Functional connectivity analysis using whole brain and regional network metrics in MS patients

  • In the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order to obtain information of differences in entire brain network, we focused on both, local and global network connectivity parameters. And the regional connectivity differences were assessed using regional network parameters. RRMS patients presented a significant increase of modularity in comparison to HC, pointing towards a network structure with densely interconnected nodes within one module, while the number of connections with other modules outside decreases. This higher decomposable network favours cost-efficient local information processing and promotes long-range disconnection. InIn the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order to obtain information of differences in entire brain network, we focused on both, local and global network connectivity parameters. And the regional connectivity differences were assessed using regional network parameters. RRMS patients presented a significant increase of modularity in comparison to HC, pointing towards a network structure with densely interconnected nodes within one module, while the number of connections with other modules outside decreases. This higher decomposable network favours cost-efficient local information processing and promotes long-range disconnection. In addition, at the regional anatomical level, the network parameters clustering coefficient and local efficiency were increased in the insula, the superior parietal gyrus and the temporal pole. Our study indicates that modularity as derived from fMRI can be seen as a characteristic connectivity feature that is increased in MS patients compared to HC. Furthermore, specific anatomical regions linked to perception, motor function and cognition were mainly involved in the enhanced local information processing.show moreshow less

Download full text files

  • 110239.pdfeng
    (231KB)

    Postprint. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Export metadata

Statistics

Number of document requests

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:V. C. Chirumamilla, V. Fleischer, A. Droby, T. Anjum, Muthuraman MuthuramanORCiDGND, F. Zipp, S. Groppa
URN:urn:nbn:de:bvb:384-opus4-1102392
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/110239
ISBN:978-1-4577-0219-8OPAC
Parent Title (English):2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 16-20 August 2016, Orlando, FL, USA
Publisher:IEEE
Place of publication:Piscataway, NJ
Editor:Bruce Wheeler, May Dongmei Wang, James Patton
Type:Conference Proceeding
Language:English
Year of first Publication:2016
Publishing Institution:Universität Augsburg
Release Date:2023/12/15
First Page:4039
Last Page:4042
DOI:https://doi.org/10.1109/embc.2016.7591613
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Professur für Informatik in der Medizintechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht