Independent channels stats question

[Matthew Davidson]

Jan-Mathis, thanks for the response.

Unfortunately, we tend to have a lot of channels (~120-200), and once
we start using microelectrodes in the patients, it'll only get worse.

If we were to divide our alpha by 120-200, wouldn't we have to run
120-200 times as many permutations in order to get p-values low enough
to survive Bonferroni correction? That's a large jump; we might have
to run 100,000 permutations!

What do you think about something like FDR correction instead?

Matthew

On Tue, Jul 6, 2010 at 7:21 AM, jan-mathijs schoffelen
<jan.schoffelen at donders.ru.nl> wrote:
> Dear Matthew,
>
> Your sensitivity problem is a known issue when using cluster-based test
> statistics, in which it is difficult to get small clusters significant in
> the presence of large clusters. This could also occur within a single
> channel (for example with a time-frequency decomposition, in which the
> summed spectro-temporal extent of an alpha-band effect could be much bigger
> than a gamma-band effect).
> In your case I think it would be statistically valid to do the cluster-based
> permutation test on each channel separately (which will involve a for loop
> around ft_freqstatistics, because it is not implemented in the fieldtrip
> code) and doing a post-hoc Bonferroni correction on the resulting p-values.
> If the number of channels is not too big, this might work.
>
> Good luck,
>
> Jan-Mathijs
>
>
> On Jul 6, 2010, at 3:26 AM, Matthew Davidson wrote:
>
>> Hi, this is Matthew Davidson. I recently took the Fieldtrip EEG/MEG
>> Toolkit (Hi Robert and Jan-Mathis!), and have been diving into using
>> Fieldtrip more directly.
>>
>> My question pertains to cluster-based correction when channels are
>> independent. My data is primarily intracranial EEG, and due to the
>> 1/f^2 power drop-off, electrodes directly on the brain reflect local
>> activity much more strongly than sensors further away. As a result, we
>> treat them as independent. Now, I can force the Fieldtrip clustering
>> algorithm to not cluster across channels by setting:
>>
>> cfg.neighbours = [];
>> cfg.minnbchan = 0;
>>
>> but it still computes the maximum cluster size for a particular
>> permutation based on *all* the data. This seems... less sensitive
>> somehow, as if large clusters in one channel negatively impact the
>> significance of clusters in another channel.
>>
>> Is there a better way to do this and still solve the MCP? E.g.,
>> compute the maxsum on each channel separately, and then use something
>> like FDR or Bonferroni correction on the maxsums across channels?
>>
>> Thanks for any advice you may have, and thanks for producing fieldtrip!
>> Matthew
>>
>> ----------------------------------
>> The aim of this list is to facilitate the discussion between users of the
>> FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG
>> and EEG analysis. See also
>> http://listserv.surfnet.nl/archives/fieldtrip.html and
>> http://www.ru.nl/neuroimaging/fieldtrip.
>>
>
> Dr. J.M. (Jan-Mathijs) Schoffelen
> Donders Institute for Brain, Cognition and Behaviour,
> Centre for Cognitive Neuroimaging,
> Radboud University Nijmegen, The Netherlands
> J.Schoffelen at donders.ru.nl
> Telephone: 0031-24-3668063
>
> ----------------------------------
> The aim of this list is to facilitate the discussion between users of the
> FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG
> and EEG analysis. See also
> http://listserv.surfnet.nl/archives/fieldtrip.html and
> http://www.ru.nl/neuroimaging/fieldtrip.
>

----------------------------------
The aim of this list is to facilitate the discussion between users of the FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis. See also http://listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/neuroimaging/fieldtrip.