%0 Journal Article
%T Detection of chronic blast-related mild traumatic brain injury with diffusion tensor imaging and support vector machines
%J Diagnostics (Basel, Switzerland)
%D 2022
%A Harrington, Deborah L.
%A Hsu, Po-Ya
%A Theilmann, Rebecca J.
%A Angeles-Quinto, Annemarie
%A Robb-Swan, Ashley
%A Nichols, Sharon
%A Song, Tao
%A Le, Lu
%A Rimmele, Carl
%A Matthews, Scott
%A Yurgil, Kate A.
%A Drake, Angela
%A Ji, Zhengwei
%A Guo, Jian
%A Cheng, Chung-Kuan
%A Lee, Roland R.
%A Baker, Dewleen G.
%A Huang, Mingxiong
%V 12
%N 4
%P e987-e987
%X 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.<p /> <p>Language: en</p>
%G en
%I MDPI: Multidisciplinary Digital Publishing Institute
%@ 2075-4418
%U http://dx.doi.org/10.3390/diagnostics12040987