[FieldTrip] Question about cluster-based permutation tests on linear mixed models

Elisabeth May elisabethsusanne.may at gmail.com
Tue Sep 27 14:46:55 CEST 2016

Dear FieldTripers,

I have a question about the potential use of cluster-based permutation
tests for results obtained using linear mixed models.

We are working with data from a 10 min EEG experiment on source level with
the aim to quantify the relationship of brain activity in different
frequency bands with continous perceptual ratings across 20 subjects in
different experimental conditions. Thus, we have 10 min time courses of
brain activity and ratings for each voxel for different conditions and want
to test a) if there are significant relationships in the single conditions
and b) if these relationships differ between two conditions. To this end, I
have calculated linear mixed models in R using the lme4 toolbox. For both
the single condition relationships and the condition contrasts, they result
in a single t-value (and a corresponding p-value), which is based on
information on both the single subject and the group level (i.e. we perform
a multi-level analysis). However, with more than 2000 voxels, we have a lot
of t-values and are wondering if there is a way to apply cluster-based
tests to correct for multiple comparisons.

The main problem I see is that I only have one multilevel t-value for the
effect across all subjects, i.e. I don't have single subjects values, which
I could then e.g. randomize between conditions as normally done in
cluster-based permutation tests. (Or rather, I would be able to extract
single subject values but would then loose the advantage of the multi-level

I found an old thread in the mailinglist archive where it was suggested to
flip the signs of the t-statistic for cluster-level correction (
https://mailman.science.ru.nl/pipermail/fieldtrip/2012-July/005375.html). I
understand that, in our case, I would do this randomly for all voxels in
each randomization and then build spatial clusters on the resulting (partly
flipped) t-values. However, I am not sure if that is a valid approach based
on the null hypothesis that there are no significant relations in my single
conditions (a) or no significant relationship differences in my condition
contrasts (b).

For the condition contrasts, I would be able to permute the condition
labels as normally done in cluster-based permutation tests,I think, but
would then have to recalculate the linear mixed models for all voxels in
every permutation. This would result in a very high computational load.

Does anyone have any experience with this kind of analysis? Would the
flipping of t-values be a valid approach (and if yes, is there anything to
keep in mind in particular)? Can you think of other ways to combine linear
mixed models with a multiple comparison correction on the cluster level?

Any help would be greatly appreciated!

Best wishes from Munich,

Elisabeth S. May, PhD
Klinikum rechts der Isar
Technische Universität München
Ismaninger Str. 22
81675 München
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