gRAICAR: an exploratory tool for sub-group discovery in neuroimaging data

Author: Zhi Yang, Ph.D.

Institute of Psychology, Chinese Academy of Sciences

To download this package, simply download the zip file (see the last button on the right sidebar) or clone the repository using "git clone https://github.com/yangzhi-psy/gRAICAR".

This package is also a component of the Connectome Computing System (CCS, https://github.com/zuoxinian)

• gRAICAR setup GUI

• Example of output

Release note

Feb 15, 2015: Version 1.1

• This version provides an option to perform ICA on individual subjects using RAICAR. Users can provide fMRI datasets and a group-level mask (both registered to standard space), and leave the ICA and gRAICAR work to the software.
• A few bug fixes.

Jan 16, 2015: Version 1.0

The first release of gRAICAR after a major reconstruction.

Key features:

• A GUI to input file paths and parameters
• Automatically generate html reports
• Manage multi-core computation

Documentation

Please see this tutorial for installing gRAICAR, running gRAICAR, and interpreting its outputs.

Demonstration

An example dataset with 4 subjects can be downloaded here. To extract the zip file, type tar zxvf demo.tgz.

This example shows recommended directory structure for gRAICAR analyses. Please refer to a full directory tree of the demonstration data.

Two configuration files, 'gRAICAR_settings_rest_MELODIC.mat' and 'gRAICAR_settings_rest_RAICAR.mat' are included in 'demo/0scripts/'. These configuration files can be loaded using the GUI to run the example analysis.

Alternatively, the user can run the batch file, 'batch_setup_gRAICAR.m' to perform the example analysis without using GUI.

The outputs of the demonstration are in 'demo/output'. There are two versions of webpage reports, one for RAICAR mode, the other for MELODIC mode. Start browsing from 'demo/output/rest_webreport_RAICAR/00index1.html'.

Reference

Please consider to cite the following publications:

Original algorithms

Yang Z, Zuo X, Wang P, Li Z, Laconte S, Bandettini PA, Hu X (2012). Generalized RAICAR: Discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks. NeuroImage 63, 403-414

Yang Z, LaConte S, Weng X, and Hu X* (2008). Ranking and averaging independent component analysis by reproducibility (RAICAR). Human Brain Mapping 29, 711-25

Improvements and applications

Yang Z, Chang C, Xu T, Jiang L, Handwerker D, Castellanos F, Milham M, Bandettini P, Zuo X (2014). Connectivity Trajectory across Lifespan Differentiates the Precuneus from the Default Network. NeuroImage 89, 45-56.

Yang Z, Xu Y, Xu T, Hoy C, Handwerker D, Chen G, Northoff G, Zuo X, Bandettini P (2014). Brain network informed subject community detection in early-onset schizophrenia. Scientific Reports 4, 5549.