Dear experts,

I want to clarify the setting of longitudinal studies since I found my results (correlation matrices) are different. 

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

I import my data by "New (import)" -> "from fMRIprep dataset", after preprocessing and ICA-AROMA denoising the data. Then, I replace all functional data with ICA-AROMA denoised data in "SETUP" -> "Functional".

I tried 2 different ways to import my data and found them result in different correlation matrices.

(1) import baseline and follow-up data as different subjects, resulting in 32 subjects in "Basic information" of "SETUP". CONN automatically generates one rs condition

(2) import 16 subjects, each with 2 sessions (baseline, and follow-up). CONN automatically generates 2 rs conditions (baseline_rs, followup_rs).

All the other settings are the same.

I know the first setting is wrong since it leads to a wrong GLM model which doesn't take the subject random effect into account. I thought that correlation matrices would be the same for both ways, but, I found the correlation matrices are different. It looks like the subject random effect is also considered during the calculation. However, I want to extract the correlation matrices (I use "tanh()" function to convert them back to the Pearson correlation), and use them to calculate topological properties with the Brain Connectivity Toolbox. 

I'm confused about which way to set my analyses since I plan to calculate longitudinal changes in patients (baseline vs. follow-up) and compare healthy controls (at the baseline) with patients at follow-up (HC-baseline vs. Patients-follow-up).

I also checked the result of another analysis (3) which imports healthy controls and patients at baseline, each subject has one session.

(2) and (3) generated the same correlation matrix for the same patient, however, (1) generated a different matrix.

Best regards,

Chih-Hao