The effects of cognitive behavioral therapy on resting‐state functional brain network in drug‐naive patients with obsessive–compulsive disorder
Bibliographic record
Abstract
Objectives: Although cognitive behavioral therapy (CBT) is an effective treatment for obsessive-compulsive disorder (OCD), the treatment mechanisms remain poorly understood. This study aimed to investigate the effects of CBT on changes in the intrinsic whole-brain functional network of OCD patients. Materials and Methods: Twenty drug-naive and noncomorbid OCD patients were recruited, and resting-state functional magnetic resonance imaging was performed before and after 12 weeks of CBT. Moreover, 20 healthy controls were scanned twice with a 12-week interval. A graph-theory degree centrality (DC) approach and functional connectivity method were used to analyze the whole-brain functional network hub and connectivity changes in OCD patients before and after CBT treatment. Results: A significant group × time interaction on DC was found in the left dorsolateral prefrontal cortex (DLPFC); the DC in the left DLPFC was significantly reduced after CBT treatment. Resting-state functional connectivity (RSFC) between the left DLPFC and right orbitofrontal cortex was increased in the OCD patients at baseline, and normalized after CBT treatment. RSFC changes between the left DLPFC and default mode network (DMN) positively correlated with changes in clinical symptoms in OCD patients. Conclusions: These findings suggest that CBT can modulate changes in intrinsic functional network hubs in the cortico-striato-thalamo-cortical circuit in OCD patients. Cognitive control network and DMN connectivity may be a potential imaging biomarker for evaluating CBT treatment for OCD.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".