Thanks Anderson. To tell you more about the analysis, I ran dual regression and I found an area with a significant difference between the two groups for a certain component. Now I want to investigate connections between subregions of the component with the significant area through DCM. Extracting time series from the non-decomposed resting state data won't do the job cause the initial finding is specific for the component; that's why I would like to have voxel or region-wise time-course for the component to run the analysis between those subregions. Is multiplying the component map with the component time course the way to get what I want? 

Someone on this forum also suggested to run dual regression again but after dividing the component of interest into subregions and feeding the analysis the new melodic_IC 4D image, in order to get component time series for the images. I appreciate your help.

Best,

George

2015-09-23 9:41 GMT+02:00 Anderson M. Winkler <[log in to unmask]>:

Hi George,

Not impossible: the dual regression are two regressions, and in Y=X*b+e, once you have an estimated b, the fitted response, without the noise, is X*b. It isn't something interesting, though: it will be just the spatial map for the DMN multiplied by its estimated timecourse from the previous step. I think the best is just leave the dual regression as it is.

All the best,

Anderson

On 22 September 2015 at 14:10, G Ch <[log in to unmask]> wrote:

Thank you Anderson. It would be impossible then to regress out all components of non-interest + noise and leave DMN signal? I'm asking because I'm interested in voxel-wise DMN residuals for further DCM analyses.

Best, 

George

2015-09-22 10:36 GMT+02:00 Anderson M. Winkler <[log in to unmask]>:

Hi George,

I'm a bit worried with this suggestion: regressing out all other timecourses will leave DMN + noise. Perhaps better than is to simply take the DMN fit (or the fit for whatever other component that is of interest), which is what the dual regression does.

All the best,

Anderson

On 21 September 2015 at 15:29, G Ch <[log in to unmask]> wrote:

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,