[Eeglablist] LIMO toolbox - difference between clusters surviving correction with R^2 vs. F statistics

PERNET Cyril cyril.pernet at ed.ac.uk
Sat Aug 1 21:43:45 PDT 2020


Hi Dan

Yes the new version is almost ready addressing most of your issues - and will patch current version too before releasing.

1st level - do you let LIMO zscore that binary variable? If so then the beta reflects the presence of 2 conditions (-0.5 and 0.5) via a difference (equivalent to a contrast) on which you can indeed 2nd level regress whatever. If it is not zscored, as a binary 0/1 variable only one condition is modelled relative to the biased constant (because that constant now include all variance of the missing condition + the mean across conditions).

Note the single continuous file is all that is needed to encode conditions as well... At import each value is used as a condition.

Best
Cyril


--
Dr Cyril Pernet,
Senior Academic Fellow
Neuroimaging Sciences

Centre for Clinical Brain Sciences
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The University of Edinburgh
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cyril.pernet at ed.ac.uk
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________________________________
From: Dan Kleinman <kleinman at gmail.com>
Sent: 01 August 2020 16:31
To: PERNET Cyril <cyril.pernet at ed.ac.uk>
Cc: EEGLAB List <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] LIMO toolbox - difference between clusters surviving correction with R^2 vs. F statistics

Hi Cyril,

Thanks so much for your quick response.

1) I have uploaded the folder containing the 2nd-level analysis to OSF and will send you a private link to it via email.

2) I *think* I downloaded this version of LIMO (v2.0) from GitHub, but it’s been a while.

3) Since my condition only had 2 factor levels, I coded them as 0 and 1 and then told LIMO to treat it as a continuous variable at the 1st level of analysis. I realize this gives up some functionality as far as contrasts, but I often add other continuous variables at the first level – trial number, for instance – and it’s (very slightly) easier to generate a single continuous variable file for each subject rather than creating both categorical and continuous variable files for each subject.

Thanks,
Dan

—
Daniel Kleinman, Ph.D.
Postdoctoral Fellow
Haskins Laboratories


On Aug 1, 2020, at 3:51 AM, PERNET Cyril <cyril.pernet at ed.ac.uk<mailto:cyril.pernet at ed.ac.uk>> wrote:

Hi Dan,


  1.  With a single varialbe, the covariate effect is the R^2 --> should give the same results. Can you give me access at that 2nd level analysis please? So I can check what is going on.
  2.  is LIMO toolbox (v2.0) from GitHub or the eeglab plug-in (might not be the same)
  3.  I don’t understand your 1st level, if that is conditions, how did you coded this as a continuous variable? (might actually be ok, all is a single linear model anyway – but that’s a separate issue from clustering above)

Thx
Cyril


From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu<mailto:mmiyakoshi at ucsd.edu>>
Sent: 31 July 2020 22:52
To: EEGLAB List <eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>>; PERNET Cyril <cyril.pernet at ed.ac.uk<mailto:cyril.pernet at ed.ac.uk>>
Subject: Re: [Eeglablist] LIMO toolbox - difference between clusters surviving correction with R^2 vs. F statistics

Dear Cyril,

When you have time, could you please take a look for your comment?

Makoto

On Thu, Jul 30, 2020 at 12:42 PM Dan Kleinman <kleinman at gmail.com<mailto:kleinman at gmail.com>> wrote:
Hello All,

I have a question about results obtained using the LIMO toolbox (v2.0). Specifically, I performed an analysis in two different – but, I think, equivalent – ways, and obtained substantially different results depending on how it was conducted. I am wondering if others who have experience using (or programming) the toolbox could please shed light on why the results are different.

At the first level, I coded for (binary) trial condition using a single continuous variable (no categorical variables) for all participants. At the second level, I conducted a Regression analysis to identify spatiotemporal clusters at which a continuous between-subjects variable correlated significantly with the effect of condition. Importantly, *I only entered one between-subjects variable at this stage*.

There are (at least) two ways to view the results:
(1) Show clusters at which r^2 is significant (by selecting R2.mat; “Model fit")
(2) Show clusters at which F is significant (by selecting Covariate_effect_1.mat; “F test for a continuous regressor")

If I do not apply a correction (MC Correction=None), the cluster maps with uncorrected thresholds look identical (as I would expect with only one regressor). However, if I apply a correction (MC Correction=Clustering), method (1) yields a significant cluster but method (2) does not. This pattern holds true across a number of different datasets, in that method (1) often yields a significant result even when (2) does not and only a single regressor is used.

Is this expected behavior? If so, how should I interpret the r^2 cluster results vs. the F cluster results with one regressor?

Many thanks,
Dan Kleinman

—
Daniel Kleinman, Ph.D.
Postdoctoral Fellow
Haskins Laboratories
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