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

[Dan Kleinman]

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> wrote:
> 
> Hi Dan,
>  
> With a single varialbe, the covariate effect is the R^2 à should give the same results. Can you give me access at that 2nd level analysis please? So I can check what is going on. 
> is LIMO toolbox (v2.0) from GitHub or the eeglab plug-in (might not be the same)
> 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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