[Eeglablist] Group level brain connectivity analyses in sourcelevel

[Makoto Miyakoshi]

Dear Iman and Robert,

Oh ok that what he means. Sorry. The group-level SIFT scheme I've been
developing normalizes individual data into AAL-defined anatomical ROIs. As
long as ICA results are more or less comparable (or by applying wider 3-D
Gaussian smoothing size), you can assume consistency across pre and post
within subject.

Makoto

On Wed, Jul 13, 2016 at 3:38 PM, Iman Mohammad-Rezazadeh <
irezazadeh at ucdavis.edu> wrote:

> Hi Makoto,
>
> I think what Rob’ point is if you have two different recordings ( pre-/
> post-treatment , for example ) then you may get two different sets of
> dipoles.  It is like single-subject longitudinal design .Your new approach
> would be useful to see the changes from pre to post.
>
> Best,
>
> Iman
>
>
>
> *From:* Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu]
> *Sent:* Wednesday, July 13, 2016 3:33 PM
> *To:* Rob Coben <drcoben at gmail.com>
> *Cc:* Iman Mohammad-Rezazadeh <irezazadeh at UCDAVIS.EDU>; 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 Rob,
>
>
>
> > Would this also work on individual eeg’s (e.g., pre-post in a single
> subject)? Very interesting..
>
>
>
> For a single-subject data, use SIFT. It's been there since 2010. My
> solution is for the group-level, which does not even work with n==1 (for
> technical reason for now; in theory it works).
>
>
>
> Makoto
>
>
>
> On Wed, Jul 13, 2016 at 7:01 AM, Rob Coben <drcoben at gmail.com> wrote:
>
> Makoto,
>
>
>
> Would this also work on individual eeg’s (e.g., pre-post in a single
> subject)? Very interesting..
>
>
>
> Rob
>
>
>
> Sent from Mail <https://go.microsoft.com/fwlink/?LinkId=550986> for
> Windows 10
>
>
>
> *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
>
>
>
>
>
>
>
> --
>
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>



-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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