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. Author manuscript; available in PMC: 2021 Jan 1.
Published in final edited form as: Neuroinformatics. 2020 Jan;18(1):87–107. doi: 10.1007/s12021-019-09422-1

Fig. 3.

Fig. 3

A. A toy illustration of the analysis workflow. gRAICAR analysis on ICs estimated from fMRI data corresponding to Group 1 is assumed to produce group-level components grpIC1,1 and grpIC1,2. Let us suppose that grpIC1,1 has highest inter-subject consistency in Group 1 and hence be selected for further processing. B. A toy illustration of the analysis workflow. gRAICAR Analysis on ICs estimated from fMRI data corresponding to Group 2 is assumed to produce group-level components grpIC2,1 and grpIC2,2. Let us suppose that grpIC2,2 has highest inter-subject consistency in Group 2 and hence be selected for further processing. C. The spatial maps at the individual subject level corresponding to group-level components from Fig. 3 A and Fig. 3 B, i.e. the most reproducible ICs within each group, are obtained and entered into a k-means clustering analysis in order to determine their ability to discriminate between the groups.