[FieldTrip] Cluster-based permutation test on single channel in time-domain

[Pia Reinfeld]

Dear FieldTrip Community,

I'm currently working on a project where I'm interested in exploring single channel differences between two conditions using cluster-based permutation tests.  My aim is to only identify significant temporal clusters, without considering spatial clusters across multiple channels. I've configured my analysis as follows:

cfg_stats = [];
cfg_stats.neighbours = [];
cfg_stats.channel = 'ECG';
cfg_stats.latency = time_stat;
cfg_stats.avgovertime = 'no';
cfg_stats.minnbchan = 0;
cfg_stats.parameter = 'avg';
cfg_stats.correctm = 'cluster';
cfg_stats.method = 'montecarlo';
cfg_stats.statistic = 'depsamplesT';
cfg_stats.clusterstatistic = 'maxsum';
cfg_stats.alpha = 0.05;
cfg_stats.correcttail = 'prob';
cfg_stats.clustertail = 0;
cfg_stats.clusteralpha = 0.025;
cfg_stats.numrandomization = 1000;

Nsubj = length(reference);
design = zeros(2, Nsubj*2);
design(1,:) = [1:Nsubj 1:Nsubj];
design(2,:) = [ones(1,Nsubj) ones(1,Nsubj)*2];
cfg.design = design;
cfg.uvar = 1;
cfg.ivar = 2;
cfg_stats.design = design;
cfg_stats.uvar = 1;
cfg_stats.ivar = 2;

stat = ft_timelockstatistics(cfg_stats, reference{:}, target{:});

My question is whether this approach of focusing solely on temporal clusters within a single channel is justifiable and valid?
Additionally, I am curious about the comparability of the clusters found solely in the time domain with those identified through both time and space domain analyses. Does focusing only on the time domain potentially simplify the detection of significant clusters? In other words, are clusters which are only identified in time more prone to being detected due to the reduced complexity of the analysis, or does this approach yield results that are comparable in validity to those obtained through spatial-temporal cluster analyses?

Further, if there are any suggestions for improving this configuration or if there are alternative approaches that I should consider, I would greatly appreciate your input.

Thank you for your time and assistance.

All the best
Pia
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