Quantifying generalization error in machine learning prediction of cognitive decline - 09/08/26

Doi : 10.1016/j.tjpad.2026.100646

Roya Melanie Hüppi ^a,^b,^c,^⁎ , Nicolas Langer ^a,^b, Bruno Hebling Vieira ^a,^b,^⁎

for the Alzheimer’s Disease Neuroimaging Initiative ^¹
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( adni.loni.usc.edu/ ). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: ADNI_Acknowledgement_List.pdf .

^a Methods of Plasticity Research, Department of Psychology, University of Zurich, 8050, Zurich, Zurich, Switzerland

^b Neuroscience Center Zurich (ZNZ), University of Zurich & ETH Zurich, 8057, Zurich, Zurich, Switzerland

^c Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, 8032, Zurich, Zurich, Switzerland

^⁎ Corresponding author at: Lenggstrasse 31, 8032, Zurich, Zurich, Switzerland. Lenggstrasse 31 Zurich Zurich 8032 Switzerland ^⁎⁎ Corresponding author at: Binzmühlestrasse 14, 8050, Zurich, Zurich, Switzerland. Binzmühlestrasse 14 Zurich Zurich 8050 Switzerland

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Highlights

•	Adding structural MRI to non-brain data improves prediction of cognitive decline.
•	Cross-cohort evaluation reveals limits of model generalizability.
•	Key-feature models match full-model performance in predictions across datasets.
•	Continuous decline prediction captures interindividual variation in change.

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Abstract

Background

Predicting cognitive decline as a continuum, from healthy age-related decline to mild cognitive impairment and dementia, enables more precise individual-level predictions. However, the practical value of such models for early intervention and prevention depends on their ability to generalize to independent cohorts, a property that is often not evaluated.

Objectives

This study investigated whether adding structural magnetic resonance imaging (MRI) to non-brain data improved machine learning predictions of continuous cognitive decline and analyzed the models’ generalizability.

Design

Multi-target random forest regression models predicted annual decline in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB) and Mini-Mental State Examination (MMSE) using non-brain data, structural MRI data, or their combination from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 1237) and Open Access Series of Imaging Studies (OASIS-3; N = 662) datasets. Cross-site generalizability was evaluated.

Setting

Data from ADNI and OASIS-3 were used for this study.

Participants

A total of 1899 participants who had demographic, clinical, and brain imaging data from a baseline session and clinical data from at least 2 follow-up sessions were included.

Measurements

Baseline non-brain (demographics, clinical and neuropsychological scores, information on APOE genotype, cognitive diagnosis, health, and number of sessions before baseline) and/or structural MRI data were used to predict the yearly rate of change in CDR-SOB and MMSE scores.

Results

Including structural MRI data improved prediction of CDR-SOB and MMSE change, reaching respective R ² values of .41 and .33 in ADNI and .42 and .33 in OASIS-3. Model performance for across-dataset predictions was reduced ( R ² between .18 and .35), unexplained by distributional shifts of target variables. Models using only top predictive features performed similarly to full models when tested externally ( R ² between .18 and .34), suggesting predictor redundancy.

Conclusions

Incorporating structural MRI data enhances within-dataset prediction of continuous cognitive decline, allowing for more precise individual-level prediction and advancing towards precision medicine. Even though external validation remains limited, quantifying the generalizability gap is a crucial step towards the responsible use of ML models in clinical intervention and prevention.

El texto completo de este artículo está disponible en PDF.

Keywords : Cognitive decline, Structural MRI, Generalizability, Machine learning, Predictive modeling

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Vol 13 - N° 9

Artículo 100646- novembre 2026 Regresar al número

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