Hello,
I switched your message to a public topic because it can be useful for other users.
Then, epochs are imported for the 2 experimental conditions (for -200ms to 600ms)
This might be a bit short for time-frequency or connectivity analysis. If you can, think maybe about increasing the duration of your epochs, pre and post stim.
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)?
Indeed, if you have removed all the components of the signal under 1Hz, you don't need to remove again the signal mean.
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?
This is up to your decision, connectivity analysis can be done in sensor space or in source space.
(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.)
To my knowledge, there are still no tools working correctly for connectivity analysis on unconstrained source models (3 dipoles with orthogonal directions for each 3D location). Therefore, I recommend doing your source analysis with constrained locations and constrained orientations.
@hossein27en Can you confirm?
(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?
Looks good. If you increase the baseline in your epochs, increase it here as well.
I used "Unconstrained" because my focus for this dataset is on the hippocampal activity=>Would this be fine for later connectivity processing?
No, this is not a good idea because I think the connectivity processes in Brainstorm are not well equipped to handle unconstrained sources.
Additionally, Attal et al showed that the hippocampus sources are best modelled with constrined sources:
https://neuroimage.usc.edu/brainstorm/Tutorials/DeepAtlas#Location_and_orientation_constraints
(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?
Connectivity and time-frequency analysis require long signals and fine fine signal dynamics, it is not advised to use averaged responses. Use continuous recordings or individual trials instead.
(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?
This part is still under construction.
@hossein27 might be able to address some of your questions.
And then, how do I compute the connectivity sources?
Run your connectivity analysis either on sensor or on source signals.
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?
Most connectivity measures will not have any time resolution anymore.
(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?)
This is needed only if you want to average your results in source space.
If you goal is to compute one connectivity measure for each subject and condition and then test for the significance of the differences between the two conditions, you don't necessarily have to project your full source maps to a template.
(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?
Everything you can do in terms of statistics is described in the Statistics tutorial. There are no multivariate tests available yet. You can export your subject-level results and do your statistical analysis outside of Brainstorm if needed.
https://neuroimage.usc.edu/brainstorm/Tutorials/Statistics
(10) Regarding the ROI, is there any documentation on the approaches of how to generate it and use it for connectivity analysis?
No, sorry. There are in general two strategies: starting with a strong anatomical hypothesis and designing your region of interest independently from the data, or guiding your ROI analysis based on the results you obtain.
Keep them relatively small, especially with constrained sources. Many of the ROIs available in the FreeSurfer anatomical atlases are too big and mix multiple functional regions.
E.g., should I de-project the standard-space ROI back to individual anatomy space to do the connectivity analysis?
You can design your ROIs for each subject individually, or your can design them on the template and then project them to each subject (menu Scout > Project in the Scout tab).