Gutenberg Open Science: Structural brain network characteristics can differentiate CIS from early RRMS

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7756

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DC Field	Value	Language
dc.contributor.author	Muthuraman, Muthuraman	-
dc.contributor.author	Fleischer, Vinzenz	-
dc.contributor.author	Kolber, Pierre	-
dc.contributor.author	Lüssi, Felix	-
dc.contributor.author	Zipp, Frauke	-
dc.contributor.author	Groppa, Sergiu	-
dc.date.accessioned	2022-09-14T08:20:19Z	-
dc.date.available	2022-09-14T08:20:19Z	-
dc.date.issued	2016
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/7771	-
dc.description.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.	en_GB
dc.description.sponsorship	DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	Structural brain network characteristics can differentiate CIS from early RRMS	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-7756	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Frontiers in neuroscience	de
jgu.journal.volume	10	de
jgu.pages.alternative	Art. 14	de
jgu.publisher.year	2016	-
jgu.publisher.name	Frontiers Research Foundation	de
jgu.publisher.place	Lausanne	de
jgu.publisher.uri	http://dx.doi.org/10.3389/fnins.2016.00014	de
jgu.publisher.issn	1662-4548	de
jgu.publisher.issn	1662-453X	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
opus.date.modified	2019-01-16T09:05:50Z
opus.subject.dfgcode	00-000
opus.organisation.string	FB 04: Medizin: Klinik und Poliklinik für Neurologie	de_DE
opus.identifier.opusid	56230
opus.institute.number	0435
opus.metadataonly	false
opus.type.contenttype	Keine	de_DE
opus.type.contenttype	None	en_EN
opus.affiliated	Muthuraman, Muthuraman
opus.affiliated	Lüssi, Felix
opus.affiliated	Zipp, Frauke
opus.affiliated	Groppa, Sergiu
jgu.publisher.doi	10.3389/fnins.2016.00014	de
jgu.organisation.ror	https://ror.org/023b0x485	-
Appears in collections:	DFG-OA-Publizieren (2012 - 2017)

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