[FieldTrip] Correction for MCP using ft_statfun_correlationT

[Pia Reinfeld]

Dear community,

I am currently working on statistically testing the correlation between EEG data (dependent variable) and quantitative data (independent variable).

I'm using the ft_statfun_correlationT function to correlate and test the quantitative independent variable (e.g. reaction time), which is one value per subject, with the subject's averaged ERP data (31channelx500timepoints) (~100 subjects). I used this configuration from the tutorial and it worked well:

% compute statistics with correlationT
cfg = [];
cfg.statistic        = 'ft_statfun_correlationT';
cfg.method           = 'montecarlo';
cfg.numrandomization = 1000;

n1 = 99;
design(1,1:n1)       = qualitative_variable; % e.g. [0.25, 0.22, ...]
cfg.design           = design;
cfg.ivar             = 1;

stat = ft_freqstatistics(cfg, data_brain{:});

I saw that the t-values are calculated by using this:
tstat = rho*(sqrt(nrepl-2))./sqrt((1-rho.^2))

I'm afraid I don't know much about statistics. But wouldn't it be appropriate to correct for the multiple comparison problem by using a cluster-based approach?
So I tried to add this to my configuration:

cfg.alpha = 0.025;
cfg.correcttail = 'alpha';
cfg.correctm = 'cluster';
cfg.clustertail = 0;
cfg.clusteralpha = 0.05;
cfg.clusterstatistic = 'maxsum';
cfg.neighbours = neighbours;

I get the error:
Error using ft_statistics_montecarlo could not determine the parametric critical value for clustering
Error in ft_timelockstatistics (line 185)
[stat, cfg] = statmethod(cfg, dat, design);

Is there already a correction for MCP and am I thinking wrongly by using a cluster based approach?
How is the cfg.numrandomization implemented in this statistic? I may have missed something, but it didn't look like random permutation was being used at all.

Any help would be highly appreciated.

Thank you very much and all the best,
Pia
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