Software introduction:

Based on the Matlab interactive development environment, the IAMI (Intelligent Analysis of Medical Image) laboratory led by Professor Du Yuhui from the School of Computer and Information Technology of Shanxi University developed the GIG-ICA (Group information guided independent component analysis) toolbox, which integrates our previously proposed GIG-ICA method. We release the GIG-ICA toolbox here for use by researchers in the field of brain science. The toolbox can analyze the brain functional MRI data of multiple subjects at the same time, and can calculate the brain functional networks of individual subjects, the time series related to the brain functional networks, and the functional network connectivity (FNC). Using our toolbox, brain functional networks and related information can be visualized as well, and the estimated measures can be used by researchers for subsequent statistical analysis, clustering, and classification studies.

Sharing files：

Download	Version	Upload Time
GIG-ICA.rar	Version 1	2022.01.24
GIG-ICA.zip GIG-ICA.rar	Version 2	2022.03.31
GIG-ICA.rar GIG-ICA.zip	Version 3	2022.05.06

Please cite as:

• [1] Du, Y. H., E. A. Allen, H. He, J. Sui, L. Wu and V. D. Calhoun (2016). "Artifact removal in the context of group ICA: A comparison of single-subject and group approaches." Hum Brain Mapp 37(3): 1005-1025.

• [2] Du, Y. H. and Y. Fan (2013). "Group information guided ICA for fMRI data analysis." Neuroimage 69: 157-197.

• [3] Du, Y. H., G. D. Pearlson, J. Y. Liu, J. Sui, Q. B. Yu, H. He, E. Castro and V. D. Calhoun (2015). "A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders." Neuroimage 122: 272-280.