Hi REST experts,
I'd like to perform Degree Centrality analysis within restricted ROI (e.g., only PFC region).
To do so, I do make a correlation matrix between voxels in the restricted ROI and voxels in whole gray matter mask (or default whole brain mask) before applying threshodling (r>0.025) and counting or averaging.
1) Can I perform such Degree Centrality analysis (restricted ROI x whole brain mask) using REST v1.8?
If yes, How can I do this?
2-1) I know that there is still a debate about negative correlation in resting-state functional connectivity in terms of global singal regression. So, I used r>0 or r>0.025 as a thresholding value for Degree Centrality analysis according to Buckner et al. (2009). I understand that means there is no negative correlation values in a correlation matrix between voxels of whole brain mask. As far as I understand, all values of PositiveBinarizeSumBrainMap must be positive values because these values are the number counting voxels that have values more than r>0.025. However, I found negative values on the "PositiveBinarizeSumBrainMap". Why are there negative values on the map?
2-2) After completing z-normalization (with mean and S.D. values for each subject), there are negative values. As far as I know, statistical group analysis in seed-based functioanl connectivity use a binary mask for positive correlation network to remove negative correlation values. When I perform between-group comparison analysis with DC maps, I also use a binary mask for only positive DC values.
There are two Degree Centrality analysis methods; weighted DC (Cole et al., Neuroimage 2010; 39:3132-48) and binary DC (Buckner et al., 2009). According to Cole et al. (2010), they made a correlation matrix between voxels in whole gray matter mask and then performed Fisher's r-to-z transformation. After that, they did averaged values in the correlation matrix.
3) Does REST v1.8 perform Fisher's r-to-z transformation during process of "DC Positive Weighted Sum Brain map" before summing or averaging values? So, I think that the "DC PositiveWeightedSumBrainMap" methods is a little different from the method developed by Cole et al. (2010).
4) What do you think about applying spatial smoothing (e.g., 4~6mm FWHM) to "DC PositiveWeightedSumBrainMap" and "DC PositiveBinarizeSumBrainMap" rather than z-normalization (with mean and S.D.) before statistic group analysis?
Thank you in advance