RT journal article
T1 Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation
A1 Gómez Ramírez, Jaime D.
A1 Fernández-Blázquez, Miguel A.
A1 González Rosa, Javier Jesús
A2 Psicología
K1 aging
K1 MRI
K1 machine learning
K1 XGBoost
K1 feature importance
K1 shapley values
K1 brain segmentation
K1 cortical parcellation
K1 age prediction
K1 biological aging
AB Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitativeunderstanding of age-related brain changes can shed light on successful aging. To investigate theeffect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonanceimaging scans were analyzed using automated brain segmentation and parcellation methods inelderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradientboosting (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 importantfeature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporaland parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights fromthis approach that integrate model prediction and interpretation may help to shorten the currentexplanatory gap between chronological age and biological brain age.
PB MDPI
SN 2076-3425
YR 2022
FD 2022-05
LK http://hdl.handle.net/10498/27224
UL http://hdl.handle.net/10498/27224
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 12-nov-2026