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<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Hi Allen,<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US>I just now came
across the FieldTrip toolbox and have a few questions – I apologize in
advance if I am misusing this discussion forum or if my questions are too
obvious. I am working with a very large dataset of simultaneous LFP,
unit, and NIRS signal from macaques for the purpose of examining time and
frequency dynamics of the signals in various cognitive conditions. I
would like to implement the clustering/bootstrapping methodology outlined in
[Journal of Neuroscience Methods 164 (2007) 177–190] to identify
significant changes in synchronization and phase-locking. <o:p></o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US>The specific
data in question are LFPs recorded while the monkeys perform a working-memory
task, which consists of a 2s visual sample, 20s delay, and subsequent choice
period. A 20s baseline precedes the sample (t=0), which is time-locked to
the animal’s foveation on the visual sample. TF plots are computed
using Morlet wavelets for each trial, averaged across trials, and then
normalized with respect to the average baseline. The first question
I’d like to address is: what time-frequency regions exhibit significant
difference between Correct (left plot) and Incorrect (right plot) trials.
<o:p></o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US><img width=352
height=264 id="Picture_x005f_x0020_2" src="cid:image001.jpg@01CAD1EC.7F56EA50"
alt="cid:image002.jpg@01CAA0E0.B7CEC3E0"> <img width=352 height=264
id="Picture_x005f_x0020_3" src="cid:image002.jpg@01CAD1EC.7F56EA50"
alt="cid:image003.jpg@01CAA0E0.B7CEC3E0"><o:p></o:p></span></p>
<p class=MsoNormal style='margin-left:35.4pt'><span lang=EN-US>I have a number
of questions about the appropriateness of applying bootstrapping to our
specific dataset:<o:p></o:p></span></p>
<p class=MsoListParagraph style='margin-left:71.4pt;text-indent:-18.0pt;
mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=EN-US><span
style='mso-list:Ignore'>1)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>Because we use wavelets for
spectral decomposition (with frequency-dependent changes in spectral and
temporal resolution) is it valid to simply cluster across different
frequencies?<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Yes it is. However, with your 20 sec. trials, I would not go for
the high temporal resolution that he wavelet transform gives you. I would do one
Fourier-based analyses for the first 2 seconds (encoding stage) and another one
for the rest of the trial (retention stage).<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:71.4pt;text-indent:-18.0pt;
mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=EN-US><span
style='mso-list:Ignore'>2)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>We are interested in comparing
different conditions, where each is computed relative to its own
baseline. Is there anything wrong with using the baselines of the same
dataset for the null distribution as opposed to using a different set of
baseline data?<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Do not baseline correct your data. Compare the raw power spectra
for the correct response trials with those of the incorrect response condition.<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:71.4pt;text-indent:-18.0pt;
mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=EN-US><span
style='mso-list:Ignore'>3)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>The data have been recorded at
1KHz, and this oversampling results in high spatial-frequency noise. Do I
need to do any downsampling/smoothing before applying the statistics?<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>No. However, with such long trials, I would definitely smooth in
the frequency domain using multitapers (also available in Fieldtrip).<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoListParagraph style='margin-left:71.4pt;text-indent:-18.0pt;
mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=EN-US><span
style='mso-list:Ignore'>4)<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>For evaluating the relationship
between channels, we compute the phase-locking value (PLV), which has no
meaningful single-trial measuremetnt. Is there any way to apply
statistical analyses directly to the average TF plots without resorting back to
the individual trials?<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-size:10.5pt;font-family:Consolas;
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Have look at the paper by Maris, Schoffelen, and Fries (2007,
JNM) that describes cluster-based permutation tests for coherence differences.
This may be what you need.<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'>Best,<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-family:"Verdana","sans-serif";
color:#1F497D'><o:p> </o:p></span></p>
<p class=MsoNormal><span lang=EN-US style='font-size:10.5pt;font-family:Consolas'>__________________________________________________________________________________</span><span
lang=EN-US><o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Allen Ardestani Email: <a
href="mailto:aardesta@ucla.edu">aardesta@ucla.edu</a><o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Phone: (310) 825-5528<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Medical Scientist Training Program<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>David Geffen School of Medicine at UCLA<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Semel Institute for Neuroscience and Human
Behavior<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>760 Westwood Plaza<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>Los Angeles, CA 90095-1759<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US>USA<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p>----------------------------------<o:p></o:p></p>
<p>The aim of this list is to facilitate the discussion between users of the
FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and
EEG analysis.<o:p></o:p></p>
<p>http://listserv.surfnet.nl/archives/fieldtrip.html<o:p></o:p></p>
<p>http://www.ru.nl/fcdonders/fieldtrip/<o:p></o:p></p>
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<p>----------------------------------</p>
<p>The aim of this list is to facilitate the discussion between users of the FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis.</p>
<p> http://listserv.surfnet.nl/archives/fieldtrip.html</p>
<p> http://www.ru.nl/fcdonders/fieldtrip/</p>