A Multi-Symptom Circuit Architecture of Obsessive–Compulsive Disorder
Bibliographic record
Abstract
Abstract Obsessive–compulsive disorder (OCD) manifests with diverse symptom constellations that likely arise from dysfunction in partially distinct neural circuits. Deep brain stimulation (DBS), paired with high-resolution connectomics, offers a unique window into these pathways in humans. Here, we analyzed clinical outcomes and stimulation sites from 77 treatment-refractory patients with OCD and 39 with Tourette’s syndrome (TS) exhibiting comorbid obsessive–compulsive behaviors (244 electrodes across 15 cohorts and eight targets). Engagement of the previously defined OCD response tract predicted global obsessive-compulsive symptom improvement across heterogeneous targets and diagnoses, spanning both OCD and TS. Beyond broad symptoms, obsessions, compulsions, anxiety, and depression mapped onto distinct yet overlapping subcircuits fragmenting the anterior limb of the internal capsule along a dorsoventral axis. This fine-scale architecture of OCD circuit dysfunction was reproducible across cross-validation schemes and patient subsets. Exploratory analyses identified additional subcircuits for cognitive control and flexibility. Global functional recovery was better explained by combined engagement of multiple symptom-specific rather than a single tract. Collectively, these findings illustrate how invasive neuromodulation can delineate a multi-symptom circuit taxonomy of compulsivity that may guide personalized neuromodulation.
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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 | 0.001 | 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".