I am working on an analysis looking at the association between ALFF and depression change, including head motion as a covariate of no interest. So far, I have used fmriprep to preprocess then XCP-D to generate ALFF images, resampled to MNI152 space, applied a mask, calculated the Z-stat image, and merged into a 4D image so that each volume is one subject’s ALFF Z-stat image.

Then, I am using FSL’s GLM, not within FSL FEAT but the separate tool, to perform Higher Level / non-timeseries design. The # of inputs is # of participants, and then including 3 EVs: column of 1s, column of demeaned % depression change values, column of demeaned head motion values. Then for the contrasts, the contrast of interest is

gm dep change head motion

0 1 0

I save that, which saves the .con and .mat files I need for randomise. Then, I run randomise using the basic structure of randomise -i 4D_merged_image_ALFF_zstat -o zstat_alff_randomise -d <design.mat> -t <design.con> -m mask -n 5000 -x --uncorrp

which outputs a t-stat image, an uncorrected p value image, and a corrected p value image. I am then using that t-stat image as the basis of a TDP exploratory analysis.

- Does the above sound like an appropriate use and set up of FSL GLM’s tool?
- Is there a way for me to have randomise consider negative t-stat values when creating the p-value image? Right now, it’s a one-sided test, but negative t-stat values are relevant.

Thanks in advance for help!