Negative Valence 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 altered brain function related to processing of negative emotions. To investigate neural correlates of negative valence in OCD, we pooled functional magnetic resonance imaging data of 633 individuals with OCD and 453 healthy control participants from 16 studies using different negatively valenced tasks across the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium's OCD Working Group. METHODS: Participant data were processed uniformly using HALFpipe, to extract voxelwise participant-level statistical images of one common first-level contrast: negative versus neutral stimuli. In preregistered analyses, parameter estimates were entered into Bayesian multilevel models to examine whole-brain and regional effects of OCD and its clinically relevant features-symptom severity, age of onset, and medication status. RESULTS: We provided a proof of concept that participant-level data can be combined across several task paradigms and observed one common task activation pattern across individuals with OCD and control participants that encompasses frontolimbic and visual areas implicated in negative valence. Compared with control participants, individuals with OCD showed very strong evidence of weaker activation of the bilateral occipital cortex (P+ < 0.001) and adjacent visual processing regions during negative valence processing that was related to greater OCD severity, late onset of the disorder, and an unmedicated status. Individuals with OCD also showed stronger activation in the orbitofrontal, subgenual anterior cingulate, and ventromedial prefrontal cortex (all P+ < 0.1) that was related to greater OCD severity and late onset. CONCLUSIONS: In the first mega-analysis of this kind, we replicated previous findings of stronger ventral prefrontal activation in OCD during negative valence processing and highlight the lateral occipital cortex as an important region implicated in altered negative valence processing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".