| Autor: |
Deborah L. Harrington, Po-Ya Hsu, Rebecca J. Theilmann, Annemarie Angeles-Quinto, Ashley Robb-Swan, Sharon Nichols, Tao Song, Lu Le, Carl Rimmele, Scott Matthews, Kate A. Yurgil, Angela Drake, Zhengwei Ji, Jian Guo, Chung-Kuan Cheng, Roland R. Lee, Dewleen G. Baker, Mingxiong Huang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Diagnostics, Vol 12, Iss 4, p 987 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2075-4418 |
| DOI: |
10.3390/diagnostics12040987 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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