How understanding the neurobiology of complex post-traumatic stress disorder can inform clinical practice: a social cognitive and affective neuroscience approach
Bibliographic record
Abstract
OBJECTIVE: In this review, we examine the relevance of the social cognitive and affective neuroscience (SCAN) paradigm for an understanding of the psychology and neurobiology of complex post-traumatic stress disorder (PTSD) and its effective treatment. METHOD: The relevant literature pertaining to SCAN and PTSD was reviewed. RESULTS: We suggest that SCAN offers a novel theoretical paradigm for understanding psychological trauma and its numerous clinical outcomes, most notably problems in emotional/self-awareness, emotion regulation, social emotional processing and self-referential processing. A core set of brain regions appear to mediate these collective psychological functions, most notably the cortical midline structures, the amygdala, the insula, posterior parietal cortex and temporal poles, suggesting that problems in one area (e.g. emotional awareness) may relate to difficulties in another (e.g. self-referential processing). We further propose, drawing on clinical research, that the experiences of individuals with PTSD related to chronic trauma often reflect impairments in multiple social cognitive and affective functions. CONCLUSION: It is important that the assessment and treatment of individuals with complex PTSD not only addresses traumatic memories but also takes a SCAN-informed approach that focuses on the underlying deficits in emotional/self-awareness, emotion regulation, social emotional processing and self-referential processing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".