EEG-fMRI reveals biological motion network disruptions as early markers of Multiple Sclerosis progression

Multiple sclerosis (MS) is a demyelinating disease that disrupts neuronal connectivity and alters functional networks. While resting-state approaches are common, task-based paradigms, especially those probing connectivity, remain underexplored. The biological motion (BM) task stands out by engaging highly myelinated, clinically relevant regions and targeting social cognition, a domain often overlooked in MS. Coupling this task with EEG-fMRI increases sensitivity to subtle disease-related changes, making it valuable for tracking progression and treatment effects. We acquired longitudinal simultaneous EEG-fMRI data during a BM task from 18 early relapsing-remitting MS patients and 18 matched healthy controls at baseline and 10-month follow-up. EEG source activity was constrained using concurrent fMRI priors, and effective connectivity (EC) within the BM network was computed using Dynamic Causal Modelling and Parametric Empirical Bayes. We identified alterations driven by disease (group differences), time (longitudinal changes), and their interaction, and examined their associations with disability, fatigue, and cognitive performance including social cognition. We found early disruption of BM network, associated with greater disability and cognitive deficits, particularly in social cognition. While connectivity, disability, and cognition may improve over time, likely due to treatment, new altered connections emerged at follow-up, negatively correlating with cognition. Decreased EC was generally associated with worse function, whereas increased EC, particularly in emotion-related regions, may reflect compensatory mechanisms, though it was also linked to greater fatigue. The confirmed sensitivity of the combined methods to detect early and subtle alterations in MS underscores their potential to predict cognitive decline and to inform interventions aimed at preserving brain function.