Graph Neural Network Reveals the Cortical Morphology of Local Brain Aging in Normal Cognition and Alzheimer's Disease.
Authors
Affiliations (10)
Affiliations (10)
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA.
- Corwin D. Denney Research Center, Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, USA.
- Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA.
- Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA. [email protected].
- Corwin D. Denney Research Center, Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA. [email protected].
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, USA. [email protected].
- Department of Quantitative & Computational Biology, Dana & David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA, USA. [email protected].
- Centre for Healthy Brain Aging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. [email protected].
Abstract
Estimating brain age (BA) from T<sub>1</sub>-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-cortical thickness, surface area, curvature, gray/white matter intensity ratio (GWR), sulcal depth-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 better test-retest performance 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 fidelity than prior work.