Open AccessTract-Based Bayesian Multivariate Analysis of Mild Traumatic Brain Injury
Author(s) -
Yongkang Liu,
Tianyao Wang,
Xiao Chen,
Jianhua Zhang,
Guoxing Zhou,
Zhongqiu Wang,
Rong Chen
Publication year - 2014
Publication title -
computational and mathematical methods in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 48
eISSN - 1748-6718
pISSN - 1748-670X
DOI - 10.1155/2014/120182
Subject(s) - traumatic brain injury , fractional anisotropy , diffusion mri , white matter , neuroimaging , multivariate statistics , multivariate analysis , concussion , psychology , medicine , poison control , neuroscience , magnetic resonance imaging , radiology , injury prevention , computer science , machine learning , psychiatry , emergency medicine
Purpose . Detecting brain regions characterizing mild traumatic brain injury (mTBI) by combining Tract-Based Spatial Statistics (TBSS) and Graphical-model-based Multivariate Analysis (GAMMA). Materials and Methods . This study included 39 mTBI patients and 28 normal controls. Local research ethics committee approved this prospective study. Diffusion-tensor imaging was performed in mTBI patients within 7 days of injury. Skeletonized fractional anisotropy (FA) maps were generated by using TBSS. Brain regions characterizing mTBI were detected by GAMMA. Results . Two clusters of lower frontal white matter FA were present in mTBI patients. We constructed classifiers based on FA values in these two clusters to differentiate mTBI and controls. The mean accuracy, sensitivity, and specificity, across five different classifiers, were 0.80, 0.94, and 0.61, respectively. Conclusions . Combining TBSS and GAMMA can detect neuroimaging biomarkers characterizing mTBI.
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