[Eeglablist] Two step source connectivity analysis (as implemented in SIFT)

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Wed Feb 19 12:27:14 PST 2014


Dear Iman,

Is it possible that X(t) and Y(t) are independent but X(t) and Y(t+1) are
dependent, given that X(t) has auto-correlation?

Makoto


2014-02-19 11:44 GMT-08:00 Iman M.Rezazadeh <irezazadeh at ucdavis.edu>:

> Hi Makoto and all,
>
> Actually I am thinking about ICA and GC without considering their
> applications in ERP/EEG. What I said is the IC sources are "independent' at
> time point t but ICA does not guarantee to remove any dependency between
> event X and Y at different time points like X(t) and (Y+1)
>
> ~Iman
>
>
>
> *From:* Aleksandra Vuckovic [mailto:Aleksandra.Vuckovic at glasgow.ac.uk]
> *Sent:* Wednesday, February 19, 2014 11:31 AM
> *To:* mmiyakoshi at ucsd.edu; Iman M.Rezazadeh
> *Cc:* EEGLAB List
> *Subject:* RE: [Eeglablist] Two step source connectivity analysis (as
> implemented in SIFT)
>
>
>
> Hi
>
> we've looked at Granger causality of ICAs during motor imagery task, yes
> you can definitively see that some sources 'speak' to each other at certain
> points of time, related to the event while some other seem to be there
> always (in mu rhythm) independent on the event.
>
> Regards,
>
> Alex
>
>
> ------------------------------
>
> *From:* eeglablist-bounces at sccn.ucsd.edu [eeglablist-bounces at sccn.ucsd.edu]
> On Behalf Of Makoto Miyakoshi [mmiyakoshi at ucsd.edu]
> *Sent:* 19 February 2014 18:18
> *To:* Iman M.Rezazadeh
> *Cc:* EEGLAB List
> *Subject:* Re: [Eeglablist] Two step source connectivity analysis (as
> implemented in SIFT)
>
> Dear Iman and all,
>
>
>
> So are you saying independent sources can Granger cause each other?
>
>
>
> I agree with Joe and you. I'm not a specialist, but I would imagine
> (correct me if I'm wrong) that ICs are *usually* independent *except*when they are perturbed event-relatedly. In such moments independence are
> transiently lost and ICs start to Granger cause each other... I tend to
> think in this way because stationarity depends on time scale. So in the
> sense it's correct to say ICs are *not always* independent, because its
> independency changes from timepoint to timepoint. You can see this
> visualization with one of AMICA tools. However I haven't seen a log
> likelihood drop around the event, which contradicts my explanation above,
> so I could be wrong somewhere. Multiple model AMICA does extract
> peri-event-onset periods as a different model though.
>
>
>
> Note also that there is an issue of IC subspace within which ICs are
> always intra-dependent.
>
>
>
> Makoto
>
>
>
> 2014-02-19 0:53 GMT-08:00 Iman M.Rezazadeh <irezazadeh at ucdavis.edu>:
>
> I would like step in and add more comments which may be helpful
> (hopefully):
>
>
>
> The assumption of ICA is : The observed data is the sum of a set of inputs
> which have been mixed together in an unknown fashion and the aim of ICA is
> to discover both the inputs and how they were mixed. So, after ICA we have
> some sources which are temporally independent. In other words, they are
> independent at time t  McKeown, et al. (1998)
>
>
>
> However and based on Clive Granger talk at 2003 Nobel Laureate in
> Economics "The basic "Granger Causality" definition is quite simple.
> Suppose that we have three terms, Xt, Yt, and Wt, and that we first
> attempt to forecast Xt+1 using past terms of Yt and Wt. We then try to
> forecast Xt+1 using past terms of Xt, Yt, and Wt. If the second forecast
> is found to be more successful, according to standard cost functions, then
> the past of Y appears to contain information helping in forecasting Xt+1that is not in past X
> t or Wt. ... Thus, Yt would "Granger cause" Xt+1 if (a) Yt occurs before X
> t+1 ; and (b) it contains information useful in forecasting Xt+1 that is
> not found in a group of other appropriate variables."  So, in Granger
> causality we try to relate time t+1 to t.
>
>
>
> So, ICA and Granger causality are not contradicting each other and finding
> causality btw sources would not have anything to do with source space or
> channel space data. In my point of view, using ICA and source signal for
> Granger causality is good because you do not have to worry about the volume
> conductance problem. However, one can apply Granger causality in the
> channel space as well since the dipole localization has its own
> limitations. One clue code be transforming the channel space data to
>  current source density (CSD) format and then applying any
> causality/connectivity analysis you would like to study.
>
>
>
> Best
>
> Iman
>
>
>
> *-------------------------------------------------------------*
>
> *Iman M.Rezazadeh, Ph.D*
>
> Research Fellow
>
> Semel Intitute, UCLA , Los Angeles
>
> & Center for Mind and Brain, UC DAVIS, Davis
>
>
>
>
>
> *From:* eeglablist-bounces at sccn.ucsd.edu [mailto:
> eeglablist-bounces at sccn.ucsd.edu] *On Behalf Of *Makoto Miyakoshi
> *Sent:* Tuesday, February 18, 2014 3:54 PM
> *To:* mullen.tim at gmail.com
> *Cc:* eeglablist at sccn.ucsd.edu
> *Subject:* Re: [Eeglablist] Two step source connectivity analysis (as
> implemented in SIFT)
>
>
>
> Dear Tim,
>
>
>
> Why don't you comment on the following question: If independent components
> are truly independent, how do causality analyses work?
>
>
>
> Dear Joe,
>
>
>
> Your inputs are too difficult for me to understand. In short, are you
> saying causality analysis works on independent components because they are
> not completely independent?
>
>
>
> Makoto
>
>
>
> 2014-02-18 15:46 GMT-08:00 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:
>
> Dear Bethel,
>
>
>
> > say A=sunrise and B=ice-cream-sale, then the ICA in EEGLAB should find
> that A is maximally  temporaly independent from B.
>
>
>
> ICA would find a correlation between sunrise and ice-cream-sale.
>
>
>
> Makoto
>
>
>
> 2014-02-10 4:57 GMT-08:00 Bethel Osuagwu <b.osuagwu.1 at research.gla.ac.uk>:
>
>
>
> Hi
> I am not an expert but I just want to give my own opinion!
>
> I do not think that temporal independence of two variables (A and B)
> violets causality between them as implemented in SIFT. In fact if  say
> A=sunrise and B=ice-cream-sale, then the ICA in EEGLAB should find that A
> is maximally  temporaly independent from B. However we know there is causal
> flow from A to B.
>
> This is what I think, but I wait to be corrected so that I can learn!
>
> Thanks
> Bethel
> ________________________________________
> From: eeglablist-bounces at sccn.ucsd.edu [eeglablist-bounces at sccn.ucsd.edu]
> On Behalf Of IMALI THANUJA HETTIARACHCHI [ith at deakin.edu.au]
> Sent: 07 February 2014 01:27
> To: mullen.tim at gmail.com
> Cc: eeglablist at sccn.ucsd.edu
> Subject: [Eeglablist] Two step source connectivity analysis (as
> implemented     in SIFT)
>
>
> Hi Tim and the list,
>
> I am just in need of a clarification regarding the ICA source
> reconstruction and the subsequent MVAR -based effective connectivity
> analysis using the components, which is the basis of the SIFT toolbox. I
> was trying to use this approach in my work but was questioned on the
> validity using ICA and subsequent MVAR analysis by my colleagues.
>
> "When using independent component analysis (ICA), we assume the mutual
> independence
> of underlying sources, however when we try to estimate connectivity
> between EEG sources,
> we implicitly assume that the sources may be  influenced by each other.
> This contradicts the
> fundamental assumption of mutual independence between sources in ICA
> [Cheung et al., 2010, Chiang et al., 2012, Haufe et al., 2009 ]. "
>
> So due to this reason different approaches such as MVARICA,
> CICAAR(convolution ICA+MVAR),  SCSA and state space-based methods have been
> proposed as ICA+MVAR based source connectivity analysis techniques.
>
>
> ·         So, how would you support the valid use of SIFT ( ICA+MVAR as a
> two-step procedure) for the source connectivity analysis?
>
>
> ·         If I argue that I do not assume independent sources but rely on
> the fact that ICA will decompose the EEG signals and output 'maximally
> independent' sources and then, I subsequently model for the dependency,
> will you agree with me? How valid would my argument be?
>
> It would be really great to see different thoughts and opinions.
>
> Kind regards
>
> Imali
>
>
> Dr. Imali Thanuja Hettiarachchi
> Researcher
> Centre for Intelligent Systems research
> Deakin University, Geelong 3217, Australia.
>
> Mobile : +61430321972
>
> Email: ith at deakin.edu.au<mailto:ith at deakin.edu.au>
> Web :www.deakin.edu.au/cisr<http://www.deakin.edu.au/cisr>
>
> [cid:image001.jpg at 01CF23FF.F8259940]
>
>
>
>
>
>
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>
>
>
> --
>
> 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
>



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