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Hi Omar

In general during the functional realignment preprocessing step CONN/SPM perform realignment across multiple runs/sessions in two steps, realigning first each individual run separately to the first image in each run, and then following that by coregistering all runs to the first run. After that process CONN/SPM create a single "mean" image combining all time points (across all runs). When, later in a preprocessing pipeline, CONN uses the outlier detection algorithm to identify potential outlier timepoints, this step also creates a new/revised mean image by combining all time points (also across all runs) but now without the potential-outlier timepoints.

Hope this helps

Alfonso

Originally posted by Omar Maximo:

Hi,

Here's something I noticed across all versions I have used:

When I processed subjects with 3 timepoints, I noticed that it only created a mean image for the first timepoint and not for the remaining 2 but still created outputs. Is CONN using a mean image from timepoint 1 to correct for motion and coregistration for the other 2 sessions? Why isn't CONN creating a mean image for each timepoint and using them accordingly? or is it creating a mean image across all sessions?

There is also an issue when you have multiple T1w images. When you tell CONN that you have multiple images, then it only preprocess subjects with one timepoint and get errors for the other subjects with multiple errors. Then, when I change the setting to one scan per subject, then the errors go away. But then again, that brings me back to my first point.