[FieldTrip] Beamforming resting state MEG results in highly correlated time series at sEEG contact locations along the same electrode

[Coyne, Caila Ann]

Dear FieldTrip community,
My name is Caila Coyne and I am a PhD student in Dr. Rachel Smith’s lab at the University of Alabama at Birmingham. Our lab uses dynamical network modelling of sEEG data to improve seizure onset zone localization in epilepsy patients. I am currently trying to apply similar techniques to source localized MEG signals.
Our MEG was recorded on a 148 channel 4D system, and I have a ~10 minute resting state clip per patient (no noise recordings were saved). My current pipeline involves bandpass filtering the MEG data from 2-50 Hz, computing covariance matrices on 500 ms trials, calculating the average covariance matrix over the trials, constructing a 3-layer BEM volume conduction model using OpenMEEG, then using FieldTrip’s scalar LCMV beamformer algorithm to estimate the time series at the coordinates corresponding to the implanted sEEG contact locations. What I expected to see was high correlation between the beamformed time series for virtual sensors that are in close spatial proximity but what I am finding is that time series are highly correlated along electrodes (i.e., along linear trajectories moving from the skull into the brain). I’ve attached a figure of an example of a beamformed trial separated by electrode.
My question is if this is expected behavior for a beamformer algorithm for resting state data or if there is likely something wrong with my pipeline? I have so far tried different filters, trial durations for computing data covariance matrices (500 ms - 7 seconds), volume conduction models (BEM vs single sphere), and multiple patients but these have all primarily impacted signal amplitude rather than the correlation trends we’re seeing.
The figure of the sEEG electrode locations, my code, and the data can be found at the box link:
https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fuab.box.com%2Fs%2Fevymg1x4vg8qhy38pxhq2ydztwp2umuw&data=05%7C02%7Cfieldtrip%40science.ru.nl%7Ce24b144bc3ca45b4029808ddf5667793%7C084578d9400d4a5aa7c7e76ca47af400%7C1%7C0%7C638936542639973929%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=tifP2Y7Vd0l93ukz8QzNmBLWhBSMl%2Bb8bTXfU8Q3z1E%3D&reserved=0
Any help would be greatly appreciated.
All the best,
Caila



Caila Coyne | Graduate Student

Neuroengineering Ph.D. Program

UAB | Neural Signal Processing and Modeling Lab

cacoyne at uab.edu
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