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Hi Vladimir, Thank you for the information. If I may ask, what kind of statistics are you referring to with respect to assessing significance of winning model parameters between sessions? I guess more specifically, is it not a result in itself if two different models are selected as the winning model in the two sessions (if both models were included as potential options in both sessions)? If I can, I think I would prefer being able to run the data separately, but would like to be able to make inferences about changes in network activity between the sessions within one experiment as well as between experiments. I am creating a head model for source reconstruction for each session, which I think should resolve the issue of head position changes between sessions, correct? Thank you for the LFP suggestion, because I was also curious as to what would be the best neuronal model to use in this paradigm. Best, Vivek From: Vladimir Litvak [mailto:[log in to unmask]] Sent: Wednesday, January 25, 2012 5:00 PM To: Buch, Vivek (NIH/NINDS) [G] Cc: [log in to unmask] Subject: Re: [SPM] DCM analysis set-up Dear Vivek, Both are possible but I think the second (concatenating) approach is more common. You could also find one winning model for all subjects/sessions fitted separately and then do classical stats on the parameters of that model. Since the head position is likely to differ between the two experiments I'd suggest that you use some kind of source extraction (e.g. beamformer) and then LFP option in DCM. Vladimir On Tue, Jan 24, 2012 at 10:01 PM, Buch, Vivek (NIH/NINDS) [G] <[log in to unmask]<mailto:[log in to unmask]>> wrote: Hi SPM experts, I have a conceptual question regarding the best way to set up some DCM analysis. Subject Experiment 1: Four separate MEG sessions Experiment 2: Four separate MEG sessions As above, basically I have two experiments with four MEG sessions each. I would like to compare how network activity changes across the different sessions within experiment and then compare those results between experiments. There is no event related modulation during any of the scans. Experimental influence is occurring between sessions, outside of the scanner, and is different for Experiment 1 and 2. Therefore using DCM SSR, I am interested in the "most prominent" average network activity (of a particular known network within the brain) during the different sessions and how this network activity changes between the four sessions within each experiment. For example if I am comparing say 10 different models to characterize the "most prominent" network activity, I would like to know how the model selection changes between all of the sessions within each experiment; and then compare those model selection results to make inferences between the two experiments. What would be the best way to set up the analysis? Can I run the same 10 model BMS for each session and see if the winning model changes across sessions? Or should I concatenate the sessions into one dataset and analyze how A and B matrix coupling parameters characterize the changes? Thank you for the suggestions and help