Dear Brainstorm team,
I have a question concerning the sign interpretation of sources.
What I did is to localise (constrained, dSPM) the time course of beta coefficients characterising the linear relation between the brain signal and a variable of interest.
To understand which areas contribute to this process I run two tailed ttests (against 0). As clearly explained in the brainstorm tutorials, however, the sign of these ttest is ambiguous as it mainly reflects source orientation.
The tutorial (https://neuroimage.usc.edu/brainstorm/Tutorials/Difference) explains how to obtain the correct amplitude when we compare two conditions.
What is not clear to me is what happens in my scenario, where I have one condition and I contrast it against 0 (two tailed). Is the sign (positive and negative tvals) interpretable (respectively reflecting positive and negative betas)?
Looking at the results, I see stripes of positive and negative values, so I assume that dipole orientation is contributing to my statistical maps.
How could I obtained the correct signal direction, in other words, how could I tell where positive and negative betas are encoded in the brain?
Thank you for your time!
Kind regards,
Lorevi
What I did is to localise (constrained, dSPM) the time course of beta coefficients characterising the linear relation between the brain signal and a variable of interest.
You can't apply the Brainstorm inverse routines to anything but MEG or EEG signals. Multiplying regression coefficients with the inverse models computed with Brainstorm will not give any meaningful results.
See: Can I do source estimation for a single spatial map (not time series) - #2 by Francois
I'm not sure I understand this sentence correctly. If this comment is off-topic, please ignore it.
How could I obtained the correct signal direction, in other words, how could I tell where positive and negative betas are encoded in the brain?
Dear Francois,
thank you for your kind reply. What I am doing is to localise the time series of regressor coefficients, where I have a coefficient for each time point, MEG sensor and subject (see Chen, Davis, Pulvermüller, & Hauk, 2013; Hauk, Davis, Ford, Pulvermüller, & Marslen-Wilson, 2006; Hauk, Pulvermüller, Ford, Marslen-Wilson, & Davis, 2009; Miozzo, Pulvermüller, & Hauk, 2015)... I hope this clarifies what I am doing...