<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class="">Dear Daniel,<div class=""><br class=""></div><div class="">Did you get an answer already?</div><div class="">I’m just wondering, what is the null hypothesis?</div><div class="">Do you want to see if individual PLV values are significantly different from the null value, or do you want to compare PLV values between conditions?</div><div class=""><br class=""></div><div class="">Best,</div><div class=""><br class=""></div><div class=""><div class="">
<span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; " class=""><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; " class=""><div class="">Florian Gerard-Mercier</div><div class="">Lab. for Cognitive Brain Mapping</div><div class="">RIKEN Brain Science Institute</div><div class="">2-1 Hirosawa, Wako, Saitama</div><div class="">351-0198, Japan</div><div class="">Tel: 048 462 1111 and 7106</div><div class="">Mob: 080 3213 6851</div></div></span></div></span></span>
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<br class=""><div><blockquote type="cite" class=""><div class="">On 7 Nov, 2016, at 10:49 PM, Hähnke, Daniel <<a href="mailto:daniel.haehnke@tum.de" class="">daniel.haehnke@tum.de</a>> wrote:</div><br class="Apple-interchange-newline"><div class=""><div class="">Dear FT community,<br class=""><br class="">I’m currently working on spike and LFP data from a behavioural experiment that contained three different stimulus conditions. The conditions were unequally distributed across trials: condition A was in 60 % of trials and conditions B and C each in 20 % of trials.<br class=""><br class="">I want to compare the spike-field PLV between the conditions using a z-scoring approach similar to Buschman et al. 2012, Neuron (<a href="http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf" class="">http://download.cell.com/neuron/pdf/PIIS0896627312008823.pdf</a>). In that paper they shuffle the trial associations between spike trials and LFP trials to generate a null distribution from which they compute the z-score.<br class=""><br class="">Since I have unequal number of trials across conditions, I also need to equalise the number of spikes across conditions. There are two methods I used to try to accomplish this.<br class=""><br class="">Method 1:<br class="">1. Within each condition, shuffle the trial association between spike trials and LFP trials (this is for the null distribution). Do this e.g. 100 times. Compute STS.<br class="">2. From each trial shuffle (see 1.) use a random subset of spike phases (matched to the condition with the lowest number of spikes) to compute the PLV. Do this random subsampling e.g. 1000 times.<br class="">3. For each trial shuffle (see 1.) average across subsamples (see 2.).<br class="">4. Compute z-score from the trial shuffles' subsampling-average (see 3.), by computing mean and SD across the trial shuffles’ subsampling averages.<br class=""><br class="">Method 2:<br class="">1. Within each condition, use a random subset of trials (matched to condition with lowest number of trials). Do this e.g. 1000 times.<br class="">2. For each subsample (see 1.) shuffle the trial associations between spike trials and LFP trials. Do this e.g. 100 times. Compute STS and PLV.<br class="">3. For each trial-subset (see 1.) compute z-score by using SD and mean across trial shuffles (see 2.).<br class=""><br class="">Now I see that for method 1, there is a lower SD for condition A, which is why I get higher z-scores. Using method 2 I get unlikely low z-scores.<br class=""><br class="">Despite the differences in the steps, there are also the following differences in the two methods.<br class="">In method 1 I shuffled the spike trains so that they can also be referred to an LFP trial that didn’t have any spikes (i.e. I didn’t limit LFP trials to only trials in which the units were recorded). This of course gives condition A a much bigger “shuffling pool” than the other two conditions. In method 2 I only shuffled within the LFP trials that actually also had spikes.<br class="">Another difference is that in method 2, the spike numbers are only very similar but not equal, since I only equalised the trial numbers.<br class=""><br class="">Is there another approach to accomplish what I am looking for? Basically, I want to reduce PLV bias by equalising the spike numbers and I also want to normalise the PLV.<br class="">I could imagine that limiting the “shuffling pool” in method 1 would maybe equalise the conditions better, but I’m not sure whether the general approach is statistically sound.<br class=""><br class="">It would be great if someone could comment on the methods above and/or propose another method (e.g. would bootstrapping be alright for the generation of the null distribution?).<br class=""><br class="">Best wishes,<br class=""><br class="">Daniel<br class="">--<br class="">Daniel Hähnke<br class="">PhD student<br class=""><br class="">Technische Universität München<br class="">Institute of Neuroscience<br class="">Translational NeuroCognition Laboratory<br class="">Biedersteiner Straße 29, Bau 601<br class="">80802 Munich<br class="">Germany<br class=""><br class="">Email: <a href="mailto:daniel.haehnke@tum.de" class="">daniel.haehnke@tum.de</a><br class="">Phone: +49 89 4140 3356<br class=""><br class=""><br class="">_______________________________________________<br class="">fieldtrip mailing list<br class=""><a href="mailto:fieldtrip@donders.ru.nl" class="">fieldtrip@donders.ru.nl</a><br class="">https://mailman.science.ru.nl/mailman/listinfo/fieldtrip</div></div></blockquote></div><br class=""></div></body></html>