The effects of trauma on brain and body: A unifying role for the midbrain periaqueductal gray
Bibliographic record
Abstract
Post-traumatic stress disorder (PTSD), a diagnosis that may follow the experience of trauma, has multiple symptomatic phenotypes. Generally, individuals with PTSD display symptoms of hyperarousal and of hyperemotionality in the presence of fearful stimuli. A subset of individuals with PTSD; however, elicit dissociative symptomatology (i.e., depersonalization, derealization) in the wake of a perceived threat. This pattern of response characterizes the dissociative subtype of the disorder, which is often associated with emotional numbing and hypoarousal. Both symptomatic phenotypes exhibit attentional threat biases, where threat stimuli are processed preferentially leading to a hypervigilant state that is thought to promote defensive behaviors during threat processing. Accordingly, PTSD and its dissociative subtype are thought to differ in their proclivity to elicit active (i.e., fight, flight) versus passive (i.e., tonic immobility, emotional shutdown) defensive responses, which are characterized by the increased and the decreased expression of the sympathetic nervous system, respectively. Moreover, active and passive defenses are accompanied by primarily endocannabinoid- and opioid-mediated analgesics, respectively. Through critical review of the literature, we apply the defense cascade model to better understand the pathological presentation of defensive responses in PTSD with a focus on the functioning of lower-level midbrain and extended brainstem systems.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".