Intrinsic Connectivity Networks in post‐traumatic stress disorder during sub‐ and supraliminal processing of threat‐related stimuli
Bibliographic record
Abstract
OBJECTIVE: To investigate the functional connectivity of large-scale intrinsic connectivity networks (ICNs) in post-traumatic stress disorder (PTSD) during subliminal and supraliminal presentation of threat-related stimuli. METHOD: Group independent component analysis was utilized to study functional connectivity within the ICNs most correlated with the Default-mode Network (DMN), Salience Network (SN), and Central Executive Network (CEN) in PTSD participants (n = 26) as compared to healthy controls (n = 20) during sub- and supraliminal processing of threat-related stimuli. RESULTS: Comparing patients with PTSD with healthy participants, prefrontal and anterior cingulate cortex involved in top-down regulation showed increased integration during subliminal threat processing within the CEN and SN and during supraliminal threat processing within the DMN. The right amygdala showed increased connectivity with the DMN during subliminal processing in PTSD as compared to controls. Brain regions associated with self-awareness and consciousness exhibited decreased connectivity during subliminal threat processing in PTSD as compared to controls: the claustrum within the SN and the precuneus within the DMN. CONCLUSION: Key nodes of the ICNs showed altered functional connectivity in PTSD as compared to controls, and differential results characterized sub- and supraliminal processing of threat-related stimuli. These findings enhance our understanding of ICNs underlying PTSD at different levels of conscious threat perception.
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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".