 |
 |
I have performed a dual regression analysis including running the final randomise step. I would now like to threshold the maps using the gaussian/gamma mixture model as was done in the Filippini paper. Is there a way to implement this using FSL tools? My best guess is that I need to convert the tstat images output from randomise to zstat images, and then somehow implement melodic using a design.mat and a design.con file to show areas of significant group differences controlling for the local FDR? Alternatively, I could use TFCE from the standard output of randomise. Or another recent paper (Napadow, "Intrinsic Brain Connectivity in fibromyalgia is associated with chronic pain intensity") used a mixed effects model implemented in FLAME. I appreciate any advice, on the best way to handle the dual regression output. Thanks! One more question. In the context of group ICA, the gaussian/gamma mixture model delineates between significantly activated and non-activated voxels. Is there even a way to use the GGMM to test group differences, or am I misinterpreting the paper? Filippini et. al. "(ii) using these time-course matrices in a linear model fit (temporal regression) against the associated fMRI data set to estimate subject-specific spatial maps. Finally, the different component maps are collected across subjects into single 4D files (1 per original ICA map, with the fourth dimension being subject identification) and tested voxel-wise for statistically significant differences between groups using nonparametric permutation testing (5,000 permutations) (55). This results in spatial maps characterizing the between-subject/group differences. These maps were thresholded using an alternative hypothesis test based on fitting a Gaussian/gamma mixture model to the distribution of voxel intensities within spatial maps (see ref. 31 for further details) and controlling the local false-discovery rate at P < 0.05." Chris Bell University of Minnesota