Multimodal neuroimaging and explainable deep learning for characterizing brain aging: insights into biomarkers of healthy and pathological aging

Abstract

The aging brain undergoes complex structural and functional transformations that differ- entiate healthy aging from pathological trajectories such as dementia. This study pioneers a multimodal neuroimaging and explainable deep learning framework to characterize brain aging, identify biomarkers of neurodegeneration, and elucidate the interplay between local anatomical changes and global network reorganization. Leveraging structural MRI-derived volumetrics and graph theory-based connectivity metrics extracted from resting-state fMRI from a heterogeneous cohort of cognitively healthy individuals and patients with Dementia attributed to Alzheimer’s and non-Alzheimer’s Disease, two predictive models were devel- oped: (1) a brain-age regression model to quantify deviations from normative aging patterns and (2) a dementia classification model to distinguish pathological from healthy aging. Both models achieved robust performance (mean absolute error = 0.68 years for controls in re- gression; F1-score = 0.93 for classification), with interpretable feature contributions revealed through SHAP (SHapley Additive exPlanations) analyses. Explainable AI (SHAP) analyses revealed non-linear feature interactions and highlighted established and novel neuroanatom- ical correlates of brain aging and dementia. By synthesizing computational innovation with clinical neuroimaging, this research provides actionable biomarkers for aging research, re- fines the conceptual framework of compensatory brain reorganization, and establishes a new contribution for AI-driven precision diagnostics in neurodegenerative disorders.