Visual processing capacity and cognitive decline in Parkinson’s disease
Bibliographic record
Abstract
Background Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised by motor symptoms. However, approximately half of patients with PD exhibit signs of dementia within a decade of diagnosis. While deficits in working memory and visuospatial abilities are recognised as hallmarks of cognitive decline in PD, these populations are rarely studied using detailed cognitive tools that link cognitive impairments to formal theoretical models, such as the theory of visual attention (TVA). Methods This cross-sectional study addresses this gap by employing the TVA whole report paradigm to assess visual processing in a cohort of patients with PD, both with and without cognitive impairment. Participants were divided based on their Montreal Cognitive Assessment (MoCA) scores into two PD groups (n=25 each) and a healthy control group (n=25). Results Our principal finding is that the visual processing speed (C) and visual short-term memory capacity (K) are significantly diminished in patients with PD with MoCA scores below 26 (Analysis of variance, p=0.016 for C and p<0.001 for K), while no notable differences were observed between controls and patients with PD with MoCA scores of 26 or above. Using a generalised linear model to assess the impact of factors such as age, gender and disease duration, we discovered that the C-parameter was significantly influenced by age, while the K - parameter was notably affected by gender. Conclusion TVA parameters demonstrate their suitability for detecting cognitive deficits in PD. Given their independence from motor and non-motor symptoms, TVA parameters may prove to be valuable tools for early diagnosis and longitudinal monitoring of cognitive deficits in individual patients with PD.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".