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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 . The biological basis of brain aging is not well understood, but it has implications for human health. Here, the authors explore the genetic basis of human brain aging, finding genetic variants, genes and potential causal relationships with disease.

Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70–0.85)/0.75 (95% CI 0.64–0.79) and 0.75 (95% CI 0.65–0.84)/0.63 (95% CI 0.52–0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6–0.7) for clinical data improving to 0.75 (95% CI 0.72–0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.

•Multiscale functional connectivity pattern of the aging brain were learned from a large-scale multisite fMRI datasets.•A machine learning model built on the multiscale functional connectivity measures achieved accurate brain age prediction.•Functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction.•Data harmonization significantly improved the brain age prediction performance. To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

Glioblastoma is a highly heterogeneous disease, with variations observed at both phenotypical and molecular levels. Personalized therapies would be facilitated by non-invasive in vivo approaches for characterizing this heterogeneity. In this study, we developed unsupervised joint machine learning between radiomic and genomic data, thereby identifying distinct glioblastoma subtypes. A retrospective cohort of 571 IDH-wildtype glioblastoma patients were included in the study, and pre-operative multi-parametric MRI scans and targeted next-generation sequencing (NGS) data were collected. L21-norm minimization was used to select a subset of 12 radiomic features from the MRI scans, and 13 key driver genes from the five main signal pathways most affected in glioblastoma were selected from the genomic data. Subtypes were identified using a joint learning approach called Anchor-based Partial Multi-modal Clustering on both radiomic and genomic modalities. Kaplan–Meier analysis identified three distinct glioblastoma subtypes: high-risk, medium-risk, and low-risk, based on overall survival outcome ( p  < 0.05, log-rank test; Hazard Ratio = 1.64, 95% CI 1.17–2.31, Cox proportional hazard model on high-risk and low-risk subtypes). The three subtypes displayed different phenotypical and molecular characteristics in terms of imaging histogram, co-occurrence of genes, and correlation between the two modalities. Our findings demonstrate the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities can aid in better understanding the molecular basis of phenotypical signatures of glioblastoma, and provide insights into the biological underpinnings of tumor formation and progression.

Glioblastoma (GBM) has high metabolic demands, which can lead to acidification of the tumor microenvironment. We hypothesize that a machine learning model built on temporal principal component analysis (PCA) of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI can be used to estimate tumor acidity in GBM, as estimated by pH-sensitive amine chemical exchange saturation transfer echo-planar imaging (CEST-EPI). We analyzed 78 MRI scans in 32 treatment naïve and post-treatment GBM patients. All patients were imaged with DSC-MRI, and pH-weighting that was quantified from CEST-EPI estimation of the magnetization transfer ratio asymmetry (MTR asym ) at 3 ppm. Enhancing tumor (ET), non-enhancing core (NC), and peritumoral T2 hyperintensity (namely, edema, ED) were used to extract principal components (PCs) and to build support vector machines regression (SVR) models to predict MTR asym values using PCs. Our predicted map correlated with MTR asym values with Spearman’s r equal to 0.66, 0.47, 0.67, 0.71, in NC, ET, ED, and overall, respectively ( p  < 0.006). The results of this study demonstrates that PCA analysis of DSC imaging data can provide information about tumor pH in GBM patients, with the strongest association within the peritumoral regions.

Background Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events. Methods We conducted a retrospective analysis of 357 IDH-wildtype glioblastomas with pre-operative multiparametric MRI and targeted genetic sequencing data. Radiogenomic signatures and spatial distribution maps were generated for key mutations in genes such as EGFR , PTEN , TP53 , and NF1 and their corresponding pathways. Machine and deep learning models were used to identify imaging biomarkers and stratify tumors based on their genetic profiles and molecular heterogeneity. Results Here, we show that glioblastoma mutations produce distinctive imaging signatures, which are more pronounced in tumors with less molecular heterogeneity. These signatures provide insights into how mutations affect tumor characteristics such as neovascularization, cell density, invasion, and vascular leakage. We also found that tumor location and spatial distribution correlate with genetic profiles, revealing associations between tumor regions and specific oncogenic drivers. Additionally, imaging features reflect the cross-sectionally inferred evolutionary trajectories of glioblastomas. Conclusions This study establishes clinically accessible imaging biomarkers that capture the molecular composition and oncogenic drivers of glioblastoma. These findings have potential implications for noninvasive tumor profiling, personalized therapies, and improved patient stratification in clinical trials. Plain language summary Glioblastoma is a type of brain cancer that grows and spreads quickly, making treatment challenging. The changes that lead to cancer and parts of the brain in which it grows are also very variable. We explored how specific genetic changes in glioblastoma influence its appearance on brain scans and how tumor location within the brain relates the genetic changes seen. We applied computational models to the imaging data to identify patterns in where cancers with particular genetic changes are found in the brain. These findings could help doctors predict genetic changes in tumors without the need for invasive procedures, improving patient selection for targeted therapies and clinical trials. Kazerooni et al. explore the associations between tumor imaging and spatial characteristics with cancer gene mutations and the inferred sequence of mutational events. Radiogenomics reflects glioblastoma's molecular heterogeneity and imaging biomarkers and spatial patterns can reveal key oncogenic drivers.

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. •Multi-site harmonization method that pools volumetric data from 18 studies, controlling for nonlinear age effects.•Resulting dataset covers ages 3 to 96 and used to derive age trends of brain structure through the lifespan.•Interactive visualization tool provided for exploring age trends and comparing new data.

Alzheimer's disease (AD) is characterized by progressive cognitive decline and genetic influences on cognition are well-established. Investigating single nucleotide polymorphisms (SNPs) associated with cognitive function across dementia progression stages may identify early genetic markers of cognitive decline. The Alzheimer's Disease Neuroimaging Initiative (ADNI), which categorized participants as cognitively normal, mild cognitive impairment, or dementia, provide a valuable resource for such studies. We therefore conducted genome-wide association study (GWAS) on ADAS scores in ADNI participants, to identify SNPs associated with cognitive performance, serving as early markers of dementia progression. Genotyped data from three ADNI phases were downloaded and merged. Quality control retained common SNPs with a variant call rate >0.98, sample call rate >0.95, Hardy-Weinberg Equilibrium p -value >10 , and minor allele frequency (MAF) ≥0.01. Individuals were included if their genetic sex matched reported sex and they had no up-to-third-degree relationships with other participants. Genotype imputation was performed using the Michigan Imputation Server. Functional annotation was performed using ANNOVAR. Separate GWAS of ADAS11 and ADAS13 were performed using linear regression in PLINK2, adjusting for age, sex, years of education, and the first ten principal components. Genetic loci were determined based on index SNPs (p -value <10 ) with more than one nominally associated SNPs (p -value <10 ) in linkage disequilibrium (r ≥0.02) within 250kb using PLINK clump function. A total of 1,236 participants and 8,416,387 common autosomal SNPs were included in the analyses. Thirteen genetic loci were identified as associated with ADAS11, and six with ADAS13, five of which were shared (Figure 1A and 1B). Four index SNPs reached the genome-wide significant threshold (p -value <5×10 ), including the strongest signal at the APOE locus, primarily driven by dementia, and three novel but less frequent SNPs (MAF <0.05) located on chromosomes 3, 8 and 13. The chromosome 3 locus is near the SUCLG2 gene, previously reported having an SNP linked to cerebrospinal fluid Aβ levels in AD patients. Novel genetic loci identified in ADNI provide insights into the genetic basis of cognitive performance, warranting further research on whether well-established A/T/N imaging or fluid biomarkers mediate their effects on cognitive and diagnostic outcomes.

Background Alzheimer’s disease (AD) is characterized by progressive cognitive decline and genetic influences on cognition are well‐established. Investigating single nucleotide polymorphisms (SNPs) associated with cognitive function across dementia progression stages may identify early genetic markers of cognitive decline. The Alzheimer's Disease Neuroimaging Initiative (ADNI), which categorized participants as cognitively normal, mild cognitive impairment, or dementia, provide a valuable resource for such studies. We therefore conducted genome‐wide association study (GWAS) on ADAS scores in ADNI participants, to identify SNPs associated with cognitive performance, serving as early markers of dementia progression. Method Genotyped data from three ADNI phases were downloaded and merged. Quality control retained common SNPs with a variant call rate >0.98, sample call rate >0.95, Hardy‐Weinberg Equilibrium p ‐value >10‐6, and minor allele frequency (MAF) ≥0.01. Individuals were included if their genetic sex matched reported sex and they had no up‐to‐third‐degree relationships with other participants. Genotype imputation was performed using the Michigan Imputation Server. Functional annotation was performed using ANNOVAR. Separate GWAS of ADAS11 and ADAS13 were performed using linear regression in PLINK2, adjusting for age, sex, years of education, and the first ten principal components. Genetic loci were determined based on index SNPs (p ‐value <10‐6) with more than one nominally associated SNPs (p ‐value <10‐4) in linkage disequilibrium (r2 ≥0.02) within 250kb using PLINK clump function. Result A total of 1,236 participants and 8,416,387 common autosomal SNPs were included in the analyses. Thirteen genetic loci were identified as associated with ADAS11, and six with ADAS13, five of which were shared (Figure 1A and 1B). Four index SNPs reached the genome‐wide significant threshold (p ‐value <5×10‐8), including the strongest signal at the APOE locus, primarily driven by dementia, and three novel but less frequent SNPs (MAF <0.05) located on chromosomes 3, 8 and 13. The chromosome 3 locus is near the SUCLG2 gene, previously reported having an SNP linked to cerebrospinal fluid Aβ1–42 levels in AD patients. Conclusion Novel genetic loci identified in ADNI provide insights into the genetic basis of cognitive performance, warranting further research on whether well‐established A/T/N imaging or fluid biomarkers mediate their effects on cognitive and diagnostic outcomes.

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. Assessing brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies, a generative model identifies five dominant patterns of brain atrophy, with specific associations with biomedical, lifestyle and genetic factors.