Frontoparietal and salience network alterations in obsessive–compulsive disorder: insights from independent component and sliding time window analyses
Bibliographic record
Abstract
Background: Resting-state functional MRI (fMRI) studies commonly report alterations in 3 core networks in obsessive–compulsive disorder (OCD) — the frontoparietal network, the default mode network and the salience network — defined by functionally connected infraslow oscillations in ongoing brain activity. However, most of these studies observed static functional connectivity in the brains of patients with OCD. Methods: To investigate dynamic functional connectivity alterations and widen the evidence base toward the triple network model in OCD, we performed group-based independent component and sliding time window analyses in 49 patients with OCD and 41 healthy controls. Results: The traditional independent component analysis showed alterations in the left frontoparietal network as well as between the left and right frontoparietal networks in patients with OCD compared with healthy controls. For dynamic functional connectivity, the sliding time window approach revealed peak dysconnectivity between the left and right frontoparietal networks and between the left frontoparietal network and the salience network. Limitations: The number of independent components, noise in the resting-state fMRI images, the heterogeneity of the OCD sample, and comorbidities and medication status in the patients could have biased the results. Conclusion: Disrupted modulation of these intrinsic brain networks may contribute to the pathophysiology of OCD.
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 | 0.002 |
| 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) | 0.001 | 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".