Hi Andrew,

I would like to suggest another method though perhaps someone else can confirm if this is valid as I also thought about this.

You can use the filtered_func_data and regress out from time courses all components except the DMN so you would end up with the contribution of the DMN to each voxel. Then from the residuals you can use a mask for each region and measure the functional connectivity between regions of interest in a seed analysis.

Best,

George

2015-09-21 15:49 GMT+02:00 Andrew Song <[log in to unmask]>:

Dear FSL experts,

I am interested in figuring out functional connectivity between sub-components of independent components (IC) by MELODIC.
For instance, I am interested in functional connectivity between pcc and IPL/IPS within DMN.

The first method I tried was to simply increase the number of IC to be identified in MELODIC (from 25 to 60/70), in hopes of replicating the Smith PNAS 2009 paper. However, DMN did not seem to break down - It still emerged as a single IC.

The second method I am thinking of is 'manually' breaking down the ICs. I would break down DMN into several sub-regions manually using masks and re-integrate them as 'separate components' into the dataset to be fed into dual regression. This was suggested a while ago in this forum.

Following this, I have two questions.

1. In the first method, is simply increasing the number of ICs not the correct answer to get the sub-regions?
2. Perhaps more important, is the second method valid? I am worried that manually breaking down the components will violate the spatial independence between components, which is the very premise of ICA/MELODIC.

Thank you very much,