Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations.

ABSTRACT: 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. Restin

SUBMITTER: Chen C 

PROVIDER: S-EPMC6890557 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature