Learning fMRI activations dictionaries across individual geometries via optimal transport
Two new research papers explore advanced geometric and optimal transport methods for analyzing functional magnetic resonance imaging (fMRI) data. The first paper introduces an 'Off-log metric' and Grassmannian subspace discrimination to model the geometry of correlation matrices, improving sensitivity and classification performance in clinical and aging cohorts. The second paper uses optimal transport, specifically the Fused Gromov-Wasserstein distance, to learn fMRI activation dictionaries that account for individual brain geometry variations without relying on common templates. AI
IMPACT These novel geometric and optimal transport techniques offer more sensitive and robust methods for extracting insights from complex fMRI data, potentially improving diagnostic and predictive capabilities in neuroscience research.