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

Dylan Mann-Krzisnik dylan.mann.krzisnik at gmail.com
Sat Mar 14 13:54:48 EDT 2020
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Hello Alexandre,

Thank you for your response. I’ll try contacting the people you’ve mentioned below.

Kind regards,

Dylan MK

> On 14 Mar 2020, at 12:36, Alexandre Gramfort <alexandre.gramfort at inria.fr> wrote:
> 
>        External Email - Use Caution        
> 
> Dear Dylan,
> 
> Sorry for the late reply
> 
> I don't have any opinion myself but maybe if you contact our beamformer
> maintainers directly it might help (Marijn Britta or maybe Sarang)
> 
> Alex
> 
> 
> On Thu, Mar 5, 2020 at 6:19 PM Dylan Mann-Krzisnik
> <dylan.mann.krzisnik at gmail.com> wrote:
>> 
>>        External Email - Use Caution
>> 
>> 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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