[FieldTrip] Question regarding the meaning of cluster statistic

Inbal Shapira inbalots at gmail.com
Sun Mar 2 08:37:51 CET 2014

Hi all.

My question is: to know if I understand correctly the non parametric
process using permutations, and to understand the process for the
clustering part in it.

I wanted to make sure I understand correctly the meaning of the results I'm
getting when using the non parametric approach* for multiple subjects* and
the cluster statistics. i.e. I compare an 'Active' and 'Control' data sets
that each subject has (for example, for 8 subject I have 16 averages over

As far as I understand:
Each subject has a subject-specific-average that is calculated for each
condition (i.e. one vector (1X248 channels) for each condition that
contains the average over selected time, freq and trials).
Then, randomly permute the subject-specific averages of the two conditions,
independently for every subject (i.e. in each permutation there will be one
average selected from each subject) and calculate the statistic results for
each channel.
We repeat this step many times as to construct a "permutation distribution"
from the statistics value from each permutation.
finally, we look at the real "active vs control" statistics to see where
their value lies in this permutation distribution, and that is the value we
will save for that channel in the XXX.stat field. (we have 248 values and
permutation distributions, one for each channel).

When we want the "clusters" - I am not totally understand the process here.
In the paper by Maris and  Oostenveld , 2007 they talk about cluster in the
following manner:
A Cluster based permutation test, is a test in which for each partition we
cluster an *adjacent time samples* that exhibit a similar difference (in
sign and magnitude). This is done by selecting all samples whose t-values
is larger than some threshold, cluster the selected sample in connected set *on
the basis of temporal adjacency* and calculate cluster level statistics by
taking the sum of t-values within a cluster. Finally, for each partition we
take the largest cluster statistics as a value for the permutation
distribution. This distribution will serve us for testing the observed
largest cluster statistics as was explained earlier.

When I use *cfg.correctm = 'cluster'; *Do we look for adjacent channels
that are significant? so the clustering process is a spatial one (and not

Now for what I did:
I applied the statistic test using the following definitions and [stat] =

sublist = {'2','3','8','11','15','17','18','19'};% 8 subjects...
    cfg = [];
    cfg.channel          = {'MEG'};
   cfg.latency  = [1.8 1.95];
*    cfg.method           = 'montecarlo';*
    cfg.frequency        =  'all';

*    cfg.statistic = 'depsamplesT';    cfg.correctm         = 'cluster';*
    cfg.clusteralpha     = 0.05;
    cfg.clusterstatistic = 'maxsum';
    cfg.minnbchan        = 2;
    cfg.tail             = 0;
    cfg.clustertail      = 0;
    cfg.alpha            = 0.025;
    cfg.numrandomization = 500;
    cfg_neighb.method    = 'distance';
    cfg.neighbours       =

    Nsub = length(sublist);
    cfg.design(1,1:2*Nsub) = [ones(1,Nsub) 2*ones(1,Nsub)];% condition
    cfg.design(2,1:2*Nsub) = [1:Nsub 1:Nsub];% subject number
    cfg.ivar     = 1;
    cfg.uvar     = 2;
    % cfg.avgovertime = 'yes';
    cfg.avgoverfreq = 'yes';

    [stat] = ft_freqstatistics(cfg, alphaAvgSub2note3, alphaAvgSub3note3,
alphaAvgSub8note3, alphaAvgSub11note3,...
        alphaAvgSub15note3, alphaAvgSub17note3, alphaAvgSub18note3,
        alphaAvgSub2note2,  alphaAvgSub3note2,  alphaAvgSub8note2, ...
        alphaAvgSub11note2, alphaAvgSub15note2,  alphaAvgSub17note2,
alphaAvgSub18note2,  alphaAvgSub19note2);

I then plot the results using ft_clusterplot(...)

Thanks a lot

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