<div dir="ltr">Dear Iman and Robert,<div><br></div><div>Oh ok that what he means. Sorry. The group-level SIFT scheme I've been developing normalizes individual data into AAL-defined anatomical ROIs. As long as ICA results are more or less comparable (or by applying wider 3-D Gaussian smoothing size), you can assume consistency across pre and post within subject.</div><div><br></div><div>Makoto</div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Jul 13, 2016 at 3:38 PM, Iman Mohammad-Rezazadeh <span dir="ltr"><<a href="mailto:irezazadeh@ucdavis.edu" target="_blank">irezazadeh@ucdavis.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">Hi Makoto,
<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">I think what Rob’ point is if you have two different recordings ( pre-/ post-treatment , for example ) then you may get two different sets of dipoles. It is
like single-subject longitudinal design .Your new approach would be useful to see the changes from pre to post.<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">Best,<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">Iman
<u></u><u></u></span></p>
<p class="MsoNormal"><a name="m_3925304234929231003_m_-49199889331194567__MailEndCompose"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d"><u></u> <u></u></span></a></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">From:</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> Makoto Miyakoshi [mailto:<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>]
<br>
<b>Sent:</b> Wednesday, July 13, 2016 3:33 PM<br>
<b>To:</b> Rob Coben <<a href="mailto:drcoben@gmail.com" target="_blank">drcoben@gmail.com</a>><br>
<b>Cc:</b> Iman Mohammad-Rezazadeh <<a href="mailto:irezazadeh@UCDAVIS.EDU" target="_blank">irezazadeh@UCDAVIS.EDU</a>>; EEGLAB List <<a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a>>; Scott Makeig <<a href="mailto:smakeig@ucsd.edu" target="_blank">smakeig@ucsd.edu</a>>; <a href="mailto:s.sanei@surrey.ac.uk" target="_blank">s.sanei@surrey.ac.uk</a></span></p><div><div><br>
<b>Subject:</b> Re: [Eeglablist] Group level brain connectivity analyses in sourcelevel<u></u><u></u></div></div><p></p><div><div>
<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">Dear Rob,<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">> Would this also work on individual eeg’s (e.g., pre-post in a single subject)? Very interesting..<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">For a single-subject data, use SIFT. It's been there since 2010. My solution is for the group-level, which does not even work with n==1 (for technical reason for now; in theory it works).<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">Makoto<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">On Wed, Jul 13, 2016 at 7:01 AM, Rob Coben <<a href="mailto:drcoben@gmail.com" target="_blank">drcoben@gmail.com</a>> wrote:<u></u><u></u></p>
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<p class="MsoNormal">Makoto,<u></u><u></u></p>
<p class="MsoNormal"> <u></u><u></u></p>
<p class="MsoNormal">Would this also work on individual eeg’s (e.g., pre-post in a single subject)? Very interesting..<u></u><u></u></p>
<p class="MsoNormal"> <u></u><u></u></p>
<p class="MsoNormal">Rob<u></u><u></u></p>
<p class="MsoNormal"> <u></u><u></u></p>
<p class="MsoNormal">Sent from
<a href="https://go.microsoft.com/fwlink/?LinkId=550986" target="_blank">Mail</a> for Windows 10<u></u><u></u></p>
<p class="MsoNormal"> <u></u><u></u></p>
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<p class="MsoNormal"><b>From:
</b><a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">Makoto Miyakoshi</a><br>
<b>Sent: </b>Wednesday, July 13, 2016 12:00 AM<br>
<b>To: </b><a href="mailto:irezazadeh@ucdavis.edu" target="_blank">Iman Mohammad-Rezazadeh</a><br>
<b>Cc: </b><a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">EEGLAB List</a>;
<a href="mailto:smakeig@ucsd.edu" target="_blank">Scott Makeig</a>; <a href="mailto:s.sanei@surrey.ac.uk" target="_blank">
s.sanei@surrey.ac.uk</a><br>
<b>Subject: </b>Re: [Eeglablist] Group level brain connectivity analyses in sourcelevel<u></u><u></u></p>
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<p class="MsoNormal">Dear Iman,<u></u><u></u></p>
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<p class="MsoNormal">I have an alpha version. I'll present it in SfN this year (see below for poster info). I'm currently working on ULCA data sets using it. I plan to release a beta version of the
tool this summer (of course, it's a pluging for EEGLAB). I'll meet my colleagues in Qusp soon to report finalization of it and discuss publication.<u></u><u></u></p>
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<p class="MsoNormal">Makoto<u></u><u></u></p>
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<p class="MsoNormal">%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<u></u><u></u></p>
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<p class="MsoNormal">Your abstract, "<b>Group-level statistics on EEG effective source connectivity</b>," has been accepted into the program as a traditional poster presentation<br>
<br>
Session Type: Poster<br>
Session Number: 851<br>
Session Title: Computational Tools for Human Data II<br>
Date and Time: Wednesday Nov 16, 2016 1:00 PM - 5:00 PM<br>
Location: San Diego Convention Center: Halls B-H<br>
Abstract Control Number: 9417<u></u><u></u></p>
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<p class="MsoNormal"> <u></u><u></u></p>
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<p class="MsoNormal">Multivariate connectivity measures in EEG have been gathering attention to investigate causal information flow in dynamics across signals. There are known issues in applying this
method on scalp-recorded channel EEG data, such as volume conductance and scalp mixing, which makes the original scalp channel data highly correlated. To address these issues, applying independent component analysis (ICA) as preprocess is effective. It finds
a linear transform to obtain effective source signals that are temporally maximally independent to each other. Thanks to this, the issues raising from volume conductance and scalp mixing are both addressed cleverly without estimating any parameters in electrophysiological
forward model. However, because ICA reveals individual differences, it creates problems in the group-level statistics. For example, in the conventional EEG analysis, group-level statistics was straightforward; selecting a channel e.g. Cz from all the subjects
was regarded to be sufficient. But after ICA preprocessing, there is no exact common independent comonent across all the subjects. Hence we developed a following statistical framework: 1. Preprocess individual data with ICA, estimate equivalent current dipoles,
and apply multivariate connectivity measures across all pairs of ICs; 2. Compute dipole density that distributes within MNI brain space by applying 3-D Gaussian kernel; 3. Segment dipole density into anatomical regions; 4. Compute region-to-region pairwise
dipole density that is weighted by connectivity measures; 5, Repeat above process for all subjects; 6, Perform statistics between conditions using variance across subjects. We developed a free, open-source source toolbox that plugs into EEGLAB. With this solution,
effective EEG source connectivity can be evaluated in time-frequency domain at the group-level statistics with multiple comparison corrections. It is also generate a movie to visualize information flow. We expect it will also impact MEG, ECoG, and other electrophysiological
data analysis.<u></u><u></u></p>
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<p class="MsoNormal"> <u></u><u></u></p>
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<p class="MsoNormal">On Tue, Jul 12, 2016 at 4:11 PM, Iman Mohammad-Rezazadeh <<a href="mailto:irezazadeh@ucdavis.edu" target="_blank">irezazadeh@ucdavis.edu</a>> wrote:<u></u><u></u></p>
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<p class="MsoNormal">Hi EEGLABers,
<u></u><u></u></p>
<p class="MsoNormal"> <u></u><u></u></p>
<p class="MsoNormal">My concern is because in any experiment subjects don’t necessarily share common EEG sources ( nodes in the network) in terms of their locations, number of sources , etc.
<u></u><u></u></p>
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<p>1-<span style="font-size:7.0pt"> </span>Does anyone has experience on doing group level connectivity analyses on source ( not channel level).
<u></u><u></u></p>
<p>2-<span style="font-size:7.0pt"> </span>In a single subject design ( pre-/ post-treatment , for example) how can we compare the brain connectivity in source level by considering the above concern?
<u></u><u></u></p>
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<p class="MsoNormal">I would greatly appreciate any input and also please share any resources that you might think it is helpful.
<u></u><u></u></p>
<p class="MsoNormal"> <u></u><u></u></p>
<p class="MsoNormal">Thanks<u></u><u></u></p>
<p class="MsoNormal">Iman
<u></u><u></u></p>
<p class="MsoNormal"><b>-------------------------------------------------------------</b><u></u><u></u></p>
<p class="MsoNormal"><b>Iman Rezazadeh, Ph.D</b><u></u><u></u></p>
<p class="MsoNormal">Project Scientist | Semel Intitute, UCLA , Los Angeles, CA<u></u><u></u></p>
<p class="MsoNormal">Adjunct Researcher | Center for Mind and Brain, Davis, CA<u></u><u></u></p>
<p class="MsoNormal"> <u></u><u></u></p>
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<p class="MsoNormal">--
<u></u><u></u></p>
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<p class="MsoNormal">Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">-- <u></u><u></u></p>
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<p class="MsoNormal">Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego<u></u><u></u></p>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br><div data-smartmail="gmail_signature"><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div></div>
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