Distinct intrinsic network connectivity patterns of post‐traumatic stress disorder symptom clusters
Bibliographic record
Abstract
OBJECTIVE: Post-traumatic stress disorder (PTSD) is considered a multidimensional disorder, with distinct symptom clusters including re-experiencing, avoidance/numbing, hyperarousal, and most recently depersonalization/derealization. However, the extent of differing intrinsic network connectivity underlying these symptoms has not been fully investigated. We therefore investigated the degree of association between resting connectivity of the salience (SN), default mode (DMN), and central executive (CEN) networks and PTSD symptom severity. METHOD: Using resting-state functional MRI data from PTSD participants (n = 21), we conducted multivariate analyses to test whether connectivity of extracted independent components varied as a function of re-experiencing, avoidance/numbing, hyperarousal, and depersonalization/derealization. RESULTS: Hyperarousal symptoms were associated with reduced connectivity of posterior insula/superior temporal gyrus within SN [peak Montréal Neurological Institute (MNI): -44, -8, 0, t = -4.2512, k = 40]. Depersonalization/derealization severity was associated with decreased connectivity of perigenual anterior cingulate/ventromedial prefrontal cortex within ventral anterior DMN (peak MNI: 8, 40, -4; t = -3.8501; k = 15) and altered synchrony between two DMN components and between DMN and CEN. CONCLUSION: Our results are consistent with prior research showing intrinsic network disruptions in PTSD and imply heterogeneous connectivity patterns underlying PTSD symptom dimensions. These findings suggest possible biomarkers for PTSD and its dissociative subtype.
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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 | 0.001 | 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".