Threatening faces induce fear circuitry hypersynchrony in soldiers with post-traumatic stress disorder
Bibliographic record
Abstract
BACKGROUND: Post-traumatic stress disorder (PTSD) is associated with atypical responses to emotional face stimuli with preferential processing given to threat-related facial expressions via hyperactive amygdalae disengaged from medial prefrontal modulation. METHOD: We examined implicit emotional face perception in soldiers with (n = 20) and without (n = 25) PTSD using magnetoencephalography to define spatiotemporal network interactions, and a subsequent region-of-interest analysis to characterize the network role of the right amygdala and medial prefrontal cortex in threatening face perception. RESULTS: Contrasts of network interactions revealed the PTSD group were hyperconnected compared to controls in the phase-locking response in the 2-24 Hz range for angry faces, but not for happy faces when contrasting groups. Hyperconnectivity in PTSD was greatest in the posterior cingulate, right ventromedial prefrontal cortex, right parietal regions and the right temporal pole, as well as the right amygdala. Graph measures of right amygdala and medial prefrontal connectivity revealed increases in node strength and clustering in PTSD, but not inter-node connectivity. Additionally, these measures were found to correlate with anxiety and depression. CONCLUSIONS: In line with prior studies, amygdala hyperconnectivity was observed in PTSD in relation to threatening faces, but the medial prefrontal cortex also displayed enhanced connectivity in our network-based approach. Overall, these results support preferential neurophysiological encoding of threat-related facial expressions in those with PTSD.
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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) | 0.002 | 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".