Functional and Emotional Predictors of Cognitive Decline: Insights from the ADNI Dataset
Bibliographic record
Abstract
Abstract Background Cognitive decline is a hallmark of aging and neurodegenerative diseases, including Alzheimer's disease, and is considered a cornerstone of early identification efforts. Previous studies have reported that cognitive decline is associated with structural changes in the brain, functional impairment (FAQ), and mood alterations (GDS). However, understanding the combined predictive power of structural changes in the brain, functional impairment (FAQ), and mood alterations (GDS) is critical for identifying their roles as determinants of cognitive decline, thereby improving early detection and targeted interventions. Method We analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify predictors of cognitive decline using the Montreal Cognitive Assessment (MoCA), a tool that evaluates multiple cognitive domains, in participants with a score ≥4 ( n = 312). The dependent variable, cognitive change, was quantified as the beta coefficient obtained from the regression analysis of MoCA scores over time (4+ time points). LASSO regression evaluated the baseline relationships between hippocampal volume (HIPPO), functional abilities (FAQ), and depressive symptoms (GDS), with 5‐fold cross‐validation to minimize overfitting. The model was trained on 80% of the data and evaluated on 20% using Mean Squared Error (MSE), Mean Absolute Error (MAE), and R 2 . Result In predicting cognitive trajectories (COGN), the LASSO regression model achieved R2=0.51R^2 = 0.51R2=0.51 on the test set, explaining 51% of the variance in cognitive decline. FAQ accounted for the largest proportion of the explained variance (86.2%), followed by HIPPO (7.7%) and GDS (6.0%). These findings highlight the dominant role of functional abilities (FAQ) in predicting cognitive decline, with depressive symptoms (GDS) and hippocampal volume (HIPPO) contributing minimally. Conclusion The current findings demonstrate that baseline functional and emotional measures can predict future cognitive decline, with functional difficulties (FAQ) contributing the most, followed by depressive symptoms (GDS), and hippocampal volume (HIPPO). These results suggest that functional and emotional measures vary alongside cognitive decline, with neuroimaging offering complementary insights when combined with behavioral evaluations for early detection.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".