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Ciao Andrea,
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<div class="">I have never thought about this in much detail, but naively I would say that what you sketch is not a good way to perform statistics. </div>
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<div class="">Using an inferential statistical test with permutations and clustering for FWER correction, the clusters are just a byproduct of the inferential logic. Strictly, the only thing that can be derived from the test is a binary decision: ‘either or
not reject the null hypothesis of exchangeability’. Any downstream stuff done with the clusters (i.e. strong interpretation of effects based on their location, or a second level test based on masked data) is in my view not allowed. See <a href="http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test" class="">http://www.fieldtriptoolbox.org/faq/how_not_to_interpret_results_from_a_cluster-based_permutation_test</a> for
more information (also check out the references at the bottom of that page).</div>
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<div class="">What you could do, however, is to think into the direction of using prevalence statistics to do your inference (to get started you could check Allenfeld et al. 2016, Neuroimage, their code is on github). In this approach, first level permutations
are used to estimate a distribution of a test statistic under some null hypothesis, and group inference is done using this prevalence framework.</div>
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<div class="">Perhaps Bruno and Robin might want to chime in here (preferably in Inglese), rumour has it that they have contacted you offline about this, but I think that the community would benefit from being able to read along.</div>
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<div class="">Best wishes,</div>
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<div class="">Jan-Mathijs</div>
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J.M.Schoffelen, MD PhD<br class="">
Senior Researcher, VIDI-fellow - PI, language in interaction<br class="">
Telephone: +31-24-3614793</div>
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Physical location: room 00.028</div>
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Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands<br class="">
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<div class="">On 28 Feb 2019, at 17:37, Andrea Brovelli <<a href="mailto:andrea.brovelli@univ-amu.fr" class="">andrea.brovelli@univ-amu.fr</a>> wrote:</div>
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<div class="">Dear all,<br class="">
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we would like to perform group-level analyses of the results obtained from single-subject cluster-based statistics. In our case, each subject has a statistical map of size (time x channels) thresholded using cluster-based statistics method.<br class="">
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Given you expertise in the cluster-based methods, what do you think would be a good way to perform group-level statistics?<br class="">
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And could you point me to some tools, codes, papers please?<br class="">
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Or simply send a link of previous discussions on the mailing list? I cannot find them....<br class="">
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Thanks a lot.<br class="">
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Best,<br class="">
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Andrea<br class="">
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https://doi.org/10.1371/journal.pcbi.1002202<br class="">
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