Multiple sclerosis: new imaging-based markers for clinical practice.
© 2012 EPFL
Discriminating Early Multiple Sclerosis Based on Resting-State Functional Connectivity.
Multiple sclerosis (MS), a variable and diffuse disease affecting white and grey 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, the group of Prof. Dimitri Van De Ville (IBI), together with researchers from HUG, UniGE, CHUV and CIBM, developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11 Hz) of region-averaged time-series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Their findings suggest that predictive models of resting-state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.