Hello--
I have a pediatric dataset that is hampered by motion-related artifacts. I
have tried using motion as a covariate, ArtRepair and FSL's melodic algorithim
to remove artifacts, but have found inconsistent results (good for some
participants but not others). I have also started to remove the individual
scans that are artifact ridden and modeled them as a separate condition in my
event-related design. This last solution appears to reduce the artifact
activations more than my previous attempts. Despite the disadvantage of
losing power in an event-related design, are there other reasons that speak
against using this method for artifact correction?
Thanks so much for your input,
Ben
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