Functional connectivity for discrimination between mild cognitive impairment and subjective cognitive decline in Alzheimer disease: A study on resting‐state EEG rhythms in the Peruvian population
Bibliographic record
Abstract
Abstract Background The increase in cases of dementia worldwide has promoted the development of research for its detection and early intervention. However, the strategies, procedures, and tools designed to address the problem from the international level have found significant barriers in its implementation in Latin American countries, where factors such as socio‐demographic variability and clinical techniques limit their scope in terms of identification and intervention. Under this framework, research with signals in EEG / MEG has shown that the analysis of functional connectivity can be a sensitive biomarker in neurodegenerative processes, including the analysis of pre‐symptomatic stages with subsequent conversion to Alzheimer disease. This study seeks to develop robust methods for the diagnosis and characterization of Alzheimer's disease (AD) in the phase from the analysis of 160‐channel EEG signals in the Peruvian population. Method 500 screening evaluations were carried out, with 75 adults/seniors between 50 and 75 years of age being selected: 17 subjective cognitive declines, 24 with a family history of dementia and 31 mild cognitive impairment. The participants underwent four evaluation sessions, which included an EEG record in a state of rest (eyes closed and eyes open) and neuropsychological tests. Result The functional connectivity patterns are consistent with similar studies using MEG in European and North‐American population. Conclusion Our results demonstrate the utility of the EEG for the diagnosis and distinction of the previous stages of AD and the variation of the default mode network between QSM, DCL, and AF.
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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 | 0.001 | 0.002 |
| 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".