Robust Brain Age Estimation via Regression Models and MRI-derived
  Features
Mansoor Ahmed Usama Sardar Sarwan Ali Shafiq Alam Murray Patterson Imdad Ullah Khan
The determination of biological brain age is a crucial biomarker in the
assessment of neurological disorders and understanding of the morphological
changes that occur during aging. Various machine learning models have been
proposed for estimating brain age through Magnetic Resonance Imaging (MRI) of
healthy controls. However, developing a robust brain age estimation (BAE)
framework has been challenging due to the selection of appropriate MRI-derived
features and the high cost of MRI acquisition. In this study, we present a
novel BAE framework using the Open Big Healthy Brain (OpenBHB) dataset, which
is a new multi-site and publicly available benchmark dataset that includes
region-wise feature metrics derived from T1-weighted (T1-w) brain MRI scans of
3965 healthy controls aged between 6 to 86 years. Our approach integrates three
different MRI-derived region-wise features and different regression models,
resulting in a highly accurate brain age estimation with a Mean Absolute Error
(MAE) of 3.25 years, demonstrating the framework's robustness. We also analyze
our model's regression-based performance on gender-wise (male and female)
healthy test groups. The proposed BAE framework provides a new approach for
estimating brain age, which has important implications for the understanding of
neurological disorders and age-related brain changes.