Neurocognitive performance in the context of acute symptom reduction in OCD: Treatment effects and the impact of BDNF
Bibliographic record
Abstract
BACKGROUND: Obsessive-compulsive disorder (OCD) has been associated with neurocognitive impairments. The present study examined the effect of treatment on neurocognitive performance in OCD and the relationship between neurocognitive change and symptom change. The present study also examined polymorphisms influencing brain derived neurotrophic factor (BDNF) as predictors of neurocognitive change. METHOD: Treatment-seeking participants with OCD (N = 125) were assigned to cognitive behavioural therapy (CBT) alone, CBT combined with regular physical exercise, exercise alone, or a waitlist control group. Measures of OCD symptom severity and a neuropsychological battery were completed pre- and post-treatment. Blood or saliva samples were used to genotype the BDNF Val66Met polymorphism. RESULTS: OCD symptom severity was not cross-sectionally associated with neurocognitive performance. Several neurocognitive measures improved over treatment. The BDNF Val66Met polymorphism was significantly associated with worse performance on the Stroop test but did not significantly predict change in neurocognitive performance over time. LIMITATIONS: Limitations include lack of a healthy control group. CONCLUSION: Improvement in neurocognitive performance corresponded to symptomatic improvement and was independent of the BDNF Val66Met genotype.
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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 | 0.000 | 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".