ERP biomarkers for go/no-go tasks to detect potential cognitive impairment in community-dwelling older adults
Bibliographic record
Abstract
With an aging population, detecting cognitive dysfunction at an early stage is important. However, no current neurophysiological assessments examine brain activity status in community-dwelling older adults. Therefore, this study aimed to determine the relationship between event-related potential (ERP) and neuropsychological scores during go/no-go tasks among community-dwelling older adults. The participants were 34 young adults and 46 older adults. They underwent a go/no-go task to measure ERP, and older adults also underwent neuropsychological testing. Older adults were divided into two groups for analysis based on the results of the Japanese version of the Montreal Cognitive Assessment (MoCA-J): cognitively normal and cognitively impaired. N2 latency was significantly delayed in the cognitively impaired group compared to the cognitively normal and younger groups. In addition, the younger group exhibited a significant increase in P3 amplitude and faster latency compared to both older adults groups, with no notable difference between the two older adult groups. Results of a correlation analysis between ERP and neuropsychological test scores showed that the N2 latency was correlated with neuropsychological test scores, which suggested that receiver operating characteristic (ROC) curve analysis could discriminate cognitive function. These results indicated that the N2 latency reflected potential cognitive dysfunction in community-dwelling older adults. Furthermore, P3 amplitude may be useful in detecting age-related decline in inhibitory function.
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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 | 0.001 | 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) | 0.002 | 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".