[Eeglablist] [External] Re: Source analysis trade-offs: ICA + ECD versus distributed methods

[Fine, Justin Michael]

To Makoto (and eeglab peeps):

I appreciate the thoughtful response alongside Scott’s.  But, I think the nature of my question might not have been clear.   

Just to build background: I have read Nuñez, cohen’s book, Steve luck’s, the whole gamut. My question wasn’t about the indeterminacy of inverting a forward model.  That issue is clear.  For example, I regularly build custom neurostimulation optimization routines.  So the issues of solving for the inverse was not the question neither the issues of field cancellation of dipoles.  I’m saying this to clarify what the question is not. There’s a lot of material on this, what is lacking imho is an earnest comparison of different source methods in terms of utility, interpretational capacity, ground truth comparisons across all types of methods (e.g., a phantom study).  

But first, you state well why a priori sources
Matter. Presumably the confidence in solving an inverse problem is always bolstered by putting more functional (e.g., functional localizer) and physiological constraints on a problem.  Leading into my question, but first, given the advent of available meta-analysis from fmri (e.g., neurosynth), it would seem a good idea for eeg source analysis should be to always make a meta-analysis map of a priori regions (for a given task or concept, e.g., response inhibition) from fMRi data.  This places a strong constraint on spatial locations of, say, ECDs that would dominate variance in scalp maps. What to make of this idea?

My main question was more practical in nature, given a certain level of resources (ranging from a standard study with only scalp eeg to one with T1s and recorded electrode positions, and an fmri prior map of activations) what source methods are preferable? Specifically, when the goal is to a priori examine how certain anticipated sources, such as rifg  (confirmed in fmri with 100s of studies), which method offers a good trade off: at simplest ICA + field trip style dipfit, to Bayesian constrained dipole analysis,  to distributed methods like eLORETA or nonlinear ones like MSP?  What are the tradeoffs in interpretation? Why prefer individual bem/fem meshes versus canonical meshes (a la spm).  For example, I think only 3-5 sources really drive the dominant activity during the time window of interest in my data.  Thus, it seems like Bayesian ECD is best because it allows directly testing this in a statistical model framework (see VB-ECD in spm). 

So, which method should someone consider first given these types of resources at hand and question?

A final point/question  I would like to bring up about model constraints: it seems the strongest also be model-based Temporal constraints. I have dynamic causal modeling in mind here. Thoughts on this, regardless of connectivity, but really about neural mass like models as a generator of predicted eeg sources as a means to decompose scalp activity.

I look forward to discussing this more. 
Cheers,
Justin Fine


Justin M. Fine, PhD

> On Jan 11, 2021, at 2:46 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
> 
> This message was sent from a non-IU address. Please exercise caution when clicking links or opening attachments from external sources.
> -------
> 
> Dear Justin,
> 
>> why would someone prefer finding a priori known sources in EEG with a
> given source methods?
> 
> It is because the inverse problem of the head volume conductor does not
> have a unique solution. Because of this fundamental limitation, there can
> be multiple reasonable assumptions about the spatial distribution of
> 'source of EEG' to reach the unique solution (to a reasonable degree, given
> the assumption).
> 
> In the 'Electric Field of the Brain' (2006) by Nunez and Srinivasan, they
> advocate an alternative approach in which you do get a unique solution in
> estimating EEG source distribution at the cost of resolution in sulci. In
> other words, you accept the limitation that you will only analyze
> continuous gyri with no sulci and you get an unique solution. The
> justification for this trade-off is that apparently potentials cancel each
> between the two cortices facing to each other at a sulcus. You can find
> quantitative evaluation on this in their book.
> 
> In sum, what you can give up determines what you can say for sure. But the
> critical difference concerns the qualitative differences, such as whether
> the solution is unique or non-unique. For non-unique solutions, you may be
> able to take advantage of electrophysiological facts by incorporating
> sparticy and/or smoothness of the 'EEG sources' (such as
> sparse-compact-smooth constraint by Chen used in Zeynep's SCALE, not SCORE)
> as well as dynamics (such as Stefan Haufe's method that can be used for
> SIFT which Tim Mullen always talk highly of) to 'improve' the result, or
> better to say, 'inform' the result. Hence the goodness of the result
> depends on the goodness of the assumptions.
> 
> I'm not super knowledgeable in this topic but do have a strong interest to
> learn. I'd be happy to discuss this topic with you.
> 
> Makoto
> 
> 
> 
>> On Sat, Jan 9, 2021 at 9:46 AM Fine, Justin Michael <justfine at iu.edu> wrote:
>> 
>> Dear list:
>> 
>> I have a question that the literature does not really seem to answer
>> regarding source analysis: why would someone prefer findings a priori known
>> sources in EEG with a given source methods? Specifically, I am asking about
>> the benefits and obvious trade-offs if (1) ICA + ECD ,(2) distributed (and
>> sparse or group hierarchical methods) source methods (e.g., MSP in SPM) or
>> (3) a Bayesian ECD approach which does not rely on fitting separate IC
>> components but relies on specifying a prior source locations?
>> 
>> Quick background, I have T1s and recorded electrode positions (64 channel
>> acticap) for all participants.  The main goal here is extracting
>> time-frequency and evoked (ERP) from an rIFG, pre-SMA/ACC/MCC, and left M1
>> source.The study was a standard stop signal task, of which the literature
>> tends to prefer the method (1) of ICA + ECD. But I gather that might have
>> something to do with researchers typically (1) not having electrode
>> locations and (2) T1s?
>> 
>> Any thoughts and feedback would be greatly appreciated.
>> 
>> Thanks!
>> Justin Fine
>> Post-doctoral researcher
>> Indiana University
>> -----Original Message-----
>> From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Scott
>> Makeig
>> Sent: Friday, January 8, 2021 2:32 PM
>> To: JULIANA CORLIER <corlier at g.ucla.edu>; Johanna Wagner <
>> joa.wagn at gmail.com>
>> Cc: eeglablist at sccn.ucsd.edu
>> Subject: [External] Re: [Eeglablist] Analysis of TMS-induced harmonics in
>> the EEG
>> 
>> This message was sent from a non-IU address. Please exercise caution when
>> clicking links or opening attachments from external sources.
>> -------
>> 
>> Juliana -
>> 
>> These will also soon be a new toolbox, the 'Independent Modulator Analysis
>> Toolbox' (IMAT), that can separate harmonic from non-harmonic activity. If
>> you might like to test its use, write Johanna Wagner <joa.wagn at gmail.com>.
>> 
>> Scott Makeig
>> 
>>> On Thu, Jan 7, 2021 at 3:20 PM JULIANA CORLIER <corlier at g.ucla.edu> wrote:
>>> 
>>> Dear list,
>>> 
>>> I would like to get some expert advice on how to assess/quantify the
>>> presence of harmonics in the EEG.
>>> Notably, our lab is using EEG recordings during repetitive
>>> transcranial magnetic stimulation (TMS) and we would like to assess
>>> whether the stimulation at a certain frequency elicits an entrainment
>>> at the stimulation frequency but also at other frequencies.
>>> 
>>> My first approach was to check for ‘ratios of frequencies’ that show
>>> activation in the time-frequecy domain post stimulation, but that
>>> turned out to be a more tricky than I have aniticipated.
>>> I was wondering if there is a proper approach to harmonics analysis?
>>> One would think that engineers and signal processing experts outside
>>> of neurosciences deal all the time with that.
>>> 
>>> Any advice is much appreciated!
>>> 
>>> Thank you!
>>> 
>>> Juliana Corlier
>>> 
>>> 
>>> 
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>> 
>> 
>> 
>> --
>> Scott Makeig, Research Scientist and Director, Swartz Center for
>> Computational Neuroscience, Institute for Neural Computation, University of
>> California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott
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