cmehta126

According to Enders, et al. (Front. Neuroinform., 2014), the permutation tests via _RandomiseGroupLevel_ use the same functions as the first-level analysis. Are the subject-level spatial maps that are inputs for...

I used 3dMVM with univarate response model of the form ~a0 + a1*age_i + a2*sex_i + a3*Z_i, where age_i, sex_i, and Z_i are subject-specific predictors. I'm testing the null hypothesis...

3dMVM certainly has advanced modeling, but I'm restricting to a simple univariate linear regression model and simple hypothesis that's equivalent to 3dttest++. I confirmed this by re-running my analysis with...

@Metasoliton, what you say about permutation testing in this context makes complete sense to me. However, the t-statistics observed at individual voxels are not related to clusters. It's not clear...

@wanderine, thank you. Is it right that the intercept must be represented by a column of ones in the Design matrix input into BROCCOLI? Also, would you confirm whether the...

@Metasoliton, thank you for the insight. I performed all pre-processing and subject-level analysis in AFNI. The resulting subject-spatial map inputs to 3dttest++ and BROCCOLI were identical (there were no differences...

@wanderine I think I figured out the reason for the discrepancy. Thank you.

I believe the issue was in how I was concatenating subject-level spatial maps into the "volumes.nii" input file for group analysis with Broccoli. I used AFNI's "3dTcat" function to do...

I figured out my error was with incorrect use of 3dTcat for combining subjects' spatial maps. In case anyone else has a similar problem, here is a link to a...

RandomiseGroupLevel worked as I expected on my dataset after downsampling spatial maps in the input volume from 256x256x256 (1mm x 1mm x 1mm) to 128x128x128 (2mm x 2mm x 2mm)....