[FieldTrip] calculating behavioural-power correlation -- follow-up questions

[Arjen Stolk]

Hi Xiaoming,

Thanks for clarifying. Could you perhaps provide an (replicable) example on
how 'the permutation method interacts with the correlations'?

Yours,
Arjen

2015-10-19 15:20 GMT-07:00 Xiaoming Du <XDu at mprc.umaryland.edu>:

> Hi Arjen,
>
> Sorry for the confusion.
>
> The stat.rho matrix is similar to the one I calculated using corr.
>
> My questions is about the permutation method and the correlation matrix
> after each permutation for calculating cluster distribution. The
> permutation method seems interacting with correlation (but not with t
> tests).
>
> Looking forward to your suggestions.
>
> Xiaoming
>
> >>> Arjen Stolk <a.stolk8 at gmail.com> 10/19/2015 6:01 PM >>>
> Hey Xiaoming,
>
> Not sure if I understand, but shouldn't the directions of the correlations
> be independent of the scaling of the two variables? Looking at the code of
> ft_statfun_correlationT it doesn't seem the conversion from correlation to
> T value (tstat = rho*(sqrt(max(nunits)-2))/sqrt((1-rho^2))) would result in
> a direction change either. Perhaps you could try to first manually
> calculate a correlation between signal power and behavioral power, and see
> whether anything is behaving unexpectedly?
>
> Yours,
> Arjen
>
> 2015-10-19 14:25 GMT-07:00 Xiaoming Du <XDu at mprc.umaryland.edu>:
>
>> Dear FieldTrip users,
>> This is Xiaoming from University of Maryland Baltimore. My current
>> project requires to calculate behavioral-power correlation across subjects.
>> Similar topic was discussed here early this year.
>> http://mailman.science.ru.nl/pipermail/fieldtrip/2015-February/008953.html
>> According to the suggestions in above mentioned thread, I duplicate my
>> power dataset and replace the power values at each time-frequency point
>> with behavioral data. Therefore, those two datasets have same structure and
>> dimension. I used the following script to test if there are significant
>> clusters of correlations.
>> cfg = [];
>> cfg.parameter = 'powspctrm';
>> cfg.method = 'montecarlo';
>> cfg.statistic = 'ft_statfun_correlationT';
>> ...
>> etc
>> ...
>> design = zeros(2, n1 * 2); % n1 is the number of subjects.
>> design(1,1:n1) = 1;
>> design(1,(n1 + 1):(n1 * 2)) = 2;
>> design(2, :) = [[1:n1 ] [1 : n1]];
>> cfg.design = design;
>>
>> cfg.ivar = 1;
>> cfg.uvar = 2;
>> stat = ft_freqstatistics(cfg, dataBeh{:}, dataDX1{:});
>> However, it seems when each time the design matrix is permuted, FieldTrip
>> is using the same method as for 'ft_statfun_depsamplesT', meaning cfg.uvar
>> remains the same while cfg.ivar (1 or 2) is randomly assigned to each
>> subject in design matrix. Although I confirmed this by uncommenting line
>> 313 (i.e., tmpdesign = design(:,resample(i,:))) in
>> ft_statistics_montecarlo.m which allows to display the permuted design
>> matrix in command line, please correct me if this is not the case.
>> In my mind, this kind of permutation will cause trouble when dealing with
>> correlation. For example, in my case, the behavioral data and power data
>> have different scales. The power data are much larger than behavioral data
>> in general. When assigning behavioral data into power group or vice versa,
>> it will induce huge negative correlations between power and behavioral
>> measurement. Therefore, no negative clusters will survive from permutation
>> test.
>> Please let me know if I have mis-understanding or if I did anything
>> wrong. Any suggestions will be highly appreciated!
>> Thanks.
>> Xiaoming
>>
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>
>
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