Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations - Chen, Chen; Cao, Xuyu; Tian, Lixia (Frontiers in Neuroscience, 2019)
Estimation of individuals' cognitive, behavioral and demographic (CBD) variables based on MRI has attracted much research interest in the past decade, and effective machine learning techniques are of great importance for these estimations. Partial least squares regression (PLSR) is an attractive machine learning technique that can accommodate both single- and multi-label learning in a simple framework, while its potential for MRI-based estimations of CBD variables remains to be explored. In this study, we systemically investigated the performance of PLSR in MRI-based estimations of individuals’ CBD variables, especially its performance in simultaneous estimation of multiple CBD variables (multi-label learning). We performed the study on the dataset included in the HCP S1200 release. Resting state functional connections (RSFCs) were used as features, and a total of ten CBD variables (e.g., age, gender, grip strength and picture vocabulary) were estimated. The results showed that PLSR performed well in both single- and multi-label learning. In fact, the present estimations were better than those reported in literatures, as indicated by stronger correlations between the estimated and actual CBD variables (e.g., 0.546 for the estimation of picture vocabulary in this study, as compared to not more than 0.35 in literatures), as well as high gender classification accuracy (97.8% in this study). Moreover, the RSFCs that contributed to the estimations were observed to be of biological significance. Besides, the estimation accuracies based on RSFCs among 100, 200 and 300 ROIs were higher than those based on RSFCs among 15, 25 and 50 ROIs; the estimation accuracies based on RSFCs evaluated using partial correlation were higher than those based on RSFCs evaluated using full correlation. In addition to the aforementioned virtues, PLSR is efficient in model training and testing, and it is simple and easy to use. Therefore, PLSR can be a favorable choice for future MRI-based estimations of CBD variables.