# [FieldTrip] Question about Dr Eric Maris et al. paper

Roemer van der Meij r.vandermeij at donders.ru.nl
Tue Jan 25 17:40:02 CET 2011

```Hi Shogo,

Haven't earned that title yet ;). My apologies for my late reply.
> In your 2nd step, I should compute cluster-level-statistics from
> random permutation data.
> Here, I have an question.
> When I define clusters from random permutation data, which should I
> define where clusters are the same place (e.g. time, sensor and so on)
> as "observed clasters" or should I define newly where clusters are
> from random permutation data regardless of the places of "observed
> clasters"?
The idea is that for every random permutation of data, you calculate
your statistics and cluster over them. Then, select the largest of
these, whether it occurs at different time-points than your observed
clusters or not. The null-hypothesis that your are trying to refute is
not of the form "this cluster is significant is bigger than zero", but
of the form "the data is interchangeable between conditions". Therefore,
you gather 'largest-clusters' from 1000 random combinations of data. If
your biggest cluster is bigger than 95% of the biggest clusters (alpha =
0.05, single-sided test), than the data is /not /interchangeable, and
thus significantly different between conditions. This is exemplified by
/all /clusters that surpass the test based on the /same
/permutation-distribution of 'biggest-clusters'. Your data is different
between conditions, and all peaks in the mountain-range are representing
this (if they surpass the cluster-level test) (my favorite analogy).

> I think the latter is right, this is OK?
Yes.
>
> Second, If I have interests in the cluster that has the second or
> third... non first largest cluster-level-statistics from the
> experimental hypothesis, how should I test these clusters?
All clusters are tested against the permutation-distribution of
'biggest-clusters'. Any of the smaller observed-clusters that are still
bigger than 95% of your distribution-of-biggest-clusters (alpha = 0.05,
single-sided), are part of /*all* /the clusters that /*together *show
that the data differs between conditions/.

Best,
Roemer

--
Roemer van der Meij M.Sc.
PhD student
Donders Institute for Brain, Cognition and Behaviour
Centre for Cognition
P.O. Box 9104
6500 HE Nijmegen
The Netherlands
Tel: +31(0)24 3655932
E-mail: r.vandermeij at donders.ru.nl
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