Brain injury · 2025
An iterative ROC procedure identifies white matter tracts diagnostic for traumatic brain injury: an exploratory analysis in U.S. Veterans.
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Main Keith L, Vakhtin Andrei A, Zhuo Jiachen, Marion Donald, Adamson Maheen M, Ashford J Wesson, Gullapalli Rao, Furst Ansgar J
Аннотация
OBJECTIVE: Understanding the pathophysiology of traumatic brain injury (TBI) is crucial for effectively managing care. Diffusion tensor imaging (DTI) is an MRI technology that evaluates TBI pathology in brain white matter. However, DTI analysis generates numerous measures. Choosing between them remains an obstacle to clinical translation. In this study, we leveraged an iterative receiver operating characteristic (ROC) analysis to examine white matter tracts in a group of 380 Veterans, consisting of TBI (n = 243) and non-TBI patients (n = 137).
METHOD: For each participant, we obtained a whole brain tractography and extracted DTI measures from 50 tracts. The ROC analyzed these variables and produced decision trees of tracts diagnostic for TBI. We expanded our findings by applying jackknife resampling. This procedure removed potential outliers and yielded tracts not observed in the initial ROCs. Finally, we used logistic regression to confirm the tracts predicted TBI status.
RESULTS: Our results indicate ROC can identify tracts diagnostic for TBI. We also found that groups of tracts are more predictive than any single one.
CONCLUSIONS: These analyses show that ROC is a useful tool for exploring large, multivariate datasets and may inform the design of clinical algorithms.
METHOD: For each participant, we obtained a whole brain tractography and extracted DTI measures from 50 tracts. The ROC analyzed these variables and produced decision trees of tracts diagnostic for TBI. We expanded our findings by applying jackknife resampling. This procedure removed potential outliers and yielded tracts not observed in the initial ROCs. Finally, we used logistic regression to confirm the tracts predicted TBI status.
RESULTS: Our results indicate ROC can identify tracts diagnostic for TBI. We also found that groups of tracts are more predictive than any single one.
CONCLUSIONS: These analyses show that ROC is a useful tool for exploring large, multivariate datasets and may inform the design of clinical algorithms.