[Eeglablist] Is dipfit ever used for purposes other than localising source space data?

[Scott Makeig]

1) the scalp map 0 is arbitrary ... so a red+green map and a red+blue map
*may* represent the same source projection.

2) a deep equiv. dipole can match the projection of a shallow distributed
source (for example, a source constituted of all the scalp 'source' voxels
with different values/colors! - active follicular source areas!?!).

3) the result with Julie on the most focal source in which HFB co-varies
with emotional valence (good-meh-bad) - the near-bilateral temporal source
pair that showed up in that ICA analysis has appeared again in several
(differing) fMRI experiments.  ICs are spatial filters, spatially filtering
the whole spectrum to emphasize (in the case of a 'dipolar' IC map)
activity in a particular brain source area.  Whether whole-band ICA
decomposition captures as much of the spatial structure of high-freq LFP as
at lower frequencies, I do not know -- but Julie's result shows that it can
well capture significant spatial pattern differences.

On Tue, Mar 15, 2022 at 7:56 PM Makoto Miyakoshi via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Dear Scott,
>
> Thank you for pointing me to the Tanaka et al. (2016).
> I agree that this could be the case of independent subspace. But I do not
> really see how independent subspace explains or relates to deep dipoles.
> Why do you always refer to this example as if an independent subspace is
> supposed to be deep?
>
> Related to this Hiro's nice IC scalp topos, I found it really interesting
> that my simple 4-shell model forward simulation (i.e. scalp projection from
> a cortical dipole) CANNOT make the same scalp topos generated by ICs.
> Please see the 'Projection from UDL' for the case of 1 dipole with various
> depth (2-52mm)
> chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/
>
> https://sccn.ucsd.edu/mediawiki/images/c/cb/SupplementaryFiguresForSimuUDL_BSCR80.pdf
> In order to create a deep 'blue' in the skirt of the scalp topos, the
> dipole actually needs to be deep. But this makes it impossible to make a
> focul 'red'.
> In other words, having a focal 'red' and a deep 'blue' skirt at the same
> time is impossible with a single dipole model.
> Why is that?
>
> Let me take advantage of this opportunity and ask one more question.
> I recently found in your TICS 2004 paper 'Box 2 For future research', you
> asked 'Can ICA model near-DC and high-frequency gamma band dynamics?'
> What's your answer in 2022?
>
> Makoto
>
>
>
> On Wed, Mar 9, 2022 at 1:19 PM Scott Makeig <smakeig at gmail.com> wrote:
>
> > Makoto - Our strategy (Zeynep Akalin Acar, C. Acar & S Makeig,
> > *Neuroimage*, 2016) is to stop SCALE iterations when the relative
> > compactness of the IC effective source distributions and the goodness of
> > fit of the estimated source projection to the IC map are maximized.  As
> the
> > SCALE paper states (p. 171), the goal of the iterative SCALE estimation
> > process is "to best maximize the compactness of the estimated cortical IC
> > source distributions while also maximizing the goodness of fit of their
> > modeled scalp channel projections to the selected IC source maps." Since
> > the orientations of the voxel dipoles are constrained in the model to be
> > perpendicular to the *local* cortical surface (as modeled from the MR
> > head image), simply increasing the size (diameter) of the source areas
> > would not necessarily increase their goodness of fit to the respective IC
> > maps.
> >
> > Makoto - the apparently deep central ICs returned by decomposition of
> > Hirokzazu's data (Tanaka, H., Makoto M., Makeig, S. "Coordinate Systems
> > in the Motor System: Computational Modeling and EEG Experiment." In:
> *Advances
> > in Cognitive Neurodynamics (V).* Springer, Singapore, 85-92, 2016) - for
> > which the issue of apparently deep central IC sources was most evident -
> > almost certainly form a dependent IC subspace - for which the single
> dipole
> > solutions of the individual maps do NOT necessarily represent the
> location
> > of the extended source area.  e.g., say 3 such ISs actually arose as
> > non-independent (dependent) activities in three ~small CORTICAL area (ABC
> > below):
> >
> >      A          B
> >
> >             C
> >
> > then ICA would find a basis (the 3 ICs) for this subspace, each with a
> map
> > that might reflect some function of the activity in all 3 source areas
> (of
> > course, the 3 areas ABC need not be spatially distinct).   To explore
> this
> > possibility, the first thing to do would be to explore the nature of the
> > activity dependence between the IC time courses of the 3 ICs. Then a
> movie
> > of their summed scalp projections might reveal more about the subspace
> > activity properties. Finally, if, e.g., fit a single dipole to each of
> the
> > resulting subspace scalp activity maps (e.g. for 100+ random time
> points).
> > Then represent the resulting dipoles as a dipole distribution in the
> > template head model. This would be revealing only depending on the nature
> > of the time course dependency in the subspace. (If there were an MR head
> > image for the participant, then SCALE/SCS might be more revealing).
> >
> > Whenever I've brought it up, you have declined to explore further.
> Without
> > doing that, it seems risky of you to base a theory on what you imagine
> the
> > implications might be ...   As an old English saying goes, 'Look before
> you
> > leap.'
> >
> > Scott
> >
> >
> > On Wed, Mar 9, 2022 at 2:33 PM Makoto Miyakoshi via eeglablist <
> > eeglablist at sccn.ucsd.edu> wrote:
> >
> >> Dear Scott,
> >>
> >> For record, I disagree with the premise of SCALE. There are multiple
> >> papers
> >> reporting that the size of the EEG sources are not that compact:
> >>
> >> 6 cm^2 (Cooper et al., 1965; Ebersol, 1997; Nunez and Srinivasan; 2006)
> >> 10 cm^2 (Tao et al., 2010)
> >> 8-15 cm2 (Hashiguchi et al., 2007; Cosandier-Rimele et al., 2008)
> >>
> >> Even for the case of MEG, 4 cm^2 (Oishi et al., 2002). The authors of
> the
> >> SCALE paper own responsibility to prove how such a 'small patch
> >> hypothesis'
> >> can generate scalp-measurable potentials in the scale of 20-30 microV
> >> without violating biophysics. If you could do so, I would be convinced.
> >>
> >> Here is some food for thoughts. See the comparison between the small
> patch
> >> vs. large patch comparison.
> >>
> >>
> https://sccn.ucsd.edu/wiki/Makoto%27s_preprocessing_pipeline#Does_a_broad_dipole_layer_produce_a_depth_bias_when_fitted_with_a_single_dipole.3F_.28For_190.2C000_page_views.2C_02.2F22.2F2022_added.29
> >>
> >> In thinking about this issue, however, I noticed that your ICA-based
> >> approach actually mixes the effect of temporal averaging, while these
> >> authors I listed above are apparently discussing the unaveraged scalp
> >> potentials. If SCALE tries to recover the compactness of the sources of
> >> the
> >> 'averaged' (i.e. long-data decomposition) potentials--how does this
> change
> >> the whole interpretation? That is, if you give up the claim 'ICA
> sources =
> >> biological truth' to open possibilities to other interpretations, what
> >> other explanations become possible? However, I think there is little
> >> possibility for this anyway, because even with averaging/long-data
> >> decomposition ICA seems to return a scalp projection from a broad dipole
> >> sheet.
> >>
> >> Makoto
> >>
> >> On Wed, Jan 19, 2022 at 3:28 PM Scott Makeig <smakeig at gmail.com> wrote:
> >>
> >> > Makoto and all -
> >> >
> >> > The SCALE algorithm for building head models including an estimate of
> >> > skull conductivity (the most important unknown in all electrical head
> >> > models)  does NOT optimize for the *size* of the obtained IC source
> >> > distributions. Instead, it optimizes for the *compactness* of the
> >> > estimated source distributions. This is, essentially, a source
> >> distribution
> >> > *shape* parameter (penalizing distributed 'chicken pox'-like,
> >> > physiologically non-plausible source distribution estimates).
> >> >
> >> >    Acar, Z.A., Acar, C.E. and Makeig, S., 2016. Simultaneous head
> tissue
> >> > conductivity
> >> >    and EEG source location estimation. *NeuroImage*, *124*,
> pp.168-180.
> >> >
> >> > It should be of interest to apply this (SCALE-optimized,
> SCS-localized)
> >> > method to the seeming 'deep' sources we have seen in some SCCN EEG
> data
> >> > involving arm movements. What the results of that assessment will be I
> >> > cannot predict in advance.
> >> >
> >> > Certainly, Arno is correct that dipfit estimates single (or
> >> > dual-symmetric) *equivalent dipole* models. A single-equivalent dipole
> >> > model of the scalp map of an independent component process found by
> ICA
> >> > decomposition is a useful pointer to the equivalent of the 'electrical
> >> > center of gravity' of the active patch of cortex from which the source
> >> > activity is projecting.  Michael Scherg clearly explained all this in
> >> his
> >> > 1990 chapter
> >> >
> >> >    Scherg, M., 1990. Fundamentals of dipole source potential analysis.
> >> > *   Auditory evoked magnetic fields and electric potentials. Advances
> in
> >> > audiology*, *6*, pp.40-69.
> >> >
> >> > although its physical usefulness for EEG source localization was first
> >> > pointed out by physicists in the 1960s, as I recall.
> >> >
> >> > As a simple equivalent example. one might define an 'equivalent
> dipole'
> >> > for your current position on earth (using GPS coordinates, say) with
> its
> >> > orientation pointing out from the center of your head through the
> >> bridge of
> >> > your nose.  This could well model your position and (head) orientation
> >> in
> >> > 3D space, though It would *not* capture any information about your
> body
> >> > *size* (height, weight) or shape.
> >> >
> >> > One can attempt to fit a single-equivalent dipole model to any EEG
> scalp
> >> > map, although very few maps are 'dipolar', meaning they match the
> >> > projection of a brain-centered single equivalent dipole model. In the
> >> past,
> >> > EEG researchers attempted to fit scalp maps representing peaks in
> >> > trial-average ERPs using equivalent dipoles. Typically, the fits were
> >> poor
> >> > - this is because the evoking events perturbed potential dynamics in
> >> > several or many parts of cortex in different ways, so the average ERP
> >> peak
> >> > maps represented combinations of source projections - and,
> >> unfortunately,
> >> > fitting multiple equivalent dipoles to such maps is under-determined,
> >> > meaning many different spatial muli-dipole solutions could account for
> >> the
> >> > same maps equally well.
> >> >
> >> > ICA decomposition avoids this problem by finding component processes
> >> with
> >> > fixed scalp projection patterns that account for some portion of the
> >> entire
> >> > scalp data, whose activity time courses are distinct from all the
> >> remaining
> >> > EEG.  As Arno pointed out, many of the brain-based IC source process
> >> > scalp projection maps found by adequate ICA decomposition of
> sufficient
> >> > data are almost perfectly 'dipolar' (i.e., exhibiting a difference
> >> between
> >> > the best-fitting brain single-equivalent dipole model and the
> >> data-driven
> >> > IC scalp map of as low as ~1%). This is compatible with a conclusion
> >> that
> >> > the activity accounted for by the IC arises, entirely or nearso, in
> >> > spatially near-synchronous activity across *some* cortical source
> patch,
> >> > whose spatial *center* and central *orientation* is estimated by the
> >> > equivalent dipole position and orientation. This model is supported by
> >> the
> >> > fact that cortical connectivity is highly weighted toward short-range,
> >> > neighbor-to-neighbor connections, allowing the emergence of areal
> >> synchrony
> >> > or near-sychrony in local field potential.
> >> >
> >> > The SCS (Sparse, Compact, Smooth) algorithm that Zeynep Akalin Acar
> and
> >> I
> >> > use (in the NFT plug-in) for distributed source estimation also takes
> >> into
> >> > account the physical orientations of each cortical surface model
> voxel,
> >> > which strongly limits the physiologically possible shapes and sizes of
> >> the
> >> > source patch estimates -- more so as the electrical current flow head
> >> model
> >> > used in the analysis is more accurate.
> >> >
> >> >    Acar, Z.A. and Makeig, S., 2013. Effects of forward model errors on
> >> > EEG source localization.
> >> > *   Brain topography*, *26*(3), pp.378-396.
> >> >
> >> > Scott
> >> >
> >> > On Mon, Jul 22, 2019 at 7:34 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu
> >
> >> > wrote:
> >> >
> >> >> Dear Arno,
> >> >>
> >> >> > We have never claimed that ICA components localize to point like
> >> regions
> >> >> (the flux of current in a small patch of cortex can be well
> represented
> >> >> with a point like dipole at a slightly deeper location
> >> >>
> >> >> SCCN has been claiming what I call 'small patch hypothesis' for the
> >> >> ICA-resolved cortical effective source. For example, see Delorme et
> al.
> >> >> (2012).
> >> >>
> >> >> ...There are strong biological reasons to believe that under
> favorable
> >> >> circumstances ICA should separate signals arising from local field
> >> >> activities in physically distinct, compact cortical source areas:
> >> First,
> >> >> short-range (<100 micrometer) lateral connections between cortical
> >> neurons
> >> >> are vastly more dense than longer-range connections [3,4]...
> >> >>
> >> >> The extremity of this view is SCALE algorithm by Akalin-Acar and
> Makeig
> >> >> where patch size serves as a cost function to be minimized by
> changing
> >> >> skull conductivity as independent variable.
> >> >>
> >> >> According to Nunez and Srinivasan (2006), cortical patch size should
> >> be >
> >> >> 1
> >> >> inch^2 to be detected at the scalp sensor. See my talk on
> >> >>
> >>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__youtu.be_lRyZxt2WeKk-3Ft-3D1392&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=p0fIQdceEvkFW6h1570IYZADc7CbMsKvsNG6TVCJC6jKaigQRJcKlPOR3GVHkXIP&s=mkfUSsONJje5Ci0Up6iiEsMShvuVE-rSOqIUjHpFE_Y&e=
> >> >> <
> >>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__youtu.be_lRyZxt2WeKk-3Ft-3D1392&d=DwMFaQ&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=xpqx0L--Dny38T0rb0EvFRb-oMVaHSZoDczZM55F6A6zKoZ-zjm5AOQZC3fAMauU&s=TcDXaq170FBkYC4HghbjnoVhFtxRc2g5udZWnVu9g3I&e=
> >> >
> >> >> Also Nunez and Srinivasan (2006) Fig.
> >> >> 1-20.
> >> >>
> >> >> Makoto
> >> >>
> >> >>
> >> >>
> >> >> On Fri, Jul 12, 2019 at 9:12 PM Arnaud Delorme <arno at ucsd.edu>
> wrote:
> >> >>
> >> >> > > Let me tell you dirty secret of dipfit.
> >> >> > > In order for cortical activation to be recorded at the scalp
> level
> >> >> > without
> >> >> > > averaging, the patch size should be > 1 inch^2 (Nunez and
> >> Srinivasan,
> >> >> > > 2006). However, dipole model assumes a spatial point with 6
> >> >> parameters.
> >> >> > > Therefore, estimating dipolar sources of scalp-recorded potential
> >> >> means
> >> >> > to
> >> >> > > model spatially spread cortical patch with a point. This is a
> >> >> fundamental
> >> >> > > violation of the dipole model.
> >> >> >
> >> >> > The dipole model is a mathematical tool that works well to
> >> >> > summarize/compress data (6 values; 3 position; 2 moment; 1
> amplitude)
> >> >> can
> >> >> > often explain scalp topographies with 256 channels with 99%
> accuracy
> >> >> which
> >> >> > indicates that this is a “good” model (6 numbers explaining 256
> >> >> numbers).
> >> >> > We have never claimed that ICA components localize to point like
> >> regions
> >> >> > (the flux of current in a small patch of cortex can be well
> >> represented
> >> >> > with a point like dipole at a slightly deeper location - I believe
> >> it is
> >> >> > mathematically equivalent but would welcome validation from a
> source
> >> >> > localization expert).
> >> >> >
> >> >> > If you do not like dipoles, in Dipfit 3 you can also use eLoreta to
> >> >> > calculate distributed source models associated with ICA components.
> >> >> >
> >> >> > Best wishes,
> >> >> >
> >> >> > Arno
> >> >> >
> >> >> > _______________________________________________
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> to
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> >> >>
> >> >>
> >> >>
> >> >> --
> >> >> Makoto Miyakoshi
> >> >> Assistant Project Scientist, Swartz Center for Computational
> >> Neuroscience
> >> >> Institute for Neural Computation, University of California San Diego
> >> >> _______________________________________________
> >> >> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
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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
> >
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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