<div dir="ltr">Dear Iman,<div><br></div><div>The core ideas and code comes from Nima's previous works in SCCN. I want to discuss the current status of the tool with Nima in more detail and for validation before providing it to outside the lab, which would take a few more months. Please wait.</div><div><br></div><div>Makoto</div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Jul 13, 2016 at 10:21 AM, 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,
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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">Thanks for your response. Dr Loo already told me about your project at UCLA. My colleagues and I are going to submit a paper in Frontiers and compare different
connectivity methods in children with autism. Is there any way that we can have access to the Alpha version of the plug-in since our due date is end of July.<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">Many thanks,<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_4886733549034654183_m_3969170269191162417__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>]
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<b>Sent:</b> Tuesday, July 12, 2016 10:00 PM<br>
<b>To:</b> Iman Mohammad-Rezazadeh <<a href="mailto:irezazadeh@UCDAVIS.EDU" target="_blank">irezazadeh@UCDAVIS.EDU</a>><br>
<b>Cc:</b> 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><br>
<b>Subject:</b> Re: Group level brain connectivity analyses in source level<u></u><u></u></span></p><div><div>
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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">Your abstract, "<b>Group-level statistics on EEG effective source connectivity</b>," has been accepted into the program as a traditional poster presentation<br>
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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">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">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,
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<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.
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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).
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<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?
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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.
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<p class="MsoNormal">Thanks<u></u><u></u></p>
<p class="MsoNormal">Iman
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<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>
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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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