Dear Chih-Hao

I imagine one possible explanation for your observations would be the differences caused by different inter-subject normalization results when preprocessing your subjects using your four different approaches (but I would expect this to produce very small differences, so let me know if that is not your case). Mainly in option (3) you would be running a single normalization step jointly across both sessions, while in options (1) (2) and (4) you would be normalizing each session separately. Your observations would be consistent with having used the 'First functional volume as reference' option during normalization, which would cause the joint normalization to be identical to the normalization of the first session alone, but different from the normalization of the second session alone. 

In general, when having multiple sessions the recommendation is to preprocess them all jointly (i.e. option (3)) with the exception of cases when you may expect to find anatomical differences between the sessions (e.g. if the longitudinal data spans a relatively large period of time) when it can be preferable to preprocess each session separately (e.g. you may do that by unchecking the 'all sessions' checkbox when running preprocessing and running one session at a time). In either case I would probably recommend not using the 'First functional volume as reference' option and let instead the reference be computed from the averaged functional data across all sessions (unless of course you had a good reason to do that, e.g. your second functional run may have worse data quality)

Hope this helps

Alfonso

Originally posted by Chihhao Lien:

Dear experts,

It's my first time doing longitudinal analysis with CONN.

According to previous replies to the setting for longitudinal studies, there are 2 approaches.

1:  Enter the different sessions as if they were different subjects (https://www.nitrc.org/forum/forum.php?th...)

2: Enter 2 as the "number of sessions" per subject (https://www.nitrc.org/forum/message.php?... https://www.nitrc.org/forum/message.php?...)

I thought that CONN would generate the same ROI-to-ROI matrices with both approaches since I extracted these matrices from the result of 1st level analysis. However, I got different correlation matrices (Fisher's Z values, extracted from "resultsROI_Subject00*_Condition00*.mat") for my follow-up data.

I tested 4 different settings:

(1) Import HC and patient data at baseline, each subject has 1 session

(2) Import baseline and follow-up data (patients only) as different subjects (e.g. import baseline data as Subject 1-10, and then import follow-up data as Subject 11-20)

(3) Import baseline and follow-up data (patients only) as 2 sessions for 1 subject. Each subject has 2 sessions: baseline and follow-up

(4) Import follow-up data only, each subject has 1 session. 

While comparing the Z matrices for baseline data, (1), (2), and (3) generated the same matrices. However, while focusing on follow-up data, (3) generated different correlation matrices, compared with (2) and (4). 

I plan to extract ROI-to-ROI matrices for other analyses (e.g. graph theory), but I', confused about which setting I should use for my longitudinal data. Does anyone know which one is the better approach?

Best regards,

Chih-Hao