Establishing an individualized model of conversion from normal cognition to Alzheimer's disease after 4 years, based on cognitive, brain morphology and neuropsychiatric characteristics
Bibliographic record
Abstract
OBJECTIVES: The impact of neuropsychiatric symptoms (NPS) on cognitive performance has been reported, and this impact was better defined in the aging population. Yet the potential of using the impact of NPS on brain and cognitive performance in a longitudinal setting, as prediction of conversion - have remained questionable. This study proposes to establish a predictive model of conversion to Alzheimer's disease (AD) and mild cognitive impairment (MCI) based on current cognitive performance, NPS and their associations with brain morphology. METHODS: 156 participants with MCI from the Alzheimer's Disease Neuroimaging Initiative database cognitively stable after a 4-year follow-up were compared to 119 MCI participants who converted to AD. Each participant underwent a neuropsychological assessment evaluating verbal memory, language, executive and visuospatial functions, a neuropsychiatric inventory evaluation and a 3 Tesla MRI. The statistical analyses consisted of 1) baseline comparison between the groups; 2) analysis of covariance model (controlling demographic parameters including functional abilities) to specify the variables that distinguish the two subgroups and; 3) used the significant ANCOVA variables to construct a binary logistic regression model that generates a probability equation to convert to a lower cognitive performance state. RESULTS: Results showed that MCI who converted to AD in comparison to stable MCI, exhibited a higher NPS prevalence, a lower cognitive performance and a higher number of involved brain structures. Functional abilities, memory performance and the sizes of inferior temporal, hippocampal and amygdala sizes were significant predictors of MCI to AD conversion. We also report two models of conversion that can be implemented on an individual basis for calculating the percentage risk of conversion after 4 years. CONCLUSION: These analytical methods might be a good way to anticipate cognitive and brain declines.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".