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http://hdl.handle.net/10609/123166
Title: Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value
Author: Solana Díaz, Elisabeth
Martínez de las Heras, Eloy
Casas Roma, Jordi
Calvet Liñan, Laura  
López Soley, Elisabet
Sepúlveda, María
Solà Valls, Nuria
Montejo, Carmen
Blanco, Yolanda
Pulido Valdeolivas, Irene
Andorrà Inglés, Magí
Saiz, Albert
Prados Carrasco, Ferran
Llufriu, Sara
Others: University College London
Universitat de Barcelona
Universitat Oberta de Catalunya (UOC)
Keywords: multiple sclerosis
structural brain connectivity
magnetic resonance imaging
Issue Date: 27-Dec-2019
Publisher: Scientific Reports
Citation: Solana, E., Martinez-Heras, E., Casas-Roma, J., Calvet, L., Lopez-Soley, E., Sepulveda, M., Solà-Valls, N., Montejo, C., Blanco, Y., Pulido-Valdeolivas, I., Andorra, M., Saiz, A., Prados, F. & Llufriu, S. (2019). Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value. Scientific Reports, 9(), 1-8. doi: 10.1038/s41598-019-56806-z
Project identifier: info:eu-repo/grantAgreement/FIS2015/PI15/00587
info:eu-repo/grantAgreement/FIS2018/PI18/01030
info:eu-repo/grantAgreement/REEM/RD16/0015/0002
info:eu-repo/grantAgreement/REEM/RD16/0015/0003
info:eu-repo/grantAgreement/REEM/RD12/0032/0002
info:eu-repo/grantAgreement/REEM/RD12/0060/01-02
Also see: https://doi.org/10.1038/s41598-019-56806-z
Abstract: Brain structural network modifications in multiple sclerosis (MS) seem to be clinically relevant. The discriminative ability of those changes to identify MS patients or their cognitive status remains unknown. Therefore, this study aimed to investigate connectivity changes in MS patients related to their cognitive status, and to define an automatic classification method to classify subjects as patients and healthy volunteers (HV) or as cognitively preserved (CP) and impaired (CI) patients. We analysed structural brain connectivity in 45 HV and 188 MS patients (104 CP and 84 CI). A support vector machine with k-fold cross-validation was built using the graph metrics features that best differentiate the groups (p<0.05). Local efficiency (LE) and node strength (NS) network properties showed the largest differences: 100% and 69.7% of nodes had reduced LE and NS in CP patients compared to HV. Moreover, 55.3% and 57.9% of nodes had decreased LE and NS in CI compared to CP patients, in associative multimodal areas. The classification method achieved an accuracy of 74.8-77.2% to differentiate patients from HV, and 59.9-60.8% to discriminate CI from CP patients. Structural network integrity is widely reduced and worsens as cognitive function declines. Central network properties of vulnerable nodes can be useful to classify MS patients.
Language: English
URI: http://hdl.handle.net/10609/123166
ISSN: 2045-2322MIAR
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