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

[Fine, Justin Michael]

Makoto:

I appreciate that thoughtful response and ecog/fmri comparison. I think framing the issue as assumptions (e.g., in terms of prior covariance matrices — see wipf 2008) rather than accuracy really captures the essence of the problem, at all levels. 

Cheers!

Justin M. Fine, PhD

> On Jan 13, 2021, at 12:37 AM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
> 
> Dear Justin,
> 
>> But, I think the nature of my question might not have been clear.
> 
> Yes, it was not very clear to me. But I appreciated your enthusiasm.
> 
>> 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).
> 
> If you say you read Electric Field of the Brain (EFB), I hope you did not
> miss this part: ''If one type of data transformation suggests genuine brain
> properties, we should be able to find supplementary computer methods that
> yield similar physiological or cognitive conclusions. Otherwise, we run the
> well-known scientific risk that our conclusions are no more than the
> summation of assumptions made in the process of choosing experimental and
> analysis methods. For example, some physiological interpretations have
> depended on the unwarranted assumption that a few isolated sources generate
> EEG. Another example is the implicit assumption that the brain is a system
> with only a few degrees of freedom when interpreting measures of chaotic
> dynamics." (EBF p.354-355)
> 
> The principle of functional brain map tells us that each brain region is
> specialized to some specific functions. However, to the eyes that saw too
> many nice activation maps in neuroimaging studies, watching a movie of raw
> ECoG signal activation rendered on the cortex is purely mind boggling (was
> it not?) We become surprised to see it because we realize that the speed
> and complexity of the genuine brain dynamics is beyond our imagination.
> 
> Here is an interesting story--My colleague once said to me 'Where are the
> independent components in this ECoG movie?' I liked this question as a
> humble expression of curiosity at that time, but now I like it for another
> reason: This symbolic question, which resonated in my mind for years since
> then, finally brought one clear answer to me--independent components are
> not in the brain.
> 
> Getting back to your discussion, what does it mean to compare equivalent
> current dipole models with distributed source models?
> Let me tell you--it compares their assumptions. It does not compare their
> performance in terms of anatomical accuracy.
> 
> I generally agree to your opinion that using fMRI-based ROI is useful for
> analyzing EEG, despite the cautions raised by the authors of EFB. We may
> want to use as much prior information as possible, it is worth trying. Our
> colleague Lars Kai Hansen once proposed a similar idea, a key hole
> hypothesis (https://urldefense.com/v3/__https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492868/__;!!Mih3wA!VpfjbanSu4M3WT7FQBGr_NEBib4APVhNTRsprLjkJYEItfdFbvxBmluGWbSSvImYaQDABQ$ ) But
> again, we want to be careful when we say 'localize' or 'decompose' the 'EEG
> sources': if we are implicitly or explicitly looking for an isolated,
> stationary, independent generator as an EEG source, we may be wrong from
> the beginning. It is worth remembering that the authors of EFB did not
> choose to localize EEG sources because of radical skepticism on this point.
> The authors actually criticized the popular implicit assumption that EEG
> source are localizable.
> 
> Finally, this is my recent solution--talk only about models, and be always
> ready to explain the difference between the model and the reality. Even if
> your ruler is bent, as long as you can explain to others how bent it is,
> there is a chance that your measurement is still meaningful. If you meet a
> person who speak of EEG's source localization accuracy as if it were some
> kind of neuroimaging modality, carefully ask them about his assumptions on
> source distribution and dynamics.
> 
> Makoto
> 
>> On Mon, Jan 11, 2021 at 7:07 PM Fine, Justin Michael <justfine at iu.edu>
>> wrote:
>> 
>> 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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