Prediction of Cognitive Function via Brain Region Volumes with Applications to Alzheimer’s Disease Based on Space-Factor-Guided Functional Principal Component Analysis
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a prevalent and irreversible brain disorder and early prediction of cognitive function is vital for detecting the onset. The volumes of brain regions can serve as features for predicting cognitive decline, facilitating early detection and intervention. In order to offer a comprehensive representation of brain tissue changes in AD, we employ volume density curves to investigate the relationship between brain regions and cognitive function. However, analyzing these volume curves is complex due to their highly spatial and intrinsic dependence and piecewise structure. To address these challenges, we propose Space-Factor-Guided Functional Principal Component Analysis (SF-FPCA). This method utilizes factor processes to extract low-dimensional features for intrinsic correlations among regions of interest (ROIs) and applies Functional Principal Component Analysis (FPCA) to these processes to address temporal dependence. Furthermore, by decomposing the loadings into smooth functions of spatial coordinates and a piecewise constant matrix, we identify regions exhibiting smoothness within each region while discontinuities between these regions. We apply SF-FPCA to analyze data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our results demonstrate that SF-FPCA provides the best fit compared to other methods. In addition, features extracted from volume curves using SF-FPCA enable more accurate prediction of cognitive function compared to scalar volumes alone. Leveraging these extracted features, we identify 36 important ROIs influencing cognitive decline. Our investigation into brain atrophy also reveals distinct mechanisms between the left and right hemispheres, shedding light on the nuanced effects of brain region changes on cognitive decline in AD.
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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 | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".