[FieldTrip] cluster-based permutation test on time-frequency data

David Groppe david.m.groppe at gmail.com
Thu May 10 16:42:45 CEST 2012

Hi Shen-Mou,
      Cluster-based permutation tests have great power for detecting
broadly distributed effects, but this comes at the cost at being less
sensitive to less broadly distributed effects.  A good alternative to
cluster-based permutation tests is Benjamini & Hochberg's false
discovery rate (FDR) control algorithm.  Like cluster-based
permutation tests FDR control provides weak control of family-wise
error, and we've found that it has relatively good power for broadly
and narrowly distributed effects:

Groppe, D.M., Urbach, T.P., & Kutas, M., Mass univariate analysis of
event-related brain potentials/fields I: A critical tutorial review,
Psychophysiology, 2011, DOI: 10.1111/j.1469-8986.2011.01273.x

I'd recommend using that FDR control procedure over the cluster-based
permutation procedure unless you are only interested in the broadly
distributed effects.

On Thu, May 10, 2012 at 12:31 AM, Shen-Mou Hsu <explena at gmail.com> wrote:
> Dear Users,
> I wonder if anyone could provide any suggestion regarding an issue I
> encountered. According to the article (Maris, 2007), it seems that
> cluster-based permutation test is calculated under the permutation
> distribution of the maximum cluster-level statistics. In my tome-frequency
> data, there are two big clusters. Only one cluster reaches statistical
> significance; however, if I exclude this significant cluster and then rerun
> the test, the other cluster also reaches statistical significance. It seems
> to suggest that the sensitivity is lost for the second cluster. I wondered
> if there is any approach to resolve this issue. Many thanks!
> Best regards,
> Shen-Mou Hsu
> _______________________________________________
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> fieldtrip at donders.ru.nl
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David Groppe, Ph.D.
Postdoctoral Researcher
North Shore LIJ Health System
New Hyde Park, New York

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