Hi Laura,

Sorry for my delay. All the strategies I've considered reduce to either the sum of the ICs to be merged scaled by some factor, or are not better than simply doing the sum. So, all and all, I'd say simply add the two spatial maps and carry on with the dual regression. Yes, it will add some noise from one IC on top of another, but when we look at the ranges of the values, these become diluted and shouldn't become an major issue.

Another option is the one I commented in the earlier email to Sheena in this thread: keep the original ICs, and then run a NPC (Non-Parametric Combination) over those that you'd like to combine. Given that these come from spatial ICA, we expect little overlap between the components, such that the Tippett combining function should be the most powerful. There are details on how to run NPC here.

All the best,

Anderson

On 7 June 2016 at 09:56, Anderson M. Winkler <[log in to unmask]> wrote:
Hi Laura,

Please give me a few days to prepare a script for this. I'll try to send details soon.

All the best,

Anderson


On 6 June 2016 at 19:51, Laura Korthauer <[log in to unmask]> wrote:
Hello,

I ran group ICA and got two default mode network components, one that is more anterior and one more posterior. I'd like to combine them into one single component. From a previous listserv response that Anderson made to another user with a similar problem, I see that:

"Another option to consider would to multiply these two components by their respective lines in the mixing matrix (that is, mix them back, "undoing" the ICA for them), then run ICA again only on this "undone" data, with dimensionality 1."

I'm confused about how exactly to go about this. The mixing matrix has 20 columns (corresponding to my 20 components, presumably) and 30444 rows (129 subjects x 236 vols). What files am I to be multiplying to mix them back and "undo" the ICA?

Thanks for your help,

Laura

--
Laura Korthauer
Ph.D. Candidate, Clinical Psychology
University of Wisconsin-Milwaukee