publications

2025

1. Prediction of brain age using structural magnetic resonance imaging: A comparison of clinical validity of publicly available software packages

   Ruben P. Dörfel, Brice Ozenne, Melanie Ganz, and 2 more authors

   medRxiv, Mar 2025

   Abs

   Predicting brain age from structural magnetic resonance images is commonly used as a biomarker of biological aging and brain health. Ideally, as a clinically valid biomarker, brain age should indicate the current state of health and also be predictive of future disease onset and detrimental changes in brain biology. In this preregistered study, we evaluated and compared the clinical validity, i.e., diagnostic and prognostic abilities, of six publicly available brain age prediction packages using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Baseline brain age significantly differed between groups consisting of individuals with normal cognitive function, mild cognitive impairment, and Alzheimer\textquoterights Disease for all packages, but with comparable performance to gray matter volume. Further, estimates were not centered around zero for cognitively normal subjects and showed considerable variation between packages. Finally, brain age did not show strong correlation with disease onset, memory decline, nor gray matter atrophy within four years from baseline in individuals without neurodegenerative disease. The substantial bias and variability in cross-sectional predictions, combined with the weak associations between brain age and longitudinal changes in memory performance or grey matter volume, suggested limited validity of current brain age estimates as a biomarker for biological aging.

2024

1. Multimodal Brain Age Prediction Using Machine Learning: Combining Structural MRI and 5-HT2AR PET-derived Features

   Ruben P. Dörfel, Joan M. Arenas-Gomez, Claus Svarer, and 4 more authors

   GeroScience, Apr 2024

   Abs

   To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also reduced in neurodegenerative disorders, such as Alzheimer’s disease. This study investigates whether the decline in 5-HT2AR binding, measured in vivo using positron emission tomography (PET), can be used as a biomarker for brain aging. Specifically, we aim to (1) predict brain age using 5-HT2AR binding outcomes, (2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and (3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean\,= 38, std\,= 18) and estimated 5-HT2AR binding and GM volume for 14 cortical and subcortical regions. Different machine learning algorithms were applied to predict chronological age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison. We find that both the cerebral 5-HT2AR binding (mean MAE\,= 6.63 years, std\,= 0.74 years) and GM volume (mean MAE\,= 6.95 years, std\,= 0.83 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE\,= 5.54 years, std\,= 0.68). In conclusion, 5-HT2AR binding measured using PET might be useful for improving the quantification of a biomarker for brain aging.

2023

1. 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, Joan M. Arenas-Gomez, Patrick M. Fisher, and 4 more authors

   Human Brain Mapping, Apr 2023

   Abs

   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 \textpm 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.