Brain structural and functional impairment network localization in obsessive–compulsive disorder
Bibliographic record
Abstract
BACKGROUND: Numerous neuroimaging studies investigating the neural substrates of obsessive-compulsive disorder (OCD) have yielded inconsistent findings, and growing evidence suggests that psychiatric disorders are more accurately localized to brain networks rather than discrete brain regions. We sought to identify brain network localization in OCD. METHODS: We initially examined brain locations of structural and functional alterations among patients with OCD and healthy controls using neuroimaging studies. Employing a novel technique called functional connectivity network mapping (FCNM) and large-scale human brain connectome data, we mapped these damaged brain regions to 2 brain impairment networks in OCD. RESULTS: We included 62 neuroimaging studies involving 2578 patients with OCD and 2502 healthy controls. For FCNM, we used data from 556 healthy adults. Among patients with OCD, the grey matter volume (GMV) and resting-state activity impairment networks encompassed a broad range of brain regions, primarily involving the default mode, sensorimotor, and limbic networks, as well as the bilateral middle frontal gyrus and bilateral middle temporal gyrus. Additionally, the GMV impairment network specifically involved bilateral inferior frontal gyrus. LIMITATIONS: We used large-scale human brain connectome data from healthy people, rather than the samples clinically and demographically matched to the original study participants, to examine brain networks in OCD. CONCLUSION: Our study integrated an FCNM method with large-scale human brain connectome data to map heterogeneous abnormal brain locations of OCD to structural and functional impairment networks. Our findings deepen our understanding of the neuropathological mechanisms of OCD from a network perspective and may inform future neuromodulation treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".