The R-fMRI Lab, led by Prof. YAN Chaogan from Institute of Psychology of Chinese Academy of Sciences released the brain imaging standardized processing platform DPABI (a toolbox for Data Processing & Analysis for Brain Imaging, V2.1) on April 15, 2016. The article that describes principles, implementation and application of DPABI was published at Neuroinformatics on April 13, 2016.

DPABI is a key component of The R-fMRI Maps Project under The Human Brain Data Sharing Initiative. The aim of the R-fMRI Maps project is to build a big data of intrinsic brain activity indices, which has the potential to allow us addressing critical questions about the brain. This project utilizes DPABI to process the open R-fMRI data and user-own R-fMRI data with the standard processing pipeline, and share the R-fMRI indices to the global research community, eventually accumulate a big data for the intrinsic brain activity.

DPABI integrates the advanced R-fMRI data processing pipeline DPARSF (Data Processing Assistant for Resting-State fMRI, which incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies.

This open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies. With DPABI, more researchers could join The R-fMRI Maps Project, to accumulate a big data of the intrinsic brain activity, thus facilitate the investigation of the association of brain function and behavior.