Neuroanatomical features in soldiers with post-traumatic stress disorder
Bibliographic record
Abstract
BACKGROUND: Posttraumatic stress disorder (PTSD), an anxiety disorder that can develop after exposure to psychological trauma, impacts up to 20 % of soldiers returning from combat-related deployment. Advanced neuroimaging holds diagnostic and prognostic potential for furthering our understanding of its etiology. Previous imaging studies on combat-related PTSD have focused on selected structures, such as the hippocampi and cortex, but none conducted a comprehensive examination of both the cerebrum and cerebellum. The present study provides a complete analysis of cortical, subcortical, and cerebellar anatomy in a single cohort. Forty-seven magnetic resonance images (MRIs) were collected from 24 soldiers with PTSD and 23 Control soldiers. Each image was segmented into 78 cortical brain regions and 81,924 vertices using the corticometric iterative vertex based estimation of thickness algorithm, allowing for both a region-based and a vertex-based cortical analysis, respectively. Subcortical volumetric analyses of the hippocampi, cerebellum, thalamus, globus pallidus, caudate, putamen, and many sub-regions were conducted following their segmentation using Multiple Automatically Generated Templates Brain algorithm. RESULTS: Participants with PTSD were found to have reduced cortical thickness, primarily in the frontal and temporal lobes, with no preference for laterality. The region-based analyses further revealed localized thinning as well as thickening in several sub-regions. These results were accompanied by decreased volumes of the caudate and right hippocampus, as computed relative to total cerebral volume. Enlargement in several cerebellar lobules (relative to total cerebellar volume) was also observed in the PTSD group. CONCLUSIONS: These data highlight the distributed structural differences between soldiers with and without PTSD, and emphasize the diagnostic potential of high-resolution MRI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".