Stress‐induced reduction in hippocampal volume and connectivity with the ventromedial prefrontal cortex are related to maladaptive responses to stressful military service
Bibliographic record
Abstract
Previous studies have shown that people who develop psychopathology such as posttraumatic stress disorder (PTSD) following stress exposure are characterized by reduced hippocampal (HC) volume and impaired HC functional connectivity with the ventromedial prefrontal cortex (vmPFC). Nevertheless, the exact interrelationship between reduced HC volume and HC-vmPFC connectivity deficits in the context of stress has yet to be established. Furthermore, it is still not clear whether such neural abnormalities are stress induced or precursors for vulnerability. In this study, we combined measurements of MRI, functional MRI (fMRI), and diffusion tensor imaging (DTI) to prospectively study 33 a priori healthy Israeli soldiers both pre- and post-exposure to stress during their military service. Thus, we were able to assess the contributions of structural and functional features of the HC and its connectivity to the onset and progression of maladaptive response to stress (i.e., increased PTSD symptoms post-exposure). We found that soldiers with decreased HC volume following military service (i.e., post-exposure) displayed more PTSD-related symptoms post-exposure as well as reduced HC-vmPFC functional and structural connectivity post-exposure, compared to soldiers with increased HC volume following military service. In contrast, initial smaller HC volume pre-exposure did not have an effect on any of these factors. Our results therefore suggest that reduction in HC volume and connectivity with the vmPFC together mark a maladaptive response to stressful military service. As stress-induced HC volume reductions were previously shown to be reversible, these localized biological markers may carry valuable therapeutic potential.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".