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help > RE: Is small cluster size problematic?
Jul 11, 2018 02:07 PM | wzhong
RE: Is small cluster size problematic?
Hi Alfonso,
I should note that I performed the preprocessing steps including denoising and smoothing outside conn in a custom in-house pipeline, then I imported the data into conn using batch scripting skipping preprocessing and denoising steps. In my preprocessing I used 3dBlurToFWHM smooting with a kernel size of 6mm.
Would this have caused problems for smoothing when importing into conn? I also have non-smoothed, denoised image files, should I have used these in the conn analysis and used smoothing in conn? If these do not work I will try nonparametrics.
I ran the second level as follows:
Subject effects: [AllSubjects behav age_mean_centered male female mean_fd], contrast = [0 1 0 0 0 0]
Where behav is the outcome measure of interest (it is a continuous measures with a fairly small range between 1 and 10 and is not mean-centered); for the covariates, age has been mean centered, male and female are two binary indicators with values of 0 or 1 for being male or female (and vice versa), and mean_fd is the subject motion measure (not mean-centered since I think 0 is meaningful here indicating subjects without motion).
Conditions: Rest [1]
For Sources I entered my ROIs of interest and run a F-test contrast (eye(n)). I also tried running separate t-tests for each source ROI and bonferroni correct for the cluster FDR alpha-level, the resulting clusters are all small.
Thanks!
I should note that I performed the preprocessing steps including denoising and smoothing outside conn in a custom in-house pipeline, then I imported the data into conn using batch scripting skipping preprocessing and denoising steps. In my preprocessing I used 3dBlurToFWHM smooting with a kernel size of 6mm.
Would this have caused problems for smoothing when importing into conn? I also have non-smoothed, denoised image files, should I have used these in the conn analysis and used smoothing in conn? If these do not work I will try nonparametrics.
I ran the second level as follows:
Subject effects: [AllSubjects behav age_mean_centered male female mean_fd], contrast = [0 1 0 0 0 0]
Where behav is the outcome measure of interest (it is a continuous measures with a fairly small range between 1 and 10 and is not mean-centered); for the covariates, age has been mean centered, male and female are two binary indicators with values of 0 or 1 for being male or female (and vice versa), and mean_fd is the subject motion measure (not mean-centered since I think 0 is meaningful here indicating subjects without motion).
Conditions: Rest [1]
For Sources I entered my ROIs of interest and run a F-test contrast (eye(n)). I also tried running separate t-tests for each source ROI and bonferroni correct for the cluster FDR alpha-level, the resulting clusters are all small.
Thanks!
Threaded View
Title | Author | Date |
---|---|---|
wzhong | Jul 6, 2018 | |
Alfonso Nieto-Castanon | Jul 11, 2018 | |
wzhong | Jul 11, 2018 | |
Alfonso Nieto-Castanon | Jul 12, 2018 | |