Abstract:Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a powerful framework for quantifying anatomical brain aging. Whereas global BA (GBA) summarizes overall brain health, local BA (LBA) provides cortically specific patterns of aging at the subject level. Although previous studies have examined anatomical contributors to GBA, to our knowledge, no framework has been established to estimate LBA using cortical morphology. To address this gap, we introduce a graph neural network (GNN) that uses morphometric features$\unicode{x2013}$cortical thickness, surface area, curvature, gray/white matter intensity ratio (GWR), sulcal depth$\unicode{x2013}$to estimate LBA across the cortical surface at high spatial resolution (mean inter-vertex distance = 1.37 mm). Trained on cortical surface meshes extracted from the MRIs of cognitively normal (CN) adults (N = 14,423), our model achieves lower mean absolute error (MAE) than the existing state-of-the-art while identifying more biologically plausible patterns of aging in Alzheimer's disease (AD) on the ADNI dataset. Association cortices emerge as primary sites of morphometric aging in CNs, whereas mild cognitive impairment is characterized by widespread aging that is pronounced in the parahippocampal gyrus. AD subjects demonstrate significant aging across the entire cortex, particularly within medial temporal regions and associated cortical networks. Feature ablation highlights curvature and GWR as preferentially sensitive to AD pathology. Regional LBA gaps are significantly associated with neuropsychological measures of AD-related cognitive impairment, linking cortical aging patterns to clinical outcomes. These results demonstrate that GNN-based modeling of cortical morphometry enables biologically interpretable mapping of local brain aging with greater interpretability than prior work.
From: Samuel Anderson [view email]
[v1]
Fri, 16 Jan 2026 00:06:39 UTC (2,038 KB)
[v2]
Tue, 20 Jan 2026 19:45:20 UTC (2,041 KB)
[v3]
Thu, 22 Jan 2026 03:04:39 UTC (2,041 KB)
[v4]
Fri, 23 Jan 2026 20:36:01 UTC (2,041 KB)
[v5]
Wed, 27 May 2026 18:31:30 UTC (1,910 KB)