Study of face recognition event-related potential in Parkinson's disease patients
Bibliographic record
Abstract
Objective To study changes of N170 component of event-related potential in PD patients with impairment of visuospatial function and investigate methods of detecting cognitive impairment in the early stage of PD.Methods Fifty PD patients participated in the study.All patients fulfilled the diagnostic criteria for PD proposed by the Queen Square Brain Bank.They were assessed by neuropsychological scales,and were divided into impairment in visuospatial function (group Ⅰ ,n = 30) and normal visuospatial function(group Ⅱ ,n = 20).The face learning-recognition experimental model was used.EEG data of 10 channels were recorded and face-specific N170 component in all subjects was measured.In addition,reaction time and correct reaction rate were recorded.Statistical analysis was applied to peak latency and amplitude of N170,behavioral indexes and scores of neuropsychological scale in the two groups.Results There were no statistically significant differences in scores of MMSE between the two groups.The scores of MoCA of the group Ⅰ were lower than those of the group Ⅱ.Correct reaction rate in group Ⅰ was much lower than that in group Ⅱ (P0.05).The wave amplitudes of N170 evoked by familiar face event in only T6 electrode in group Ⅰ was increased as compared with that in group Ⅱ (P 0.05). There was statistical difference between peak latency or amplitude of N170 evoked by familiar face event and strange face event in F7,F8,O1 and T5 electrodes in group Ⅰ .Conclusion MoCA can identify cognitive function impairment in PD patients in early stage.The method of face recognition has clinical value in detecting impairment of visuospatial function in PD patient.N170 is insensitive for detecting impairment of visuospatial function in face recognition stage.
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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".