Functional connectivity of brain areas related to social cognition and anxiety in cervical dystonia
Bibliographic record
Abstract
Introduction Recent studies highlighted the importance of non-motor symptoms, including emotional processing dysfunction, in individuals with cervical dystonia (CD). The resting state functional connectivity of areas involved in emotional processing, and the modulatory role of social anxiety on this connectivity, remain unexplored in CD. We hypothesized that CD patients would have altered functional connectivity between limbic areas involved in emotional processing as compared to healthy subjects and examined how variations in social anxiety affect connectivity. Methods 14 CD patients and 26 age- and sex-matched healthy controls completed a series of questionnaires and underwent functional magnetic resonance imaging (fMRI). Resting state functional connectivity was investigated between seeds (amygdala and insula) and whole brain ROIs, and in conventional functional networks. The modulatory role of social anxiety was investigated. Results CD patients showed reduced intra-regional connectivity in the insula, reduced connectivity between the right insula, left parietal operculum and left central opercular cortex. CD patients also showed clear reductions in connectivity in the salience, dorsal attention and sensorimotor resting state networks, as well as modest inter-network connections between language and fronto-parietal networks. In CD patients, higher anxiety scores and performance on affect naming tasks were associated with lower connectivity between right and left insula and between right insula and left central opercular cortex. Conclusion This study demonstrates that the previously observed deficits in emotional processing in CD patients may be underpinned by reduction in resting state functional connectivity in limbic areas and salience network with anxiety and social perception as a modulating factor.
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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) | 0.002 | 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".