Autoren:	Muthuraman, Muthuraman Fleischer, Vinzenz Kolber, Pierre Lüssi, Felix Zipp, Frauke Groppa, Sergiu
Titel:	Structural brain network characteristics can differentiate CIS from early RRMS
Online-Publikationsdatum:	14-Sep-2022
Erscheinungsdatum:	2016
Sprache des Dokuments:	Englisch
Zusammenfassung/Abstract:	Focal demyelinated lesions, diffuse white matter (WM) damage, and gray matter (GM) atrophy influence directly the disease progression in patients with multiple sclerosis. The aim of this study was to identify specific characteristics of GM and WM structural networks in subjects with clinically isolated syndrome (CIS) in comparison to patients with early relapsing-remitting multiple sclerosis (RRMS). Twenty patients with CIS, 33 with RRMS, and 40 healthy subjects were investigated using 3 T-MRI. Diffusion tensor imaging was applied, together with probabilistic tractography and fractional anisotropy (FA) maps for WM and cortical thickness correlation analysis for GM, to determine the structural connectivity patterns. A network topology analysis with the aid of graph theoretical approaches was used to characterize the network at different community levels (modularity, clustering coefficient, global, and local efficiencies). Finally, we applied support vector machines (SVM) to automatically discriminate the two groups. In comparison to CIS subjects, patients with RRMS were found to have increased modular connectivity and higher local clustering, highlighting increased local processing in both GM and WM. Both groups presented increased modularity and clustering coefficients in comparison to healthy controls. SVM algorithms achieved 97% accuracy using the clustering coefficient as classifier derived from GM and 65% using WM from probabilistic tractography and 67% from modularity of FA maps to differentiate between CIS and RRMS patients. We demonstrate a clear increase of modular and local connectivity in patients with early RRMS in comparison to CIS and healthy subjects. Based only on a single anatomic scan and without a priori information, we developed an automated and investigator-independent paradigm that can accurately discriminate between patients with these clinically similar disease entities, and could thus complement the current dissemination-in-time criteria for clinical diagnosis.
DDC-Sachgruppe:	610 Medizin 610 Medical sciences
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 04 Medizin
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-7756
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	Frontiers in neuroscience 10
Seitenzahl oder Artikelnummer:	Art. 14
Verlag:	Frontiers Research Foundation
Verlagsort:	Lausanne
Erscheinungsdatum:	2016
ISSN:	1662-4548 1662-453X
URL der Originalveröffentlichung:	http://dx.doi.org/10.3389/fnins.2016.00014
DOI der Originalveröffentlichung:	10.3389/fnins.2016.00014
Enthalten in den Sammlungen:	DFG-OA-Publizieren (2012 - 2017)