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I want to analyze functional MRI (fMRI) data as follows:

  1. I am comparing the brain networks of two groups of subjects (patients and matched).

  2. For each subject I have a correlation (Pearson) matrix with the correlations between multiple brain regions (above 50000 correlations).

  3. I want to get a single measure to represent every subject (maybe the mean of correlations of each subject network). What is the most suitable measure? Should I use Fisher transformation to average the correlation matrix?
  4. I assume that the variance of the two groups is different and that the variance in each group is similar: How can check that without doing multiple comparisons?
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    $\begingroup$ Do you really want to do an FMRI analysis with absolutely no chance of localization of the functional differences? $\endgroup$ – John Dec 27 '11 at 15:02
  • $\begingroup$ I am comparing voxels correlations within specific regions of interest, so it is localized. $\endgroup$ – gid3a1 Dec 27 '11 at 15:17
  • $\begingroup$ ah, so you just want to see which group's region X better correlates. $\endgroup$ – John Dec 27 '11 at 15:34
  • $\begingroup$ Several regions, but yes. $\endgroup$ – gid3a1 Dec 27 '11 at 15:38
  • $\begingroup$ Whether it's physically the same region is irrelevant, only whether you're treating the same functionally... since that's what you're studying. $\endgroup$ – John Dec 27 '11 at 16:06
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Yes, z-transform to average the functional regions within S and then compare them as you'd normally compare correlations between groups... is your simplest path. Otherwise, I thought FSL had a facility to handle such comparisons of the magnitude of an effect with multi-level modelling.

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  • $\begingroup$ I'm using brain voyager. Anyway I wanted to know how to do it theoretically. Thank you. $\endgroup$ – gid3a1 Dec 27 '11 at 16:46

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