You can do it that way. If you use the -per-run option, it will motion correct and coregister each run separately and only combine them in the common space (eg, surface space). The alternative is to analyze each separately, then combine the results using a fixed effect model using mri_glmfit. The difference, if any, will depend on your design. If you do not have any event types that are the same across sessions, then the results should be nearly identical.

On 10/03/2016 03:03 PM, Taylor, Johnmark wrote:

Hello,

I am looking to run a GLM on a single subject, where data was collected across several different sessions. What is the best way to go about doing this in the FSFAST pipeline? I was thinking that I should just 1) drop the data for all the runs into a single BOLD directory as though they were from a single session, and 2) let the registration and motion correction do as well as they can. Is this a reasonable approach, and if so, are there any settings in the preprocessing or GLM that would advisable (for instance, in terms of running the GLM on the surface versus the volume, or doing the registration per-run versus per-session)?

Thank you very much,

JohnMark

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