000181663 001__ 181663
000181663 005__ 20190316235505.0
000181663 0247_ $$2doi$$a10.1016/j.neuroimage.2012.05.078
000181663 022__ $$a1053-8119
000181663 02470 $$2ISI$$a000307369000069
000181663 037__ $$aARTICLE
000181663 245__ $$aClassifying minimally disabled multiple sclerosis patients from resting state functional connectivity
000181663 269__ $$a2012
000181663 260__ $$bElsevier$$c2012$$aSan Diego
000181663 300__ $$a13
000181663 336__ $$aJournal Articles
000181663 520__ $$aMultiple 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 (<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. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These 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.
000181663 6531_ $$abrain decoding
000181663 6531_ $$abrain networks
000181663 6531_ $$aclassification
000181663 6531_ $$afunctional magnetic resonance imaging
000181663 6531_ $$aimaging marker
000181663 700__ $$0244755$$g103349$$aRichiardi, Jonas
000181663 700__ $$aGschwind, Markus
000181663 700__ $$aSimioni, Samanta
000181663 700__ $$aAnnoni, Jean-Marie
000181663 700__ $$aGreco, Beatrice
000181663 700__ $$aHagmann, Patric
000181663 700__ $$aSchluep, Myriam
000181663 700__ $$aVuilleumier, Patrik
000181663 700__ $$0240173$$aVan De Ville, Dimitri$$g152027
000181663 773__ $$j62$$tNeuroImage$$k3$$q2021-33
000181663 8564_ $$uhttps://infoscience.epfl.ch/record/181663/files/MSconnectivity_postReview_authorsVersion.pdf$$zPostprint$$s3811088$$yPostprint
000181663 909C0 $$xU12143$$0252169$$pMIPLAB
000181663 909CO $$ooai:infoscience.tind.io:181663$$qGLOBAL_SET$$pSTI$$particle
000181663 917Z8 $$x103349
000181663 937__ $$aEPFL-ARTICLE-181663
000181663 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000181663 980__ $$aARTICLE