Detecting Early Preclinical Alzheimer's Disease via Cognition, Neuropsychiatry, and Neuroimaging: Qualitative Review and Recommendations for Testing
Bibliographic record
Abstract
In this paper, we review studies that have investigated whether neuropsychological, neuropsychiatric, and neuroimaging measures predict decline to Alzheimer's disease (AD). Prospective neuropsychological studies indicate that cognitive performance may be an excellent indicator of future progression from mild cognitive impairment (MCI) to AD, particularly when episodic memory is combined with tasks relying on executive control and language tasks. Research on neuropsychiatric symptoms reveal that depression, apathy, anxiety, and sleep disturbances can contribute to predictive models, though their sensitivity is typically lower than that found with cognitive measures. Finally, different structural brain imaging markers reveal excellent predictive accuracy. The paper discusses issues that will have to be addressed in future studies. First, it will be necessary to increase the evaluation of combined markers, as this may considerably improve predictive accuracy. Second, it will be necessary to move to earlier stages than MCI in order to expand the detection window. Third, processes of compensation and plasticity will have to be better investigated as research moves into earlier stages. The Consortium for the early identification of AD-Quebec (CIMA-Q) is presented as an instance of this approach, and potential batteries of measures are proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".