Dear Brainstorm Expert:
I am wondering if I may get some feedback & help for my first MEG analysis using Brainstorm? I downloaded Brainstorm last week (the latest version). The online tutorial is so helpful and I've followed them carefully, but I still need some help to achieve my goal - The ultimate goal is to look at hippocampal connectivity with the rest of the brain and compare its "connectivity difference between 2 experimental conditions (condition A and B)" between Group 1 and Group 2.
Below is my pipeline and questions. Please also let me know if each step looks fine? This is so much appreciated!
(1) Each subject's anatomy is processed through Freesurfer reconstruction pipeline, and each subject has one single run of MEG recording (CTF format). Subjects' MEG data are first filtered by 1 to 50 Hz on the continuous data, and the first & last transient time window of the continuous data (a few seconds, based on information given by the 'view filter response') were manually marked as bad segments.
(2) Stimulus delay is first corrected, then power spectrum inspected and a notch filter applied, followed by bad channel inspection & removal, and artifact removal with SSP projectors (for eye blinks and heart beats, etc.). Lastly, head motion is corrected (shifting to the center position of the run), and the entire data were inpected throughout again and marked with a few additional bad segments. Then, epochs are imported for the 2 experimental conditions (for -200ms to 600ms)=> When importing epochs, should I uncheck the "remove DC offset" option (given that I've filtered the continuous data for 1-50Hz in the beginning)?
**Below are based on the tutorial for event-related source localization and I wonder if they should still be done before I can do any connectivity analysis?
(3) Computing head model => for the Source space, should I select "Cortex surface" or the "MRI volume"? (This is regarding precise localization and connectivity estimates for deep-brain sources like hippocampus.) For the Forward modeling methods, I used "MEG: Overlapping spheres".
(4) Computing noise covariance: Compute from recordings using baseline of -200ms - 0 ms; and "Remove DC offset: Block by block, to avoid effects of slow shifts in data" =>Are these good? There is no additional noise recording available
Comuting sources: Method: "Minimum norm imaging", Measure: "Current density map"; and for Source model, I used "Unconstrained" because my focus for this dataset is on the hippocampal activity=>Would this be fine for later connectivity processing? I then normalize the density map and generated the "z-scored" source maps for each subject for each condition
(5) For connectivity analysis, now my question is which file should I drag and drop to the Processing Box1? The link to all the trials for each experimental condition (and not the averaged link), right?
(6) And, somehow the options for the type of connectivity seem not matching the tutorial documentaiton onine, so I don't know which connectivity analysis is the best for me to go for? If I am interested in examining the degree of of phase synchronisation, or something like 'lagged coherence' or 'phase lag index', which one is the one to go for I wonder? And what parameters should I use?
(7) Finally, how can I subtract connectivity between trial conditions? I assume I can then average the sum of these differences for each subject, right?
And then, how do I compute the connectivity sources? Just like the above event-related source localization? And after this, is there any normalization like normalizaing the event-related sources above, using the pre-stimulus window of -200ms - 0 ms, that should be done before group analysis?
(8) Before group analysis, I should project the connectivity sources to a standard brain template, right? I wonder how? Should I follow the tutorial of "Group Analysis : Subject coregiateration"? (But which file should I use for the projection?)
(9) Finally, I imagine I'll get something like a Group folder containing the (normalized?) and projected 1xN connectivity sources, with the connectivity difference files of [connectivity (A) - connectivity (B)] per subject, and how do I run a t-test with them between Group 1 and Group 2. And is it also possible to control for covariates (e.g. age, and IQ) in this group comparison (simple ANCOVA) and how?
(10) Regarding the ROI, is there any documentation on the approaches of how to generate it and use it for connectivity analysis? E.g., should I de-project the standard-space ROI back to individual anatomy space to do the connectivity analysis? Or this can be done just on the standard space (i.e. MNI)? But somehow in all the connectivity processing tabs, I didn't see any place for me to load the ROI file..
Thank you so much for your time. Your expert feedback and information would be greatly appreciated.
University of Toronto