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

Arjen Stolk a.stolk8 at gmail.com
Tue Oct 20 00:01:20 CEST 2015

```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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