ArticlemedRxiv : the preprint server for health sciences2025
Regional brain aging patterns reveal disease-specific pathways of neurodegeneration.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The heterogeneity of brain aging is a hallmark of neurological and psychiatric disorders, yet machine-learning tools used to characterize this process, including the 'brain age' paradigm, have largely relied on global metrics that lack the specificity to map these complex patterns. Here, we introduce BrainAgeMap, an interpretable deep learning framework that generates fine-grained, voxel-wise maps of brain-predicted age difference (brain-PAD) from T1-weighted magnetic resonance imaging scans. We provide converging lines of evidence for the framework's clinical, prognostic, and neurobiological utility. Disorder-specific topographies of accelerated aging were identified in Alzheimer's disease (AD), frontotemporal dementia, and schizophrenia. Longitudinal analysis of the hippocampus revealed accelerated aging in individuals with progressive versus stable mild cognitive impairment (MCI), demonstrating prognostic value. Regional brain-PAD in the temporal lobe correlated strongly with
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