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

Zhiying Long,Li Yao,Yubao Wang,Xuanping Liu,Yong Fan

doi:10.1371/journal.pone.0214937

Abstract

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.

Highlights

One of the key challenges in cognitive neuroscience is to map the human brain activities to different brain states
The group voxel pattern of the simulated data with contrastto-noise ratio (CNR) = 0.1 using partial least squares regression (PLSR) and general linear model (GLM) are displayed in Fig 1(b) and 1(c)
Compared to the pre-defined regions of interest (ROI) in Fig 1a, PLSR detected regions that were only activated by each task and did not detect regions that were jointly activated by the four tasks

Summary

Introduction

One of the key challenges in cognitive neuroscience is to map the human brain activities to different brain states. Multi-voxel pattern analysis (MVPA) using machine learning models has been widely applied to functional magnetic resonance imaging (fMRI) datasets to address this question [1]. The models that are trained on stimulus-evoked brain activity can be used to discriminate multiple cognitive processes [2,3,4]. Two critical steps that include feature selection and classification are involved in decoding brain states. Among the various MVPA methods, the partial least squares regression (PLSR) is a powerful method for multivariate data analysis that can be used in either feature/variable.

Methods

Results

Discussion

Conclusion