[Eeglablist] Group level brain connectivity analyses in sourcelevel

Thu Jul 14 10:48:28 PDT 2016

Dear James,

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.
1. Wes & Tim's 'Bayesian Hierarchical Modeling' with interpolating
'missing' ICs acorss subjects.
2. Nima's 'Network Projection' which blurs existing ICs and average across
subjects.
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.

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.

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.

Makoto

On Thu, Jul 14, 2016 at 6:31 AM, James Jones-Rounds <jj324 at cornell.edu>
wrote:

> I have been struggling with this issue for a couple years, also, and have
> two responses to your questions, Iman.
>
>
> 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.
>
>
> http://sccn.ucsd.edu/~scott/pdf/Thompson_and_Mullen_Poster_ICONXI.pdf
>
>
> 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!
>
>
> 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.
>
>
> Then i average the connectivity values accordingly, and simply ignore any
> subjects that are not co-occurring in both clusters of interest.
>
>
> This is not ideal but does generate plausible results.
>
>
> I'm so glad you brought this issue back onto the front burner, Iman!
>
>
> James
>
>
> *From: *Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> *Sent: *Wednesday, July 13, 2016 12:00 AM
> *To: *Iman Mohammad-Rezazadeh <irezazadeh at ucdavis.edu>
> *Cc: *EEGLAB List <eeglablist at sccn.ucsd.edu>; Scott Makeig
> <smakeig at ucsd.edu>; s.sanei at surrey.ac.uk
> *Subject: *Re: [Eeglablist] Group level brain connectivity analyses in
> sourcelevel
>
>
>
> Dear Iman,
>
>
>
> 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.
>
>
>
> Makoto
>
>
>
>
>
>
>
> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
>
> Your abstract, "*Group-level statistics on EEG effective source
> connectivity*," has been accepted into the program as a traditional
> poster presentation
>
> Session Type: Poster
> Session Number: 851
> Session Title: Computational Tools for Human Data II
> Date and Time: Wednesday Nov 16, 2016 1:00 PM - 5:00 PM
> Location: San Diego Convention Center: Halls B-H
> Abstract Control Number: 9417
>
>
>
> 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.
>
>
>
>
>
> On Tue, Jul 12, 2016 at 4:11 PM, Iman Mohammad-Rezazadeh <
> irezazadeh at ucdavis.edu> wrote:
>
> Hi EEGLABers,
>
>
>
> 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.
>
>
>
> 1-      Does anyone has experience on doing group level connectivity
> analyses on source  ( not channel level).
>
> 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?
>
>
>
> I would greatly appreciate any input and also please share any resources
> that you might think it is helpful.
>
>
>
> Thanks
>
> Iman
>
> *-------------------------------------------------------------*
>
> *Iman Rezazadeh, Ph.D*
>
> Project Scientist  | Semel Intitute, UCLA , Los Angeles, CA
>
> Adjunct Researcher | Center for Mind and Brain, Davis, CA
>
>
>
>
>
>
>
>
>
> --
>
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
> --
> James Jones-Rounds
> Laboratory Manager
> Human Development EEG and Psychophysiology (HEP) Laboratory,
> Department of Human Development,
> --------------------------------------------
> Cornell University | Ithaca, NY
> 607-255-9883
> eeg at cornell.edu
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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