Task-based functional connectivity in nilearn via bsc

I want to ask whether I’m doing beta-series correlation based contrasts sensibly.

Picking up where this nilearn tutorial left off:


Beta-Series Modeling for Task-Based Functional Connectivity and Decoding

This example shows how to run beta series GLM models, which are a common modeling approach for a variety of analyses of task-based fMRI data with an event-related task design, including functional …

Am I right to think that after we calculate beta-series for e.g: sub-1’s condition 1 and 2 (e.g.: “string” and “language” following the tutorials example) we wanted to do a group-level “string-language” task-based FC contrast… we could subtract the string_connectivity_image from the language_connectivity_image and - following the same process for all subs - just do a one sample t-test to arrive at the group-level task dependent FC difference between the conditions?

You probably want to do a paired t-test between the connectivities obtained in the two conditions, across subjects. With the paired statistic, you remove the subject-effect of no interest that would otherwise fold into the variance term.
Does that answer your question ?

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Yes, thank you! Just wanted to double check!