Applying AAL or Desikan-Killiany Parcellation Atlas to Source Reconstructed (stc) Data

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Hi all,

I have recently started using MNE-Python to perform a source-reconstruction of EEG data to the fs-average source-space. I have succeeded in applying the LCMV source-reconstruction however I would now like to apply an AAL or Desikan-Killiany parcellation atlas to my source-reconstructed data (stc structure) as I intend to perform network (connectivity) inference on the data.

I have not managed to find any documentation on this unfortunately and would be grateful for any help and advice on how to go about doing this.

Thanks in advance.

Best wishes,
Boki

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Dear Boki,
Did you solve this? I have the same question. Thanks a lot!

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In the connectivity example script - they are using the Deskian-Killiany parcellation (34 labels per hemisphere and is the default):
https://mne.tools/mne-connectivity/stable/auto_examples/mne_inverse_label_connectivity.html

More info on extracting label time courses:
https://mne.tools/stable/auto_examples/inverse/label_source_activations.html#sphx-glr-auto-examples-inverse-label-source-activations-py

There are a few other parcellations that are available by default. You can use volumetric labels to extract volume time courses, but I don’t think AAL is one of the volume parcels available.

–Jeff

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