Diffusion Tensor Imaging in Acute, Chronic, and Remote Mild Traumatic Brain Injury: A Systematic Review of Cross-Sectional and Longitudinal Studies
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI) is a global health concern that remains difficult to clinically evaluate due to variable diagnostic criteria and a lack of objective biomarkers. Diffusion tensor imaging (DTI) has been shown to be a sensitive measure of microstructural injury caused by head injury that cannot be visualized by conventional neuroimaging, which may potentially aid in the diagnosis and prognosis of mTBI. This review seeks to evaluate the available literature concerning the role of DTI in evaluating microstructural alterations in white matter (WM) associated with mTBI. An initial systematic search from PubMed, CENTRAL, Embase, MEDLINE, Web of Science, and Scopus yielded 1507 articles published between 2007 and 2024. A total of 79 studies met the full eligibility criteria for inclusion in this qualitative synthesis. The majority of studies demonstrated DTI abnormalities in the setting of acute, chronic, and remote mTBI, predominantly in the WM tracts of the corpus callosum, corona radiata, internal capsule, and longitudinal fasciculus. Many studies identified associations between DTI parameters and clinical measures of mTBI, such as cognitive performance, executive functioning, and comorbidities, including post-concussion syndrome and post-traumatic stress disorder. Overall, the weight of evidence in this review supports the cautious integration of DTI in the clinical assessment of mTBI. The observed discrepancies reported in the literature on DTI may be explained by significant variability in study design, analytical technique, and measured clinical outcomes. Further research that implements consistent methodology is crucial to fully realize the use of DTI as an imaging biomarker of mTBI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".