[Eeglablist] FW: Beyond good and evil of ICA

Robert Thatcher rwthatcher2 at yahoo.com
Mon Aug 7 07:58:43 PDT 2017


Pedro,

      Althoughmy comments were not about lagged coherence and instead about the phase slopeindex and distortion of phase differences by ICA reconstruction I agree thatthe volume conductor that contains heterogeneous tissue compartments can also altertime or phase differences.  However, thefact is that the magnitude of effect on phase differences by a heterogeneousvolume is small (e.g., a few milliseconds or 1 to 2degrees) compared to physiological factors such as white matter conductionvelocity which is as much as 40 degrees at Euclidean distances of 120millimeters between Brodmann area center voxels.  Here is a url to a publication showing this,see figure 6 at:

http://www.appliedneuroscience.com/Default_Network_LORETA_Phase_Reset-Thatcher_et_al.pdf

 

Also, one can correlate phase differences with whitematter fiber spacing such as the ‘U’ shaped cortico-cortical white matter atthe scalp surface.   If one transformsphase differences into milliseconds then as one increases scalp interelectrodedistance phase differences vary from 5 msec for closely spaced electrodes toover 100 msec for Fp1/2-O1/2 spacings.  Hereis a url to a publication showing this, see figures 7 & 8 at:

http://www.appliedneuroscience.com/TWO-COMPARTMENTAL_MODEL_EEG_COHERENCE.pdf

 

What is more important is the false assumption thatICA reconstruction some how corrects for a heterogeneous volume conductor whenthere is no evidence to support this claim. Further the correlations with underlying physiological factors such as synapticdelays and white matter conduction velocities are obliterated by ICA reconstruction.  Therefore the effects of a heterogeneoustissue volume conductor are also distorted by ICA reconstruction. For example,once phase differences in the original time series are altered by ICAreconstruction for each and every time point then covariance calculations areno longer valid since the primary level of data has been altered.  Although I did not mention lagged coherence,I just compared lagged coherence between the original EEG recording and theAustralian and Delorme ICA Reconstructions (i.e., using 17 ICs to create 19channels) and 100% of the values were different and statistically all were greaterthan 0.0000001 different.  The argument onthis list server is that there is no ground truth of the EEG and that the originalEEG is nothing but noise and artifact and therefore it is ok to alter all of the phase differences in the original EEG recording.   In my opinion and the opinion of many others (not on the Eeglablist) once the phase differences in the original recording are altered then all subsequent network analyses including lagged coherence, directedcoherence and phase slope index are decoupled from the physiological ground truth of the EEG.   

 

As always it is great to hear from you!

 

Warm regards,

 

Bob


On Sunday, August 6, 2017, 3:40:07 PM EDT, Pedro Antonio Valdés-Sosa <pedro.valdes at neuroinformatics-collaboratory.org> wrote:

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Great Summary of EEG generation RobertI
 
I agree that there is great confusion regarding instantaneous effects of volume conduction and phase relations.
 
I must warn that the volume conductor model is not only instantaneous. It affects all the elements used to calculate phases at the electrodes.
 
I reproduce a summary that Thomas Koenig and I are carrying out in the form of the usual phase fallacy arguments (in black) and a a criticism (in red)
 
  
 
1: Volume conduction leads to instantaneous correlations on the scalp (correct).
 
CORRECT
 
  
 
BUT  it also induces mixing of lagged interactions
 
V(t)=K j(t) + e(t), V voltage, K lead field, J currents, e sensor noise. Everything is indexed by time.
 
Lets look at covariance since correlation and coherence—and phase as a consequence-- comes from it. Assuming stationarity (without loss of generality).
 
SigmaV(0) = K SigmaJ(0) K’ + sigmae^2  K’ is lead field transposed
 
SigmaV (0) covariance of EEG at lag 0—that is cov [V(t) with V(t)], SigmaJ(0) idem for sources,  sigmae^2 sensor noise variance
 
Now let look at lagged covariance SigmaV(lag) that it cov(V(t), V(t=tau))
 
Assuming sensor white noise the sigmae^2 disappears
 
SigmaV(tau)= K SigmaJ(tau) K’
 
Where is the disappearance of the effect of volume conduction persists even on lagged influences?
 
 
 
2: by using phase-lagged quantifiers of dependencies among pairs of channels, we can eliminate the effect of instantaneous correlations that are potentially explained by volume conduction in the given pair of channels.
 
FALSE because of the argument above this is totally false and is the phase fallacy
 
Volume condution, modeled by the lead field affects lagged measures as much as instantaneous ones
 
 
 
3: Thus, we have completely eliminated the volume conduction problem.
 
FALSE since 2 is not true
 
 
 
There are a number of measures that purport to avoid volume conduction effects, both instantaneous and lagged and allow inference of brain interactions at the scalp. These measures are “magical” since they solve the effect of the Lead field without factoring it into the model.
 
  
 
  
 
From:eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of Robert Thatcher <rwthatcher2 at yahoo.com>
Date: Sunday, 6 August 2017 at 15:23
To: Marius Klug <marius.s.klug at gmail.com>, "tarikbelbahar at gmail.com" <tarikbelbahar at gmail.com>
Cc: Eeglablist <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] FW: Beyond good and evil of ICA
 
  
 
Marius,
 
    The ground truth of the EEG and phase delays begins with the physics of the electrical field potential (or the electrostatic potential) defined as the amount of work needed to move a unit positive charge from a reference point to a specific point inside the field without producing any acceleration. When the curl ∇ × E is zero then the electrical potential is determined by the gradient of the electrical field.  EEG differential amplifiers are used to measure the difference or gradient of the electrical field and therefore the electrical potential is the difference between the two inputs to the amplifier.  If one measures the electrical field and then removes ICs and starts with a smaller number of EEG channels to create a larger number of time series to replace the original time series then this changes the gradients of the electrical field produced by the brain and also distorts the time differences between samples then the physics of the electrical field no longer applies.
 
The majority of neuroscientists agree that the physiological ground truth for the genesis of the EEG as being summated synaptic potentials in groups of neurons that are connected by axons.   The ground truth of phase differences between separated scalp locations are physiological factors of the electrical sources and the connections between sources (i.e., networks) such as axonal conduction velocities, length of connecting axons, synaptic rise times, synaptic delays and synaptic integration times, etc.   This is well established science and it was quite surprising to read posts on this forum by individuals that claim that there is no ground truth of the EEG, EEG is all noise and therefore this justified altering phase differences using a small number of ICs to create different time series (i.e., reconstruction) to thereby decoupling the brain from the subject and the physics of electrical fields.   Like Pedro Valdez I have also recorded EEG from the dura and pia surface and simultaneously from the scalp.  I also have impaled neurons and correlated the intracellular EEG with field potentials as a function of distance from the impaled neurons.  These recordings were when I was a post doc at Albert Einstein College of Medicine   With E. Roy John we moved multiple micro-electrodes through wide regions of the cat brain while also recording EEG from the scalp.  It was during this time that Roy and myself and others quantified how phase differences increase or decrease as a function of interelectrode distance.  At the University of Maryland myself and colleaques, while still collaborating with Roy, did extensive studies that linked phase differences to the cortical white matter with short distance ‘U’ shaped fibers vs long distance fasciculi.  These measures helped Paul Nunez to use scalp EEG phase differences to estimate white matter conduction velocities.  Additional validation studies of the network physiological ground truth of EEG phase differences was when I worked at NIH with patients before and after craniotomy.   Further validations also involved correlations with MRI T2 relaxation time.  
 
  
 
Luckily ICA reconstruction was not invented in the 1960s because if it had then today we would have no idea that there is a replicable and verifiable ground truth of EEG nor know about the relationship between EEG phase differences and the cerebral white matter.  
 
 
 
As for volume conduction and phase delays between separated sensors I think that there may be some confusion between the instantaneous physics of the electrical potential and the volume conductor model used for the inverse solution.  The volume conductor model pertains to a single instant of time where the accuracy of the spatial localization of sources is affected by inhomogeneities of tissue conductivities that primarily affect amplitudes and only negligibly time differences which are near to the speed of light.  A good book on this topic is Malmuvo and Plonsey “Bioelectricity”, Oxford University, 1995 or a standard physics text book, for example, Richard Feynman’s book.  Here is a url to a study where we evaluated phase differences as a function of the Euclidean distance between Brodmann areas that comprise the default network - see figure 6 at:
 
http://www.appliedneuroscience.com/Default_Network_LORETA_Phase_Reset-Thatcher_et_al.pdf
 
 
 
We have also evaluated the distortion of effective connectivity by ICA reconstruction.  For example, we computed the phase slope index before and after ICA reconstruction and 100% of the effective connectivity between 171 electrode pairs for all frequencies is adulterated.  This is another example of decoupling the “ground truth” of the physiology of the brain from the subject.   We recently published a study that correlated intelligence with effective connectivity where we found significant differences between low and high I.Q. subjects.   If we had used ICA reconstruction then because phase differences are scrambled and distorted and thereby decoupled from the brain it is unlikely that there would have been any significant differences. 
 
Here is a url to this study: http://www.appliedneuroscience.com/Intelligence%20&%20information%20flow-Thatcher%20et%20al%202016.pdf
 
 
 
For those interested, here is a url to a book that E. Roy John and myself published in 1977 that reviews the history of how the ground truth of EEG was discovered.  The ICA replacement violates the electricity and physiology of the brain and contradicts this history:  http://www.appliedneuroscience.com/FuncNeuro_v1n.pdf
 
 
 
Best wishes,
 
 
 
Robert
 
  
 
  
 
On Friday, August 4, 2017, 1:10:52 PM EDT, Marius Klug <marius.s.klug at gmail.com> wrote:
 
  
 
  
 
Hi everyone!
 
One thing after the other...:
 
  
 
Georges, sure, feel free to use the example, I'm always happy to contribute :-)
 
A comparison of high to low density ICA cleaning would definitely be interesting and my hypothesis is that the ICA decomposition might be significantly worse (meaning more changes in the posterior channels) in the 19-channels set. If I have some spare time I might do that and post the results. Will take a while, though, I won't have time until end of September I think...


 
  
 
Robert, if you read again my very first email on the topic, I have not changed my mind the slightest bit... I have said from the beginning that ICA does indeed change the data, which is rather obvious. It's only about the question where this change is a positive or a negative one. However, as I said, it might be that 19 channels is not a sufficiently high number for a decent ICA decomposition for cleaning. It sure is not for working on the source level, but by now I did indeed assume that the eye components are so strong that even 19 channels are enough to cleanly take them out. If that's not the case, it's an important information and everyone needs to decide for themselves if they want to use ICA and risk losing a slight bit of brain data or other cleaning methods which might prove to be better in the low-density segment. I would, however still HIGHLY encourage you to test whether or not the results with the ICA-based cleaning are significantly better or worse than without. As long as you have not tested this, you keep rejecting a method that might prove useful for you.
 
The as you call it false belief of continuous artifacts has in my opinion already been adressed enough and I guess you just must have special means of keeping eye and neck muscles and heart beats from continuous signals in the EEG, since in ordinary participants eye components seem to correlate highly with eye tracker signals and I can still see the heart beats of my subjects in their EEG. As I've stated elsewhere, there are many means of increasing signal-to-noise ratio, ICA just being one of them (one other would for example be the averaging of ERPs). Many other published sources just tell us that those other means work as well and that the signal had been strong enough for statistically significant results. It does not say there are no continuous artifacts, that's a logical fallacy.
 
As for the statistically independent sources in ICA and connected brain regions: This is an interesting thing to think about, that's true. One answer would be that ICA has no temporal knowledge while separating the sources, it just works with point clouds, but the brain connections are temporal. One other might be that ICA doesn't separate based on correlation but more complex measures of the data set. I must admit I didn't have the time yet to fully understand the different ICA algorithms, though. My last answer to this is the fact that ICA gets nicely dipolar components which can be projected back to the brain, and this also works in simulations where the true sources are known. So for me, at least with sufficiently high density (64 at least, better 128) ICA components that have a neat separation from other components are brain sources. This has to be taken with a grain of salt though, since one needs to be experienced in understanding inhowfar a component might still be mixed sources. For example residual variance of the component gives at least a hint about this question. Unless proven otherwise, I will keep my working definition of ICA components as sources, and especially dipolar sources as brain sources, since I work on the source level anyway... 
 
Be it as it may, the 18-19 bitcoin example still doesn't hold true, you can just replace "sources" with "components". You made another strawman argument which I answered because it's an interesting question, not because it's an actual counter argument against my case.
 
Also, I think exactly the central-limit theorem is being used in ICA. A singular source is always less gaussian than mixed sources, so a de-mixing matrix which maximizes non-gaussianity of components would maximize the separation of the components from each other. I think your argument here goes exactly where the basic ICA argument goes so I don't see your point. Seehttp://arnauddelorme.com/ica_for_dummies/
 



 
Makoto, yes, the dipole model is definitely far from perfect. We also attempt to create a head model including the eyes and neck muscles, since the head movements are vitally important for us. Blood vessels is an interesting point though, we hadn't thought about this, as far as I know at least. This would definitely improve localization of the muscle artifacts and simplify the discrimination of eye and muscle, but of course muscles are complex in their electrical properties so it's going to be an interesting question if this is working as intended. The distributed source model does appeal to me in the sense that it's just more realistic than a single dipole creating the signal at the scalp, but I am not familiar with the assumptions that are held for this.

Another big issue to me is that we don't get the same components for each subject. Clustering is a really bad way of combining the components, even though it's the best we have. In fact, I've realized that the clustering solution varies to a more-than-slight degree even if you re-cluster with the exact same parameters. I've thought of a method to do repeated clustering and compare the results to have a kind of probabilistic model slightly similar to Nima's Measure Projection, but it has proven to be more work than I had time. I might come back to it, though! As you said, a long way to go to make EEG a smoothly working brain imaging technique.. but all the more interesting and it's fascinating to be part of this!



 
Georges, I don't think anyone ever doubted that the data changes after one takes out eye components. It's obvious and I don't recall emails that state otherwise. The whole discussion was about the fact that the change in the data would increase the quality and make it closer to the actual brain data, that was even in Arno's very first post. It's reflected in the wording, for example you and robert use "distortion" or "adulteration", while Arno, Makoto and others from this ist use simply "change", because this implies no negative impact.
 
I think an important thing to keep in mind is that we can not really make any meaningful conclusions from sensor level activity as to where this activity originated from. Sensor-level connectivity will always have this drawback, plus the mixing in of artifactual sources, which is why Johanna has said a while ago that she performs only high-density EEG source-level connectivity analysis, nothing on the sensor-level at all. It obviously depends on your data set and application, but to me, 19 channel surface EEG is always to be treated with extra care as to any interpretations.



 
Tarik, yes, it seems to me that there is tons of method research to be done and paper to be written. We soon will have a synchronized data set containing high-density EEG with 28 as EMG on the neck, together with motion capture of the head, shoulders, hands and feet in position and orientation and eye tracking while moving around in virtual reality. This might provide useful for investigating the continuous artifacts, since we have eye movement, muscle activity, and (if we record this in addition) ECG all synchronized and we can look for it's connections to the EEG data. Well, the things to come! It all needs time and resources... Anyways, I would be glad to contribute to some of the papers if I can, since investigating the EEG methods is one of my main interests as a researcher.


 
All the best,
 
Marius
 
  
 
2017-07-28 20:23 GMT+02:00 Tarik S Bel-Bahar <tarikbelbahar at gmail.com>:
 

Greetings, it's great to see a more congenial future-focused discussion
 
​building
 
 here.
 
​ We all have knowledge we consider as true, biases that impact our perceptions and expectations, and this all sometimes gets in the way of (needed) discussion on research topics and assumptions. It seems we often need less "fixed ideas and assumptions" and to be more open to "new published data-based findings addressing specific topics". 
 
Overall, kudos for keeping the
 
​human ​
 
conversation going
 
, it may eventually lead to positive impacts for the EEG field.
 
  
 
>From my perspective,
 
​ ​
 
argument
 
​s​
 
or point of view, whether based on theory
 
​, math,​
 
or experience,
 
will
 
​remain
 
 
 
​un​
 
satisfactory until th
 
​ey
 
 are
 
​in ​1) 
 
multiple published new high-quality
 
​ empirical​
 
 ​
 
papers
 
​that 2) examine a ​
 
range of topics related to the discussion here about ICA, EEG,
 
​phase metrics, ​
 
artifacts, etc... from a
 
​3) ​
 
data-and assessment-focused point of view. 
 
​It's important to have conversations, but it's new peer-reviewed publications that will truly clarify things.​
 
  
 
​I may be too optimistic, but more collaboration amongst the discussants here could generate a half-dozen papers over the next year or two focused on these issues. These are opportunities for both young flexible researchers, as well as more mature researchers and clinicians. 
 
  
 
One thing that has struck me is that the conversation has reflected
 
​1) ​
 
a divide between low-density and high-density EEG systems users,
 
​2) the differences in researcher and clinician values, ​
 
and
 
​that 3) 
 
there is enough data
 
​already ​
 
out there to provide data-based publications addressing these topics. Further, the I think there is a lot of room for theoretical and methodological consolidation in the EEG field, but this will take time and effort, just as a clear understanding of the nature of true neural sources will take time and effort. 
 
  
 
Below, a resend of my earlier list of topics that future articles can consider
 
​ or that we can add to/clarify​
 
, and a list of articles (and authors) that
 
​can
 
 be consulted regarding current use/opinions of ICA, EEG, phase, and con
 
​n​
 
ectivity
 
​ analyses​
 
.
 
  
 
  
 
  
 
Possible dimensions/constraints to consider or compare?
 

Channel density and relative total-head coverage
 
  
 
​Low-density vs. moderate-density vs high-density​
 
  
 
With and without EOG and/or EMG recording ?​
 

Number/type of artifact
 
​ual detected, number/type of​
 
ICs removed
 
  
 
​IC-based vs. non-IC based artifact removal​
 
  
 
Number/type of artifact visible in channel record
 
​​
 


Clarity/robustness of artifact 
 
​ual ICs ​
 
(e.g., ICs that are mixed vs. ICs that
are mixed (containing both artifact and neural info)

Channel-level vs. ICA-level vs. source 
 
​-estimated
 
 connectivity metrics

Length of epochs/trials

Type of source analysis

Type of reference (e.g., Chella et al., 2016)

Type of ICA/blind source separation (e.g., Bridwell et al., 2016;
Brain Topography)

Event-related or resting data

Signal quality (e.g., gel versus saline, very noisy vs. quite clean)

Reliability of particular metric

Type of connectivity metric (e.g., various kinds of phase measures,
and the plethora of other connectivity and graph theoretical measures)

MEG vs. EEG ?

Sampling rate? 
 
  
 
Type of population ?
 
​ Rate of artifacts per population/sample?
 
  
 
Individual differences in cleanness or clarity of EEG signal?
 
  
 




Sample articles that seem to use ICA in relation to connectivity
metrics are listed below.
 
​ ​
 
Moving forward, it may be beneficial to survey these authors and their findings.
 

de Pasquale, F., Della Penna, S., Sporns, O., Romani, G. L., &
Corbetta, M. (2015). A dynamic core network and global efficiency in
the resting human brain. Cerebral Cortex, bhv185.

Lai, M., Demuru, M., Hillebrand, A., & Fraschini, M. (2017). A
Comparison Between Scalp-And Source-Reconstructed EEG Networks.
bioRxiv, 121764.

Colclough, G. L., Woolrich, M. W., Tewarie, P. K., Brookes, M. J.,
Quinn, A. J., & Smith, S. M. (2016). How reliable are MEG
resting-state connectivity metrics?. NeuroImage, 138, 284-293.

Kuntzelman, K., & Miskovic, V. (2017). Reliability of graph metrics
derived from resting‐state human EEG. Psychophysiology, 54(1), 51-61.

Siems, M., Pape, A. A., Hipp, J. F., & Siegel, M. (2016). Measuring
the cortical correlation structure of spontaneous oscillatory activity
with EEG and MEG. NeuroImage, 129, 345-355.

Toppi, J., Astolfi, L., Poudel, G. R., Innes, C. R., Babiloni, F., &
Jones, R. D. (2016). Time-varying effective connectivity of the
cortical neuroelectric activity associated with behavioural
microsleeps. NeuroImage, 124, 421-432.

Farahibozorg, S. R., Henson, R. N., & Hauk, O. (2017). Adaptive
Cortical Parcellations for Source Reconstructed EEG/MEG Connectomes.
bioRxiv, 097774.

Rueda-Delgado, L. M., Solesio-Jofre, E., Mantini, D., Dupont, P.,
Daffertshofer, A., & Swinnen, S. P. (2016). Coordinative task
difficulty and behavioural errors are associated with increased
long-range beta band synchronization, NeuroImage.

Cooper, P. S., Wong, A. S., Fulham, W. R., Thienel, R., Mansfield, E.,
Michie, P. T., & Karayanidis, F. (2015). Theta frontoparietal
connectivity associated with proactive and reactive cognitive control
processes. Neuroimage, 108, 354-363.

Nayak, C. S., Bhowmik, A., Prasad, P. D., Pati, S., Choudhury, K. K.,
& Majumdar, K. K. (2017). Phase Synchronization Analysis of Natural
Wake and Sleep States in Healthy Individuals Using a Novel Ensemble
Phase Synchronization Measure. Journal of Clinical Neurophysiology,
34(1), 77-83.

Vecchio, F., Miraglia, F., Piludu, F., Granata, G., Romanello, R.,
Caulo, M., ... & Rossini, P. M. (2017). “Small World” architecture in
brain connectivity and hippocampal volume in Alzheimer’s disease: a
study via graph theory from EEG data. Brain imaging and behavior,
11(2), 473-485.

Ranzi, P., Freund, J. A., Thiel, C. M., & Herrmann, C. S. (2016).
Encephalography Connectivity on Sources in Male Nonsmokers after
Nicotine Administration during the Resting State. Neuropsychobiology,
74(1), 48-59.

Vecchio, F., Miraglia, F., Curcio, G., Della Marca, G., Vollono, C.,
Mazzucchi, E., ... & Rossini, P. M. (2015). Cortical connectivity in
fronto-temporal focal epilepsy from EEG analysis: a study via graph
theory. Clinical Neurophysiology, 126(6), 1108-1116.

Smit, D. J., de Geus, E. J., Boersma, M., Boomsma, D. I., & Stam, C.
J. (2016). Life-span development of brain network integration assessed
with phase lag index connectivity and minimum spanning tree graphs.
Brain connectivity, 6(4), 312-325.

Chung, Jae W., Edward Ofori, Gaurav Misra, Christopher W. Hess, and
David E. Vaillancourt. "Beta-band activity and connectivity in
sensorimotor and parietal cortex are important for accurate motor
performance." NeuroImage 144 (2017): 164-173.

Shou, G., & Ding, L. (2015). Detection of EEG
spatial–spectral–temporal signatures of errors: A comparative study of
ICA-based and channel-based methods. Brain topography, 28(1), 47-61.

Kline, J. E., Huang, H. J., Snyder, K. L., & Ferris, D. P. (2016).
Cortical Spectral Activity and Connectivity during Active and Viewed
Arm and Leg Movement. Frontiers in neuroscience, 10.

van Driel, J., Gunseli, E., Meeter, M., & Olivers, C. N. (2017). Local
and interregional alpha EEG dynamics dissociate between memory for
search and memory for recognition. NeuroImage, 149, 114-128.

Castellanos, N.P., Makarov, V.A., 2006. Recovering EEG brain signals:
Artifact suppression with wavelet enhanced independent component
analysis. J. Neurosci. Methods 158, 300–312.
doi:10.1016/j.jneumeth.2006. 05.033

Mehrkanoon, S., Breakspear, M., Britz, J., & Boonstra, T. W. (2014).
Intrinsic coupling modes in source-reconstructed
electroencephalography. Brain connectivity, 4(10), 812-825.
 

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