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. 2014 Dec;35(12):5946-61.
doi: 10.1002/hbm.22596. Epub 2014 Jul 22.

Disruption of Structural and Functional Networks in Long-Standing Multiple Sclerosis


Disruption of Structural and Functional Networks in Long-Standing Multiple Sclerosis

Prejaas Tewarie et al. Hum Brain Mapp. .


Both gray matter atrophy and disruption of functional networks are important predictors for physical disability and cognitive impairment in multiple sclerosis (MS), yet their relationship is poorly understood. Graph theory provides a modality invariant framework to analyze patterns of gray matter morphology and functional coactivation. We investigated, how gray matter and functional networks were affected within the same MS sample and examined their interrelationship. Magnetic resonance imaging and magnetoencephalography (MEG) were performed in 102 MS patients and 42 healthy controls. Gray matter networks were computed at the group-level based on cortical thickness correlations between 78 regions across subjects. MEG functional networks were computed at the subject level based on the phase-lag index between time-series of regions in source-space. In MS patients, we found a more regular network organization for structural covariance networks and for functional networks in the theta band, whereas we found a more random network organization for functional networks in the alpha2 band. Correlation analysis revealed a positive association between covariation in thickness and functional connectivity in especially the theta band in MS patients, and these results could not be explained by simple regional gray matter thickness measurements. This study is a first multimodal graph analysis in a sample of MS patients, and our results suggest that a disruption of gray matter network topology is important to understand alterations in functional connectivity in MS as regional gray matter fails to take into account the inherent connectivity structure of the brain.

Keywords: functional connectivity; functional networks; magnetic resonance imaging; magnetoencephalography; multiple sclerosis; structural covariance networks.

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