Large-Scale Functional Hyperconnectivity Patterns Characterizing Trauma-Related Dissociation: A rs-fMRI Study of PTSD and its Dissociative Subtype
Bibliographic record
Abstract
Background: In 2012, a dissociative subtype of post-traumatic stress disorder (PTSD) was introduced into the DSM based on emerging clinical and neurobiological evidence of a distinct PTSD phenotype characterized by trauma-related dissociation. Ten years later, considerable research has demonstrated unique small-scale (i.e., node-based) and large-scale (i.e., network-related) functional connectivity patterns, specific to the dissociative subtype. However, the field has yet to arrive at a neurobiological framework able to account for the disparate findings across these various scales of investigation. Methods: We conducted the largest region of interest (ROI)-to-ROI analysis performed on a PTSD population to date, with a total of 132 ROIs and 197 participants, 134 of whom were diagnosed with PTSD. We implemented a whole-brain approach, comparing patterns of intra- and inter-network functional connectivity between participants with PTSD, its dissociative subtype, and non-traumatized, healthy controls. We also performed a joint factor analysis between the discovered patterns of functional connectivity and a battery of behavioural, demographic, and clinical scores. Results: Whereas participants with PTSD showed only modest differences to that of controls in temporal regions and the right frontoparietal network, participants with the dissociative subtype demonstrated widespread small-scale and large-scale functional hyperconnectivity, compared to controls. Three major joint factors were also identified, characterizing two dissociative and one PTSD symptom-linked factor. Conclusion: In the dissociative subtype, we found evidence of a general pattern of hyperconnectivity, especially among subcortical regions, sensory- and motor-related networks, and intrinsic connectivity networks, diverging from what we would expect based on a small-world organization of the brain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".