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</o:shapelayout></xml><![endif]--></head><body lang=NL link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Hi Kristen,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>If you supply ft_timelockstatistics with data containing multiple channels it will always “correct” (this is not the best word to denote what the method does, but let’s use it anyhow) for the number of channels. By requesting not to do clustering in space you just achieve that a clusters are computed in a different way (ie, assuming that the channels measure functionally independent signals). <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>If you do not want correction for multiple channels, then you must analyze the data per channel, as you did for Elec11. If you have a low number of channels, then you could combine the permutation-based p-value with regular Bonferroni correction (dividing the critical alpha-level by the number of channels).<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Good luck,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'>Eric Maris<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p> </o:p></span></p><div style='border:none;border-left:solid blue 1.5pt;padding:0cm 0cm 0cm 4.0pt'><div><div style='border:none;border-top:solid #B5C4DF 1.0pt;padding:3.0pt 0cm 0cm 0cm'><p class=MsoNormal><b><span lang=EN-US style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>From:</span></b><span lang=EN-US style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> Kristen Berry [mailto:kkb268@nyu.edu] <br><b>Sent:</b> vrijdag 2 mei 2014 22:41<br><b>To:</b> fieldtrip@science.ru.nl<br><b>Subject:</b> [FieldTrip] ft_timelockstatistics cluster correction using ECoG data<o:p></o:p></span></p></div></div><p class=MsoNormal><o:p> </o:p></p><div><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>Dear all,</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>I am using an intracranial EEG dataset to compare average high gamma power waveforms between conditions. I want to correct for multiple comparisons across time, but not across number of electrodes. Here is my input code:</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg=[];</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.latency = [0.05 .5];</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.parameter = 'trial';</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.method = 'montecarlo';</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.correctm = 'cluster';</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.neighbours = [];</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.numrandomization = 1000;</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.statistic = 'indepsamplesT';</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.channel = ‘all’;</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.alpha = 0.05;</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.clusteralpha = 0.05;</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>cfg.tail = 0;</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>stats = ft_timelockstatistics(cfg, data,control); </span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>Fieldtrip’s montecarlo reference page (</span><a href="http://fieldtrip.fcdonders.nl/reference/ft_statistics_montecarlo" target="_blank"><span style='font-family:"Calibri","sans-serif"'>http://fieldtrip.fcdonders.nl/reference/ft_statistics_montecarlo</span></a><span style='font-family:"Calibri","sans-serif";color:black'>) says, “If you specify an empty neighbourhood structure, clustering will only be done in frequency and time (if available) and not over neighbouring channels.”</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>However, my input code corrects for the number of channels as well. As an example, when cfg.channel = ‘all’ for ft_timelockstatistics, then Elec11 is not significant. However, if the analysis is limited to Elec11 (cfg.channel = 11), then Elec11 has a significant cluster.</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>Is there a mistake in my code that is causing this? Or is there an older version of fieldtrip that doesn’t correct for # of channels, when cfg.neighbours is empty? Thank you for your input!</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>Best regards,</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>Kristen</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>Kristen Berry</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>Research Assistant</span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Calibri","sans-serif";color:black'>NYU School of Medicine</span><o:p></o:p></p></div></div></div></body></html>