Dysfunctional Activation and Brain Network Profiles in Youth with Obsessive-Compulsive Disorder: A Focus on the Dorsal Anterior Cingulate during Working Memory
Bibliographic record
Abstract
Brain network dysfunction is emerging as a central biomarker of interest in psychiatry, in large part, because psychiatric conditions are increasingly seen as disconnection syndromes. Understanding dysfunctional brain network profiles in task-active states provides important information on network engagement in an experimental context. This in turn may be predictive of many of the cognitive and behavioral deficits associated with complex behavioral phenotypes. Here we investigated brain network profiles in youth with obsessive-compulsive disorder (OCD), contrasting them with a group of age-comparable controls. Network interactions were assessed during simple working memory: in particular, we focused on the modulation by the dorsal anterior cingulate cortex (dACC) of cortical, striatal, and thalamic regions. The focus on the dACC was motivated by its hypothesized role in the pathophysiology of OCD. However, its task-active network signatures have not been investigated before. Network interactions were modeled using psychophysiological interaction, a simple directional model of seed to target brain interactions. Our results indicate that OCD is characterized by significantly increased dACC modulation of cortical, striatal, and thalamic targets during working memory, and that this aberrant increase in OCD patients is maintained regardless of working memory demand. The results constitute compelling evidence of dysfunctional brain network interactions in OCD and suggest that these interactions may be related to a combination of network inefficiencies and dACC hyper-activity that has been associated with the phenotype.
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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 | 0.000 |
| 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".