P300 Wave Alterations and Cognitive Impairment in Cerebellum Lesions
Bibliographic record
Abstract
Patients with cognitive deficits have a prolonged latency and reduced amplitude of the P300 wave. However, no study has correlated P300 wave alterations with the cognitive performance of patients with cerebellar lesions. We aimed to determine if the cognitive status of these patients was associated with P300 wave alterations. We recruited 30 patients with cerebellar lesions from the wards of the N.R.S. Medical College, Kolkata, in West Bengal (India). The Kolkata Cognitive Screening Battery tasks and the Frontal Assessment Battery (FAB) were used to assess the cognitive status and the International Cooperative Ataxia Rating Scale (ICARS) for cerebellar signs. We compared the results with the normative data of the Indian population. Patients had P300 wave alterations with a significant increase in latency and a non-significant trend in amplitude. In a multivariate model, P300 wave latency was positively associated with the ICARS kinetic subscale (p = 0.005) and age (p = 0.009), regardless of sex and years of education. In the model that included cognitive variables, P300 wave latency was negatively associated with performance in phonemic fluency (p = 0.035) and construction (p = 0.009). Furthermore, P300 wave amplitude was positively associated with the FAB total score (p < 0.001). In closing, patients with cerebellar lesions had an increase in latency and a decrease in the amplitude of the P300 wave. These P300 wave alterations were also associated with worse cognitive performance and some of the subscales of the ICARS, reinforcing that the cerebellum has motor, cognitive, and affective functions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".