[Mne_analysis] Time-frequency Beamforming - and possible implications for EEG-fMRI

Britta Westner britta.wstnr at gmail.com
Mon Mar 16 10:24:40 EDT 2020
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        External Email - Use Caution        

Hi Dylan,

sorry for my late reply - your e-mail was on my todo list!
I cannot give much recommendation regarding the fMRI parts of your
question, however, I can comment on the 5D-beamformer. I am in the lab of
Sarang Dalal, and we have moved on from this type of beamformer, as it
restricts the samples that you can use for your covariance matrix as well
as the time-resolution of your output (since you compute the covariance
matrix over a short time window).
We are now using the "Hilbert beamformer" for time-frequency resolved data,
where we combine the Hilbert transform with the beamformer computation. You
can find a description of how we do this in this preprint:
https://www.biorxiv.org/content/10.1101/153551v2
I also wrote a blogpost on how to do this in MNE-Python a while ago:
https://brittas-summerofcode.blogspot.com/2017/08/the-hilbert-beamformer-pipeline_29.html
Maybe this could be an alternative?

Hope this helps,
Britta

On Mon, Mar 16, 2020 at 3:09 PM Dylan Mann-Krzisnik <
dylan.mann.krzisnik at gmail.com> wrote:

>         External Email - Use Caution
>
> Dear Dr Marijn van Vliet
>
> I have posted an inquiry on the MNE mailing list regarding source-space
> time-frequency analysis using beamformers. Dr Gramfort suggested I
> contacted you, Britta Westner, or perhaps Sarang Dalal. I would have liked
> to email Dr Westner and yourself jointly, although I could not find Dr
> Westner’s email address.
>
> The concerns I have regard some of the particularities of EEG data
> recorded simultaneously with fMRI (EEG-fMRI). The original post is
> presented below, herein. It is quite a long post; do not hesitate to skip
> over some of the questions. Any clarification would be appreciated.
>
> Kind regards,
>
> Dylan Mann-Krzisnik
>
>
>
> On 5 Mar 2020, at 12:17, Dylan Mann-Krzisnik <
> dylan.mann.krzisnik at gmail.com> wrote:
>
> Dear MNE experts,
>
> Our group is testing different beamforming approaches to EEG data
> simultaneously recorded with fMRI using MNE Python. We'd like to analyze
> the time-frequency EEG source-space correlates of BOLD-fMRI, both at rest
> and following primary sensory tasks. We are trying to figure out which
> beamforming approach would best suite our needs.
>
> There seems to be 3 main candidates of beamformers for our analysis. I'll
> lay out what seems to me to be the potential advantages and nuances of
> these 3 candidate approaches. Apologies for any conceptual mistakes on my
> behalf.
>
>  - LCMV, as suggested for EEG-fMRI in Brookes et al. 2008. The gradient
> and ballistocardiogram (BCG) artifacts induced within EEG data by the EPI
> sequence can be accounted for by proper design of noise covariance
> matrices. Linear time-frequency transforms could be performed in sensor
> space and projected into source space before performing additional
> non-linear transforms (eg Hilbert > source-space projection > compute
> magnitude). Good for blocking out interfering signals.
>
> - DICS, similar to LCMV but in frequency-domain. Moreover, to my
> knowledge, DICS differ from LCMV due to their respective linear
> constraints. DICS à la Gross et al. 2001 imposes a unit-gain constraint for
> scanning location *r*, whereas LCMV further imposes a null-gain
> constraint onto regions other than the scanning region (this is my
> understanding from Sekihara & Nagarajan 2008). Good for reconstructing
> networks of coherent sources.
>
> - 5D time-frequency beamforming à la Dalal et al. 2008, where weights are
> optimized for individual narrowbands in a time-resolved manner. Whereas
> DICS use cross-spectra to optimize the fllter weights, this 5D beamforming
> use time-domain correlations for optimizing the filters, which are then
> later used for frequency-domain analysis. Good for resolving
> frequency-specific time-varying source power.
>
> My impression is that the 5D beamforming approach, as implemented by Dalal
> and colleagues, could be of interest to us in light of our research
> activities. Perhaps this is especially true if we incorporate the EPI
> gradient and BCG artifacts into estimation of noise covariance matrices,
> similarly to Brookes et al 2008.
> The MNE-Python implementation of this method (ie tf_dics) uses DICS for
> every time-frequency window rather than SAM, as noted in
> https://mne.tools/stable/generated/mne.beamformer.tf_dics.html#r24787c541d0a-1.
> This implies that frequency-domain CSD matrices are computed rather than
> time-domain covariance matrices for optimizing spatial filters for each
> time-frequency window.
>
> In light of all of this, here are a few questions:
>
> - Is it reasonable to incorporate gradient and BCG artifacts within
> MNE-Python's tf_dics method?
> - If so, does calculating the CSD matrices rather than covariance matrices
> for these artifacts make a difference?
> - Is the pass-band constraint employed for LCMV desirable for
> time-frequency analysis, if the source-space *network coherence* is not
> of primary interest?
> - Would tf_dics be preferred over time-frequency SAM if source-space
> network coherence is of primary interest? Otherwise, would time-frequency
> SAM be preferred over tf_dics for resolving time-varying narrowband power?
>
> I have additional thoughts and questions, but the email might start being
> a bit heavy. Any clarification, even if partial, is deeply appreciated. I'd
> be glad to provide more explanations if this helps clarify any question
> I've asked or statement I've mentioned.
>
> Kind regards,
>
> Dylan Mann-Krzisnik - M.Sc. Graduate Researcher
> Biosignals and Systems Analysis Lab, McGill University
>
>
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