<div dir="ltr">Dear Avniel,<div><br></div><div>> One question: why not simply interpolate between sources on the brain and then morph all of the individual source spaces into a common source space?<br></div><div class="gmail_extra"><br></div><div class="gmail_extra">Yes, that's almost what Nima's Network Projection approach does. Instead of interpolation, his approaches uses 3-D smoothing on equivalent dipole points and made them into probabilistic distributions of EEG sources. Simple interpolation would fail, for example, when one has only three ICs.</div><div class="gmail_extra"><br></div><div class="gmail_extra">Makoto</div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Jul 14, 2016 at 12:21 PM, Ghuman, Avniel <span dir="ltr"><<a href="mailto:ghumana@upmc.edu" target="_blank">ghumana@upmc.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">One question: why not simply interpolate between sources on the brain and then morph all of the individual source spaces into a common source space? This is what MNE does. I personally then build my connectivity matrices etc. in the common source space.<br>
<br>
From: Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu">mmiyakoshi@ucsd.edu</a><mailto:<a href="mailto:mmiyakoshi@ucsd.edu">mmiyakoshi@ucsd.edu</a>>><br>
Reply-To: <<a href="mailto:mmiyakoshi@ucsd.edu">mmiyakoshi@ucsd.edu</a><mailto:<a href="mailto:mmiyakoshi@ucsd.edu">mmiyakoshi@ucsd.edu</a>>><br>
Date: Thursday, July 14, 2016 2:25 PM<br>
To: Aleksandra Vuckovic <<a href="mailto:Aleksandra.Vuckovic@glasgow.ac.uk">Aleksandra.Vuckovic@glasgow.ac.uk</a><mailto:<a href="mailto:Aleksandra.Vuckovic@glasgow.ac.uk">Aleksandra.Vuckovic@glasgow.ac.uk</a>>><br>
Cc: EEGLAB List <<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a><mailto:<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>>>, James Jones-Rounds <<a href="mailto:jj324@cornell.edu">jj324@cornell.edu</a><mailto:<a href="mailto:jj324@cornell.edu">jj324@cornell.edu</a>>><br>
<span class="">Subject: Re: [Eeglablist] Group level brain connectivity analyses in sourcelevel<br>
<br>
</span><span class="">Dear Alex,<br>
<br>
In theory yes, but instead of dipole density you need to have dipole pair density. All of this minor difference from Measure Projection is summarized as Network Projection which is unpublished but presented in Nima's PhD thesis.<br>
<br>
I chose, however, not to create domains. Creating 'domains' based on measure similarity smells like double dipping to me. Instead, I separate the dipole pair density cloud into anatomical regions (note that this was also prepared by Nima, so I'm still using his option). Using anatomical ROIs is as good as creating across-subject domains to prepare common data structure across subjects.<br>
<br>
It uses AAL (Tzourio-Mazoyer et al., 2002) that has 88 anatomical regions, but I merged upper and lower basal regions such as caudate, thalamus, amygdala, hippocampus, etc. Thus the current scheme uses 76 regions, and the connectivity matrix size is 76x76. Each of the graph edge has time x freq x subject data.<br>
<br>
Makoto<br>
<br>
</span><span class="">On Thu, Jul 14, 2016 at 11:10 AM, Aleksandra Vuckovic <<a href="mailto:Aleksandra.Vuckovic@glasgow.ac.uk">Aleksandra.Vuckovic@glasgow.ac.uk</a><mailto:<a href="mailto:Aleksandra.Vuckovic@glasgow.ac.uk">Aleksandra.Vuckovic@glasgow.ac.uk</a>>> wrote:<br>
Hi<br>
Does it mean that if you do MPT analysis and create domains you extend to group connectivity analysis between domains?<br>
Regards<br>
Alex<br>
<br>
Sent from my iPhone<br>
<br>
</span><span class="">On 14 Jul 2016, at 18:49, Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu">mmiyakoshi@ucsd.edu</a><mailto:<a href="mailto:mmiyakoshi@ucsd.edu">mmiyakoshi@ucsd.edu</a>>> wrote:<br>
<br>
Dear James,<br>
<br>
I'm surprised you know a lot about development of the group-level SIFT. Let me tell you a little bit about the current development. When I started it, there were two possibilities for me.<br>
1. Wes & Tim's 'Bayesian Hierarchical Modeling' with interpolating 'missing' ICs acorss subjects.<br>
2. Nima's 'Network Projection' which blurs existing ICs and average across subjects.<br>
I chose the second plan since Bayesian Hierarchical Modeling was too fancy. Nima's concept of 'Network Projection', which is a variation of Measure Projection, was technically more straightforward and familiar to me. So I bothered Nima a lot to learn how to make it work.<br>
<br>
SCCN has been struggling with IC inconsistency across subjects, and the difficulty in the solution is actually only a variation of the same issue... it's always there as long as you use ICA on individual data, which is Scott's philosophy and I don't want to give it up.<br>
<br>
Thus, the solution I've been developing is not Tim's. What I do is Nima's 'blur, weight, and average' approach using his code. It would be interesting if we could compare the results from the two methods.<br>
<br>
Makoto<br>
<br>
<br>
</span><span class="">On Thu, Jul 14, 2016 at 6:31 AM, James Jones-Rounds <<a href="mailto:jj324@cornell.edu">jj324@cornell.edu</a><mailto:<a href="mailto:jj324@cornell.edu">jj324@cornell.edu</a>>> wrote:<br>
I have been struggling with this issue for a couple years, also, and have two responses to your questions, Iman.<br>
<br>
First, some of the (fomer) researchers at UCSD, including Tim Mullen (who developed the SIFT connectivity toolbox), were working hard on a group-level, source-space connectivity toolbox, that would use markov chain monte carlo estimations of posterior distribution densities to deal with the missing data problem that is inherent to multi-subject connectivity analyses. Here is a link to a poster they presented a couple years ago on it.<br>
<br>
<a href="http://sccn.ucsd.edu/~scott/pdf/Thompson_and_Mullen_Poster_ICONXI.pdf" rel="noreferrer" target="_blank">http://sccn.ucsd.edu/~scott/pdf/Thompson_and_Mullen_Poster_ICONXI.pdf</a><br>
<br>
However, due to some extenuating circumstances, they were not able to complete it. I am so thankful Makoto has taken up the charge to address the issue with his own toolbox!<br>
<br>
Second, a work-around that I have been using has been to conduct k-means clustering (using the standard STUDY function in EEGLAB), with a heavy weight on "dipole location" in the pre-clustering step. Then, I use these across-subject clusters (choosing the clusters that have the highest recruitment, i.e. have IC's from as many subjects as possible) to determine which IC's I will measure connectivity between. In other words, if Cluster 5 and Cluster 9 both recruited IC's from >90% of my subjects, then for each subject that has IC's in both clusters, I will measure the connectivity between the components, for that subject, that are in both clusters.<br>
<br>
Then i average the connectivity values accordingly, and simply ignore any subjects that are not co-occurring in both clusters of interest.<br>
<br>
This is not ideal but does generate plausible results.<br>
<br>
I'm so glad you brought this issue back onto the front burner, Iman!<br>
<br>
James<br>
<br>
</span>From: Makoto Miyakoshi<mailto:<a href="mailto:mmiyakoshi@ucsd.edu">mmiyakoshi@ucsd.edu</a>><br>
<span class="">Sent: Wednesday, July 13, 2016 12:00 AM<br>
</span>To: Iman Mohammad-Rezazadeh<mailto:<a href="mailto:irezazadeh@ucdavis.edu">irezazadeh@ucdavis.edu</a>><br>
Cc: EEGLAB List<mailto:<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>>; Scott Makeig<mailto:<a href="mailto:smakeig@ucsd.edu">smakeig@ucsd.edu</a>>; <a href="mailto:s.sanei@surrey.ac.uk">s.sanei@surrey.ac.uk</a><mailto:<a href="mailto:s.sanei@surrey.ac.uk">s.sanei@surrey.ac.uk</a>><br>
<span class="">Subject: Re: [Eeglablist] Group level brain connectivity analyses in sourcelevel<br>
<br>
Dear Iman,<br>
<br>
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.<br>
<br>
Makoto<br>
<br>
<br>
<br>
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br>
Your abstract, "Group-level statistics on EEG effective source connectivity," 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<br>
<br>
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.<br>
<br>
<br>
</span><span class="">On Tue, Jul 12, 2016 at 4:11 PM, Iman Mohammad-Rezazadeh <<a href="mailto:irezazadeh@ucdavis.edu">irezazadeh@ucdavis.edu</a><mailto:<a href="mailto:irezazadeh@ucdavis.edu">irezazadeh@ucdavis.edu</a>>> wrote:<br>
Hi EEGLABers,<br>
<br>
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.<br>
<br>
<br>
1- Does anyone has experience on doing group level connectivity analyses on source ( not channel level).<br>
<br>
2- 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?<br>
<br>
I would greatly appreciate any input and also please share any resources that you might think it is helpful.<br>
<br>
Thanks<br>
Iman<br>
-------------------------------------------------------------<br>
Iman Rezazadeh, Ph.D<br>
Project Scientist | Semel Intitute, UCLA , Los Angeles, CA<br>
Adjunct Researcher | Center for Mind and Brain, Davis, CA<br>
<br>
<br>
<br>
<br>
<br>
--<br>
Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego<br>
<br>
--<br>
James Jones-Rounds<br>
Laboratory Manager<br>
Human Development EEG and Psychophysiology (HEP) Laboratory,<br>
Department of Human Development,<br>
--------------------------------------------<br>
Cornell University | Ithaca, NY<br>
</span><a href="tel:607-255-9883" value="+16072559883">607-255-9883</a><tel:<a href="tel:607-255-9883" value="+16072559883">607-255-9883</a>><br>
<a href="mailto:eeg@cornell.edu">eeg@cornell.edu</a><mailto:<a href="mailto:eeg@cornell.edu">eeg@cornell.edu</a>><br>
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<span class=""><br>
<br>
<br>
--<br>
Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego<br>
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<div class=""><div class="h5"><br>
<br>
<br>
--<br>
Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego<br>
</div></div></blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature" 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>
</div></div>