Self-Focused Brain Predictors of Cognitive Behavioral Therapy Response in a Transdiagnostic Sample
Bibliographic record
Abstract
Background: Effective biomarkers of cognitive behavioral therapy (CBT) response provide information beyond available behavioral or self-report measures and may optimize treatment selection for patients based on likelihood of benefit. No single biomarker reliably predicts CBT response. In this study, we evaluated patterns of brain connectivity associated with self-focused attention (SFA) as biomarkers of CBT response for anxiety and obsessive-compulsive disorders. We hypothesized that pre-treatment as well as pre- to post-treatment changes in functional connectivity would be associated with improvement during CBT in a transdiagnostic sample. Methods: =13) were scanned before and after 12 sessions of CBT targeting their primary disorder. Eligibility was based on elevated trait SFA scores on the Public Self-Consciousness Scale. Seed-based resting state functional connectivity associated with symptom improvement was computed using a seed in the posterior cingulate cortex/precuneus that delineated a self-other functional network. Results: At pre-treatment, stronger positive connectivity of the seed with the cerebellum, insula, middle occipital gyrus, postcentral gyrus, and precuneus/superior parietal lobule, and stronger negative connectivity with the putamen, were associated with greater clinical improvement. Between pre- to post-treatment, greater anticorrelation between the seed and precuneus/superior parietal lobule was associated with clinical improvement, although this did not survive thresholding. Conclusions: Pre-treatment functional connectivity between regions involved in attentional salience, self-generated thoughts, and external attention predicted greater CBT response. Behavioral and self-report measures of SFA did not contribute to predictions, thus highlighting the value of neuroimaging-based measures of SFA. Clinical Trials Registration: ClinicalTrials.gov Identifier: NCT02808702 https://clinicaltrials.gov/ct2/show/NCT02808702.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".