Mind the Gap: Does Brain Age Improve Alzheimer’s Disease Prediction?

Brain age is widely regarded as a powerful marker of general brain health. Brain age models are typically trained on large datasets to predict chronological age, which may offer advantages in predicting specific health outcomes, much like the success of finetuning large language models for specific applications. However, it is also well accepted that machine learning models trained to directly predict specific outcomes (i.e., direct models) often outperform those trained on surrogate objectives. Therefore, despite their much larger training data, it is unclear whether brain age models outperform direct models in predicting specific brain health outcomes. Here, we compare large-scale brain age models (pretrained on 53,542 participants) and direct models for predicting specific health outcomes related to Alzheimer's disease (AD) dementia. Using anatomical T1 scans from three continents (N = 1,848), we find that summarizing brain age with a single scalar (i.e., brain age gap) led to poor prediction performance. Using higher-dimensional intermediate representations of brain age models led to better prediction, but was still worse than direct models without finetuning. Using intermediate representations of finetuned brain age models was necessary to achieve similar performance to direct models. Overall, our results do not discount brain age as a useful marker of general brain health but suggest that using chronological age as a pretraining target might be suboptimal for predicting specific health outcomes.

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Publication Details

Journal
Human Brain Mapping
Published
2025-08-15
DOI
https://doi.org/10.1002/hbm.70276
Citations
5
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
7.36

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article

Mind the Gap: Does Brain Age Improve Alzheimer’s Disease Prediction?

Narayanaswamy Venketasubramanian, B.T. Thomas Yeo, Christopher Chen, Fang Ji et al.
5 citations
Human Brain Mapping
Dementia and Cognitive Impairment Research
7.36
article

Mind the Gap: Does Brain Age Improve Alzheimer’s Disease Prediction?

Narayanaswamy Venketasubramanian, B.T. Thomas Yeo, Christopher Chen, Fang Ji, Kim‐Ngan Nguyen, Trevor Wei Kiat Tan, Joanna Su Xian Chong, Csaba Orbán, Juan Helen Zhou, Susan F Cheng, Ru Kong, Eddie Chong, Chen Zhang, Michael W.L. Chee
article en
5 citations

Abstract

Brain age is widely regarded as a powerful marker of general brain health. Brain age models are typically trained on large datasets to predict chronological age, which may offer advantages in predicting specific health outcomes, much like the success of finetuning large language models for specific applications. However, it is also well accepted that machine learning models trained to directly predict specific outcomes (i.e., direct models) often outperform those trained on surrogate objectives. Therefore, despite their much larger training data, it is unclear whether brain age models outperform direct models in predicting specific brain health outcomes. Here, we compare large-scale brain age models (pretrained on 53,542 participants) and direct models for predicting specific health outcomes related to Alzheimer's disease (AD) dementia. Using anatomical T1 scans from three continents (N = 1,848), we find that summarizing brain age with a single scalar (i.e., brain age gap) led to poor prediction performance. Using higher-dimensional intermediate representations of brain age models led to better prediction, but was still worse than direct models without finetuning. Using intermediate representations of finetuned brain age models was necessary to achieve similar performance to direct models. Overall, our results do not discount brain age as a useful marker of general brain health but suggest that using chronological age as a pretraining target might be suboptimal for predicting specific health outcomes.

Human Brain MappingVol. 46(12)
National University of Singapore (SG), Raffles Institution (SG), National University Health System (SG), Clinical Nutrition Research Centre (SG)
U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Ministry of Health -Singapore, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research, Medical Research Council, National Medical Research Council
Quality Education
Openalex Percentile: Top 3%
Dementia and Cognitive Impairment Research
7.36
DOIOpen Access PDFOpenAlex
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