[FieldTrip] Single-subject Monte Carlo PLV or WPLI test?

[Matthew Davidson]

Thanks for your reply, Dr. Maris. I'm sure you're very busy, so I
really appreciate your taking time out to answer me.

I'd already tried clustering across only time/frequency and not across
channel, but what I found was that the strongest channels "set the
bar" for all the others, so to speak. I would see 2-3 strong channels
with long significant durations reaching significance, and everything
else would be silenced. Whereas with parametric stats, I had enough
strong signals to detect changes under FDR and Bonferroni correction
across a wide range of times and channels. Would z-scoring to
compensate for electrode sensitivity differences have helped?

I considered doing Monte Carlo stats for each channel independently,
and then adjusting critical p-vals via FDR or Bonferroni, but for 100
channels, I would need at least 20000 permutations just to have the
Monte Carlo p-val resolution *approach* the adjusted Bonferroni
p-vals, and would probably need more to be safe. Factor in several
subjs and contrasts, and I computed my analysis would take a few weeks
to run.

This was why I asked about a parametric method; while I'd prefer
permutation methods, I fear the same problem will occur with my
connectivity analysis. I know your focus is on permutation stats, but
do you have any insight into how to proceed? Think I could generate a
permutation distribution of the WPLI differences from a random
sampling of electrodes and contrasts, and then, if they look
sufficiently close to normal (or transformable via something like a
log transform), use that as an argument for using t-tests if I have
to?

Thanks again,
Matthew


On Fri, Feb 18, 2011 at 4:36 AM, Eric Maris <e.maris at donders.ru.nl> wrote:
> Hi Matthew,
>
>
> Permutation inference is valid for comparing two experimental conditions
> using ANY statistic. If your channels are more or less independent (no
> common pick-up via volume conduction), then don't use the cluster-based
> statistics (at least not for the spatial dimension; clustering along the
> spectral dimension may still be wise).
>
> Best,
>
> Eric Maris
>
>
>
>
>> -----Original Message-----
>> From: fieldtrip-bounces at donders.ru.nl [mailto:fieldtrip-
>> bounces at donders.ru.nl] On Behalf Of Matthew Davidson
>> Sent: vrijdag 18 februari 2011 2:32
>> To: fieldtrip at donders.ru.nl
>> Subject: [FieldTrip] Single-subject Monte Carlo PLV or WPLI test?
>>
>> Hi everyone, I'm looking to see if there's an equivalent to the
>> statfun_indepsamplesZcoh function, but for other connectivity
>> measures, like PLV or WPLI. I need to do several single-subject,
>> between-trials analyses of differences between two conditions. Since
>> my data are intracranial EEG, there's no meaningful group test I could
>> use, which I gather is how many people make inferences on connectivity
>> measures. So, has anyone implemented this, or something like it? Am I
>> missing something obvious in how to do this?
>>
>> If I implement it myself, I guess I should randomly partition the
>> trials, compute the WPLIs of the two groups, take the difference,
>> compute the max cluster size, and build a permutation distribution of
>> the max cluster WPLI difference. Is that generally correct? Should I
>> use jackknife variance to transform them into Z-scores for
>> thresholding?
>>
>> Alternatively, if I wanted to do this parametrically, how should I do
>> that? (I ask because Monte Carlo methods w/clustering haven't worked
>> as well as analytic methods on intracranial data where the electrodes
>> are more independent than in MEG or scalp EEG.) What's the proper
>> reference distribution of differences in these bounded connectivity
>> metrics? Do I just compute the jackknife variance, and then do a mass
>> univariate t-test on the connectivity measures (for a single electrode
>> pair and freq bin)?
>>
>> Thanks for any insight or advice you might have,
>> Matthew Davidson
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