How to Inspect and Visualize Significant Clusters on Anatomical Maps from Paired t-Stats on Source PSDs (freq data)

Greetings everybody,

We have computed the subject wise single trial PSDs on source level for each of the two conditions (space and time) of study. Then we ran the paired t-test on average source PSDs of 17 subjects for the group analysis across two conditions. To visualize the results and significant values we exported the paired t-Test results to Matlab which resulted in the following attached image 1. Here we observed several significant scouts at different frequencies. It is possible to see the individual scouts on anatomical maps.
But our question is, how can we inspect and visualize the significant clusters of scouts at different frequencies on anatomical maps by projecting the sources to default anatomy, in the group analysis?

Any help would be really appreciated. Thank you!

Best regards,
Abrar

For reference, MATLAB script and paired t-test images are attached, and the pipeline of Source Estimation and Stats in source space includes the following steps;

  1. Data and noise covariance.
  2. Compute head model (OpenMEEG BEM).
  3. Compute sources (Single trials, LCMV Beamformer, PNAI: MEG GRAD, Full, constrained, Destrieux atlas-148 scouts).
  4. Compute single trial source PSDs across two conditions.
  5. Compute subject average source PSD across two conditions.
  6. Permute paired t-stats on subjects average source PSDs (with average over time).
  7. Visualize results of t-maps and p-maps in Matlab by exporting the resultant file of t-test.

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If you were doing this for a single frequency, you could maybe find a way to use the process "Simulate > Full source maps from scouts". But this is designed only to produce a "source map" (="result" file type) from a list of values or signals (="matrix" file type).
https://neuroimage.usc.edu/brainstorm/Tutorials/Simulations#Generate_full_source_maps_from_scouts

If you want all the frequency bins in the same files, you probably need to do this manually.
Start by computing a file that looks like what you want (eg. a PSD source on full source files, without selecting scouts in the input options), then replace the values with your own values. You'd basically need to copy each scout value into all the vertices associated with this scout.

To get the list of vertices for each scout, you need to look into the cortex surface:

Thank you for your answer. The 2nd option is more liable as we are interested in different frequency bands. I will have a look into the process but it is not clear how this will create the clusters across scouts based on neighboring data points? Let's say a scout no.5 at 9 Hz might be a neighbor of scout no. 123 at 9 Hz.