Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (
Dimitri Nestor Alice Van De Ville, Alessandra Griffa, Enrico Amico, Ekansh Sareen, Sélima Zahar
Tobias Kober, Bénédicte Marie Maréchal, Reto Meuli, Jonas Richiardi, Mario Joao Fartaria de Oliveira