Neural correlates of heart rate variability in PTSD during sub‐ and supraliminal processing of trauma‐related cues
Bibliographic record
Abstract
BACKGROUND: Posttraumatic stress disorder (PTSD) is characterized by dysregulated arousal and altered cardiac autonomic response as evidenced by decreased high-frequency heart rate variability (HF-HRV), an indirect measure of parasympathetic modulation of the heart. Indeed, subtle threatening cues can cause autonomic dysregulation, even without explicit awareness of the triggering stimulus. Accordingly, examining the neural underpinnings associated with HF-HRV during both sub- and supraliminal exposure to trauma-related cues is critical to an enhanced understanding of autonomic nervous system dysfunction in PTSD. METHODS: We compared neural activity in brain regions associated with HF-HRV in PTSD (n = 18) and healthy controls (n = 18) during exposure to sub- and supraliminal processing of personalized trauma-related words. RESULTS: As compared to controls, PTSD exhibited decreased HF-HRV reactivity in response to sub- and supraliminal cues. Notably, during subliminal processing of trauma-related versus neutral words, as compared to controls, PTSD showed decreased neural response associated with HF-HRV within the left dorsal anterior insula. By contrast, during supraliminal processing of trauma-related versus neutral words, decreased neural activity associated with HF-HRV within the posterior insula/superior temporal cortex, and increased neural activity associated with HF-HRV within the left centromedial amygdala was observed in PTSD as compared to controls. CONCLUSIONS: Impaired parasympathetic modulation of autonomic arousal in PTSD appears related to altered activation of cortical and subcortical regions involved in the central autonomic network. Interestingly, both sub- and supraliminal trauma-related cues appear to elicit dysregulated arousal and may contribute to the maintenance of hyperarousal in PTSD. Hum Brain Mapp 38:4898-4907, 2017. © 2017 Wiley Periodicals, Inc.
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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 | 0.000 | 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".