Mixed Models Quantify Annual Volume Change; Linear Regression Determines Thalamic Volume as the Best Subcortical Structure Volume Predictor in Alzheimer’s Disease and Aging
Bibliographic record
Abstract
Abstract Background Thalamus-hippocampus-putamen and thalamus-cerebellar interconnections are dense. The extent this connectivity is paralleled by each structure’s volume impact on another is unquantified in Alzheimer’s disease (AD). Mixed model quantification of annual volume change in AD is scarce and absent inclusive of the cerebellum, hippocampus, putamen and lateral ventricles and thalamus. Among these structures, autopsy evidence of early-stage AD seems largely but not entirely restricted to the hippocampus and thalamus. Objective Variation in annual volume related to time and baseline age was assessed for the hippocampus, putamen, cerebellum, lateral ventricles and thalamus. Which subcortical structure’s volume had the largest explanatory effect of volume variation in other subcortical structures was also determined. Method The intraclass correlation coefficient was used to assess test-retest reliability of structure automated segmentation. Linear regression ( N = 45) determined which structure’s volume most impacted volume of other structures. Finally, mixed models ( N = 36; 108 data points) quantified annual structure volume change from baseline to 24-months. Results High test-retest reliability was indicated by a mean ICC score of .989 ( SD = .012). Thalamic volume consistently had the greatest explanatory effect of hippocampal, putamen, cerebellar and lateral ventricular volume. The group variable proxy for AD significantly contributed to the best-fitting hippocampal linear regression model, hippocampal and thalamic longitudinal mixed models, and approached significance in the longitudinal lateral ventricular mixed model. Mixed models determined time (1 year) had a negative effect on hippocampal, cerebellar and thalamic volume, no effect on putamen volume, and a positive effect on lateral ventricular volume. Baseline age had a negative effect on hippocampal and thalamic volume, no effect on cerebellar or putamen volume and a positive effect on lateral ventricular volume. Interpretation Linear regression determined thalamic volume as a virtual centralized index of hippocampal, cerebellar, putamen, and lateral ventricular volume. Relative to linear regression, longitudinal mixed models had greater sensitivity to detect contribution of early AD, or potential AD pathology (MCI), via the group variable not just to volume reduction in the hippocampus but also in the thalamus.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".