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

[Xiaoming Du]

Dear Arjen,
 
Thanks for the updates. 
 
I was trying to run the following code on Fieldtrip website (http://www.fieldtriptoolbox.org/faq/how_can_i_test_for_correlations_between_neuronal_data_and_quantitative_stimulus_and_behavioural_variables)
 
% compute statistics with correlationT
cfg = [];
cfg.statistic	    = 'ft_statfun_correlationT';
cfg.method		   = 'montecarlo';
cfg.numrandomization = 1000;

n1 = 3;    % n1 is the number of subjects
design(1,1:n1)	   = [0.6 0.9 0.1]; %here we insert our independent variable (behavioral data) in the cfg.design matrix, in this case reaction times of 3 subjects.

cfg.design		   = design;
cfg.ivar			 = 1; 

stat = ft_freqstatistics(cfg, data_brain{:});
  However, it gives me this error: 
Error using ft_statfun_correlationT (line 81)
uvar must be specified for dependent samples statistics
 
Error in ft_statistics_montecarlo (line 276)
  [statobs, cfg] = statfun(cfg, dat, design);
 
Error in ft_freqstatistics (line 190)
    [stat, cfg] = statmethod(cfg, dat, design);
 
 
Could you provide a sample data_brain, so I can organize my data into same format? Thanks!
 
 
-Xiaoming
 
 


>>> Arjen Stolk <a.stolk8 at gmail.com> 11/7/2015 11:21 PM >>>
Dear participants in the discussion on behavioural-power correlation, and interested folks,

Following recent discussion on this mailing list (thanks to Xiaoming Du and Martin Krebber), we have updated ft_statfun_correlationT, a function that can be used for correlating neural and behavioral variables. 

Following the update, the correlation values calculated on genuine data have not changed. However, the permutation procedure for calculating the randomization distribution has. Namely, prior to the update the permutation procedure would randomly permute across both the independent (e.g., behavior) and dependent variables (e.g., neural data). This procedure is prone to systematic bias across the data belonging to these variables. And conceptually, as outlined in a new wiki page (see below), the independent and dependent variables should be statistically independent, meaning that any association between these variables should be broken by randomly permuting the values of the independent variable.

http://www.fieldtriptoolbox.org/faq/how_can_i_test_for_correlations_between_neuronal_data_and_quantitative_stimulus_and_behavioural_variables?

Those that have been using ft_statfun_correlationT for calculating a randomization distribution using the permutation procedure are advised to update to the latest fieldtrip version and re-calculate those distributions. We are sorry for any inconvenience this may cause. 

On a related note, the functionality of ft_statfun_correlationT (under Pearson) is highly similar to that of ft_statfun_indepsamplesregrT. To make the latter function, and others in the statfun suite, more accessible, we would like to forward those interested to the above wiki page where an overview is provided of the different approaches to correlating neural and behavioral variables, with some example fieldtrip code.

Yours, Arjen
on behalf of Eric Maris and Egbert Hartstra

2015-10-22 2:53 GMT-07:00 Maris, E.G.G. (Eric) <e.maris at donders.ru.nl>:


Dear participants in the discussion on behavioural-power correlation,

My name is Eric Maris and have contributed most of the older statfuns (but not ft_statfun_correlationT). Together with Arjen, I will try to resolve some of the issues that have been discussed. Give us some time, and we will return to you via the Discussion List.

best,
Eric


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