P.105 Using mobile electroencephalography for rapid detection of mild cognitive impairment
Bibliographic record
Abstract
Background: Mild cognitive impairment (MCI) is a concern for our aging population as it can be a pre-cursor to dementia. However, the diagnosis of MCI can be quite problematic and can come long after initial onset. Here, we sought to use a new technology we have previously validated for research – mobile electroencephalography (mEEG) – to measure brain function to see if we could rapidly detect differences in brain activity between people with and without MCI. Methods: Participants (60: mean age 65) were recruited for a control (30) and an MCI group (30). All participants were screened for MCI using standard RBANS and the MOCA assessments. Participants completed a standard n-Back assessment of working memory while mEEG data was recorded. A key feature here is that we used mEEG technology thus application of the device and the n-Back test was completed in under 10 minutes for each participant. Results: Our key finding is that we observed increased frontal mEEG theta power (brain oscillations between 4 and 7 Hz) for MCI participants relative to controls (p < 0.001). Conclusions: Importantly, our work demonstrates a potential novel rapid brain-based assessment for MCI that would afford earlier detection of disease onset.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".