Atypical oscillatory dynamics during emotional face processing in paediatric obsessive–compulsive disorder with MEG
Bibliographic record
Abstract
Children and youth with obsessive-compulsive disorder (OCD) demonstrate difficulties with social, emotional and cognitive functions in addition to the core diagnosis of obsessions and compulsions. This is the first magnetoencephalography (MEG) study to examine whole-brain neurophysiological functional connectivity of emotional face processing networks in paediatric OCD. Seventy-two participants (OCD: n = 36; age 8-17 yrs; typically developing controls: n = 36, age 8-17 yrs) completed an implicit emotional face processing task in the MEG. Functional connectivity networks in canonical frequency bands were compared between groups, and within OCD and control groups between emotions (angry vs. happy). Between groups, participants with OCD showed increased functional connectivity in the gamma band to angry faces, suggesting atypical perception of angry faces in OCD. Within groups, the OCD group showed greater engagement of the beta band, suggesting the over-use of top-down processing when perceiving happy versus angry emotions, while controls engaged in bottom-up gamma processing, also greater to happy faces. Over-activation of top-down processing has been linked to difficulties modifying one's cognitive set. Findings establish altered patterns of neurophysiological connectivity in children with OCD, and are striking in their oscillatory specificity. Our results contribute to a greater understanding of the neurobiology of the disorder, and are foundational for the possibility of alternative targets for intervention.
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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".