AUTHOR=Jiang Huiting , Lu Na , Chen Kewei , Yao Li , Li Ke , Zhang Jiacai , Guo Xiaojuan TITLE=Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks JOURNAL=Frontiers in Neurology VOLUME=10 YEAR=2020 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2019.01346 DOI=10.3389/fneur.2019.01346 ISSN=1664-2295 ABSTRACT=
Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain undergoes age-related neuroanatomical changes not only regionally but also on the network level during the normal development and aging process. In recent years, many studies have focused on estimating age using structural MRI measurements. However, the age prediction effects on different structural networks remain unclear. In this study, we established age prediction models based on common structural networks using convolutional neural networks (CNN) with data from 1,454 healthy subjects aged 18–90 years. First, based on the reference map of CorticalParcellation_Yeo2011, we obtained structural network images for each subject, including images of the following: the frontoparietal network (FPN), the dorsal attention network (DAN), the default mode network (DMN), the somatomotor network (SMN), the ventral attention network (VAN), the visual network (VN), and the limbic network (LN). Then, we built a 3D CNN model for each structural network using a large training dataset (