Hi Jeff,

That is an interesting/complex question. ICA spatial components (backprojected to each subject) have somewhat of a dual conceptual interpreation, both as: 1) as a measure of the spatial distribution of a given network for each subject/condition; and 2) as a measure of the funcional connectivity between a given network and every voxel in the brain for each subject/condition. In more practical/mechanistic terms, what these "spatial component" backprojected map values actually represent is the functional connectivity between a given network and every voxel, something like a "network-to-voxel" analyses, just like standard "seed-to-voxel" analyses but now using a distributed network as a seed (in other words, the exact values/metric in the GICA1 backprojected spatial maps represent regression coefficients from a seed-to-voxel multivariate-regression analysis that used ICA group-level maps as seeds). Of course, just in the same way that you may use "seed-to-voxel" connectivity measures to look both at local connectivity effects (which may be interpreted in terms of functional homogeneity of the seed ROI) as well as distant/distributed connectivity patterns (which may be interpreted in terms of functional connectivity between the seed ROI and other regions), the equivalent "network-to-voxel" measures from the ICA spatial component analyses can be used to look both at local within-network effects, which may be more representative of the spatial distribution of a network, as well as distant/between-network effects, which may be more representative of the connectivity between this network and other areas. 

So, coming back to your question, if you select the ICA_3 and ICA_10 components in the "spatial component" tab in CONN (with between-measures contrast [1 0;0 1]) and define a between-subjects regression with a covariate of interest (e.g. AllSubjects, age; contrast [0 1]) and a followup-baseline comparison (e.g. baseline, followup; contrast [-1 1]), what you are asking, conceptually, is whether the change between baseline and followup in any of these two networks spatial distribution / connecivity is associated with your covariate of interest. And, just to be precise, the "spatial distribution / connectivity" in the sentence above is represented by multivariate regression measures between the network ROIs and every voxel in the brain. If it helps, you may also think of the same analyses if you were to use two seeds instead of two networks, and simply consider that instead of looking at the seed-to-voxel connectivity profiles with two seeds now you are looking at the same seed-to-voxel connectivity profiles with two distributed networks.

Hope this helps
Alfonso 

Originally posted by Jeff Browndyke: