[Eeglablist] FW: Beyond good and evil of ICA

Sun Aug 6 19:24:22 PDT 2017

These are sound ideas and I agree that there is much research that can come out of these conversations.

To respond to one of Marius posts we are currently working on comparing findings with different numbers of electrodes and sampling rates and what differences this leads to.

I would also like to invite anyone who wishes to submit research articles to our special issue on mathematical models in the use of eeg and meg. I hope that arno, yourself and others will present your paper for publication here. I have also invited Robert, Georges and anyone elses who wishes to present different views.

Please follow this link for more information about this special issue and how to submit abstracts/articles:

http://journal.frontiersin.org/researchtopic/5265/computational-methodologies-in-brain-imaging-and-connectivity-eeg-and-meg-applications

About this Research Topic

Over the last several years, sophisticated mathematical and computational approaches (including independent component analysis and neural source localization) have been utilized to study neurological disorders using different neuroimaging modalities such as electroencephalography (EEG) and magnetoencephalography (MEG). These approaches have recently been applied to analyze functional and effective brain connectivity to models underlying neurobiological processes in subjects with neurological disorders.

In this Research Topic we welcome papers with novel approaches to these applications including those that include (but not limited to) the use of both multi-channel data and their source estimations in brain connectivity approaches such as: Granger causality, multivariate estimators of directedness, estimators of dynamical propagation (such as Dual Kalman Filter), and Bayesian hierarchical modeling. In this way, our aim is to promote and move forward conceptualization and computational methodologies that will help to more accurately depict neural connectivity and how it changes over time.

If anyone has any questions please let me know,

Rob

From: eeglablist [mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Tarik S Bel-Bahar
Sent: Friday, July 28, 2017 1:24 PM
To: Eeglablist <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] FW: Beyond good and evil of ICA

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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