[Eeglablist] Group level brain connectivity analyses in sourcelevel
jj324 at cornell.edu
Thu Jul 14 06:31:52 PDT 2016
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
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!
*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
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.
Your abstract, "*Group-level statistics on EEG effective source
connectivity*," has been accepted into the program as a traditional poster
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:
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.
*Iman Rezazadeh, Ph.D*
Project Scientist | Semel Intitute, UCLA , Los Angeles, CA
Adjunct Researcher | Center for Mind and Brain, Davis, CA
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
Human Development EEG and Psychophysiology (HEP) Laboratory,
Department of Human Development,
Cornell University | Ithaca, NY
eeg at cornell.edu
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