ArticleBrain imaging and behavior2015
Statistical estimation of physiological brain age as a descriptor of senescence rate during adulthood.
Article in Brain imaging and behavior, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cross-Sectional Volumes and Trajectories of the Human Brain, Gray Matter, White Matter and Cerebrospinal Fluid in 9473 Typically Aging Adults.Neuroinformatics · 2021Pooled it
- Women's reproductive factors predict local brain aging profiles mapped using deep neural networks.GeroScience · 2026Article
- Article
- Deep learning to quantify the pace of brain aging in relation to neurocognitive changes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation.Brain sciences · 2024Article
- Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.Neuroinformatics · 2024Article
- Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- MRI-based brain age prediction model for children under 3 years old using deep residual network.Brain structure & function · 2023Article
- Significant Acceleration of Regional Brain Aging and Atrophy After Mild Traumatic Brain Injury.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
- Regional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
- Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- White matter degradation near cerebral microbleeds is associated with cognitive change after mild traumatic brain injury.Neurobiology of aging · 2022Article
- Brain age estimation reveals older adults' accelerated senescence after traumatic brain injury.GeroScience · 2022Article
- Article
- Functional Connectome Dynamics After Mild Traumatic Brain Injury According to Age and Sex.Frontiers in aging neuroscience · 2022Article
- Review
- Pitfalls in brain age analyses.Human brain mapping · 2021Article
- Acute cognitive impairment after traumatic brain injury predicts the occurrence of brain atrophy patterns similar to those observed in Alzheimer's disease.GeroScience · 2021Article
- Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis.Brain imaging and behavior · 2021Article
- Acute cognitive deficits after traumatic brain injury predict Alzheimer's disease-like degradation of the human default mode network.GeroScience · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Mapping aging-related brain structure and connectivity changes can be helpful for assessing physiological brain age (PBA), which is distinct from chronological age (CA) because genetic and environmental factors affect individuals differently. This study proposes an approach whereby structural and connectomic information can be combined to estimate PBA as an early biomarker of brain aging. In a cohort of 136 healthy adults, magnetic resonance and diffusion tensor imaging are respectively used to measure cortical thickness over the entire cortical mantle as well as connectivity properties (mean connectivity density and mean fractional anisotropy) for white matter connections. Using multivariate regression, these measurements are then employed to (1) illustrate how CA can be predicated--and thereby also how PBA can be estimated--and to conclude that (2) healthy aging is associated with significant connectome changes during adulthood. Our study illustrates a connectomically-informed statistical approach to PBA estimation, with potential applicability to the clinical identification of patients who exhibit accelerated brain aging, and who are consequently at higher risk for developing mild cognitive impairment or dementia.
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