Rate of brain aging associates with future executive function in Asian children and older adults

The human brain undergoes coordinated, multidimensional anatomical changes throughout the lifespan, which can be measured noninvasively by MRI (Bethlehem et al., 2022). These anatomical changes occur in parallel with age-related changes to other measurable phenotypes, such as cognition (Hedden and Gabrieli, 2004; Roalf et al., 2014). Abnormal aging in late life and abnormal development in early life have both been implicated with increased risk of neuropsychiatric disorders (van der Flier and Scheltens, 2005; Marsh et al., 2008). Thus, efforts have been made to quantify the heterogenous effects of aging/development on the brain through the concept of ‘brain age’. Brain age uses machine learning to predict age from neuroimaging data. A higher brain age suggests more advanced aging/development relative to one’s chronological age. This helps summarize complex patterns into a single number that preserves individual variations (Franke et al., 2012).

Historically, most brain age studies first use specialized software to preprocess MRI data and extract features such as gray matter volume, cortical thickness, or surface area. A machine learning model is then trained to predict age from the extracted features. The model is typically trained using cognitively normal participants, with chronological age acting as the ground truth. The model is then applied to new participants to predict their brain age (Gaser et al., 2013; Cole et al., 2018; Kaufmann et al., 2019; Tian et al., 2023).

More recently, deep learning models have gained popularity over traditional machine learning models for brain age prediction (Wood et al., 2022; Hofmann et al., 2022; Yin et al., 2023; Leonardsen et al., 2022; Bashyam et al., 2020; Chen et al., 2020). Unlike previous machine learning methods, deep learning models can learn relevant features directly from the unprocessed (or minimally processed) MRI scan. This reduces the need for specialized preprocessing to extract features, which is time-consuming, requires expert knowledge, and involves laborious quality control. This also allows deep learning models to train on increasingly numerous and heterogeneous data. Some of these pretrained models have been made publicly available and shown impressive generalization performance on completely unseen test data (Leonardsen et al., 2022; Bashyam et al., 2020). However, both training and testing data still primarily consist of Caucasian participants, which could bias the models (Hahn et al., 2022; Li et al., 2022). Deep learning models can use finetuning (i.e., transfer learning) to help overcome this bias and achieve good performance even in small datasets (Chen et al., 2020). But to our knowledge, previous work has not examined the performance, with and without finetuning, of a Caucasian-centric model on non-Caucasian children and elderly participants, such as Asian children and elderly participants. This generalization is important to establish as the prevalence of developmental disorders (Qiu et al., 2020) and dementia (Catindig et al., 2012) are on the rise in Asia.

In addition to being able to predict age, predictions made by the model should show utility in relating to other phenotypes of interest (Jirsaraie et al., 2023a). The deviation from expected aging is often quantified as the brain age gap (BAG; also referred to as brainPAD, brainAGE, etc.), which is calculated by subtracting chronological age from brain age. The BAG has shown broad associations with brain disorders (Kaufmann et al., 2019), risk of mortality (Cole et al., 2018), and cognitive function (Wrigglesworth et al., 2022), to name a few (see Wrigglesworth et al., 2021; Baecker et al., 2021 for recent reviews). However, there are relatively few longitudinal studies in healthy participants. Previous work found associations with early life or cross-sectional measures (Wrigglesworth et al., 2022; Vidal-Pineiro et al., 2021; Elliott et al., 2021; Franke et al., 2018), but there was only a weak link to future age-related cognitive decline, which did not survive multiple comparison correction (Wrigglesworth et al., 2022). Notably, these studies only used brain age measured at one time point. There is evidence that cross-sectional and longitudinal brain measures may reflect different factors and predispositions of individuals (Walhovd et al., 2023a), suggesting that combining them could provide more predictive power. However, to our knowledge, the additional utility of longitudinal changes in brain age has not been tested in healthy participants.

Thus, in this work, we leverage a state-of-the-art deep learning brain age model trained on over 30,000 individuals across the lifespan (Leonardsen et al., 2022) to test generalizability to Asian elderly participants and children. We also finetune the model to explore how much predictions improve. We then examine the longitudinal utility of brain age in associating with future cognition. Finally, we investigate model interpretability using guided backpropagation. Our findings provide insight into the generalizability of brain age models and the importance of longitudinal measurements.

Figure 1 shows the study design. For the brain age model, we used the publicly available simple fully convolutional network (SFCN) pretrained on 34,285 T1 MRI scans from 21 non-overlapping datasets across the lifespan (Leonardsen et al., 2022). This pretrained model (a.k.a. pyment) was found to have the highest accuracy and test−retest reliability in a recent comparison of publicly available brain age models (Dörfel et al., 2023). While featuring an unusually large and heterogenous training set for brain age prediction, there was still a relative lack of training data from very young and/or non-Caucasian participants.

Figure 1

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Thus, to test generalizability to Asian elderly participants and children, we used three datasets from Singapore: (1) the cross-sectional Epidemiology of Dementia in Singapore (EDIS) study (Hilal et al., 2013; Hilal et al., 2017; Wong et al., 2019), consisting of 694 non-demented elderly (226 with no cognitive impairment [NCI] and 468 with cognitive impairment no dementia[CIND]); (2) the longitudinal Singapore Longitudinal Aging Brain Study (SLABS) (Chee et al., 2009), consisting of 215 healthy elderly participants; and (3) the longitudinal Growing Up in Singapore Towards healthy Outcomes (GUSTO) study (Soh et al., 2014), consisting of 678 healthy children. These datasets are detailed in ‘Materials and methods’. Table 1 and Appendix 2—table 1 summarize the participant demographic and cognitive characteristics.

The pretrained brain age model performs well in older adults, while the finetuned model performs well in both older adults and children

We first input minimally preprocessed T1 scans directly to the pretrained model (all baseline and follow-up data). We also finetuned the model for our local datasets using 10-fold cross-validation (Figure 1A and B; section ‘Brain age predictions’). Figure 2 shows the brain age predictions for the pretrained and finetuned models on all datasets.

Figure 2 with 1 supplement see all

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In EDIS and SLABS (elderly), the pretrained model performed well, as evidenced by the high correlation (${\displaystyle r=0.7389}$ for EDIS and ${\displaystyle r=0.8136}$ for SLABS) and low MAE (${\displaystyle \mathrm{M}\mathrm{A}\mathrm{E}=3.9895}$ for EDIS and ${\displaystyle \mathrm{M}\mathrm{A}\mathrm{E}=3.4668}$ for SLABS; Figure 2A, first two rows). After finetuning, correlations and MAEs slightly improved (${\displaystyle r=0.7445}$ for EDIS and ${\displaystyle r=0.8138}$ for SLABS; ${\displaystyle \mathrm{M}\mathrm{A}\mathrm{E}=3.3232}$ for EDIS and ${\displaystyle \mathrm{M}\mathrm{A}\mathrm{E}=3.2653}$ for SLABS; Figure 2B, first two rows), but the predictions were generally similar to those made by the pretrained model (correlation between finetuned and pretrained predictions = 0.9143 for EDIS and 0.9231 for SLABS).

In contrast, the pretrained model did not perform as well in GUSTO (children). The MAE was lower than in elderly (${\displaystyle \mathrm{M}\mathrm{A}\mathrm{E}=2.57}$), but the age range of GUSTO was also much smaller. Importantly, predictions did not distinguish among younger ages, leading to a low correlation (${\displaystyle r=0.5426}$; Figure 2A, last row). After finetuning, the correlation and MAE drastically improved (${\displaystyle r=0.9411}$; ${\displaystyle \mathrm{M}\mathrm{A}\mathrm{E}=0.6286}$; Figure 2B, last row). The variance in predicted ages also increased as chronological age increased (Figure 2—figure supplement 1). Unlike EDIS and SLABS, the finetuned predictions were not similar to the pretrained predictions (correlation = 0.5732).

The brain age gap is negatively associated with executive function in elderly participants

To validate the brain age model with a large and cognitively heterogeneous sample, we first tested cross-sectional associations in EDIS (N=694) using multiple linear regression models (Figure 1C). We included chronological age, sex, and years of education as covariates. Higher baseline BAG was broadly associated with lower baseline cognitive performance (i.e., negative associations; Appendix 3—table 1). The associations were significant after multiple comparison correction for global cognition (${\displaystyle \beta =-0.1125}$, ${\displaystyle p_{corr}=0.0006}$), executive function (${\displaystyle \beta =-0.1029}$, ${\displaystyle p_{corr}=0.0076}$, Figure 3A), language (${\displaystyle \beta =-0.1145}$, ${\displaystyle p_{corr}=0.0047}$), visuomotor speed (${\displaystyle \beta =-0.0825}$, ${\displaystyle p_{corr}=0.0136}$), visuoconstruction (${\displaystyle \beta =-0.0896}$, ${\displaystyle p_{corr}=0.0136}$), verbal memory (${\displaystyle \beta =-0.1096}$, ${\displaystyle p_{corr}=0.0034}$), and visual memory (${\displaystyle \beta =-0.1395}$, ${\displaystyle p_{corr}=0.0002}$). The association was not significant for attention (${\displaystyle \beta =-0.0404}$, ${\displaystyle p_{corr}=0.2461}$). These results were consistent after finetuning (Figure 3—figure supplement 1A and Appendix 3—table 1). Similar broad negative associations were also observed in SLABS at baseline (N=212), but these were not significant (Appendix 3—tables 3 and table 4).

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To investigate longitudinal utility of brain age in healthy elderly, we tested associations in a longitudinal subset of SLABS (N=81) using similar multiple linear regression models. We first related baseline BAG and early change in BAG to long-term cognitive change (Figure 1C; section ‘Associations with cognition’). Baseline BAG generally failed to show associations with longitudinal cognitive changes (Appendix 3—table 5). While higher baseline BAG was associated with faster long-term decline in executive function, it was not significant after multiple comparison correction (β =−0.2477, ${\displaystyle p=0.0406}$, ${\displaystyle p_{corr}=0.2433}$, Figure 3B). On the other hand, the early rate of BAG change was negatively associated with long-term rate of executive function change (β =−0.3807, ${\displaystyle p=0.0017}$, ${\displaystyle p_{corr}=0.0100}$, Figure 3C). This negative association held after removing the temporal overlap between BAG and cognition, looking only at the future rate of executive function change (β =−0.3807, ${\displaystyle p=0.0033}$, Figure 3D). Notably, these associations were independent of baseline BAG, chronological age, sex, and years of education. The associations were also specific to executive function (Appendix 3—table 7). Results were again consistent after finetuning (Figure 3—figure supplement 1B–D, Appendix 3—tables 6 and 8).

The longitudinal change in brain age gap is positively associated with inhibition in children

To extend our analyses to healthy children, we tested cross-sectional and longitudinal associations in GUSTO using multiple linear regression models similar to above (Figure 1C; section ‘Associations with cognition’). Since longitudinal cognitive data was not available for GUSTO, we used the cognitive scores themselves instead of the change. Furthermore, since finetuning the model drastically improved prediction accuracy in GUSTO, we used the finetuned predictions for our main analyses. We did not find a significant association between baseline BAG and baseline IQ score (β=−0.0618, ${\displaystyle p=0.3809}$, Figure 4—figure supplement 2B). Similarly, we did not find significant associations between baseline BAG and future cognitive scores (at 8.5 years old; ${\displaystyle |\beta |\le 0.0829}$ ${\displaystyle p\ge 0.4086}$, Figure 4A; Appendix 3—table 10). However, the early rate of BAG change (from 4.5 to 7.5 years old) was positively associated with future inhibition (at 8.5 years old; ${\displaystyle \beta =0.2006}$, ${\displaystyle p=0.0103,p_{corr}=0.0411}$, Figure 4B). The early rate of BAG change was also positively associated with future switching, but it was not significant after correcting for multiple comparisons (${\displaystyle \beta =0.1795}$, ${\displaystyle p=0.0221,p_{corr}=0.0663}$, Appendix 3—table 12). These associations were independent of baseline BAG, chronological age, and sex. Notably, in contrast to older adults, the direction of association was now positive, meaning increased early rate of BAG change was associated with better future executive function performance. There were no significant associations using the pretrained model (Figure 4—figure supplements 1 and 2A, Appendix 3—tables 9 and 11).

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Finetuned brain age models focus on distinct features in children and elderly participants

Finally, to investigate model interpretability, we used guided backpropagation (Springenberg et al., 2015) to derive group-level saliency maps for brain age prediction (section ‘Model interpretability’). Figure 5 shows the top 10% of contributing voxels to age prediction in four representative slices (left) for all datasets. Full 3D maps are also made available online. Relative contributions of white and gray matter features across the whole brain are shown on the right. Areas near the lateral ventricles are labeled in red, while areas more prominent in elderly than children are labeled in magenta, and areas more prominent in children than elderly are labeled in blue.

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Both EDIS and SLABS show similar profiles (Figure 5A and B), suggesting important features are stable across the elderly datasets. Regions near the lateral ventricles all make strong contributions, making up 7 of the 15 highest ranking features. Substantial contributions can also be seen in frontal/association areas corresponding to the default mode, control, and salience/ventral attention networks. Areas near the hippocampus/fornix, thalamus, and somatomotor network also contribute. These findings are consistent when using the pretrained model for EDIS and SLABS (Figure 5—figure supplement 1A and B).

In contrast, with the children of GUSTO, notable differences can be found (Figure 5C). While the fornix is still the strongest contributor, it and other anterior areas near the ventricles (genu and body of corpus callosum, caudate) do not contribute as much. The overall prominence of white matter is also increased, especially in the brainstem (corticospinal tract and pontine crossing tract) and posterior regions (sagittal stratum, superior longitudinal fasciculus, and posterior limb of internal capsule). Furthermore, gray matter networks generally decrease in prominence, except the visual and limbic networks, which increase in prominence. The hippocampus, amygdala, and thalamus continue to make substantial contributions. Unlike in elderly, these features are not consistent when using the pretrained model (Figure 5—figure supplement 1C).

Our findings are the first, to our knowledge, to show the age-dependent generalizability of a pretrained brain age model to non-Caucasian participants, specifically Singaporean children and elderly. We also present novel results on the informativeness of longitudinal changes in brain age, independent of baseline brain age, to future executive function in healthy participants. Finally, we show that accurate models focus on distinct features in elderly and children, suggesting that the brain age model can extract relevant age-related information.

Due to participant consent agreements, source data (deidentified or processed) that support the findings of this study are available from co-authors C.L.H.C (EDIS), M.W.L.C. (SLABS), and M.J.M. (GUSTO) upon collaborative request. The EDIS and SLABS data are available with a proper research agreement. Please direct inquiries to helen.zhou@nus.edu.sg. The GUSTO data is available on request, on approval by the GUSTO executive committee. Please direct inquiries to https://www.gusto.sg/request-to-collaborate/. Custom Python and R code for this study is available at https://github.com/hzlab/2024_Cheng_Longitudinal_Brain_Age (copy archived at Cheng, 2025). Some code was reused from https://github.com/estenhl/pyment-public/releases/tag/v1.0.0 (Esten, 2021).

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Author details

1. Susan F Cheng

   1. Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore
   2. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1201-2880

2. Wan Lin Yue

   1. Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore
   2. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Kwun Kei Ng

   Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0584-7679

4. Xing Qian

   Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Siwei Liu

   Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Trevor WK Tan

   1. Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore
   2. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Validation, Methodology

   Competing interests

   No competing interests declared

7. Kim-Ngan Nguyen

   Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Validation, Methodology

   Competing interests

   No competing interests declared

8. Ruth LF Leong

   Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

   Contribution

   Resources

   Competing interests

   No competing interests declared

9. Saima Hilal

   1. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
   2. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
   3. Memory Aging and Cognition Centre, National University Health System, Singapore, Singapore

   Contribution

   Resources

   Competing interests

   No competing interests declared

10. Ching-Yu Cheng

    1. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
    2. Duke-NUS Medical School, Singapore, Singapore

    Contribution

    Resources

    Competing interests

    No competing interests declared

11. Ai Peng Tan

    1. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
    2. Singapore Institute for Clinical Sciences (SICS), A*STAR Research Entities (ARES), Singapore, Singapore
    3. Brain–Body Initiative Program, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
    4. National University Health System, Singapore, Singapore

    Contribution

    Resources

    Competing interests

    No competing interests declared

12. Evelyn C Law

    1. Singapore Institute for Clinical Sciences (SICS), A*STAR Research Entities (ARES), Singapore, Singapore
    2. National University Health System, Singapore, Singapore

    Contribution

    Resources

    Competing interests

    No competing interests declared

13. Peter D Gluckman

    1. Singapore Institute for Clinical Sciences (SICS), A*STAR Research Entities (ARES), Singapore, Singapore
    2. Liggins Institute, University of Auckland, Auckland, New Zealand

    Contribution

    Resources

    Competing interests

    No competing interests declared

14. Christopher Li-Hsian Chen

    1. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
    2. Memory Aging and Cognition Centre, National University Health System, Singapore, Singapore

    Contribution

    Resources, Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

15. Yap Seng Chong

    1. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
    2. Singapore Institute for Clinical Sciences (SICS), A*STAR Research Entities (ARES), Singapore, Singapore
    3. National University Health System, Singapore, Singapore

    Contribution

    Resources

    Competing interests

    No competing interests declared

16. Michael J Meaney

    1. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
    2. Singapore Institute for Clinical Sciences (SICS), A*STAR Research Entities (ARES), Singapore, Singapore
    3. Douglas Mental Health University Institute, McGill University, Montreal, Canada
    4. Strategic Research Program, A*STAR Research Entities (ARES), Singapore, Singapore

    Contribution

    Resources, Funding acquisition

    Competing interests

    No competing interests declared

17. Michael WL Chee

    Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Contribution

    Resources, Funding acquisition, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

18. BT Thomas Yeo

    1. Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore
    2. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
    3. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
    4. N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Visualization, Methodology, Writing – review and editing

    Contributed equally with

    Juan Helen Zhou

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0119-3276

19. Juan Helen Zhou

    1. Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore
    2. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
    3. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Visualization, Methodology, Writing – review and editing

    Contributed equally with

    BT Thomas Yeo

    For correspondence

    helen.zhou@nus.edu.sg

    Competing interests

    Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0002-0180-8648

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  citation for Reviewed Preprint v1 https://doi.org/10.7554/eLife.97036.1