TY  - JOUR
PY  - 2022//
TI  - Detection of chronic blast-related mild traumatic brain injury with diffusion tensor imaging and support vector machines
JO  - Diagnostics (Basel, Switzerland)
A1  - Harrington, Deborah L.
A1  - Hsu, Po-Ya
A1  - Theilmann, Rebecca J.
A1  - Angeles-Quinto, Annemarie
A1  - Robb-Swan, Ashley
A1  - Nichols, Sharon
A1  - Song, Tao
A1  - Le, Lu
A1  - Rimmele, Carl
A1  - Matthews, Scott
A1  - Yurgil, Kate A.
A1  - Drake, Angela
A1  - Ji, Zhengwei
A1  - Guo, Jian
A1  - Cheng, Chung-Kuan
A1  - Lee, Roland R.
A1  - Baker, Dewleen G.
A1  - Huang, Mingxiong
SP  - e987
EP  - e987
VL  - 12
IS  - 4
N2  - 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>
LA  - en
SN  - 2075-4418
UR  - http://dx.doi.org/10.3390/diagnostics12040987
ID  - ref1
ER  -