect of VRFs on cognitive abilities in PD. [ABSTRACT FROM AUTHOR]

Background: Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD)., Methods: Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n = 291), resilient in both (n = 260) or advanced in one/resilient in the other (n = 163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task)., Findings: Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits., Interpretation: The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life., Funding: The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute., Competing Interests: Declaration of interests TB has received investigator-initiated research awards from the NIH, the Alzheimer’s Association, the Foundation at Barnes-Jewish Hospital, Siemens Healthineers, Hyperfine and Avid Radiopharmaceuticals (a wholly-owned subsidiary of Eli Lilly and Company). She participates as a site investigator in clinical trials sponsored by Eli Lilly and Company, Biogen, Eisai, Jaansen, and Roche. She has served as a paid and unpaid consultant to Eisai, Siemens, Biogen, Janssen, Hyperfine, Merck Lilly, and Bristol-Myers Squibb. JCM has served as a paid consultant to the Barcelona Brain Research Center and the Native Alzheimer Disease-related Resource Center in Minority Aging Research. He also received payments for presentations at the AAIM meeting, Longer Life Foundation and the International Brain Health Symposium. JCM has received travel support to attend meetings including: AAIM, DIAN, AD/PD, ATRI/ADNI, ADRC, ADC, the International conference on Health Aging & Biomarkers and the International Brain Health Symposium. He has served on the advisory board for the Cure Alzheimer’s Fund and LEADS at Indiana University. IMN has received payments from Premier, Inc for participating in an advisory board, from Peerview for an educational talk, and from Subtle Medical, Inc for consulting. DW has served as a paid consultant to Qynapse, Beckman Coulter and Eli Lilly. He also received grants from the NIH and Biogen paid to his institution and received travel support from the Alzheimer's Association. SR is an NIA IRP employee and has served on the advisory board of Dementia Platforms, UK, the Canadian Consortium on Neurodegeneration in Aging and the Adult Aging Brain Connectome. She has received travel support from the McKnight Foundation to attend an annual meeting., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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/ ., (© 2024. The Author(s).)

Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, large amounts of data are necessary for model building and optimization. To help overcome such limitations in the context of brain MRI, we present GenMIND: a collection of generative models of normative regional volumetric features derived from structural brain imaging. GenMIND models are trained on real brain imaging regional volumetric measures from the iSTAGING consortium, which encompasses over 40,000 MRI scans across 13 studies, incorporating covariates such as age, sex, and race. Leveraging GenMIND, we produce and offer 18,000 synthetic samples spanning the adult lifespan (ages 22-90 years), alongside the model's capability to generate unlimited data. Experimental results indicate that samples generated from GenMIND agree with the distributions obtained from real data. Most importantly, the generated normative data significantly enhance the accuracy of downstream machine learning models on tasks such as disease classification. Data and models are available at: https://huggingface.co/spaces/rongguangw/GenMIND., Competing Interests: Competing interests The authors declare no competing interests.

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.)

Disease heterogeneity poses a significant challenge for precision diagnostics. Recent work leveraging artificial intelligence has offered promise to dissect this heterogeneity by identifying complex intermediate brain phenotypes, herein called dimensional neuroimaging endophenotypes (DNEs). We advance the argument that these DNEs capture the degree of expression of respective neuroanatomical patterns measured, offering a dimensional neuroanatomical representation for studying disease heterogeneity and similarities of neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2) 1 , autism spectrum disorder (ASD1-3) 2 , late-life depression (LLD1-2) 3 , and schizophrenia (SCZ1-2) 4 , in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10 -8 /9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×10 -4 ) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs, along with their polygenic risk scores, significantly enhanced the predictive accuracy for 14 systemic disease categories, particularly for conditions related to mental health and the central nervous system, as well as mortality outcomes. These findings underscore the potential of the nine DNEs to capture the expression of disease-related brain phenotypes in individuals of the general population and to relate such measures with genetics, lifestyle factors, and chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine/., Competing Interests: Competing Interests None

Machine learning (ML) is revolutionizing many areas of engineering and science, including healthcare. However, it is also facing a reproducibility crisis, especially in healthcare. ML models that are carefully constructed from and evaluated on data from one part of the population may not generalize well on data from a different population group, or acquisition instrument settings and acquisition protocols. We tackle this problem in the context of neuroimaging of Alzheimer's disease (AD), schizophrenia (SZ) and brain aging. We develop a weighted empirical risk minimization approach that optimally combines data from a source group, e.g., subjects are stratified by attributes such as sex, age group, race and clinical cohort to make predictions on a target group, e.g., other sex, age group, etc. using a small fraction (10%) of data from the target group. We apply this method to multi-source data of 15,363 individuals from 20 neuroimaging studies to build ML models for diagnosis of AD and SZ, and estimation of brain age. We found that this approach achieves substantially better accuracy than existing domain adaptation techniques: it obtains area under curve greater than 0.95 for AD classification, area under curve greater than 0.7 for SZ classification and mean absolute error less than 5 years for brain age prediction on all target groups, achieving robustness to variations of scanners, protocols, and demographic or clinical characteristics. In some cases, it is even better than training on all data from the target group, because it leverages the diversity and size of a larger training set. We also demonstrate the utility of our models for prognostic tasks such as predicting disease progression in individuals with mild cognitive impairment. Critically, our brain age prediction models lead to new clinical insights regarding correlations with neurophysiological tests. In summary, we present a relatively simple methodology, along with ample experimental evidence, supporting the good generalization of ML models to new datasets and patient cohorts., Competing Interests: Competing interests The authors declare no competing interests.

Infections have been associated with the incidence of Alzheimer disease and related dementias, but the mechanisms responsible for these associations remain unclear. Using a multicohort approach, we found that influenza, viral, respiratory, and skin and subcutaneous infections were associated with increased long-term dementia risk. These infections were also associated with region-specific brain volume loss, most commonly in the temporal lobe. We identified 260 out of 942 immunologically relevant proteins in plasma that were differentially expressed in individuals with an infection history. Of the infection-related proteins, 35 predicted volumetric changes in brain regions vulnerable to infection-specific atrophy. Several of these proteins, including PIK3CG, PACSIN2, and PRKCB, were related to cognitive decline and plasma biomarkers of dementia (Aβ 42/40 , GFAP, NfL, pTau-181). Genetic variants that influenced expression of immunologically relevant infection-related proteins, including ITGB6 and TLR5, predicted brain volume loss. Our findings support the role of infections in dementia risk and identify molecular mediators by which infections may contribute to neurodegeneration., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Introduction: Plasma proteomic analyses of unique brain atrophy patterns may illuminate peripheral drivers of neurodegeneration and identify novel biomarkers for predicting clinically relevant outcomes., Methods: We identified proteomic signatures associated with machine learning-derived aging- and Alzheimer's disease (AD) -related brain atrophy patterns in the Baltimore Longitudinal Study of Aging (n = 815). Using data from five cohorts, we examined whether candidate proteins were associated with AD endophenotypes and long-term dementia risk., Results: Plasma proteins associated with distinct patterns of age- and AD-related atrophy were also associated with plasma/cerebrospinal fluid (CSF) AD biomarkers, cognition, AD risk, as well as mid-life (20-year) and late-life (8-year) dementia risk. EFEMP1 and CXCL12 showed the most consistent associations across cohorts and were mechanistically implicated as determinants of brain structure using genetic methods, including Mendelian randomization., Discussion: Our findings reveal plasma proteomic signatures of unique aging- and AD-related brain atrophy patterns and implicate EFEMP1 and CXCL12 as important molecular drivers of neurodegeneration., Highlights: Plasma proteomic signatures are associated with unique patterns of brain atrophy. Brain atrophy-related proteins predict clinically relevant outcomes across cohorts. Genetic variation underlying plasma EFEMP1 and CXCL12 influences brain structure. EFEMP1 and CXCL12 may be important molecular drivers of neurodegeneration., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Reduced brain volumes and more prominent white matter hyperintensities on MRI scans are commonly observed among older adults without cognitive impairment. However, it remains unclear whether rates of change in these measures among cognitively normal adults differ as a function of genetic risk for late-onset Alzheimer's disease, including APOE -ɛ4, APOE -ɛ2 and Alzheimer's disease polygenic risk scores (AD-PRS), and whether these relationships are influenced by other variables. This longitudinal study examined the trajectories of regional brain volumes and white matter hyperintensities in relationship to APOE genotypes ( N = 1541) and AD-PRS ( N = 1093) in a harmonized dataset of middle-aged and older individuals with normal cognition at baseline (mean baseline age = 66 years, SD = 9.6) and an average of 5.3 years of MRI follow-up (max = 24 years). Atrophy on volumetric MRI scans was quantified in three ways: (i) a composite score of regions vulnerable to Alzheimer's disease (SPARE-AD); (ii) hippocampal volume; and (iii) a composite score of regions indexing advanced non-Alzheimer's disease-related brain aging (SPARE-BA). Global white matter hyperintensity volumes were derived from fluid attenuated inversion recovery (FLAIR) MRI. Using linear mixed effects models, there was an APOE -ɛ4 gene-dose effect on atrophy in the SPARE-AD composite and hippocampus, with greatest atrophy among ɛ4/ɛ4 carriers, followed by ɛ4 heterozygouts, and lowest among ɛ3 homozygouts and ɛ2/ɛ2 and ɛ2/ɛ3 carriers, who did not differ from one another. The negative associations of APOE - ɛ 4 with atrophy were reduced among those with higher education ( P < 0.04) and younger baseline ages ( P < 0.03). Higher AD-PRS were also associated with greater atrophy in SPARE-AD ( P = 0.035) and the hippocampus ( P = 0.014), independent of APOE -ɛ4 status. APOE -ɛ2 status (ɛ2/ɛ2 and ɛ2/ɛ3 combined) was not related to baseline levels or atrophy in SPARE-AD, SPARE-BA or the hippocampus, but was related to greater increases in white matter hyperintensities ( P = 0.014). Additionally, there was an APOE -ɛ4 × AD-PRS interaction in relation to white matter hyperintensities ( P = 0.038), with greater increases in white matter hyperintensities among APOE -ɛ4 carriers with higher AD-PRS. APOE and AD-PRS associations with MRI measures did not differ by sex. These results suggest that APOE - ɛ 4 and AD-PRS independently and additively influence longitudinal declines in brain volumes sensitive to Alzheimer's disease and synergistically increase white matter hyperintensity accumulation among cognitively normal individuals. Conversely, APOE - ɛ 2 primarily influences white matter hyperintensity accumulation, not brain atrophy. Results are consistent with the view that genetic factors for Alzheimer's disease influence atrophy in a regionally specific manner, likely reflecting preclinical neurodegeneration, and that Alzheimer's disease risk genes contribute to white matter hyperintensity formation., Competing Interests: The following authors declare that they have no competing interests: A.S., J.W., C.P., C.D., G.E., T.J.H., L.D., M.B., S.M.R., L.A.R.-R., R.L., T.B., J.C.M. and J.F. C.L.M. and M.S.A. are advisors to Eli Lilly. S.C.J. has served as an advisor to Roche Diagnostics. S.M.L. is a scientific advisor to Cytox Ltd., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Introduction: The effects of sex, race, and Apolipoprotein E ( APOE ) - Alzheimer's disease (AD) risk factors - on white matter integrity are not well characterized., Methods: Diffusion MRI data from nine well-established longitudinal cohorts of aging were free-water (FW)-corrected and harmonized. This dataset included 4,702 participants (age=73.06 ± 9.75) with 9,671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FA FWcorr ) were used to assess differences in white matter microstructure by sex, race, and APOE -ε4 carrier status., Results: Sex differences in FA FWcorr in association and projection tracts, racial differences in FA FWcorr in projection tracts, and APOE -ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced., Discussion: There are prominent differences in white matter microstructure by sex, race, and APOE -ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted., Competing Interests: SCJ has served on advisory boards for Enigma Biomedical and ALZPath in the past two years. AJS receives support from multiple NIH grants (P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, U19 AG024904, R01 LM013463, R01 AG068193, T32 AG071444, U01 AG068057, U01 AG072177, U19 AG074879, and U24 AG074855). He has also received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in kind contribution of PET tracer precursor) and participated in Scientific Advisory Boards (Bayer Oncology, Eisai, Novo Nordisk, and Siemens Medical Solutions USA, Inc) and an Observational Study Monitoring Board (MESA, NIH NHLBI), as well as External Advisory Committees for multiple NIA grants. He also serves as Editor-in-Chief of Brain Imaging and Behavior, a Springer-Nature Journal.

Morphometricity examines the global statistical association between brain morphology and an observable trait, and is defined as the proportion of the trait variation attributable to brain morphology. In this work, we propose an accurate morphometricity estimator based on the generalized random effects (GRE) model, and perform morphometricity analyses on five cognitive traits in an Alzheimer's study. Our empirical study shows that the proposed GRE model outperforms the widely used LME model on both simulation and real data. In addition, we extend morphometricity estimation from the whole brain to the focal-brain level, and examine and quantify both global and regional neuroanatomical signatures of the cognitive traits. Our global analysis reveals 1) a relatively strong anatomical basis for ADAS13, 2) intermediate ones for MMSE, CDRSB and FAQ, and 3) a relatively weak one for RAVLT.learning. The top associations identified from our regional morphometricity analysis include those between all five cognitive traits and multiple regions such as hippocampus, amygdala, and inferior lateral ventricles. As expected, the identified regional associations are weaker than the global ones. While the whole brain analysis is more powerful in identifying higher morphometricity, the regional analysis could localize the neuroanatomical signatures of the studied cognitive traits and thus provide valuable information in imaging and cognitive biomarker discovery for normal and/or disordered brain research.

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases., Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories., Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points., Exposures: Individuals WODCI at baseline scan., Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed., Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7)., Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Background: Although blood-based biomarkers have been identified as cost-effective and scalable alternatives to PET and CSF markers of neurodegenerative disease, little is known about how these biomarkers predict future brain atrophy and cognitive decline in cognitively unimpaired individuals. Using data from the Baltimore Longitudinal Study of Aging (BLSA), we examined whether plasma biomarkers of Alzheimer's disease (AD) pathology (amyloid-β [Aβ 42/40 ], phosphorylated tau [pTau-181]), astrogliosis (glial fibrillary acidic protein [GFAP]), and neuronal injury (neurofilament light chain [NfL]) were associated with longitudinal brain volume loss and cognitive decline. Additionally, we determined whether sex, APOEε4 status, and plasma amyloid-β status modified these associations., Methods: Plasma biomarkers were measured using Quanterix SIMOA assays. Regional brain volumes were measured by 3T MRI, and a battery of neuropsychological tests assessed five cognitive domains. Linear mixed effects models adjusted for demographic factors, kidney function, and intracranial volume (MRI analyses) were completed to relate baseline plasma biomarkers to baseline and longitudinal brain volume and cognitive performance., Results: Brain volume analyses included 622 participants (mean age ± SD: 70.9 ± 10.2) with an average of 3.3 MRI scans over 4.7 years. Cognitive performance analyses included 674 participants (mean age ± SD: 71.2 ± 10.0) with an average of 3.9 cognitive assessments over 5.7 years. Higher baseline pTau-181 was associated with steeper declines in total gray matter volume and steeper regional declines in several medial temporal regions, whereas higher baseline GFAP was associated with greater longitudinal increases in ventricular volume. Baseline Aβ 42/40 and NfL levels were not associated with changes in brain volume. Lower baseline Aβ 42/40 (higher Aβ burden) was associated with a faster decline in verbal memory and visuospatial performance, whereas higher baseline GFAP was associated with a faster decline in verbal fluency. Results were generally consistent across sex and APOEε4 status. However, the associations of higher pTau-181 with increasing ventricular volume and memory declines were significantly stronger among individuals with higher Aβ burden, as was the association of higher GFAP with memory decline., Conclusions: Among cognitively unimpaired older adults, plasma biomarkers of AD pathology (pTau-181) and astrogliosis (GFAP), but not neuronal injury (NfL), serve as markers of future brain atrophy and cognitive decline., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

It is not well understood how neighborhood disadvantage is associated with specific domains of cognitive function and underlying brain health within older adults. Thus, the objective was to examine associations between neighborhood disadvantage, brain health, and cognitive performance, and examine whether associations were more pronounced among women. The study included 136 older adults who underwent cognitive testing and MRI. Neighborhood disadvantage was characterized using the Area Deprivation Index (ADI). Descriptive statistics, bivariate correlations, and multiple regressions were run. Multiple regressions, adjusted for age, sex, education, and depression, showed that higher ADI state rankings (greater disadvantage) were associated with poorer working memory performance ( p  < .01) and lower hippocampal volumes ( p  < .01), but not total, frontal, and white matter lesion volumes, nor visual and verbal memory performance. There were no significant sex interactions. Findings suggest that greater neighborhood disadvantage may play a role in working memory and underlying brain structure.

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10 -8 ). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine ., (© 2024. The Author(s).)

Objective: Although acute brain infarcts are common after surgical aortic valve replacement (SAVR), they are often unassociated with clinical stroke symptoms. The relationship between clinically "silent" infarcts and in-hospital delirium remains uncertain; obscured, in part, by how infarcts have been traditionally summarized as global metrics, independent of location or structural consequence. We sought to determine if infarct location and related structural connectivity changes were associated with postoperative delirium after SAVR., Methods: A secondary analysis of a randomized multicenter SAVR trial of embolic protection devices (NCT02389894) was conducted, excluding participants with clinical stroke or incomplete neuroimaging (N = 298; 39% female, 7% non-White, 74 ± 7 years). Delirium during in-hospital recovery was serially screened using the Confusion Assessment Method. Parcellation and tractography atlas-based neuroimaging methods were used to determine infarct locations and cortical connectivity effects. Mixed-effect, zero-inflated gaussian modeling analyses, accounting for brain region-specific infarct characteristics, were conducted to examine for differences within and between groups by delirium status and perioperative neuroprotection device strategy., Results: 23.5% participants experienced postoperative delirium. Delirium was associated with significantly increased lesion volumes in the right cerebellum and temporal lobe white matter, while diffusion weighted imaging infarct-related structural disconnection (DWI-ISD) was observed in frontal and temporal lobe regions (p-FDR < 0.05). Fewer brain regions demonstrated DWI-ISD loss in the suction-based neuroprotection device group, relative to filtration-based device or standard aortic cannula., Interpretation: Structural disconnection from acute infarcts was greater in patients who experienced postoperative delirium, suggesting that the impact from covert perioperative infarcts may not be as clinically "silent" as commonly assumed., (© 2023 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Objective: The effects of stroke and delirium on postdischarge cognition and patient-centered health outcomes after surgical aortic valve replacement (SAVR) are not well characterized. Here, we assess the impact of postoperative stroke and delirium on these health outcomes in SAVR patients at 90 days., Methods: Patients (N = 383) undergoing SAVR (41% received concomitant coronary artery bypass graft) enrolled in a randomized trial of embolic protection devices underwent serial neurologic and delirium evaluations at postoperative days 1, 3, and 7 and magnetic resonance imaging at day 7. Outcomes included 90-day functional status, neurocognitive decline from presurgical baseline, and quality of life., Results: By postoperative day 7, 25 (6.6%) patients experienced clinical stroke and 103 (28.5%) manifested delirium. During index hospitalization, time to discharge was longer in patients experiencing stroke (hazard ratio, 0.62; 95% confidence interval [CI], 0.42-0.94; P = .02) and patients experiencing delirium (hazard ratio, 0.68; 95% CI, 0.54-0.86; P = .001). At day 90, patients experiencing stroke were more likely to have a modified Rankin score >2 (odds ratio [OR], 5.9; 95% CI, 1.7-20.1; P = .01), depression (OR, 5.3; 95% CI, 1.6-17.3; P = .006), a lower 12-Item Short Form Survey physical health score (adjusted mean difference -3.3 ± 1.9; P = .08), and neurocognitive decline (OR, 7.8; 95% CI, 2.3-26.4; P = .001). Delirium was associated with depression (OR, 2.2; 95% CI, 0.9-5.3; P = .08), lower 12-Item Short Form Survey physical health (adjusted mean difference -2.3 ± 1.1; P = .03), and neurocognitive decline (OR, 2.2; 95% CI, 1.2-4.0; P = .01)., Conclusions: Stroke and delirium occur more frequently after SAVR than is commonly recognized, and these events are associated with disability, depression, cognitive decline, and poorer quality of life at 90 days postoperatively. These findings support the need for new interventions to reduce these events and improve patient-centered outcomes., (Copyright © 2022 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)

Introduction: Heart rate (HR) fragmentation indices quantify breakdown of HR regulation and are associated with atrial fibrillation and cognitive impairment. Their association with brain magnetic resonance imaging (MRI) markers of small vessel disease is unexplored., Methods: In 606 stroke-free participants of the Multi-Ethnic Study of Atherosclerosis (mean age 67), HR fragmentation indices including percentage of inflection points (PIP) were derived from sleep study recordings. We examined PIP in relation to white matter hyperintensity (WMH) volume, total white matter fractional anisotropy (FA), and microbleeds from 3-Tesla brain MRI completed 7 years later., Results: In adjusted analyses, higher PIP was associated with greater WMH volume (14% per standard deviation [SD], 95% confidence interval [CI]: 2, 27%, P = 0.02) and lower WM FA (-0.09 SD per SD, 95% CI: -0.16, -0.01, P = 0.03)., Discussion: HR fragmentation was associated with small vessel disease. HR fragmentation can be measured automatically from ambulatory electrocardiogram devices and may be useful as a biomarker of vascular brain injury., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes., (© 2024. The Author(s).)

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples ( N  = 1,384) of medication-free individuals with first-episode and recurrent MDD ( N  = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls ( N  = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals ( N  = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter ( N  = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter ( N  = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) ( β  = -18.3, 95% CI (-34.3 to -2.3), P  = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response., Competing Interests: Competing interestsS.R.A. has consulted for Indoc Research Canada. B.W.D. has received research support from Boehringer-Ingelheim, Compass, Pathways, NIMH, Otsuka, and Uson and honoraria for consulting from Aya Biosciences, Myriad Neuroscience, Otsuka, Sophren Therapeutics, Cerebral Therapeutics, Sage. C.H.Y.F. has received grant funding from Brain and Behavior (NARSAD), Eli Lilly and Co. Milken Institute, Flow Neuroscience, MRC, NIMH, Rosetrees Trust, and Wellcome Trust and is Section Editor of Brain Research Bulletin. C.J.H. serves as a consultant for P1vital, Lundbeck, Servier, and Compass Pathways. She holds grant income from Zogenix and J&J. S.H.K. has received funding for consulting or speaking engagements from Abbvie, Boehringer-Ingelheim, Janssen, Lundbeck, Lundbeck Institute, Merck, Otsuka Pfizer, Sunovion, and Servier. He has received research support from Abbott, Brain Canada, CIHR (Canadian Institutes of Health Research), Janssen, Lundbeck, Ontario Brain Institute, Otsuka, Pfizer, and SPOR (Canada’s Strategy for Patient-Oriented Research). He has stock/stock options in Field Trip Health. G.M.K. has received honoraria as a speaker for Sage Biogen and as a consultant for Sanos. H.S.M. has received grant funding from NIH and consulting and IP licenses fees from Abbott Labs. A.M.M. has received research support from Eli Lilly, Janssen, and The Sackler Trust. A.M.M. has also received speaker fees from Illumina and Janssen. W.E.C. serves on the National Advisory Board of the George West Mental health Foundation, as a board member of Hugarheill ehf (an Icelandic company dedicated to the prevention of depression), and on the scientific advisory boards of AIM for Mental Health and the Anxiety and Depression Association of America; he is supported by the Mary and John Brock Foundation, the Pitts Foundation, and the Fuqua family foundations, and he receives book royalties from John Wiley. H.S. has received grant funding from NIH. S.C.S. received research support from Brain Canada, CIHR (Canadian Institutes of Health Research), Ontario Brain Institute, and CFI (Canadian Foundation for Innovation). S.C.S. is a founder and shareholder of ADMdx, Inc. D.T. has received grant funding from NIH. M.H.T. received research support from NIH, PCORI, and AFSP, is a consultant for Alkermes Inc., Alto Neuroscience Inc, Axsome Therapeutics, Boegringer Ingelheim, GH Research, GreenLight VitalSign6 Inc, Heading Health, Inc., Janssen Pharmaceutical, Legion Health, Merck Sharp & Dohme Corp., Mind Medicine Inc., Navitor, Neurocrine Biosciences Inc., Noema Pharma AG, Orexo US Inc., Otsuka Canada Pharmaceutical Inc, Otsuka Pharmaceutical Development & Commercialization, Inc. (MDD section adviser), SAGE Therapeutics, Signant Health, and Takeda Pharmaceuticals Inc and receives editorial compensation from Oxford University Press. A.H.Y. reports the following conflicts of interest: paid lectures and advisory boards for the following companies: Astrazenaca, Eli Lilly, Lundbeck, Sunovion, Servier, Livanova, Janssen, Allegan, Bionomics, Sumitomo Dainippon Pharma, COMPASS, Sage, Novartis; consultant to Johnson & Johnson and to Livanova; received honoraria for attending advisory boards and presenting talks at meetings organized by LivaNova; principal investigator in the Restore-Life VNS registry study funded by LivaNova; UK chief investigator for Novartis MDD study MIJ821A12201; principal investigator on ESKETINTRD3004: ‘An Open-label, Long-term, Safety and Efficacy Study of Intranasal Esketamine in Treatment-resistant Depression’; principal investigator on ‘The Effects of Psilocybin on Cognitive Function in Healthy Participants’; principal investigator on ‘The Safety and Efficacy of Psilocybin in Participants with Treatment-Resistant Depression (P-TRD)’; no shareholdings in pharmaceutical companies; deputy editor of BJPsych Open. Grant funding (past and present): NIMH (USA); CIHR (Canada); NARSAD (USA); Stanley Medical Research Institute (USA); MRC (UK); Wellcome Trust (UK); Royal College of Physicians (Edin); BMA (UK); UBC-VGH Foundation (Canada); WEDC (Canada); CCS Depression Research Fund (Canada); MSFHR (Canada); NIHR (UK); Janssen (UK). R.Z. is a private psychiatrist service provider at The London Depression Institute and co-investigator on a Livanova-funded observational study of vagus nerve stimulation for depression. R.Z. has received honoraria for talks at medical symposia sponsored by Lundbeck as well as Janssen. He has collaborated with EMIS PLC and advises Depsee Ltd. He is affiliated with the D’Or Institute of Research and Education, Rio de Janeiro, and advises the Scients Institute, USA. Authors A. Singh, A. Stolicyn, B.R.G., B.N.F., C.C.F., C.-G.Y., C.D., D.A., D.W., G.E., H.C.W., I.H.G., I.M.A., I.S., J.F.W.D., J.Q., J.S., J.A.G., K.H., K.S.C., K.Q., M.P.P., M.A., M.D.S., M.G., Q.G., R.E., S.H., S.R., T.A.V., T.C., T.W., V.G.F., and Y.F. declare that they have no competing interests., (© The Author(s) 2024.)

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain., Competing Interests: Competing interests statement:D.H.W. served as Site PI for studies by Biogen, Merck, and Eli Lilly/Avid. He has received consulting fees from GE Healthcare and Neuronix. He is on the DSMB for a trial sponsored by Functional Neuromodulation. A.J.S. receives support from multiple NIH grants (P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, U01 AG024904, R01 LM013463, R01 AG068193, T32 AG071444, U01 AG068057, and U01 AG072177). He has also received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in-kind contribution of PET tracer precursor); Bayer Oncology (Scientific Advisory Board); Eisai (Scientific Advisory Board); Siemens Medical Solutions, Inc. (Dementia Advisory Board); and Springer-Nature Publishing (Editorial Office Support as Editor-in-Chief, Brain Imaging, and Behavior). O.C. reports having received consulting fees from AskBio (2020) and Therapanacea (2022), having received payments for writing a lay audience short paper from Expression Santé (2019), and that his laboratory has received grants (paid to the institution) from Qynapse (2017 to present). Members of his laboratory have co-supervised a Ph.D. thesis with myBrainTechnologies (2016 to 2019) and with Qynapse (2017 to present). O.C.’s spouse is an employee and holds stock options of myBrainTechnologies (2015 to present). O.C. has a patent registered at the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, J.-B. Schiratti, S. Allassonniere, O.C., S. Durrleman, A method for determining the temporal progression of a biological phenomenon and associated methods and devices) (2017). M.E. receives support from multiple NIH grants, the Alzheimer’s Association, and the Alzheimer’s Therapeutic Research Institute. M.V.Z. serves as a consultant and/or speaker for the following pharmaceutical companies: Eurofarma, Lundbeck, Abbott, Greencare, Myralis, and Elleven Healthcare.

Background: Proteins expressed by brain endothelial cells (BECs), the primary cell type of the blood-brain barrier, may serve as sensitive plasma biomarkers for neurological and neurovascular conditions, including cerebral small vessel disease., Methods: Using data from the BLSA (Baltimore Longitudinal Study of Aging; n=886; 2009-2020), BEC-enriched proteins were identified among 7268 plasma proteins (measured with SomaScanv4.1) using an automated annotation algorithm that filtered endothelial cell transcripts followed by cross-referencing with BEC-specific transcripts reported in single-cell RNA-sequencing studies. To identify BEC-enriched proteins in plasma most relevant to the maintenance of neurological and neurovascular health, we selected proteins significantly associated with 3T magnetic resonance imaging-defined white matter lesion volumes. We then examined how these candidate BEC biomarkers related to white matter lesion volumes, cerebral microhemorrhages, and lacunar infarcts in the ARIC study (Atherosclerosis Risk in Communities; US multisite; 1990-2017). Finally, we determined whether these candidate BEC biomarkers, when measured during midlife, were related to dementia risk over a 25-year follow-up period., Results: Of the 28 proteins identified as BEC-enriched, 4 were significantly associated with white matter lesion volumes (CDH5 [cadherin 5], CD93 [cluster of differentiation 93], ICAM2 [intracellular adhesion molecule 2], GP1BB [glycoprotein 1b platelet subunit beta]), while another approached significance (RSPO3 [R-Spondin 3]). A composite score based on 3 of these BEC proteins accounted for 11% of variation in white matter lesion volumes in BLSA participants. We replicated the associations between the BEC composite score, CDH5, and RSPO3 with white matter lesion volumes in ARIC, and further demonstrated that the BEC composite score and RSPO3 were associated with the presence of ≥1 cerebral microhemorrhages. We also showed that the BEC composite score, CDH5, and RSPO3 were associated with 25-year dementia risk., Conclusions: In addition to identifying BEC proteins in plasma that relate to cerebral small vessel disease and dementia risk, we developed a composite score of plasma BEC proteins that may be used to estimate blood-brain barrier integrity and risk for adverse neurovascular outcomes., Competing Interests: Disclosures None.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2023. Published by Elsevier Inc.)

Elevated plasma neurofilament light chain (NfL) is associated with dementia though underlying mechanisms remain unknown. We examined cross-sectional relationships of time-dependent plasma NfL with selected brain structural magnetic resonance imaging (sMRI) prognostic markers of dementia. The sample was drawn from the Healthy Aging in Neighborhoods of Diversity Across the Life Span (HANDLS) study, selecting participants with complete v 1 (2004-2009) and v 2 (2009-2013) plasma NfL exposure and ancillary sMRI data at v scan (2011-2015, n = 179, mean v 1 to v scan time: 5.4 years). Multivariable-adjusted linear regression models were conducted, overall, by sex, and race, correcting for multiple testing with q-values. NfL (v1) was associated with larger WMLV (both Log e transformed), after 5-6 years' follow-up, overall (β = +2.131 ± 0.660, b = +0.29, p = 0.001, and q = 0.0029) and among females. NfLv 2 was linked to a 125 mm 3 lower left hippocampal volume (p = 0.004 and q = 0.015) in reduced models, mainly among males, as was observed for annualized longitudinal change in NfL (δNfL bayes ). Among African American adults, NfL v1 was inversely related to total, gray and white matter volumes. Plasma NfL may reflect future brain pathologies in middle-aged adults., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Lipids are essential components of brain structure and shown to affect brain function. Previous studies have shown that aging men undergo greater brain atrophy than women, but whether the associations between lipids and brain atrophy differ by sex is unclear. We examined sex differences in the associations between circulating lipids by liquid chromatography-tandem mass spectrometry and the progression of MRI-derived brain atrophy index Spatial Patterns of Atrophy for Recognition of Brain Aging (SPARE-BA) over an average of 4.7 (SD = 2.3) years in 214 men and 261 women aged 60 or older who were initially cognitively normal using multivariable linear regression, adjusted for age, race, education, and baseline SPARE-BA. We found significant sex interactions for beta-oxidation rate, short-chain acylcarnitines, long-chain ceramides, and very long-chain triglycerides. Lower beta-oxidation rate and short-chain acylcarnitines in women and higher long-chain ceramides and very long-chain triglycerides in men were associated with faster increases in SPARE-BA (accelerated brain aging). Circulating lipid profiles of accelerated brain aging are sex-specific and vary by lipid classes and structure. Mechanisms underlying these sex-specific lipid profiles of brain aging warrant further investigation., (Published by Elsevier Inc.)

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate to low-resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low-field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM., (© 2022 The International Biometric Society.)