Functional connectivity in obsessive-compulsive disorder and its subtypes
Bibliographic record
Abstract
BACKGROUND: Obsessive-compulsive disorder (OCD) is an anxiety disorder with significant morbidity whose pathophysiology is not fully understood. Neuroimaging studies have characterized OCD in terms of elevated striatal and prefrontal reactivity to emotion provocation. This neural model may be informed by investigation of functional connectivity in OCD, identifying alterations in how sensory information is integrated into frontostriatal regions. METHODS: The current study employed functional magnetic resonance imaging (fMRI) to compare neural activity and connectivity in 31 OCD patients (12 washing and 19 checking subtypes) and 17 healthy volunteers in an emotion provocation paradigm using visual stimuli. RESULTS: OCD status was associated with hyper-activation of the posterior cingulate (PCg) in response to emotion provocation. Additionally, OCD patients demonstrated elevated PCg functional connectivity with the visual cortices and frontostriatal regions. Exploratory analyses suggested that stimulus-provoked activity and connectivity was elevated for checking subtypes in motor cortices, and elevated in washing subtypes in the anterior insula and orbitofrontal cortex. CONCLUSIONS: The PCg's role in moderating connectivity between the visual cortex and frontolimbic regions is muted in OCD, consistent with the PCg's suggested role in regulating attention towards emotional stimuli. Exploratory analyses suggest distinct PCg connectivity profiles in OCD subtypes, with checking linked to motor activation, but washing linked to a network supporting emotional salience. The study was not powered to fully investigate the effects of medication, patients often endorsed secondary symptom subtypes that muddied washing/checking distinctions, and the emotion provocation paradigm was of limited intensity compared to life stressors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".