Dear experts.

I'd like to provide more information about my question.

I have 16 patient data at baseline and follow-up.

All data were preprocessed and denoised via fMRIprep and ICA-AROMA denoising.

I use "New (import)" -> "from fMRIprep dataset" to import preprocessed data into CONN, and then replace functional data with denoised data.

I tested the ROI-to-ROI matrices generated by 3 different settings.

(1) only import follow-up data 
(2) import baseline and follow-up data as 2 sessions for each patient
(3) import baseline and follow-up data separately, resulting in 32 subjects in the setting, each subject has one session (baseline or follow-up)

I extracted ROI-to-ROI matrices from  “resultsROI_Subject*_Condition001.mat" in "...\results\firstlevel\SBC_01" (For (2), also from “resultsROI_Subject*_Condition002.mat"). I noticed that (1) and (2) generated the same correlation matrices, but (3) generated different results.

I know (2) is the correct way to set up a longitudinal study. But, given that (1) and (3) result in different correlation matrices, I'm wondering why they generate different results. Would the same situation happen when I compare healthy controls and patients? (e.g. separately import healthy controls and patients as 2 conn project vs. import all data as one conn project).

An update: I did the same analysis as (1) again. However, the matrices are different with (2), but as the same as (3) this time.

I noticed that the second time automatically extracted more confounds (e.g. 'scrubbing_Dim20', 'QC_aroma_motion_Dim40', 'QC_w_comp_cor_Dim27'), but I only used CSF, white matter, and effect of rs for denoising, and these confounds/regressors were not included in SBC analysis, too. Thus, I don't really know why did I get different results from the same data and the same setting.