ArticleHuman brain mapping2022
Sex differences in predictors and regional patterns of brain age gap estimates.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Brain responses to intermittent fasting and the healthy living diet in older adults.Cell metabolism · 2024Trial
- Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial.Dialogues in clinical neuroscience · 2024Trial
- Your brain doesn't look a day past 70! Cross-sectional associations with brain-predicted age in the cognitively-intact oldest-old.GeroScience · 2026Article
- Deep learning aging marker from retinal images unveils sex-specific clinical and genetic signatures.Nature communications · 2026Article
- Depression and aging: insights from brain age prediction models.Psychological medicine · 2026Article
- Developing a multi-modal neuroimaging-based BrainAge model across childhood.bioRxiv : the preprint server for biology · 2026Article
- The moderating role of psychological resilience on brain aging and PTSD in a community sample of South African women.Neuroscience · 2026Article
- A Systematic Evaluation of the Performance of Multiple Brain Age Algorithms in Two Cohorts of Youth.Human brain mapping · 2026Article
- Brain Age in Conduct Disorder:: A Mega-Analysis of the ENIGMA Antisocial Behavior Working Group.bioRxiv : the preprint server for biology · 2026Article
- Biological brain aging, cognitive-motor decline and vascular risk: a multivariate imaging analysis of 40,579 individuals.Frontiers in aging neuroscience · 2026Article
- Brain age gap is associated with cognitive abilities in captive chimpanzees.Scientific reports · 2025Article
- Metabolic Status Modulates Global and Local Brain Age Estimates in Overweight and Obese Adults.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Exploring associations between psychotic experiences and structural brain age: a population-based study in late adolescence.medRxiv : the preprint server for health sciences · 2024Article
- Common and unique brain aging patterns between females and males quantified by large-scale deep learning.Human brain mapping · 2024Article
- Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort.GeroScience · 2024Article
- Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders.Biology of sex differences · 2024Article
- Sex classification from functional brain connectivity: Generalization to multiple datasets.Human brain mapping · 2024Article
- Age Prediction Using Resting-State Functional MRI.Neuroinformatics · 2024Article
- Sex classification from functional brain connectivity: Generalization to multiple datasets Generalizability of sex classifiers.bioRxiv : the preprint server for biology · 2024Article
- Brain asymmetries from mid- to late life and hemispheric brain age.Nature communications · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.
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