Considerations on brain age predictions from repeatedly sampled data across time
M Korbmacher, MY Wang, R Eikeland… - Brain and …, 2023 - Wiley Online Library
Brain and Behavior, 2023•Wiley Online Library
Introduction Brain age, the estimation of a person's age from magnetic resonance imaging
(MRI) parameters, has been used as a general indicator of health. The marker requires
however further validation for application in clinical contexts. Here, we show how brain age
predictions perform for the same individual at various time points and validate our findings
with age‐matched healthy controls. Methods We used densely sampled T1‐weighted MRI
data from four individuals (from two densely sampled datasets) to observe how brain age …
(MRI) parameters, has been used as a general indicator of health. The marker requires
however further validation for application in clinical contexts. Here, we show how brain age
predictions perform for the same individual at various time points and validate our findings
with age‐matched healthy controls. Methods We used densely sampled T1‐weighted MRI
data from four individuals (from two densely sampled datasets) to observe how brain age …
Introduction
Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age‐matched healthy controls.
Methods
We used densely sampled T1‐weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross‐sectional datasets. Brain age was predicted by a pretrained deep learning model.
Results
We found small within‐subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross‐sectional validation data and inconclusive effects of scan quality.
Conclusion
The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.
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