Detection of Chronic Blast-Related Mild Traumatic Brain Injury with Diffusion Tensor Imaging and Support Vector Machines
Abstract
Blast-related mild traumatic brain injury (bmTBI) often leads to long-term sequalae, but diagnostic approaches are lacking due to insufficient knowledge about the predominant pathophysiology. This study aimed to build a diagnostic model for future verification by applying machine-learning based support vector machine (SVM) modeling to diffusion tensor imaging (DTI) datasets to elucidate white-matter features that distinguish bmTBI from healthy controls (HC). Twenty subacute/chronic bmTBI and 19 HC combat-deployed personnel underwent DTI. Clinically relevant features for modeling were selected using tract-based analyses that identified group differences throughout white-matter tracts in five DTI metrics to elucidate the pathogenesis of injury. These features were then analyzed using SVM modeling with cross validation. Tract-based analyses revealed abnormally decreased radial diffusivity (RD), increased fractional anisotropy (FA) and axial/radial diffusivity ratio (AD/RD) in the bmTBI group, mostly in anterior tracts (29 features). SVM models showed that FA of the anterior/superior corona radiata and AD/RD of the corpus callosum and anterior limbs of the internal capsule (5 features) best distinguished bmTBI from HCs with 89% accuracy. This is the first application of SVM to identify prominent features of bmTBI solely based on DTI metrics in well-defined tracts, which if successfully validated could promote targeted treatment interventions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 14, 2022
- Source ID
- 10.3390/diagnostics12040987
Entities
People
- Angela Drake
- Annemarie Angeles-quinto
- Ashley Robb-swan
- Carl Rimmele
- Chung-kuan Cheng
- Deborah L Harrington
- Dewleen G Baker
- Jian Guo
- Kate A. Yurgil
- Lu Q Le
- Mingxiong Huang
- Po-ya Hsu
- Rebecca J. Theilmann
- Roland R. Lee
- Scott Matthews
- Sharon Nichols
- Tao Song
- Zhengwei Ji
Organizations
- Naval Medical Research Center
- United States Department of Defense
- United States Department of Veterans Affairs