Group-level average of (individual-level average) source timecourses

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Dear MNE community,

I am extracting (per-ROI) average activity in source space via mne.extract_label_time_course() at the individual (participant) level, using mode="mean_flip", which results in timecourses that are overall positive for some participants, but negative for some other participants (in the same ROI).

What is the recommended pipeline for computing group-level average activity? Is it better to use mode="mean" (to avoid averaging positive and negative values) or to first transform (e.g., squaring) the individual-level source activity before averaging at the group level?

Thanks for your help!

Best wishes,

Ladislas

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Hi Ladislas,

that is an important topic in source reconstructed time series and I don’t think there is a correct answer on how to deal with sign ambiguity. You could just take the mean or square the data, both is valid. If you want to dig a bit deeper there is a method that basically sign flips on the group level, it’s implemented in the osl dynamics toolbox. The paper explaining the method is by Vidaurre et al (search for sign ambiguity). As far as I know this is not (yet) implemented in MNE.

Cheers,

Carina