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

Dylan Mann-Krzisnik dylan.mann.krzisnik at gmail.com
Wed Mar 18 14:18:41 EDT 2020
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Hello Sarang,

Thank you for this additional explanation. For our experiment, we don’t expect cortical source position or orientation to change so much. Indeed, computing spatial filters for the whole data might be more compelling than re-estimating the same spatial filters for every time-frequency bin.

Much appreciated,

Dylan

> On 18 Mar 2020, at 10:06, Sarang S. Dalal <sarang at cfin.au.dk> wrote:
> 
>         External Email - Use Caution        
> 
> 
> Hi Dylan,
> 
> It's also been on my list to respond to you. :-) To add to Britta's response, my original "5-D" time-frequency tiling strategy created a custom beamformer tuned to each time-frequency patch; but simply creating a single beamformer for the whole time period of each frequency band of interest works quite well in practice. We generally accomplish that with the Hilbert method these days since it retains both amplitude and phase as full time series, allowing related further analyses (e.g. intertrial phase coherence, phase-amplitude coupling, etc.).
> 
> The 5-D style may still be useful if you have especially long trials, or somehow expect your sources to substantially change orientation over time. It'd be difficult to create a continuous time series for phase or amplitude with it, though.
> 
> Hope that clarifies a bit more!
> 
> Best wishes,
> Sarang
> 
> On Tue, 2020-03-17 at 09:04 -0400, Dylan Mann-Krzisnik wrote:
>>         External Email - Use Caution        
>> 
>> Hello Britta,
>> 
>> Thank you for your response :) I had come across some of your documentation related to your Google Summer of Code. I’ll look further into the preprint you’ve provided.
>> 
>> Best regards,
>> 
>> Dylan MK
>> 
>>> On 16 Mar 2020, at 10:24, Britta Westner <britta.wstnr at gmail.com <mailto:britta.wstnr at gmail.com>> wrote:
>>>         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 <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 <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 <mailto: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 <mailto: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 inhttps://mne.tools/stable/generated/mne.beamformer.tf_dics.html#r24787c541d0a-1 <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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