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

Thu Feb 20 10:08:59 PST 2014

Dear Andrei,

I solved a question for you myself. Scott told me to remember that EEG
sources are dipoler... absolutely correct!

> However, ERP researchers have observed inverted visual potentials in
frontal channels, and also inverted N170 at vertex (Cz), and so on. Why
these ERP components change phases depending on recording sites? I thought
these are simple volume conduction.

So the ERP phase differences between channels are due to dipole sources.
Somehow I was thinking about monopoler sources (my first drawing on the
notebook was wrong)! And as long as the channel ERPs' relative phase
differences are fixed, it is accounted for by positive part.

It would be interesting to compare peak latencies between occipital visual
evoked potentials and their inverted potentials picked up at frontal
channels... if volume conduction is instantaneous, they should show the
same peak latencies. I haven't checked it yet.

Makoto

2014-02-20 9:54 GMT-08:00 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:

> Dear Iman,
>
> I did not understand your explanation. I don't know convenient terms to
> discuss it (I don't have an engineering background), so please use plain
> words.
>
> My point is that neural signal has predictive patterns, which is what I
> meant auto-correlation. This means X(t) and X(t+1) are correlated, so are
> Y(t) and Y(t+1). If this holds, then it seems impossible to assume that
> although X'(t) and Y'(t) are independent X'(t) and Y'(t+1) are dependent.
>
> Dear Andrei,
>
> I have a question about Nolte's claim.
>
> **********
> The imaginary part of coherency is only sensitive to synchronizations of
> two processes which are time-lagged to each other. If volume conduction
> does not
> cause a time-lag, the imaginary part of coherency is hence insensitive to
> artifactual 'self-interaction'.
> **********
>
> I understand it. The assumption here is that source activity should be
> observed at different channels with the *same phase*. Am I correct?
> However, ERP researchers have observed inverted visual potentials in
> frontal channels, and also inverted N170 at vertex (Cz), and so on. Why
> these ERP components change phases depending on recording sites? I thought
> these are simple volume conduction.
>
> 'zero-delay' interaction is very interesting as you point. ICA is not good
> at capturing gamma (in my opinion) unless it is coupled with theta or other
> low-frequency activities. Our lab also reported broadband gamma (Onton and
> Makeig 2009) which is a different form of gamma from well-known gamma burst
> evoked by Kanitza illusions or moony faces.
>
> It's a great opportunity for learning. Maybe my questions are naive and
> possibly based on wrong understanding. If I'm wrong I would appreciate if
> you tell me how I failed. Thank you very much.
>
> Makoto
>
>
> 2014-02-19 14:34 GMT-08:00 Iman M.Rezazadeh <irezazadeh at ucdavis.edu>:
>
>  Thanks Andrei for elaborating this in more details. Also in  my former
>> post,
>>
>> I forgot to mentioned the imaginary coherence method as suggested on
>> Nolte et al. work and I agree with you on this as well.
>>
>> -Iman
>>
>>
>>
>> *From:* eeglablist-bounces at sccn.ucsd.edu [mailto:
>> eeglablist-bounces at sccn.ucsd.edu] *On Behalf Of *Andrei Medvedev
>> *Sent:* Wednesday, February 19, 2014 12:18 PM
>> *To:* eeglablist at sccn.ucsd.edu
>>
>> *Subject:* Re: [Eeglablist] Two step source connectivity analysis (as
>> implemented in SIFT)
>>
>>
>>
>> Hi All,
>>
>> I believe Iman gave an important point for the discussion. Let me
>> reiterate it. Causality (Granger or any other causality algorithm for that
>> matter) implies that there is a TIME DELAY between the first signal (the
>> source of influence) and the second signal (the recipient of influence).
>> While, on the other hand, ICA is essentially tries to eliminate
>> INSTANTANEOUS dependence between signals i.e, at each CURRENT time point.
>> Therefore, causality and ICA do not contradict (at least, conceptually).
>> Any source reconstruction algorithm is also conceptually similar to ICA b/c
>> it minimizes this instantaneous dependence between signals. The most
>> important issue here is that this way we minimize a possible artefactual
>> component present in both signals such as 'influence' simply due to volume
>> conductance. It makes sense b/c (usually) 'real' influence is NOT
>> instantaneous and takes some time to occur (but see below for the important
>> exception).
>>
>> So, if one does ICA and then calculates Granger (or any other type of
>> autoregressive (AR) modeling) between components x(t) and y(t), the
>> expected (and ideal) result would be that the influence between x(t) and
>> y(t) should be close to zero (thanks to ICA) but there may be a non-zero
>> influence at time shifts >0 (at t and t-1 etc). All seems to be fine (I am
>> putting aside the fact that 'no algorithm is perfect' and small delays may
>> still result in some amount of instantaneous correlation b/c signals may
>> not be perfect Poisson processes and thus have some 'memory' i.e., their
>> autocorrelation functions are not delta-functions).
>>
>> This approach is similar to the imaginary coherence which is insensitive
>> to instantaneous effects of volume conductance (Nolte et al 2004).
>>
>> But to add more to the discussion, this approach means that when we
>> minimize instantaneous effects, we may overlook a real 'zero-delay'
>> interaction when two signals are synchronized at phase delay =0. The good
>> example of such zero-delay interaction is gamma-band synchrony. Here, the
>> zero-phase is achieved through the emergent property of the network itself
>> (due to mutual inhibitory and excitatory connections). To reveal this
>> zero-delay interaction in the presence of volume conductance seems to be a
>> hard problem. But I would still argue in favor of removal instantaneous
>> effects simply because they are huge in scalp EEG. Also,
>> 'physiological'/'real' zero-phase synchrony is likely to be 'not perfect'
>> giving rise to small deviations from zero from time to time, which would
>> then be 'detected' by Granger/AR/imag coh).
>>
>> I also agree that going to the source space instead of the channel space
>> (through ICA or other source reconstruction algorithms) is not free of its
>> own limitations. Perhaps, applying Granger/AR (with 'instantaneous'
>> coefficients ignored) or imaginary coh to the channel data could be a
>> method of choice as well.
>>
>> Best,
>> Andrei Medvedev
>>
>> --
>>
>> Andrei Medvedev, PhD
>>
>> Assistant Professor,
>>
>> Center for Functional and Molecular Imaging
>>
>> Georgetown University
>>
>> 4000 Reservoir Rd, NW
>>
>> Washington DC, 20057
>>
>>
>> On 2/19/2014 1:18 PM, Makoto Miyakoshi wrote:
>>
>> 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
>>
>>
>>
>>
>>
>> _______________________________________________
>>
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>>
>>
>>
>>
>>
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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
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