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

[Scott Makeig]

Justin -

You are right that the choice of inverse source localization approach
typically depends on the information available as well as the goals of the
analysis. If you have T1 images for your study participants, I assume you
also measured the 3D locations of the electrodes?

If so, I suggest you try the NFT and NIST toolboxes by Zeynep Akalin Acar <
zakalinacar at ucsd.edu>, as these build electrical head models, optionally
optimize these models using SCORE by estimating the individual skull
conductance (the largest unknown affecting EEG source localization), and
then perform (optimized) equivalent dipole localization OR high-resolution
distributed source localization using SCS (Sparse-Compact-Smooth)
localization of Cheng Cao.

I believe that extracting all the spatial source information possible from
the individual records must be optimal. Because of individual differences
in cortical folding, group source analysis must have limited spatial
resolution. However, even high-res localization, performed on individuals,
then leaves the problem of finding equivalent sources across individuals
(i.e., source clusters).  The best approach I know of is that of Arthur
Tsai (Tsai et al., 2014) using cortical model co-registration in
Freesurfer, though open source software to accomplish this has not yet been
written, so far as I know.

But in considering different methods, one must first think clearly about
how one defines an EEG 'source' -- that is, ONE EEg source.... What
identifies a source as one (functional) source? Certainly, any source
resolvable from the scalp data must represent coherent field potential
across a large (cm^2+) patch of cortex.  A raw scalp channel represents a
net spatially-weighted difference in local field potentials across more
than one broad cortical territories, as Zeynep's CRF ('cortical receptive
field')  function (to be released soon, I hope) clearly shows.

For example, does the scalp map at a peak in an ERP (or other EEG measure)
represent the projection of "a" (that is, *1*) "source" ?  It does if that
is how you define "source"(!) -- but that definition may have limited
neuroscientific value, as this "source" may actually represent the sum of
several "local source" activities in several cortical areas with different
functional significances (as ICA decomposition, followed by source
localization reveals).

The neuroscientific value of ICA decomposition, I believe, is that it finds
projections of individual (or dual-connected) cortical 'patches'
within/across which local field potential is relatively  coherent (across a
sufficient portion of the decomposed data). These 'source patch'
projections should dominate scalp recordings, as local field activities in
other cortical areas that are spatially *incoherent* will be largely
cancelled out in the scalp channel signals by phase cancellation. If they
do dominate the net scalp signals, then other inverse source estimation
approaches should also locate them at least approximately...

I hope these thoughts may be helpful to you,

Scott Makeig



On Sat, Jan 9, 2021 at 12:46 PM 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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-- 
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