Desynchronization of autonomic response and central autonomic network connectivity in posttraumatic stress disorder
Bibliographic record
Abstract
OBJECTIVES: Although dysfunctional emotion regulatory capacities are increasingly recognized as contributing to posttraumatic stress disorder (PTSD), little work has sought to identify biological markers of this vulnerability. Heart rate variability (HRV) is a promising biomarker that, together with neuroimaging, may assist in gaining a deeper understanding of emotion dysregulation in PTSD. The objective of the present study was, therefore, to characterize autonomic response patterns, and their related neuronal patterns in individuals with PTSD at rest. METHODS: PTSD patients (N = 57) and healthy controls (N = 41) underwent resting-state fMRI. Connectivity patterns of key regions within the central autonomic network (CAN)-including the ventromedial prefrontal cortex (vmPFC), amygdala, and periaqueductal gray (PAG)-were examined using a seed-based approach. Observed connectivity patterns were then correlated to resting HRV. RESULTS: In contrast to controls, individuals with PTSD exhibited lower HRV. In addition, whereas controls engaged a localized connectivity pattern of CAN-related brain regions, in PTSD, key CAN regions were associated with widespread connectivity patterns in regions related to emotional reactivity (vmPFC and amygdala to insular cortex and lentiform nucleus; PAG to insula) and motor readiness (vmPFC and amygdala to precentral gyrus; PAG to precentral gyrus and cerebellum). Critically, whereas CAN connectivity in controls was strongly related to higher HRV (insula, mPFC, superior frontal cortex, thalamus), HRV covariation was absent in PTSD subjects. CONCLUSIONS: This study provides the first evidence for a specific psychophysiological-neuronal profile in PTSD individuals characterized by lower resting HRV and a lack of HRV covariation with CAN-related brain connectivity. Hum Brain Mapp 38:27-40, 2017. © 2016 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) | 0.001 | 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".