[Eeglablist] sLORETA question

Sat Nov 20 20:43:03 PST 2021

Hi Makoto,
Thank you so much for turning your code into a tool to share! (I also appreciate your blog, btw. I have learnt many useful things there.)

As well as current density comparisons, I've used eLORETA to export time series from ROIs (to analyse further in MATLAB) and to compare average connectivity between conditions (using Connectivity 1, with permutations, and the Viewer to view significant results).

To answer your questions: 
1. I provide eLORETA with epoched data (created using EEGLAB), but it would be great to have a continuous option.
2. I am OK in MATLAB, but if a GUI is available, I use it gratefully (and then copy the commands to MATLAB batch code).
3. I have previously only compared condition averages, not multiple time points. A threshold for significance would be great.
4. Interesting question - eLORETA currently provides t-statistics and effect sizes with the pixel-based permutations, which have been useful. (This was comparing the condition averages, though, not time points.) A couple of concerns on the scalp approach: could there be a significant difference at a voxel in 3D that projects to multiple electrodes (maybe at a distance) and therefore doesn't reach significance at any scalp location, and is therefore missed? Could nonsignificant voxels look significant if they projected to an electrode which also received signals from a significant voxel? Those two concerns would make me lean towards current density stats. 
5. I would really like to extract a time series for voxels around a specified ROI coordinate to analyse further (although maybe this is already available in the dipfit function? I have not explored that yet).

Many thanks,
Neri

-----Original Message-----
From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Makoto Miyakoshi via eeglablist
Sent: Sunday, 21 November 2021 3:27 AM
To: 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.o
> rg_articles_10.3389_fnbeh.2014.00066_full&d=DwIFAg&c=-35OiAkTchMrZOngv
> JPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=Zxef-biWZfrFE9qF
> TeNsdtYkrHGyh-ZQ8zYm6OxqK8wZhat8hxJJrtPPTFIbHWD1&s=aB-mQ2PLolwFcEqW8bs
> sg_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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