Diffusion Tensor Imaging Reveals White Matter Differences in Military Personnel Exposed to Trauma with and without Post-traumatic Stress Disorder
Bibliographic record
Abstract
BACKGROUND: Post-traumatic stress disorder (PTSD) is a debilitating mental health condition that develops in response to exposure to a traumatic event. The purpose of this study was to investigate white matter differences using diffusion tensor imaging (DTI) in trauma exposed military personnel with and without PTSD. METHODS: Data were acquired in compliance with the Hospital for Sick Children and Canadian Armed Forces Research Ethics Boards for the following groups: military personnel with PTSD (PTSD, n = 23), trauma exposed military personnel with no PTSD diagnosis (TE, n = 25) and civilian controls (CC, n =13) . All participants were male. DTI was acquired on a Siemens Trio 3T MRI. Maps of Fractional Anisotropy (FA), Mean Diffusivity (MD), Axial Diffusivity (AD), and Radial Diffusivity (RD) were analyzed using Tract-Based Spatial Statistics (TBSS). RESULTS: In the PTSD and TE groups, FA was significantly greater within the hippocampus, corpus callosum, cingulum, and several associated white matter tracts. Elevated FA was shown to be largely due to reduced RD suggesting a possible structural substrate that underscores neurophysiological connectivity. CONCLUSIONS: This study reinforces previous findings showing differences in DTI metrics within the limbic system in military personnel exposed to trauma with and without PTSD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".