Probing Perceptual Decision Making in Cervical Dystonia: Employment of a Novel Paradigm Integrating Behavioural and Neuronal Measures
Bibliographic record
Abstract
Cervical dystonia (CD) is a rare neurological motor disorder characterised by involuntary head and neck movements, resulting in abnormal posturing. Although CD presents clinically as a motor disorder, recent evidence suggests it is a network disorder with abnormalities in sensory and motor processing. The pathophysiology of CD remains unclear; however, it has been suggested that impairment within the superior collicular-pulvinar-amygdala network is involved. Superior Colliculus (SC) involvement may imply abnormalities in perceptual decision making (PDM). This study assessed PDM in CD and healthy controls (HC) using a novel paradigm which integrates behavioural and neuronal measures. Seven CD subjects and six HC subjects were tested. A dichromatic dynamic translational Glass pattern task was used to test PDM ability, while 24-channel EEG recordings were taken. Event related potentials were analysed, and centro-parietal positivity (CPP), a neural measure affected by PDM, was examined. Group differences were analysed using independent sample t-tests. Results revealed no statistical difference in the reaction time in the two groups, however there was a high correlation between the HC reaction time and CPP peak amplitude (R2= 0.9876) but no correlation in the CD cohort (R2= 0.013), suggesting the CD cohort do not generate a neural CPP response while making their decision. Furthermore, the CD group performed at a lower accuracy and slower reaction time across all coherence levels.Clinical Relevance— This work aimed to assess a potential endophenotype for CD which might lead to earlier and more accurate diagnosis and the development of novel treatment strategies. There are no previous studies relating PDM to specific neural processes in CD. This work introduces a novel paradigm which integrates behavioural and neuronal measures, allowing us to investigate the impact of CD on perceptual decision making for the first time.
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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 | 0.000 | 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".