PTSD and its dissociative subtype through the lens of the insula: Anterior and posterior insula resting‐state functional connectivity and its predictive validity using machine learning
Bibliographic record
Abstract
Individuals with post-traumatic stress disorder (PTSD) typically experience states of reliving and hypervigilance; however, the dissociative subtype of PTSD (PTSD+DS) presents with additional symptoms of depersonalization and derealization. Although the insula is critical to emotion processing, its association with these contrasting symptom profiles is yet to be fully delineated. Accordingly, we investigated insula subregion resting-state functional connectivity patterns among individuals with PTSD, PTSD+DS, and healthy controls. Using SPM12 and PRONTO software, we implemented a seed-based resting-state functional connectivity approach, along with multiclass Gaussian process classification machine learning, respectively, in order to evaluate unique patterns and the predictive validity of insula subregion connectivity among individuals with PTSD (n = 84), PTSD+DS (n = 49), and age-matched healthy controls (n = 51). As compared to PTSD and PTSD+DS, healthy controls showed increased right anterior and posterior insula connectivity with frontal lobe structures. By contrast, PTSD showed increased bilateral posterior insula connectivity with subcortical structures, including the periaqueductal gray. Strikingly, as compared to PTSD and controls, PTSD+DS showed increased bilateral anterior and posterior insula connectivity with posterior cortices, including the left lingual gyrus and the left precuneus. Moreover, machine learning analyses were able to classify PTSD, PTSD+DS, and controls using insula subregion connectivity patterns with 80.4% balanced accuracy (p < .01). These findings suggest a neurobiological distinction between PTSD and its dissociative subtype with regard to insula subregion functional connectivity patterns. Furthermore, machine learning algorithms were able to utilize insula resting-state connectivity patterns to discriminate between participant groups with high predictive accuracy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".