Auditory Evoked P300 Potential in Patients With Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is the second most common neurodegenerative disorder. Though the cardinal features of PD are motor symptoms, it is also associated with many non-motor symptoms, such as cognitive impairment, autonomic dysfunction, sleep disorders, and depression, which could affect the quality of life. Early identification of PD's non-motor signs can aid in the diagnosis of PD. The current research aimed to assess the neurophysiological changes in PD patients using auditory evoked P300 potential and to determine the possible correlation between P300 wave components and cognitive impairment. MATERIALS AND METHODS: This cross-sectional research involved 32 idiopathic PD patients. The neurophysiological changes in PD patients were studied using auditory evoked P300 potential and the obtained data were compared with normative data. The patient's cognitive status was scored using the Montreal Cognitive Assessment (MoCA) questionnaire and they were divided into two groups: the patients with normal cognition and the patients with impaired cognition. RESULTS: The participants showed a significant decrease in P300 amplitude (p = 0.000) but no change in P300 latency when compared to normative data using the Wilcoxon rank sum test. Also, there was a positive correlation between the MoCA score and P300 amplitude (p < 0.05), indicating that if cognition is impaired, P300 amplitude would also be reduced. There was a significant difference between PD patients with impaired cognition and patients with normal cognition in the P300 amplitude at Cz (p = 0.001) and Fz (p = 0.003) when the Mann-Whitney U test was used. These findings indicate that it is possible to notice changes in the P300 wave components among PD patients when their cognition is impaired. CONCLUSION: Auditory evoked P300 potentials can be used to objectively evaluate cognition in PD patients and by starting supportive therapy, the quality of life for PD patients can be improved.
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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 | 0.002 |
| 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".