Hubs defined with participation coefficient metric altered following acute mTBI

  • Xiaocui Wang
  • , Chuanzhu Sun
  • , Shan Wang
  • , Jieli Cao
  • , Hui Xu
  • , Shuoqiu Gan
  • , Zhen Chen
  • , Bo Yin
  • , Guanghui Bai
  • , Meihua Shao
  • , Chenghui Gu
  • , Liuxun Hu
  • , Limei Ye
  • , Dandong Li
  • , Zhihan Yan
  • , Lijun Bai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Patients with mild traumatic brain injury (mTBI) may suffer from a widespread spectrum of symptoms that arise from the damage of long-distance white matter connections in distributed brain networks. In brain networks, an increasing attention has been devoted to assessing the functional roles of regions by estimating the spatial layout of their connections among different modules, using the participation coefficient. In the present study, we aimed to investigate the role of hubs in inter-subnetwork information coordination and integration by using participation coefficients after mTBI. 74 patients after mTBI within 7 days post-injury and 51 matched healthy controls enrolled in this study. Our results presented that hubs for mTBI patients distributed in more extensive networks such as the default mode network (DMN), ventral attention network (VAN) and frontoparietal network (FPN), somatomotor network (SMN) and visual network (VN), compared with healthy controls limited to the first three. Participation coefficients for mTBI presented significantly decreased in the DMN (P=0.015) and FPN (P=0.02), while increased in the VN (P=0.035). SVM trained with participation coefficient metrics were able to identify mTBI patients from controls with 78% accuracy, providing for its diagnose potential in clinical settings. From our point of view, difference between two groups could be related with functional network reorganization in mTBI groups.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage Processing
EditorsElsa D. Angelini, Elsa D. Angelini, Bennett A. Landman
PublisherSPIE
ISBN (Electronic)9781510616370
DOIs
StatePublished - 2018
EventMedical Imaging 2018: Image Processing - Houston, United States
Duration: 11 Feb 201813 Feb 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10574
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2018: Image Processing
Country/TerritoryUnited States
CityHouston
Period11/02/1813/02/18

Keywords

  • Support Vector Machine
  • functional brain network
  • hub
  • mild traumatic brain injury
  • participation coefficient

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