Overcoming the Brain-Body Disconnect Following Attachment Trauma: A Comment on Farina and Schimmenti.
Bibliographic record
Abstract
Farina and Schimmenti's (2025) model of Attachment Trauma (AT) reframes early relational adversity as a transdiagnostic, developmentally rooted process that disrupts emotional regulation, identity formation, and relational functioning. Rather than viewing trauma as a single catastrophic event, their model emphasizes chronic, inescapable relational disruptions, particularly in protection, attunement, and repair as core pathogenic mechanisms. This relationally situated view positions AT as a primary etiological force behind a range of clinical conditions, including complex PTSD, borderline personality disorder, and dissociative disorders. To strengthen this compelling framework, the current paper proposes the integration of somatosensory processing models, which offer neurobiologically grounded mechanisms for how AT becomes embedded neurobiologically. Dysregulation in vestibular and somatosensory systems, critical to bodily orientation, interoception, affect regulation, and social connection, may underlie the fragmentation and dissociation central to AT. A hierarchical neurobiological model is proposed, mapping disruptions from brainstem sensory circuits through limbic and cortical systems. A key implication of this model is the need to prioritize physiological regulation before initiating higher-order cognitive or relational interventions. Bottom-up approaches, including sensorimotor psychotherapy, somatic experiencing, and Deep Brain Reorienting (DBR) target shock, autonomic dysregulation and sensory fragmentation that can compromise traditional talk-based therapy. By stabilizing the nervous system and restoring sensorimotor integration, these methods prepare clients for deeper therapeutic work and support the reorganization of disrupted self and relational patterns inherent in attachment trauma.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.005 | 0.020 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.073 | 0.082 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".