Executive control in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium
Bibliographic record
Abstract
Objective: Obsessive-compulsive disorder (OCD) is associated with impaired executive function and altered activity in associated neural circuits, contributing to reduced goal-directed behavior. To investigate neural activation during executive control, we conducted a mega-analysis in the ENIGMA-OCD consortium pooling individual participant data from 475 individuals with OCD and 345 healthy controls across 15 fMRI tasks collected worldwide. Methods: Individual participant data was uniformly processed using HALFpipe to construct voxelwise statistical images of executive control and task load contrasts. Parameter estimates extracted from regions of interest were entered into multilevel Bayesian models to examine regional and whole-brain effects of diagnosis, and, within OCD, the influence of medication status, symptom severity, and age of onset on task activation. Results: We observed a robust task activation pattern across individuals with OCD and control participants in executive control regions across tasks. Relative to controls, individuals with OCD showed moderate to very strong evidence of weaker activation of the dorsolateral prefrontal cortex, precuneus, frontal eye fields, and inferior parietal lobule during executive control (all positive posterior probabilities [P+]<0.1). Individuals with OCD also showed stronger activation in regions of the default mode network during executive function relative to controls. We found little evidence for differential activation during executive control in task-positive regions related to disease onset, severity and medication status. Conclusion: In the first mega-analysis of fMRI studies of executive function in OCD, we found strong evidence of weaker frontoparietal activation during executive control tasks. Our findings also suggest a failure of default mode network regions to appropriately disengage during task performance in OCD.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".