Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Dec 1;44(17):6139-6148.
doi: 10.1002/hbm.26502. Epub 2023 Oct 16.

Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages

Ruben P Dörfel  1   2 , Joan M Arenas-Gomez  1 , Patrick M Fisher  1   3 , Melanie Ganz  1   4 , Gitte M Knudsen  1   5 , Jonas E Svensson  1   2 , Pontus Plavén-Sigray  1   2
Affiliations

Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages

Ruben P Dörfel et al. Hum Brain Mapp. .

Abstract

Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to assess the biological age of the human brain. The difference between a person's chronological age and the estimated brain age is thought to reflect deviations from a normal aging trajectory, indicating a slower or accelerated biological aging process. Several pre-trained software packages for predicting brain age are publicly available. In this study, we perform a comparison of such packages with respect to (1) predictive accuracy, (2) test-retest reliability, and (3) the ability to track age progression over time. We evaluated the six brain age prediction packages: brainageR, DeepBrainNet, brainage, ENIGMA, pyment, and mccqrnn. The accuracy and test-retest reliability were assessed on MRI data from 372 healthy people aged between 18.4 and 86.2 years (mean 38.7 ± 17.5 years). All packages showed significant correlations between predicted brain age and chronological age (r = 0.66-0.97, p < 0.001), with pyment displaying the strongest correlation. The mean absolute error was between 3.56 (pyment) and 9.54 years (ENIGMA). brainageR, pyment, and mccqrnn were superior in terms of reliability (ICC values between 0.94-0.98), as well as predicting age progression over a longer time span. Of the six packages, pyment and brainageR consistently showed the highest accuracy and test-retest reliability.

Keywords: Accuracy; Brain Age; MRI; Reliability; Test-Retest.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Predicted age versus chronological age of brainageR, DeepBrainNet, brainage, ENIGMA, pyment, and mccqrnn for age‐prediction on the cross‐sectional dataset based on 372 scans. The identity line is in dashed black, and the model regression line is in orange.
FIGURE 2
FIGURE 2
(a) Deviation between progressed brain age and the progressed chronological age for subjects with more than 1 year between MR scans. The boxplot represents the median and quartiles of the data, and the whiskers 1.5× the interquartile range. For visualization purposes, some outliers are not shown, but are instead represented by the plus‐sign to indicate their quantity and respective direction. (b) Association between chronological progressed age and progressed predicted age. The dashed black line represents the identity line, and the yellow line the fitted regression line from the LME.
See this image and copyright information in PMC

References

    1. Atkinson, D. , Hill, D. L. G. , Stoyle, P. N. R. , Summers, P. E. , & Keevil, S. F. (1997). Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion. IEEE Transactions on Medical Imaging, 16(6), 903–910. 10.1109/42.650886 - DOI - PubMed
    1. Bacas, E. , Kahhalé, I. , Raamana, P. R. , Pablo, J. B. , Anand, A. S. , & Hanson, J. L. (2023). Probing multiple algorithms to calculate brain age: Examining reliability, relations with demographics, and predictive power. Human Brain Mapping, 44, 3481–3492. 10.1002/hbm.26292 - DOI - PMC - PubMed
    1. Baecker, L. , Dafflon, J. , da Costa, P. F. , Garcia‐Dias, R. , Vieira, S. , Scarpazza, C. , Calhoun, V. D. , Sato, J. R. , Mechelli, A. , & Pinaya, W. H. L. (2021). Brain age prediction: A comparison between machine learning models using region‐ and voxel‐based morphometric data. Human Brain Mapping, 42(8), 2332–2346. 10.1002/hbm.25368 - DOI - PMC - PubMed
    1. Bashyam, V. M. , Erus, G. , Doshi, J. , Habes, M. , Nasrallah, I. M. , Truelove‐Hill, M. , Srinivasan, D. , Mamourian, L. , Pomponio, R. , Fan, Y. , Launer, L. J. , Masters, C. L. , Maruff, P. , Zhuo, C. , Völzke, H. , Johnson, S. C. , Fripp, J. , Koutsouleris, N. , Satterthwaite, T. D. , … Davatzikos, C. (2020). MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. Brain, 143(7), 2312–2324. 10.1093/brain/awaa160 - DOI - PMC - PubMed
    1. Beheshti, I. , Maikusa, N. , & Matsuda, H. (2022). The accuracy of T1‐weighted voxel‐wise and region‐wise metrics for brain age estimation. Computer Methods and Programs in Biomedicine, 214, 106585. 10.1016/j.cmpb.2021.106585 - DOI - PubMed

Publication types