Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test–retest reliability of publicly available software packages
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Prediction of brain age using structural magnetic resonance imaging : A comparison of accuracy and test–retest reliability of publicly available software packages. / Dörfel, Ruben P.; Arenas-Gomez, Joan M.; Fisher, Patrick M.; Ganz, Melanie; Knudsen, Gitte M.; Svensson, Jonas E.; Plavén-Sigray, Pontus.
I: Human Brain Mapping, Bind 44, Nr. 17, 2023, s. 6139-6148.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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 -
ID: 371281037