Functional connectivity analysis on phase-locked (not time-locked) data

Hello experts,

I have a finger tapping dataset which is phase-locked but not time-locked. I have transferred the data from time domain to frequency domain. Now I would like to do functional connectivity analysis. Could I calculate the whole brain connectivity in the frequency domain? If so, could you please tell me how to do that? If not, can I compute connectivity using the averaged data in time domain?
I dragged the frequency data in Process 1 and tried to compute the connectivity, but the Connectivity button is grey.
Thank you very much!
image

Hello

Now I would like to do functional connectivity analysis.

What measure "connectivity" are you referring to?
If you are interested about coherence or phase locking value, you should not compute a time-frequency decomposition with wavelets before, the frequency analysis is included in the computation of the connectivity measure. For instance, run the coherence 1xN directly on your source files.

can I compute connectivity using the averaged data in time domain?

You should not try to do any frequency, time-frequency or connectivity analysis on averaged signals. Averaging destroys the fine dynamics of the signals you try to explore with these measures.

Could I calculate the whole brain connectivity in the frequency domain?

If you are expecting to compute a 15000 x15000 x Nfrequency connectivity matrix: no, this is too big of a matrix to be handled by most computers. You should narrow your search based on the hypotheses behind your analysis: study the connectivity of one single ROI with the rest of the brain, or a network of a few ROIs you have localized precisely with a preliminary study.

The functional connectivity tutorial is still a work in progress but could already help you a bit:
https://neuroimage.usc.edu/brainstorm/Tutorials/Connectivity

Thank you so much, Francois!

  1. I am interested in the connectivity methods related to the "frequency" (e.g., Bivariate Granger causality (spectral), Amplitude envelop correlation). These methods include Hilbert transform, for example, which may be better for my dataset.
  2. Since averaging destroys the fine dynamics, can I select the data containing each trail ("EEProbe continuous data epochs resampled (37 files)") to do connectivity computation and then hen average the connectivity for all trails?
  3. I plan to use an atlas for computation.
    The functional connectivity is really helpful!
    THANK YOU!!

Yes, this is what you are supposed to do.
Most connectivity processes would already give you an option to either concatenate the trials or average the results on the fly. You probably won't have to do this averaging manually after.