Investigating Whole-Brain MRI Markers in Multiple Sclerosis – Emerging Dimensions in Morphometric Space

Abstract

Magnetic resonance imaging (MRI) has become a crucial criterion for diagnosis and prognosis in early Multiple sclerosis (MS). Grey matter (GM) pathology has been described. However, little is known about the cause of cortical atrophy in MS. This study aims at identifying structural neuroimaging biomarkers of interest for the investigation of MS pathophysiology. We used structural MRI-based features - GM and WM volume, cortical thickness and gyrification index - to investigate patterns of deficits in MS. We analyzed images from 59 MS patients and 64 age-matched healthy controls. Imaging data underwent univariate statistical analyses, namely voxel-based morphometry (VBM) and surface-based morphometry (SBM), to investigate regional morphometric differences. Multivariate pattern analysis (MVPA) using a Support Vector Machine (SVM) classifier was also applied to explore pattern recognition in neuroimaging as a tool with potential for development of medical imaging biomarkers. Either VBM and SBM analyses yielded several morphological disease-related changes, which can be used to highlight disease effects in MS. SVM classification yielded an accuracy of 86,51% (sensitivity 74,58%, specificity 98,44%) with GM volume, while with WM data, SVM correctly classified 82,34% of participants (sensitivity 67,80%, specificity 96,88%). As such, MVPA can be a useful predictive biomarker with potential in assisting diagnosis in MS.