help > ICA analysis across multiple conditions
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Sep 16, 2019  12:09 PM | Andria Farrens
ICA analysis across multiple conditions

I have a methodology question about how ICA works. In CONN, I understand that ICA networks are determined by concatenating each subject and each condition temporally, and then using Fast ICA to find the independent spatial components based on this dataset.

In my experiment, I have a baseline resting state scan, a post motor performance task resting state scan, and a post motor learning task resting state scan. We expect there to be differences in functional connectivity in the 3rd post learning condition. My question is, how does the change in functional connectivity, which changes how voxels in the third condition are associated, affect the identification of spatial components. If for example, in the first two scans, source A talks to source B, but in scan 3 source A talks to source C. Will ICA be able to identify this shift in network connectivity? Or should I do ICA on each condition separately and compare relevant networks?