TY - JOUR

T1 - Prediction of brain age using structural magnetic resonance imaging

T2 - A comparison of accuracy and test–retest reliability of publicly available software packages

AU - Dörfel, Ruben P.

AU - Arenas-Gomez, Joan M.

AU - Fisher, Patrick M.

AU - Ganz, Melanie

AU - Knudsen, Gitte M.

AU - Svensson, Jonas E.

AU - Plavén-Sigray, Pontus

N1 - Publisher Copyright: © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Accuracy

KW - Brain Age

KW - MRI

KW - Reliability

KW - Test-Retest

U2 - 10.1002/hbm.26502

DO - 10.1002/hbm.26502

M3 - Journal article

C2 - 37843020

AN - SCOPUS:85174322690

VL - 44

SP - 6139

EP - 6148

JO - Human Brain Mapping

JF - Human Brain Mapping

SN - 1065-9471

IS - 17

ER -