[Mne_analysis] Expand Labels

[Geller, Jason]

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Hi! 

I am trying to expand soruce labels to make them a little bigger. Is there an easy way to do this in MNE?

Thanks, 

Jason



> On Apr 10, 2020, at 11:00 AM, mne_analysis-request at nmr.mgh.harvard.edu wrote:
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> Today's Topics:
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>   1. Question about permutation testing (Dirk van Moorselaar)
>   2. Re: Question about permutation testing (Eric Larson)
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> ----------------------------------------------------------------------
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> Message: 1
> Date: Fri, 10 Apr 2020 15:12:05 +0200
> From: Dirk van Moorselaar <dirkvanmoorselaar at gmail.com>
> Subject: [Mne_analysis] Question about permutation testing
> To: mne_analysis at nmr.mgh.harvard.edu
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> Dear MNE users,
> 
> I have a question about using permutation tests to compare decoding accuracy across time between two conditions. Specifically, I am interested to see whether these conditions not only differ during stimulus presentation, but also already in anticipation of a new (search) display. I have a within subject design so I am running the following code  
> 
> t_obs, clusters, cluster_pv, H0 =  mne.stats.permutation_cluster_1samp_test(X_1 - X_2)
> 
> If I then inspect the results, I get significant clusters during stimulus presentation (which is nice. However, there are  no significant anticipatory clusters, even though numerically the conditions clearly differ.
> Interestingly, I am able to make these anticipatory clusters significant if instead of analysing the whole time course, I limit the permutation test to the anticipatory window. I assume that this is the case because the anticipatory cluster is smaller than the reactive cluster????
> 
> My question is whether this is a valid approach, and if not whether there is another alternative way to still show that the conditions already differ before stimulus processing.
> 
> Thanks in advance,
> 
> Dirk van Moorselaar
> 
> 
> 
> 
> 
> 
> ------------------------------
> 
> Message: 2
> Date: Fri, 10 Apr 2020 09:42:23 -0400
> From: Eric Larson <larson.eric.d at gmail.com>
> Subject: Re: [Mne_analysis] Question about permutation testing
> To: Discussion and support forum for the users of MNE Software
> 	<mne_analysis at nmr.mgh.harvard.edu>
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>> 
>> If I then inspect the results, I get significant clusters during stimulus
>> presentation (which is nice. However, there are  no significant
>> anticipatory clusters, even though numerically the conditions clearly
>> differ.
>> Interestingly, I am able to make these anticipatory clusters significant
>> if instead of analysing the whole time course, I limit the permutation test
>> to the anticipatory window. I assume that this is the case because the
>> anticipatory cluster is smaller than the reactive cluster????
>> 
> 
> The `step_down_p` is meant to deal with this case. Basically it runs the
> clustering, and if it finds any clusters below the step_down_p threshold
> (usually 0.05 is a good choice), it cuts those data points out of the
> analysis and redoes the permutation test. This prevents the first cluster
> from dominating the H0 values, increasing sensitivity to smaller but still
> valid effects.
> 
> My question is whether this is a valid approach, and if not whether there
>> is another alternative way to still show that the conditions already differ
>> before stimulus processing.
>> 
> 
> It seems reasonable, but keep in mind that interpreting
> cluster spatio-temporal locations from tests is considered problematic (see
> the bottom of this section in our stats tutorial
> <https://mne.tools/dev/auto_tutorials/discussions/plot_background_statistics.html#clustering>,
> and the linked Fieldtrip tutorial
> <http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test/>).
> A potential workaround is to use TFCE (see the stats tutorial), though it
> will take longer to run.
> <http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test/>
> 
> Eric
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