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Brain age prediction across the human lifespan using multimodal MRI data

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Abstract

Measuring differences between an individual’s age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6–85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.

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Data and code availability

Data used in this study are from the enhanced Nathan Klein Institute-Rocklan Sample dataset and are publicly available at http://fcon_1000.projects.nitrc.org/indi/enhanced/. In addition, you can find two publicly available programs at http://www.fil.ion.ucl.ac.uk/spm/software/spm12/, and http://www.rfmri.org/DPABI. PLSR MATLAB code can be provided according to the requirements of the author.

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Acknowledgements

We thank Prof. Guofu Wang who works at Guangxi University of Science and Technology.

Funding

This work was supported by the National Key Research and Development Program of China (2022YFE0134600; 2021YFA0715404), the National Natural Science Foundation of China (62171101; 82250410380), the Sichuan Science and Technology Program (23NSFSC2916), the Key Research and Development Program of Guangxi (2021AB05083), and the Central Universities Foundation, Southwest Minzu University (ZYN2023100).

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Authors and Affiliations

  1. College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China

    Sihai Guan

  2. Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China

    Sihai Guan

  3. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Runzhou Jiang, Chun Meng & Bharat Biswal

  4. Medical Equipment Department, Xiangyang No. 1 People’s Hospital, Xiangyang, 441000, China

    Runzhou Jiang

  5. Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Bharat Biswal

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  1. Sihai Guan
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Guan, S., Jiang, R., Meng, C. et al. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience 46, 1–20 (2024). https://doi.org/10.1007/s11357-023-00924-0

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