Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data

Mental and cognitive representations are believed to reside on low-dimensional, non-linear manifolds embedded within high-dimensional brain activity. Uncovering these manifolds is key to understanding individual differences in brain function, yet most existing machine learning methods either rely on population-level spatial alignment or assume data that is temporally structured, either because data is aligned among subjects or because event timings are known. We introduce a manifold learning framework that can capture subject-specific spatial variations across both structured and temporally unstructured neuroimaging data. On simulated data and two naturalistic fMRI datasets (Sherlock and Forrest Gump), our framework outperforms group-based baselines by recovering more accurate and individualized representations. We further show that the framework scales efficiently to large datasets and generalizes well to new subjects. To test this, we apply the framework to temporally unstructured resting-state fMRI data from individuals with schizophrenia and healthy controls. We further apply our method to a large resting-state fMRI dataset comprising individuals with schizophrenia and controls. In this setting, we demonstrate that the framework scales efficiently to large populations and generalizes robustly to unseen subjects. The learned subject-specific spatial maps our model finds reveal clinically relevant patterns, including increased activation in the basal ganglia, visual, auditory, and somatosensory regions, and decreased activation in the insula, inferior frontal gyrus, and angular gyrus. These findings suggest that our framework can uncover clinically relevant subject-specific brain activity patterns. Our approach thus provides a scalable and individualized framework for modeling brain activity, with applications in computational neuroscience and clinical research.