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Alzheimer’s Disease / Machine Learning · Journal article
Cognitive Neurodynamics · February 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This methodological study demonstrates that advanced imputation techniques (MissForest, GAIN) combined with ensemble tree models substantially improve prediction of medial temporal lobe dynamic flexibility from cognitive, genetic, and demographic data in a high-missingness aging cohort. The findings are computational and do not establish clinical validity or utility of the predicted biomarker.
Cross-sectional machine learning method comparison. 656 older adults enrolled in the Rutgers Aging and Brain Health Alliance study, with only 42 (6.40%) having complete data across all predictors. Intervention: Machine learning imputation methods (MICE, GAIN, MissForest, MIWAE, ReMasker) paired with regression models. Compared with: Complete-case deletion analysis. n = 656.
MissForest paired with Bagging Trees or Random Forest achieved lowest prediction error (R² = 0.32) GAIN combined with Bagging Trees/Random Forest yielded 57% gain in concordance over best complete-case model Only 42 participants (6.40%) had complete data from 656 older adults with 25.86% missing values
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These computational methods may improve neuroimaging data utility in aging research, but the clinical relevance of predicted MTL dynamic flexibility as an early AD biomarker remains unvalidated. Researchers should consider advanced imputation for high-missingness datasets but require external validation before clinical application.
Computational study comparing imputation methods for predicting an imaging biomarker without external validation or demonstration of clinical utility.
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These computational methods may improve neuroimaging data utility in aging research, but the clinical relevance of predicted MTL dynamic flexibility as an early AD biomarker remains unvalidated. Researchers should consider advanced imputation for high-missingness datasets but require external validation before clinical application.
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Alzheimer's disease (AD) pathology begins years before symptoms appear, and dynamic flexibility of the medial temporal lobe (MTL) may serve as an early functional biomarker. Using data from 656 older adults in the Rutgers Aging and Brain Health Alliance study, we evaluated whether cognitive, genetic, biochemical, and demographic predictors could estimate MTL dynamic flexibility, despite substantial missingness (1,866 missing values; 25.86%). Only 42 participants (6.40%) had complete data; therefore, we compared case deletion with five imputation strategies (MICE, GAIN, MissForest, MIWAE, ReMasker) and eight regression models, assessing prediction accuracy using repeated 5-fold cross-validation. Complete-case analysis yielded limited performance (average [Formula: see text], [Formula: see text]). After imputation, all methods improved accuracy, with MissForest paired with Bagging Trees or Random Forest achieving the lowest prediction error ([Formula: see text]). The greatest improvement in concordance occurred when GAIN was combined with Bagging Trees/Random Forest ([Formula: see text]), representing a 57% gain over the best complete-case model. A Scheirer-Ray-Hare ANOVA confirmed significant differences across imputation strategies ([Formula: see text]). Runtime analyses showed GAIN and MissForest to be both accurate and computationally efficient, while deep generative imputers were slower. These findings demonstrate that robust imputation is essential for maximizing data utility and predictive reliability in high-missingness neuroimaging studies and highlight the potential of ensemble tree models combined with advanced imputation techniques for estimating MTL dynamic flexibility in aging populations.
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