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

• Published in: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2015; pp. 5432 - 5435
• Main Authors: Vergara, Victor M., Damaraju, Eswar, Mayer, Andrew B., Miller, Robyn, Cetin, Mustafa S., Calhoun, Vince
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2015
• Subjects: Adult; Biomarkers; Brain injuries; Brain Injuries - diagnosis; Brain Injuries - pathology; Brain Injuries - physiopathology; Female; Head; Humans; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Middle Aged; Movement; Nerve Net - pathology; Nerve Net - physiopathology; Pipelines; Rest; Smoothing methods
• DOI: 10.1109/EMBC.2015.7319620
• ISSN: 1094-687X; 1557-170X

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.