[Eeglablist] FW: ICA misinformation

otte georges georges.otte at pandora.be
Wed Jun 14 15:15:06 PDT 2017


Subject: RE: [Eeglablist] ICA misinformation


Dear Johanna


The model of volume conduction would be completely correct if brains were a kind of  balloons filled with saline and sources would be dipoles suspended in it at fixed places with recording electrodes placed on the outside.


Although of course volume conduction is a reality in brains (Nunez) we are especially interested in communication transfert between nodes in several well defined networks and delays in the communication from a node A to a node B will be reflected in a non zero phase lag between the signals. Of course one must use a measure of information transfert (such as Nolte’s imaginary part of the coherence or other equal or better parameters to extract the phase diff NEQ zero cf Pasqual Marqui) 

So If one does find a phase difference between two scalp electrodes that is different from zero that cannot be caused by volume conduction, can it?


I agree that analysis in source space will provide even better fine grained insight in network communication patterns but I am not so convinced that ICA is the best way to pinpoint those network hub sources especially in low channel recordings where there is a real possibility of overrepresentation (more sources then recording electrodes). We know a lot of those network graphs by extensive DTI and fMRI studies (cf Catani, De Schotten)  an,d while I agree  that ICA is indeed a very elegant statistical technique I think we must also admit that it is subject to  limiting conditions that perhaps are more prominent in low grade (19 ch) recordings.


A lot of people (I include myself) have been very impressed by the power of ICA to separate sources in the toy model (cocktail party model) where all sources are nicely independent and stationary with well behaving non gaussian distributions but in brains things are seldom that simple and synchronization and network communications can result in lumping of sources into a same component.


I prefer swLoreta for source analysis as it more in relation to neurophysiological reality we know from DTI “neuroanatomy”.


Again, I absolutely admire the statistical elegance of ICA but feel that we -as a scientific community- must try to resolve all errors and pitfalls that analysis algorithms such as ICA may present to improve robustness and clinical validity of our methods.


I find the input to this list very positive and very rewarding.










From: eeglablist-bounces at sccn.ucsd.edu <mailto:eeglablist-bounces at sccn.ucsd.edu>  [mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Johanna Wagner
Sent: Wednesday, June 14, 2017 11:07 PM
To:  <mailto:tarikbelbahar at gmail.com> tarikbelbahar at gmail.com; Brunner, Clemens ( <mailto:clemens.brunner at uni-graz.at> clemens.brunner at uni-graz.at) < <mailto:clemens.brunner at uni-graz.at> clemens.brunner at uni-graz.at>; Tim Mullen < <mailto:mullen.tim at gmail.com> mullen.tim at gmail.com>;  <mailto:billinger.martin at gmail.com> billinger.martin at gmail.com
Cc: eeglablist < <mailto:eeglablist at sccn.ucsd.edu> eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] ICA misinformation


I think there is a basic misunderstanding here on what EEG is and what channels actually measure as Clemens wrote 


Connectivity should never be computed between EEG channels. Due to volume conduction, signals recorded at all channels are a linear mixture of sources. Signals at all channels are therefore highly correlated. To use No source separation before estimating connectivity is simply wrong from my understanding. 

If you reject artifacts with ICA and then go back to the channel space you have the same problem since all brain sources are still mixed at the channel level....


see also Clemens Brunners paper ( <http://journal.frontiersin.org/article/10.3389/fncom.2016.00121/full> http://journal.frontiersin.org/article/10.3389/fncom.2016.00121/full)






2017-06-14 13:30 GMT-07:00 Tarik S Bel-Bahar <tarikbelbahar at gmail.com <mailto:tarikbelbahar at gmail.com> >:

Thanks everyone for this cool ongoing conversation. Although not an
expert in these issues, it’s exciting for me to know that we will soon
very likely have new clear reports from multiple labs that will speak
to these general issues and assumptions. Arno’s proof of concept is
straightforward, and the article passed on by Clemens certainly lays
out some important points. However, to date there is simply not enough
recent published work directly examining these issues. In fact, top
journals such as Neuroimage regularly publish articles using
ICA-cleaned connectivity data. Overall, the field is dependent of
valid/robust methods, and of course it’s important to test any/all
assumptions as specifically as possible in a replicable/empirical

Possible dimensions/constraints to consider?

Number of channels (as mentioned by Rob Lawson)

Channel density and relative total-head coverage

Number/type of artifact ICs removed

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

Channel-level vs. ICA-level vs. source-level 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?

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.

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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Johanna Wagner, PhD


http://scholar.google.at/citations?user=vSJYGtcAAAAJ <http://scholar.google.at/citations?user=vSJYGtcAAAAJ&hl=en> &hl=en


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