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Hi Florian,
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<div class="">thanks for your reply! I haven’t had any replies yet.</div>
<div class="">You’re right, it’s important to form a null hypothesis before doing statistical tests.</div>
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<div class="">Explicitly, my null hypothesis is that the PLV is the same for all conditions. For that I’d need to shuffle the condition labels of the spike phases across conditions.</div>
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<div class="">The z-scoring was meant as a normalisation of the spike-lfp-combinations, so I can pool combinations via averaging. Of course, the trial association shuffling implicitly also tests the null hypothesis of zero PLV.</div>
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<div class="">Best,</div>
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<div class="">Daniel<br class="">
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<blockquote type="cite" class="">
<div class="">On 9 Nov 2016, at 01:51, Florian Gerard-Mercier <<a href="mailto:florian@brain.riken.jp" class="">florian@brain.riken.jp</a>> wrote:</div>
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Dear Daniel,
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<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>
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<div class="">Best,</div>
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<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>
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<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>
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<div class="">Dear FT community,<br class="">
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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="">
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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="">
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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="">
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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="">
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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="">
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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="">
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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="">
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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="">
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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="">
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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="">
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Email: <a href="mailto:daniel.haehnke@tum.de" class="">daniel.haehnke@tum.de</a><br class="">
Phone: +49 89 4140 3356<br class="">
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