AUTHOR=Kuo Chen-Yuan , Tai Tsung-Ming , Lee Pei-Lin , Tseng Chiu-Wang , Chen Chieh-Yu , Chen Liang-Kung , Lee Cheng-Kuang , Chou Kun-Hsien , See Simon , Lin Ching-Po 

TITLE=Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

JOURNAL=Frontiers in Psychiatry

VOLUME=12

YEAR=2021

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2021.626677

DOI=10.3389/fpsyt.2021.626677

ISSN=1664-0640

ABSTRACT=<p>Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as <italic>a priori</italic> knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, <italic>R</italic><sup>2</sup> = 0.88; support vector regression, MAE = 4.42 years, <italic>R</italic><sup>2</sup> = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; <italic>R</italic><sup>2</sup> = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.</p>