A scalable subject-specific manifold learning framework for neuroimaging data

Low-dimensional manifolds underlie mental and cognitive representations, and characterizing individual differences is critical to neuropsychiatric research. Existing manifold learning methods either distort subject-specific features through population-level alignment or limit cross-subject comparisons with individualized models. We introduce a scalable, subject-specific manifold learning framework with two modules: one capturing individual spatial variation via individualized spatial weights, and an efficient variant reducing computational cost with minimal performance loss. Simulations show that population-level alignment distorts manifold geometry, whereas our approach preserves subject-specific structure. Applied to movie-watching fMRI, our modules improve behavioral label recovery and reconstruction accuracy. In clinical resting-state fMRI, the efficient module identifies individualized spatial activation patterns highlighting sensorimotor, learning, and emotion-related regions. Further validation on an independent resting-state dataset demonstrates 95% test-retest identification accuracy. Together, these results show our framework is robust, reproducible, and broadly applicable across computational neuroscience and clinical research.