ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2023
Regional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deep learning-based MRI analysis reveals Lewy body co-pathology accelerates brain aging in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- [Research progress on intelligent brain age prediction methods in diagnosis of Parkinson's disease].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.GeroScience · 2026Review
- Women's reproductive factors predict local brain aging profiles mapped using deep neural networks.GeroScience · 2026Article
- The differential effect of chronological age and brain age on cognitive fatigue: new metrics, new insights.Journal of neurology · 2026Article
- On the replicability of diffusion weighted MRI-based brain-behavior models.Communications biology · 2025Article
- 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
- Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.Neuroinformatics · 2024Article
- Decoding MRI-informed brain age using mutual information.Insights into imaging · 2024Article
- 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
- Neurobiology of Aging: New Insights From Across the Research Spectrum.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
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8 authors.
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Abstract
The biological age of the brain differs from its chronological age (CA) and can be used as biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age (BA) is often estimated from magnetic resonance images (MRIs) using machine learning (ML) that rarely indicates how regional brain features contribute to BA. Leveraging an aggregate training sample of 3 418 healthy controls (HCs), we describe a ridge regression model that quantifies each region's contribution to BA. After model testing on an independent sample of 651 HCs, we compute the coefficient of partial determination R¯p2 for each regional brain volume to quantify its contribution to BA. Model performance is also evaluated using the correlation r between chronological and biological ages, the mean absolute error (MAE ) and mean squared error (MSE) of BA estimates. On training data, r=0.92, MSE=70.94 years, MAE=6.57 years, and R¯2=0.81; on test data, r=0.90, MSE=81.96 years, MAE=7.00 years, and R¯2=0.79. The regions whose volumes contribute most to BA are the nucleus accumbens (R¯p2=7.27%), inferior temporal gyrus (R¯p2=4.03%), thalamus (R¯p2=3.61%), brainstem (R¯p2=3.29%), posterior lateral sulcus (R¯p2=3.22%), caudate nucleus (R¯p2=3.05%), orbital gyrus (R¯p2=2.96%), and precentral gyrus (R¯p2=2.80%). Our ridge regression, although outperformed by the most sophisticated ML approaches, identifies the importance and relative contribution of each brain structure to overall BA. Aside from its interpretability and quasi-mechanistic insights, our model can be used to validate future ML approaches for BA estimation.
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