Validating Frontal Memory‐related Neuromarkers for Mild Cognitive Impairment using Identical Protocols in Two Racial and Culturally Distinct Cohorts
Bibliographic record
Abstract
Abstract Background Studies using standardized cognitive assessments across disparate populations are often confounded by sociocultural differences, leading to a lack of comparative standards to assess the degree of Mild cognitive impairment (MCI). Thus, testing in diverse populations is essential in validating biomarkers for MCI and avoiding race and cultural confounders. Applying an identical, cross‐culturally accepted, non‐invasive, electrophysiological protocol, we test the hypothesis that the frontal neuromarkers for MCI are consistent in two samples of older adults in the US and China. Method Using an identical, clinically friendly protocol with a wireless 14‐channel headset (eMotiv), we recorded EEG signals along with accuracy and reaction times during a visual object working memory task in two samples of older adults. The Kentucky sample includes 12 Normal Cognition NC; (Mean age 68); 7 MCI (age 81) while the Beijing sample includes 11 NC (age 64) and 16 MCI (age 65). Each participant also received assessments in NACC UDS 3.0. The 10‐min task asked each subject to remember two visual objects and then determine whether the subsequent object is a Match or Nonmatch to one of the two held in working memory. Result We found that left frontal sites showed significant mean amplitude differences between MCI and NC during target match retrievals. The persons with MCI showed reduced responses to memory targets at the F3 and F7 sites in Kentucky. The MCIs’ reduction patterns were significant in the left frontal F3 but not significant at F7 in Beijing sample. The Beijing participants were on average younger than KY participants, leading to a reduction in quantitative signal change, however, the effect remains robust and parallels those from Kentucky. The current results using a fast and two‐target memory task are consistent with previously reported results using 64‐channels and a longer version of the memory task with single‐target. Conclusion We have validated frontal neuromarkers for MCI risk in two diverse samples. The current finding has significant implications for clinical practice which creates a path for the next step: large‐scale, low‐cost, and culturally‐accepted screening for risk of cognitive decline in individuals, irrespective of their socio‐cultural, geographical, or societal background.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".