Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation

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2022-05Department
PsicologíaSource
Brain Sciences, Vol. 12, Núm. 5Abstract
Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative
understanding of age-related brain changes can shed light on successful aging. To investigate the
effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance
imaging scans were analyzed using automated brain segmentation and parcellation methods in
elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient
boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects.
Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important
feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal
and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from
this approach that integrate model prediction and interpretation may help to shorten the current
explanatory gap between chronological age and biological brain age.
Subjects
aging; MRI; machine learning; XGBoost; feature importance; shapley values; brain segmentation; cortical parcellation; age prediction; biological agingCollections
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