4.5 Article

Altered Rich-Club and Frequency-Dependent Subnetwork Organization in Mild Traumatic Brain Injury: A MEG Resting-State Study

Journal

FRONTIERS IN HUMAN NEUROSCIENCE
Volume 11, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fnhum.2017.00416

Keywords

magnetoencephalography (MEG); mild traumatic brain injury; network resilience; cross-frequency coupling; intrinsic networks

Funding

  1. Department of Defense Congressionally Directed Medical Research Program [W81XWH-08-2-0135]
  2. MRC grant [MR/K004360/1]
  3. Marie Curie COFUND EU-UK Research Fellowship
  4. National Centre for Mental Health (NCMH) at Cardiff University
  5. MRC [MR/K004360/1] Funding Source: UKRI

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Functional brain connectivity networks exhibit small-world characteristics and some of these networks follow a rich-club organization, whereby a few nodes of high connectivity (hubs) tend to connect more densely among themselves than to nodes of lower connectivity. The Current study followed an attack strategy to compare the rich-club and small-world network organization models using Magnetoencephalographic (MEG) recordings from mild traumatic brain injury (mTBI) patients and neurologically healthy controls to identify the topology that describes the underlying intrinsic brain network organization. We hypothesized that the reduction in global efficiency caused by an attack targeting a model's hubs would reveal the true underlying topological organization. Connectivity networks were estimated using mutual information as the basis for cross-frequency coupling. Our results revealed a prominent rich-club network organization for both groups. In particular, mTBI patients demonstrated hypersynchronization among rich-club hubs compared to controls in the delta band and the delta-gamma(1), theta-gamma(1), and beta-gamma(2) frequency pairs. Moreover, rich-club hubs in mTBI patients were overrepresented in right frontal brain areas, from theta to gamma(1) frequencies, and underrepresented in left occipital regions in the delta-beta, delta-gamma(1), theta-beta, and beta-gamma(2) frequency pairs. These findings indicate that the rich-club organization of resting-state MEG, considering its role in information integration and its vulnerability to various disorders like mTBI, may have a significant predictive value in the development of reliable biomarkers to help the validation of the recovery frommTBI. Furthermore, the proposed approachmight be used as a validation tool to assess patient recovery.

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