<div dir="ltr"><div><div><div><div><div><div>Good morning Jan-Mathijs,<br><br></div>Some weeks ago you've sent me the following answer to my question on doing some connectivity and network analysis on some frequency data separated in frequency bins.<br>
</div>I totally agree with you that electrode level analysis is a bad way for doing the connectivity analysis, but I have to use electrode level data (for the moment).<br><br></div>I also listened to your advance and only averaged the connectivity measures between subjects for the grand average comparison.<br>
</div>For the ft_freqanalysis I used the smoothing box and the foi in such a way it would overlap my frequency band of interest, but why can't one just average frequency data for a range of frequencies? And why wouldn't you do it across trials? For the trials I just used cfg.keeptrials = 'yes' and used it as an input for ft_connectivityanalysis where the dimension trials is lost.<br>
</div>So I presume averaging over trials is done in the right way within the ft_connectivityanalysis function?<br><br></div>Bests, Bart<br></div><div class="gmail_extra"><br><br><div class="gmail_quote">On 12 November 2013 10:57, jan-mathijs schoffelen <span dir="ltr"><<a href="mailto:jan.schoffelen@donders.ru.nl" target="_blank">jan.schoffelen@donders.ru.nl</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="word-wrap:break-word">Hi Bart,<div><br></div><div><span style="font-size:13px">If you 7 frequency bins within each frequency bin are considered to belong to the same frequency band, I'd suggest to use the multitaper approach to estimate the spectral parameters per frequency band, using just a single frequency bin to represent the whole band of interest. So, rather than for example estimating from 15 until 21 Hz in steps of 1 Hz, you can also estimate only at 18, using cfg.tapsmofrq = 3 in ft_freqanalysis. I suggest to have a look at the documentation on the FT-wiki that is concerned with frequency analysis. I would certainly not average the spectral representation prior to computing the coherence, neither across the individual frequency bins, and even more certainly not across trials.</span></div>
<div><span style="font-size:13px"><br></span></div><div><span style="font-size:13px">Regarding statistics, I don't want to discourage you, but I don't think it makes sense to attempt doing statistics at the channel level to begin with, due to the effects of volume conduction. Also, in any group comparison (but also when doing a comparison across conditions), if there is any between groups in terms of SNR (e.g. more or less alpha power across groups) you are bound to find a statistically significant difference in estimated connectivity as well. It remains to be motivated in such a case, that the change in estimated connectivity actually reflects a change in true connectivity.</span></div>
<div><span style="font-size:13px"><br></span></div><div><span style="font-size:13px">If you want to do statistics irrespective of these caveats, you may want to look into FieldTrip's implementation of the non-parametric permutation testing framework. There is ample documentation about this on our wiki as well.</span></div>
<div><span style="font-size:13px"><br></span></div><div><span style="font-size:13px">Best wishes,</span></div><div><span style="font-size:13px"><br></span></div><div><span style="font-size:13px">Jan-Mathijs</span></div><div>
<span style="font-size:13px"><br></span></div><div><span style="font-size:13px"><br></span></div><div><span style="font-size:13px"><br></span><div><div><div class="h5"><div>On Oct 30, 2013, at 1:22 PM, Bart Michiels wrote:</div>
<br></div></div><blockquote type="cite"><div><div class="h5"><div dir="ltr"><div><div><div><div><div>Hi,<br><br></div>I have 30 patients and 30 controls and I'm investigating their coherence (EEG, 128 electrodes). Every patient has ~30 trials resting state eyes open consisting of 7 frequency bins with 7 frequencies in each bin. My goal is to show connectivity differences between different brain regions in the control and patient group (doing electrode-level analysis now, source-level analysis is next step).<br>
</div><br>- Is it more appropriate to keep the averaging step as the latest step (ie. calculate all coherence for all the different subjects, for all trials, for all different frequencies in frequency bins) or is it better to do the averaging asap (ie. average all frequencies in 1 frequency bin at the time-frequency analysis step, average all trials at the ft_freqdescriptives step, ...)<br>
<br></div>- Is there any better way to do some statistics on the 128x128x7x7 (128 electrodes, 7 frequency bins, 7 frequencies in each bin) besides using the ttest2() matlab function?<br><br></div>Any tips & tricks are more then welcome!<br>
<br></div>Bart<br></div></div></div><div class="im">
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<div>Jan-Mathijs Schoffelen, MD PhD </div><div><br></div><div>Donders Institute for Brain, Cognition and Behaviour, <br>Centre for Cognitive Neuroimaging,<br>Radboud University Nijmegen, The Netherlands</div><div><br></div>
<div>Max Planck Institute for Psycholinguistics,</div><div>Nijmegen, The Netherlands</div><div><br></div><div><a href="mailto:J.Schoffelen@donders.ru.nl" target="_blank">J.Schoffelen@donders.ru.nl</a></div><div>Telephone: <a href="tel:%2B31-24-3614793" value="+31243614793" target="_blank">+31-24-3614793</a></div>
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<div style="color:rgb(136,136,136)">Bart Michiels, Student 2d master,<br>School of Engineering, Trinity College Institute of Neuroscience and Trinity Centre for Bioengineering,<br>Trinity College Dublin,<br>Dublin 2, Ireland</div>
<div style="color:rgb(136,136,136)">Email: <span style="color:rgb(0,0,255)">michielb<a href="mailto:edlalor@tcd.ie" target="_blank">@tcd.ie</a></span></div><div style="color:rgb(136,136,136)">Web: <a href="http://www.mee.tcd.ie/neuraleng/People/Ed" style="color:rgb(17,85,204)" target="_blank">http://www.mee.tcd.ie/neuraleng/People/Bart</a></div>
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