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Hi Khoi, Yes. One would do this: 1) fslsplit the melodic_IC 2) fsl -add on the components that are to be added. 3) fslmerge the ICs, dropping the original before the sum, and including the result of the sum. 4) Run the dual_regression. All the best, Anderson On 19 June 2016 at 11:20, Khôi Huỳnh Minh <[log in to unmask]> wrote: > Dear Anderson and Paulo, > > Sorry to jump in but I have a similar problem and want to make sure that I > understand what you mean. > > 1. By "adding", did you mean that we use "fslmaths --add" ? > 2. The ICs used in the "adding" is raw IC (split from melodic_IC by > fslsplit) or thresholded IC (thresh_zstat) ? > 3. After "adding", should I merge the result IC (by adding above) with > other IC (split from melodic_IC) to perform the dual_regression or I just > need to perform dual_regression on the result IC since I only care about it. > > Best regards, > > Khoi > > On Sun, Jun 19, 2016 at 2:11 PM, Anderson M. Winkler < > [log in to unmask]> wrote: > >> Hi Paulo, >> >> You are probably better off by simply adding up the ICs that are split >> apart before doing the dual regression. >> >> About the mixture modelling: yes, that can be used after having summed >> the ICs (in fact, there will then be no need for thresholding, and thus, no >> need for mixture modelling). >> >> All the best, >> >> Anderson >> >> >> On 18 June 2016 at 14:21, Paulo Branco <[log in to unmask]> wrote: >> >>> Dear FSL members >>> >>> We're trying to use resting-state fMRI for single-subject mapping. We're >>> discussing clean ways to solve the problem of IC splitting. For one >>> particular network (sensory-motor) we tend to consistently get 2-3 ICs when >>> using the built-in dimensionality estimation from FSL. We can solve this by >>> lowering dimensionality or simply adding the ICs together, but this is a >>> bit too subjective. We're considering doing a "dual_regression" without >>> group maps: >>> >>> In short, we run ICA, we perform a template-matching procedure, we >>> select the best-fit candidate (so, one of the "pieces" of the network), we >>> extract its timecourse and run fsl_glm on the filtered_func data, with the >>> timecourse as design and the --demean option. This is similar to a SBCA >>> analysis, but the ROI is selected based on the initial ICA. From visual >>> inspection, the output masks show the whole-network (and some noise added), >>> but we were wondering if we're missing something obvious. This is what the >>> dual_regression script does, correct? >>> >>> Also, we output the results as z values with the --out_z option. Can we >>> perform mixture-modelling on these images to get a consistent thresholding >>> value across subjects (eg. 50% false-positive rate?). If so, would the >>> following command (as seen on the MELODIC user guide) do the trick? >>> >>> melodic -i myZstat --ICs=myZstat --mix=grot.txt -o myZstatMM --Oall >>> --report -v --mmthresh=0.5 >>> >>> Thanks in advance! >>> Paulo >>> >> >> >