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

[Makoto Miyakoshi]

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
>> >
>> > _______________________________________________
>> > Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>> > To unsubscribe, send an empty email to
>> > eeglablist-unsubscribe at sccn.ucsd.edu
>> > For digest mode, send an email with the subject "set digest mime" to
>> > eeglablist-request at sccn.ucsd.edu
>>
>>
>>
>> --
>> 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
>> To unsubscribe, send an empty email to
>> eeglablist-unsubscribe at sccn.ucsd.edu
>> For digest mode, send an email with the subject "set digest mime" to
>> eeglablist-request at sccn.ucsd.edu
>
>
>
> --
> 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
>