The impact of data preprocessing in traumatic brain injury detection using functional magnetic resonance imaging

Victor M. Vergara, Eswar Damaraju, Andrew B. Mayer, Robyn Miller, Mustafa S. Cetin, Vince Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Traumatic brain injury (TBI) can adversely affect a person's thinking, memory, personality and behavior. For this reason new and better biomarkers are being investigated. Resting state functional network connectivity (rsFNC) derived from functional magnetic resonance (fMRI) imaging is emerging as a possible biomarker. One of the main concerns with this technique is the appropriateness of methods used to correct for subject movement. In this work we used 50 mild TBI patients and matched healthy controls to explore the outcomes obtained from different fMRI data preprocessing. Results suggest that correction for motion variance before spatial smoothing is the best alternative. Following this preprocessing option a significant group difference was found between cerebellum and supplementary motor area/paracentral lobule. In this case the mTBI group exhibits an increase in rsFNC.

Original languageEnglish (US)
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5432-5435
Number of pages4
ISBN (Electronic)9781424492718
DOIs
StatePublished - Nov 4 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015Aug 29 2015

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2015-November
ISSN (Print)1557-170X

Other

Other37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/TerritoryItaly
CityMilan
Period8/25/158/29/15

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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