<div style="white-space:pre-wrap">No, sorry, that's not what I meant, but thanks for giving me the opportunity to clarify. Of course everyone is familiar with the 1/f pattern across frequencies, but the distribution across time (and according to the poster, also across space), also has an extremely skewed, negative exponential distribution. I probably confused everyone by trying to show too much data in my figure, but each color represents the distribution of power values for a single frequency over time, using a histogram and a line above with circles at the mean +/- one standard deviation. <br><br>My main point was that the mean is not representative of the central tendency of such an asymmetrical distribution of power values over time. It's even more obvious which is more representative of their actual distributions when I plot e^mean(logpower) on the raw plot and log(mean(rawpower)) on the log plot, but that made the figure even more busy and confusing. <br><br>I hope that helps,<br>Teresa<br></div><br><div class="gmail_quote"><div dir="ltr">On Mon, Dec 12, 2016 at 3:47 PM Nicholas A. Peatfield <<a href="mailto:nick.peatfield@gmail.com">nick.peatfield@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr" class="gmail_msg"><div class="gmail_msg">Hi Teresa,</div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg"><div class="gmail_msg">I think what you are discussing is the 1/f power scaling of the power spectrum. This is one of the reasons that comparisons are made within a band (i.e. alpha to alpha) and not between bands (i.e. alpha to gamma), as such the assumption is that within bands there should be a relative change against baseline and this is what the statistics are performed on. That is, baseline correction is assumed to be the mean for a specific frequency and not a mean across frequencies.</div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg"> And this leads to another point that when you are selecting a frequency range to do the non-parametric statistics on you should not do 1-64 Hz but break it up based on the bands.</div></div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg">Hope my interpretation of your point is correct. I sent in individually, as I wanted to ensure I followed your point.</div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg">Cheers,</div><div class="gmail_msg"><br class="gmail_msg"></div><div class="gmail_msg">Nick</div><div class="gmail_msg"><br class="gmail_msg"></div></div><div class="gmail_extra gmail_msg"><br class="gmail_msg"><div class="gmail_quote gmail_msg"></div></div><div class="gmail_extra gmail_msg"><div class="gmail_quote gmail_msg">On 12 December 2016 at 08:23, Teresa Madsen <span dir="ltr" class="gmail_msg"><<a href="mailto:tmadsen@emory.edu" class="gmail_msg" target="_blank">tmadsen@emory.edu</a>></span> wrote:<br class="gmail_msg"></div></div><div class="gmail_extra gmail_msg"><div class="gmail_quote gmail_msg"><blockquote class="gmail_quote gmail_msg" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr" class="gmail_msg"><div class="gmail_quote gmail_msg"><div dir="ltr" class="gmail_msg"><div class="gmail_msg"><span style="font-size:12.8px" class="gmail_msg">FieldTrippers,</span><br class="gmail_msg"></div><div class="gmail_msg"><div style="font-size:12.8px" class="gmail_msg"><br class="gmail_msg"></div><span style="font-size:12.8px" class="gmail_msg">While analyzing my data for the annual Society for Neuroscience meeting, I developed a concern that was quickly validated by another poster (full abstract copied and linked below) focusing on the root of the problem: neural oscillatory power is not normally distributed across time, frequency, or space. The specific problem I had encountered was in baseline-correcting my experimental data, where, regardless of cfg.baselinetype, ft_freqbaseline depends on the mean power over time. However, I found that the distribution of raw power over time is so skewed that the mean was not a reasonable approximation of the central tendency of the baseline power, so it made most of my experimental data look like it had decreased power compared to baseline. </span><span style="font-size:12.8px" class="gmail_msg">The more I think about it, the more I realize that averaging is everywhere in the way we analyze neural oscillations (across time points, frequency bins, electrodes, trials, subjects, etc.), and many of the standard statistics people use also rely on assumptions of normality. </span><div style="font-size:12.8px" class="gmail_msg"><br class="gmail_msg"></div><div style="font-size:12.8px" class="gmail_msg"><span style="font-size:12.8px" class="gmail_msg">The most obvious solution for me was to log transform the data first, as it appears to be fairly log normal, and I always use log-scale visualizations anyway. Erik Peterson, middle author on the poster, agreed that this would at least "</span><span style="font-size:12.8px" class="gmail_msg">restore (some) symmetry to the error distribution."</span><span style="font-size:12.8px" class="gmail_msg"> </span><span style="font-size:12.8px" class="gmail_msg">I used a natural log transform, sort of arbitrarily to differentiate from the standard decibel transform included in FieldTrip as cfg.baselinetype = 'db'. </span><span style="font-size:12.8px" class="gmail_msg">The following figures compare the 2 distributions across several frequency bands (using power values from a wavelet spectrogram obtained from a baseline LFP recorded in rat prelimbic cortex). The lines at the top represent the mean +/- one standard deviation for each frequency band, and you can see how those descriptive stats are much more representative of the actual distributions in the log scale.</span></div><div style="font-size:12.8px" class="gmail_msg"><span style="font-size:12.8px" class="gmail_msg"><br class="gmail_msg"></span></div><div style="font-size:12.8px" class="gmail_msg"><span style="font-size:12.8px" class="gmail_msg"><img src="cid:ii_iwiapot72_158e5749d54b5952" style="margin-right:0px" class="gmail_msg"><br class="gmail_msg"></span></div><div style="font-size:12.8px" class="gmail_msg"><span style="font-size:12.8px" class="gmail_msg">For my analysis, I also calculated a z-score on the log transformed power to assess how my experimental data compared to the variability of the noise in a long baseline recording from before conditioning, rather than a short pre-trial baseline period, since I find that more informative than any of FieldTrip's built-in baseline types. I'm happy to share the custom functions I wrote for this if people think it would be a useful addition to FieldTrip. I can also share more about my analysis and/or a copy of the poster, if anyone wants more detail - I just didn't want to make this email too big.</span></div><div style="font-size:12.8px" class="gmail_msg"><br class="gmail_msg"></div><div style="font-size:12.8px" class="gmail_msg">Mostly, I'm just hoping to start some discussion here as to how to address this. I searched the <a href="http://www.fieldtriptoolbox.org/development/zscores" class="gmail_msg" target="_blank">wiki</a>, <a href="https://mailman.science.ru.nl/pipermail/fieldtrip/2006-December/000773.html" class="gmail_msg" target="_blank">listserv</a> <a href="https://mailman.science.ru.nl/pipermail/fieldtrip/2010-March/002718.html" class="gmail_msg" target="_blank">archives</a>, and <a href="http://bugzilla.fieldtriptoolbox.org/show_bug.cgi?id=1574" class="gmail_msg" target="_blank">bugzilla</a> for anything related and came up with a few topics surrounding normalization and baseline correction, but only skirting this issue. It seems important, so I want to find out whether others agree with my approach or already have other ways of avoiding the problem, and whether FieldTrip's code needs to be changed or just documentation added, or what?</div><div style="font-size:12.8px" class="gmail_msg"><br class="gmail_msg"></div><div style="font-size:12.8px" class="gmail_msg">Thanks for any insights,</div><div style="font-size:12.8px" class="gmail_msg">Teresa</div><div style="font-size:12.8px" class="gmail_msg"><br class="gmail_msg"></div><div style="font-size:12.8px" class="gmail_msg"><br class="gmail_msg"></div><div style="font-size:12.8px" class="gmail_msg"><h1 class="m_-2898402136910438041m_4827568291528697442gmail-m_-4943405488691602327gmail-m_-3539777104408754943gmail-m_8853517913334217492gmail-color-primary gmail_msg" style="margin:0px;font-family:"open sans",sans-serif;line-height:28px;color:rgb(45,163,189);font-size:24px;padding:0px;clear:both"><a href="http://www.abstractsonline.com/pp8/#!/4071/presentation/24150" class="gmail_msg" target="_blank">271.03 / LLL17 - Neural oscillatory power is not Gaussian distributed across time</a></h1><div class="gmail_msg"><dl><dt style="line-height:20px;font-weight:bold;color:rgb(0,0,0);font-family:"open sans",sans-serif;font-size:14px" class="gmail_msg">Authors</dt><dd style="line-height:20px;margin-left:10px;margin-bottom:5px;color:rgb(0,0,0);font-family:"open sans",sans-serif;font-size:14px" class="gmail_msg"><b class="gmail_msg">*L. IZHIKEVICH</b>, E. PETERSON, B. VOYTEK; <br class="gmail_msg">Cognitive Sci., UCSD, San Diego, CA</dd><dt style="line-height:20px;font-weight:bold;color:rgb(0,0,0);font-family:"open sans",sans-serif;font-size:14px" class="gmail_msg">Disclosures</dt><dd style="line-height:20px;margin-left:10px;margin-bottom:5px;color:rgb(0,0,0);font-family:"open sans",sans-serif;font-size:14px" class="gmail_msg"> <b class="gmail_msg">L. Izhikevich:</b> None. <b class="gmail_msg">E. Peterson:</b> None. <b class="gmail_msg">B. Voytek:</b> None.</dd><dt style="line-height:20px;font-weight:bold;color:rgb(0,0,0);font-family:"open sans",sans-serif;font-size:14px" class="gmail_msg">Abstract</dt><dd style="line-height:20px;margin-left:10px;margin-bottom:5px;color:rgb(0,0,0);font-family:"open sans",sans-serif;font-size:14px" class="gmail_msg">Neural oscillations are important in organizing activity across the human brain in healthy cognition, while oscillatory disruptions are linked to numerous disease states. Oscillations are known to vary by frequency and amplitude across time and between different brain regions; however, this variability has never been well characterized. We examined human and animal EEG, LFP, MEG, and ECoG data from over 100 subjects to analyze the distribution of power and frequency across time, space and species. We report that between data types, subjects, frequencies, electrodes, and time, an inverse power law, or negative exponential distribution, is present in all recordings. This is contrary to, and not compatible with, the Gaussian noise assumption made in many digital signal processing techniques. The statistical assumptions underlying common algorithms for power spectral estimation, such as Welch's method, are being violated resulting in non-trivial misestimates of oscillatory power. Different statistical approaches are warranted.</dd></dl></div></div></div><span class="m_-2898402136910438041HOEnZb gmail_msg"><font color="#888888" class="gmail_msg"><span class="m_-2898402136910438041m_4827568291528697442gmail-HOEnZb gmail_msg"><font color="#888888" class="gmail_msg"><div class="gmail_msg"><br class="gmail_msg"></div>-- <br class="gmail_msg"><div class="m_-2898402136910438041m_4827568291528697442gmail-m_-4943405488691602327gmail_signature gmail_msg"><div dir="ltr" class="gmail_msg"><div class="gmail_msg"><div dir="ltr" class="gmail_msg"><div class="gmail_msg"><div dir="ltr" class="gmail_msg"><font color="#0000ff" class="gmail_msg"><font size="4" class="gmail_msg"><font face="garamond, serif" class="gmail_msg">Teresa E. Madsen, PhD</font><br class="gmail_msg"></font><font face="garamond, serif" class="gmail_msg">Research Technical Specialist: <i class="gmail_msg">in vivo </i>electrophysiology & data analysis</font></font></div><div dir="ltr" class="gmail_msg"><font color="#0000ff" class="gmail_msg"><font face="garamond, serif" class="gmail_msg">Division of Behavioral Neuroscience and Psychiatric Disorders<br class="gmail_msg">Yerkes National Primate Research Center</font></font><div class="gmail_msg"><font face="garamond, serif" color="#0000ff" class="gmail_msg">Emory University<br class="gmail_msg"></font><div class="gmail_msg"><font face="garamond, serif" color="#0000ff" class="gmail_msg">Rainnie Lab, NSB 5233<br class="gmail_msg">954 Gatewood Rd. NE<br class="gmail_msg">Atlanta, GA 30329</font></div><div class="gmail_msg"><div class="gmail_msg"><font face="garamond, serif" color="#0000ff" class="gmail_msg"><a href="tel:(770)%20296-9119" value="+17702969119" class="gmail_msg" target="_blank">(770) 296-9119</a></font><br class="gmail_msg"></div></div></div><div class="gmail_msg"><font face="garamond, serif" color="#0000ff" class="gmail_msg"><a href="mailto:braingirl@gmail.com" class="gmail_msg" target="_blank">braingirl@gmail.com</a></font></div><div class="gmail_msg"><font face="garamond, serif" color="#0000ff" class="gmail_msg"><div class="gmail_msg"><a href="https://www.linkedin.com/in/temadsen" class="gmail_msg" target="_blank">https://www.linkedin.com/in/temadsen</a></div></font></div></div></div></div></div></div></div>
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<br class="gmail_msg"></blockquote></div></div><div class="gmail_extra gmail_msg"><div class="gmail_quote gmail_msg"><blockquote class="gmail_quote gmail_msg" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">_______________________________________________<br class="gmail_msg">
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<a href="mailto:fieldtrip@donders.ru.nl" class="gmail_msg" target="_blank">fieldtrip@donders.ru.nl</a><br class="gmail_msg">
<a href="https://mailman.science.ru.nl/mailman/listinfo/fieldtrip" rel="noreferrer" class="gmail_msg" target="_blank">https://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a><br class="gmail_msg"></blockquote></div></div><div class="gmail_extra gmail_msg"><br class="gmail_msg"><br clear="all" class="gmail_msg"><div class="gmail_msg"><br class="gmail_msg"></div>-- <br class="gmail_msg"><div class="m_-2898402136910438041gmail_signature gmail_msg" data-smartmail="gmail_signature"><div dir="ltr" class="gmail_msg"><div class="gmail_msg"><div dir="ltr" class="gmail_msg"><div class="gmail_msg"><font face="arial, helvetica, sans-serif" size="2" class="gmail_msg">Nicholas Peatfield, PhD</font></div><div class="gmail_msg"><br class="gmail_msg"></div></div></div></div></div>
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