Thank you both Francois and Dimitrios for your very valuable feedback. If I may build off this already started conversation, I had a few more questions I was hoping for your input on:
- In regards to the stats-related question about finding effects at the sensor level but not in the source space, I came across this paper (https://www.sciencedirect.com/science/article/pii/S1053811912009895?via%3Dihub) and if I may quote it:
Due to differential sensitivities of sensor and source space analyses it is sometimes the case that a particular effect is significant in one but not the other. When an effect is significant at the sensor level with all the proper corrections for multiple comparisons and the hypothesis is about the existence of an effect rather than about a specific area being involved, it could be acceptable to only report the peaks of a statistical map at the source level without requiring correction for multiple comparisons over the whole brain. When an effect is significant at the source level corrected for multiple comparisons and the choice of time and frequency windows for the source analysis can be motivated a-priori, a sensor-level test is not necessary. What should be avoided is doing a sensor-level test without proper MCP correction and using it to motivate a source-level test that achieves significance. This would constitute double-dipping (Kriegeskorte et al., 2009) similar to using peaks in the data to constrain a sensor-level test.
For my analysis, because I first did an EEG sensor analysis (corrected for multiple comparisons), would it be okay then to not correct for multiple comparisons in my case? What would constitute 'the peaks of a statistical map at the source level'?
- I'm a bit confused as when to apply absolute values and smoothing. I'm working on a different dataset where I have the MRI's of the participants and thus (and at your recommendation) have used constrained sources. If I may just briefly describe my workflow (following the Brainstorm workflow as best as I could for the ' Constrained cortical sources' for the ultimate goal of doing statistical testing between two independent groups), once I got the average source file for each participant I:
- normalized with Z-score with respect to the baseline (no absolute value)
- applied the default smoothing option to each individual's average file
- I then rectified (absolute value) each individual's file
- I then projected each individual's rectified source file to the template anatomy
- I then use these resulting files (under the the 'intrasubject' tab) for statistical testing (two groups; independent t-tests in my case).
I have a feeling I've done the rectification and smoothing done at the wrong stage...
Thank you again for all the help.
Paul