<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=us-ascii">
</head>
<body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">
Dear Eelke,
<div class=""><br class="">
</div>
<div class="">Thank you for an informative response. I just wonder how the discussion around the one-sample T-test finally has ended and why it was decided not to be included.</div>
<div class=""><br class="">
</div>
<div class="">I think you're right that it should be tested with the paired T-test. I thought the autocorrelation in MEG data should be minimal, I think it would make more sense to pair them, especially the signal was low-pass filtered.</div>
<div class=""><br class="">
</div>
<div class="">I think if you create a flat timeseries with the mean values of each baseline period as a "baseline trial" and do a paired t-test, the result should be identical to flipping signs of baseline-corrected activation trials. </div>
<div class=""><br class="">
</div>
<div class="">During the permutation, if the labels are not swapped, then the paired difference at one trial-pair would be y - x0 (y is a timeseries in an activation period; x0 is the mean of the baseline period). If the labels are swapped, then the difference
would be x0 - y. Since baseline-corrected activation trial is also y - x0, this is equivalent to flipping signs of the corrected activation trials.</div>
<div class=""><br class="">
</div>
<div class="">But I'm not sure if I really want to use only the mean of the baseline period because then it would discard underlying noise structure in the baseline period. But also because the baseline period is not evoked by any stimuli of interest, we don't
assume any sample-by-sample correspondence between the baseline and activation periods. So it feels a bit strange to do so too.</div>
<div class=""><br class="">
</div>
<div class="">Best,<br class="">
<div class="">
<div dir="auto" style="text-align: start; text-indent: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">
<div dir="auto" style="text-align: start; text-indent: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">
<div dir="auto" style="text-align: start; text-indent: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">
<div dir="auto" style="text-align: start; text-indent: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">
<div dir="auto" style="orphans: 2; widows: 2; letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px; text-decoration: none; -webkit-text-stroke-width: 0px; text-align: start; text-indent: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">
<font color="#7a7a7a" face="Helvetica" class=""><span style="caret-color: rgb(122, 122, 122); font-size: 11px;" class="">-SG</span></font></div>
</div>
</div>
</div>
</div>
</div>
<div><br class="">
<blockquote type="cite" class="">
<div class="">On 2018-10-05, at 04:07, Eelke Spaak <<a href="mailto:e.spaak@donders.ru.nl" class="">e.spaak@donders.ru.nl</a>> wrote:</div>
<br class="Apple-interchange-newline">
<div class="">
<div class="">Dear SG,<br class="">
<br class="">
Rather than go into the whole one-sample T test business again, I will<br class="">
respond to one aspect of your question which I think might be useful.<br class="">
<br class="">
<blockquote type="cite" class="">but the result was quite different from the two-sample t-test comparing baseline vs activation trials.<br class="">
</blockquote>
<br class="">
I don't know exactly how you were doing the baseline vs activation<br class="">
test, but I'll note two things here. First, you will typically not<br class="">
want to do a *two-sample* test, but a *paired-sample* test; i.e. each<br class="">
unit of observation (trial or subject) has both a baseline and an<br class="">
activation period, and it's the paired difference that matters.<br class="">
<br class="">
Second, you will want to test the activation period against the *mean*<br class="">
across the entire baseline period (since we typically assume a<br class="">
stationary baseline). If you were to simply do (1) select baseline<br class="">
window as condition A; (2) select activation window as condition B;<br class="">
(3) compare the two using cluster stats; then the statistic would be<br class="">
comparing each time point for the activation period against the<br class="">
matching time point in the baseline.<br class="">
<br class="">
So basically, one way of doing activation versus baseline cluster<br class="">
stats is to average the baseline window across time (probably repmat()<br class="">
the mean over time again) and then use paired statistics against the<br class="">
activation window. This should work at first or second level.<br class="">
<br class="">
Hope that helps!<br class="">
<br class="">
Best,<br class="">
Eelke<br class="">
<br class="">
<br class="">
<blockquote type="cite" class=""><br class="">
Is there really no way to use cluster-based permutation test for one-sample T-test at the second level?<br class="">
<br class="">
Best,<br class="">
--<br class="">
Seung-Goo ("SG") Kim, PhD<br class="">
<br class="">
Postdoctoral Research Associate<br class="">
O-Lab, Department of Psychology & Neuroscience, Duke University<br class="">
Postal: 308 Research Drive, Durham, NC 27708, USA<br class="">
<a href="mailto:solleo@gmail.com" class="">Email: solleo@gmail.com</a><br class="">
<br class="">
_______________________________________________<br class="">
fieldtrip mailing list<br class="">
https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=O3uZfr2pkofitVkCpUH6tjHpi5z80NBC5OwVFBKTQL8&e=<br class="">
https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=KgYvBgZIFHTGoNd1roIMW-v3VAI91zJHE2TvYPHWLvM&e=<br class="">
</blockquote>
_______________________________________________<br class="">
fieldtrip mailing list<br class="">
<a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=O3uZfr2pkofitVkCpUH6tjHpi5z80NBC5OwVFBKTQL8&e=" class="">https://urldefense.proofpoint.com/v2/url?u=https-3A__mailman.science.ru.nl_mailman_listinfo_fieldtrip&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=O3uZfr2pkofitVkCpUH6tjHpi5z80NBC5OwVFBKTQL8&e=</a><br class="">
https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1371_journal.pcbi.1002202&d=DwIGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=XJZB_02aOg0SpO6JLAKP_HYe0fIywEx0eEE02bh4ztg&s=KgYvBgZIFHTGoNd1roIMW-v3VAI91zJHE2TvYPHWLvM&e=<br class="">
</div>
</div>
</blockquote>
</div>
<br class="">
</div>
</body>
</html>