I did separate ICA analysis on 2 gropus (gift ICA), found components having highest correlations with DMN.
Should I next select the components and do one sample t-test in SPM on both groups separately (for thresholding) and then compare the results with two sample t-test?
I will be very thankful if anyone can help me.
Thank you for your answer.
I have one more issue:
when I'm selecting the apropiate folders cointaining my nifti files i get information from gift "The selected data folders are in the following order......(folder name... 5), (folder name 10), (folder name 16) etc".
In folder with all subject first 20 of my participants form a group 1 and the next 30 form a group 2.
Does it mean that gift recognize subject 1 as subject 5 (in my orignal folder), and therefore I should select my groups based on order in txt file?
Dear, i am working on EEG signal comparing t.test between young and old healthy people and I have got the follwing result. how can I analyze the following result
t(0.01) t(0.05) t(0.10) ExtremeP
One-Tailed (A>B): 1.430 1.188 1.078 0.03260
One-Tailed (A0) Prob(1Tailed>0) Thrsh(1Tailed
If you're interested in comparing groups, generally you want to conduct 1 ICA analysis and then do two tailed-tests.
The other option is using the Spatial Temporal Regression feature to apply network signal from one group onto the components selected from the other group (usually your control group).
gift keeps your subjects in the order they were inputted in through your batch file. So, when someone is 20th in that text file, gift will think of them as subject 20 as long as you're using that ica parameter file.
ICA exhibits several properties that make it challenging to perform a direct group comparison:
1) recomputing ICA on the same data does not necessarily yield identical results
2) ICA is an unsupervised method that does itself not perform a form of statistical inference - incorporating another criterion of optimality is typically helpful
3) there is hence no single best way to obtain a formal notion of group differences in ICA, which can be addressed by a number of ad-hoc procedure
-> a practical avenue can be to use the ICA projections for out-of-sample predictions in independent data
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