Discriminating mild traumatic brain injury using sparse dictionary learning of functional network dynamics

Abstract Mild traumatic brain injury (mTBI) is usually caused by a bump, blow, or jolt to the head or penetrating head injury, and carries the risk of inducing cognitive disorders. However, identifying the biomarkers for the diagnosis of mTBI is challenging as evident abnormalities in brain anatomy are rarely found in patients with mTBI. In this study, we tested whether the alteration of functional network dynamics could be used as potential biomarkers to better diagnose mTBI. We propose a sparse dictionary learning framework to delineate spontaneous fluctuation of functional connectivity into the subject‐specific time‐varying evolution of a set of overlapping group‐level sparse connectivity components (SCCs) based on the resting‐state functional magnetic resonance imaging (fMRI) data from 31 mTBI patients in the early acute phase (