[Eeglablist] Group level brain connectivity analyses in source level

[Iman Mohammad-Rezazadeh]

Hi Makoto,
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.

Many thanks,
Iman

From: Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu]
Sent: Tuesday, July 12, 2016 10:00 PM
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: Group level brain connectivity analyses in source level

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<mailto: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
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