Two-step paretial least square regression classifiers in brain-state decoding using functional magnetic resonance imaging

Multivariate analysis methods have been widely applied to decode brain states from functional magnetic resonance imaging (fMRI) data. Among various multivariate analysis methods, partial least squares regression (PLSR) is often used to select relevant features for decoding brain states. However, PLSR is seldom directly used as a classifier to decode brain states from fMRI data. It is unclear how PLSR classifiers perform in brain-state decoding using fMRI. In this study, we propose two types of two-step PLSR classifiers that use PLSR/sparse PLSR (SPLSR) to select features and PLSR for classification to improve the performance of the PLSR classifier. The results of simulated and real fMRI data demonstrated that the PLSR classifier using PLSR/SPLSR to select features outperformed both the PLSR classifier using a general linear model (GLM) and the support vector machine (SVM) using PLSR/SPLSR/GLM in most cases. Moreover, PLSR using SPLSR to select features showed the best performance among all of the methods. Compared to GLM, PLSR is more sensitive in selecting the voxels that are specific to each task. The results suggest that the performance of the PLSR classifier can be largely improved when the PLSR classifier is combined with the feature selection methods of SPLSR and PLSR.