[Eeglablist] sLORETA question

Sat Nov 20 12:32:16 PST 2021

Dear Makoto, Arno and all,

I would like to thank you two for pushing this discussion forward and for providing us with more information. It is good to know that I'm not the only one confused about the differences between eLoreta and sLoreta. My question is, if anyone can answer: Is eLoreta specific for localization of dipoles, or is that a DipFit-specific property? Because up until this conversation started, I had the impression that sLoreta allowed for whole-brain or ROI voxel activation analysis, while eLoreta was optimized for dipole-specific activation analysis. However, I see that I may have been wrong? Any thoughts would be much appreciated!

I am also interested in the EEGLAB wrapper for loreta; I offer my "services" on helping building it if necessary. My current research has epoched data but I will be using EEG (nor ERP) frequency-analysis of ROI-specific voxel activation, comparing between 4 groups and 12 sessions for a final GEE analysis - which is why you can imagine it would be much easier to get it done in EEGLAB/MATLAB instead of manually performing this transformation on sLORETA-key software, to export the data and analyze it elsewhere (i.e. on R or MATLAB).

Thank you two so much for all the info and help!

Cheers,

Ugo

Ugo Bruzadin Nunes, Ph.D. Candidate

Visiting Assistant Professor, Psychology

Webster University

Office Location: ISB room 316

Office Number: (314) 968-7677
Ugo at webster.edu<mailto:UgoBruzadinNunes at webster.edu>

________________________________
From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
Sent: Saturday, November 20, 2021 10:27 AM
To: eeglablist at sccn.ucsd.edu <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] sLORETA question

Dear Matthew, Michael, Ivano, Neri, Vahid, and others,

Thank you for your responses.
As Michael wrote, the eLORETA implementation is taken from Fieldtrip. Arno
developed the first EEGLAB wrapper to use eLORETA function a couple of
years ago, which I stripped and modified several times.
And Arno is right, it is eLORETA (published in 2005) and not sLORETA
(published in 2002). For detail, see this official website

https://urldefense.proofpoint.com/v2/url?u=http-3A__www.uzh.ch_keyinst_eLORETA_index.html&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=xphCaEnijvfiM1Vr6hU_VWnsK3CnX4b04UAWtiKp-WCYSEAWDOZesvN6kPlL7goo&s=dRD6EAVD8Vodcjanki5ZJXDtLrK21tSaP6GTOdU-Qs0&e=

Let me quote the author's description about the difference between eLORETA
and sLORETA.

*...One particular member of this family is sLORETA (standardized low
resolution brain electromagnetic tomography; Pascual-Marqui, Methods Find.
Exp. Clin. Pharmacol. 2002,
24D:5-12;http://www.unizh.ch/keyinst/NewLORETA/sLORETA/sLORETA-Math01.pdf
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.unizh.ch_keyinst_NewLORETA_sLORETA_sLORETA-2DMath01.pdf&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=xphCaEnijvfiM1Vr6hU_VWnsK3CnX4b04UAWtiKp-WCYSEAWDOZesvN6kPlL7goo&s=33s51y3NeCBlXToUvPDvo1kRbLweAyZIIFevrmH93tI&e= >). It is
shown here that sLORETA has no localization bias in the presence of
measurement and biological noise. Another member of this family, denoted as
eLORETA (exact low resolution brain electromagnetic
tomography; Pascual-Marqui 2005:
https://urldefense.proofpoint.com/v2/url?u=http-3A__www.research-2Dprojects.unizh.ch_p6990.htm&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=xphCaEnijvfiM1Vr6hU_VWnsK3CnX4b04UAWtiKp-WCYSEAWDOZesvN6kPlL7goo&s=ERAVRh0Xm1kOHLhCFwaVZPzeSPmfkPLNKBn2bxvJuvU&e=
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.research-2Dprojects.unizh.ch_p6990.htm&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=xphCaEnijvfiM1Vr6hU_VWnsK3CnX4b04UAWtiKp-WCYSEAWDOZesvN6kPlL7goo&s=ERAVRh0Xm1kOHLhCFwaVZPzeSPmfkPLNKBn2bxvJuvU&e= >), is a genuine inverse
solution (not merely a linear imaging method) with exact, zero error
localization in the presence of measurement and structured biological
noise.*

I don't know what he means by 'a genuine inverse solution (not merely a
linear imaging method)' and I can't explain the difference between sLORETA
and eLORETA. If you can explain it in a plain language, please help me
understand it.

I want to know what level of solution you need. Please answer the following
questions.

   1. Do you use continuous data (resting etc) or event-related epoched
   data (any ERP paradigm)?
   2. How comfortable it is for you to use these functions in Matlab
   command line? In other words, do you need GUI?
   3. I ask the users to input parameters for (1) latency to plot and (2)
   color scale limit (optionally, color scheme itself?). What else do you want
   to specify when generating the LORETA image?
   4. It comes with a subtraction function i.e. eLORETA result 1 - eLORETA
   result 2. This works, but the result is without stats. I know how to run a
   stats on this result using permutation test, but it is horribly
   inefficient. It's much easier if you determine statistically significant
   differences on the scalp topos, and generate a corresponding eLORETA
   difference image (a LORETA version of kernel trick) Does it make sense? Do
   you think LORETA stats on current density is still necessary?
   5. Any other general request before I start the finalization?

Please let me know what you want to see. I'd like to wrap it up into a
convenient tool for you.

Makoto

On Thu, Nov 18, 2021 at 8:18 PM Neri Baker via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Hi Arno,
>
> Thanks for this info. I have been using eLORETA, but I was interested in
> whether similar functionality was available in EEGLAB (partly to reduce
> data transfer back and forth between packages, and partly to see the source
> code to understand exactly what the various parameters do).
>
> Thank you for the suggestion of LCMV beamforming - it looks very
> interesting.
>
> Many thanks,
> Neri
>
> -----Original Message-----
> From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Delorme,
> Arnaud via eeglablist
> Sent: Friday, 19 November 2021 12:19 PM
> Cc: eeglablist at sccn.ucsd.edu
> Subject: Re: [Eeglablist] sLORETA question
>
> sLoreta is an obscure Loreta decomposition, which nobody uses except the
> NeuroGuide Neurofeedback software. As far as I know, the only way to use
> sLoreta is to use the NeuroGuide software. It has not been demonstrated to
> be superior (or inferior) to eLoreta (see the seminal article
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.frontiersin.org_articles_10.3389_fnbeh.2014.00066_full&d=DwIFAg&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=Zxef-biWZfrFE9qFTeNsdtYkrHGyh-ZQ8zYm6OxqK8wZhat8hxJJrtPPTFIbHWD1&s=aB-mQ2PLolwFcEqW8bssg_UhLPlQLXH6xXjEJNMt8Do&e=
> ). It seems relatively equivalent in numerical tests. If someone can
> enlighten us as to the difference between sLoreta and other Loreta source
> reconstruction methods, please do so.
>
> Because sLoreta is a rare and not widely accepted form of Loreta, I would
> recommend instead Pascual Marqui's original eLoreta implementation.
>
> Also try LCMV beam forming (also available in DIPFIT) which provides less
> smooth solutions, and is recommended for region of interest connectivity
> analysis by brain connectivity analysis researchers such as Stefan Haufe.
>
> Arno
>
> > On Nov 17, 2021, at 11:15 AM, Neri Baker via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
> >
> > Hi Makoto
> >
> > This sounds really useful. I am also interested in trying it out.
> >
> > Kind regards,
> > Neri
> > ________________________________
> > From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of ivano
> > triggiani via eeglablist <eeglablist at sccn.ucsd.edu>
> > Sent: Thursday, November 18, 2021 7:43:30 AM
> > To: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>; eeglablist at sccn.ucsd.edu
> > <eeglablist at sccn.ucsd.edu>
> > Subject: Re: [Eeglablist] sLORETA question
> >
> > Dear Makoto,
> >
> > I would be interested as well.
> >
> > Thank you,
> >
> > Ivano
> >
> > On Wed, Nov 17, 2021, 3:23 PM Makoto Miyakoshi via eeglablist <
> > eeglablist at sccn.ucsd.edu> wrote:
> >
> >> Dear Matthew,
> >>
> >> Dipfit supports eLORETA but it is more like a proof of concept. I
> >> recommend you check it out first.
> >> I have stripped the function and wrote my own wrapper. If you are
> >> interested, I can upload it online so that you can try it out. It is
> >> not an EEGLAB plugin so does not come with a nice GUI. My code also
> >> allows voxel-level subtraction to show current density differences
> >> between two conditions. Let me know if you want to try it out.
> >>
> >> Makoto
> >>
> >> On Tue, Nov 16, 2021 at 12:28 PM Gunn, Matthew P via eeglablist <
> >> eeglablist at sccn.ucsd.edu> wrote:
> >>
> >>> Hello,
> >>>
> >>> Does anyone know if there are any programs like sLORETA in EEGLAB or
> >>> an APP that MATlab has that could do this process. I know of
> >>> erpsource; but, didn't know if this tool existed or if a team is
> >>> currently working on
> >> one.
> >>>
> >>> Thank you for your time,
> >>>
> >>> Matt G.
> >>> _______________________________________________
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