[FieldTrip] One-sample t-test with cluster-based permutation test

Eelke Spaak e.spaak at donders.ru.nl
Fri Oct 5 10:07:18 CEST 2018

Dear SG,

Rather than go into the whole one-sample T test business again, I will
respond to one aspect of your question which I think might be useful.

> but the result was quite different from the two-sample t-test comparing baseline vs activation trials.

I don't know exactly how you were doing the baseline vs activation
test, but I'll note two things here. First, you will typically not
want to do a *two-sample* test, but a *paired-sample* test; i.e. each
unit of observation (trial or subject) has both a baseline and an
activation period, and it's the paired difference that matters.

Second, you will want to test the activation period against the *mean*
across the entire baseline period (since we typically assume a
stationary baseline). If you were to simply do (1) select baseline
window as condition A; (2) select activation window as condition B;
(3) compare the two using cluster stats; then the statistic would be
comparing each time point for the activation period against the
matching time point in the baseline.

So basically, one way of doing activation versus baseline cluster
stats is to average the baseline window across time (probably repmat()
the mean over time again) and then use paired statistics against the
activation window. This should work at first or second level.

Hope that helps!


> Is there really no way to use cluster-based permutation test for one-sample T-test at the second level?
> Best,
> --
> Seung-Goo ("SG") Kim, PhD
> Postdoctoral Research Associate
> O-Lab, Department of Psychology & Neuroscience, Duke University
> Postal: 308 Research Drive, Durham, NC 27708, USA
> Email: solleo at gmail.com
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> https://doi.org/10.1371/journal.pcbi.1002202

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