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

[PERNET Cyril]

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>
Sent: 31 July 2020 22:52
To: EEGLAB List <eeglablist at sccn.ucsd.edu>; PERNET Cyril <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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