[Eeglablist] ICA misinformation

Wed Jun 14 14:06:52 PDT 2017

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)
and
http://journal.frontiersin.org/article/10.3389/fninf.2014.00022/full

Johanna

2017-06-14 13:30 GMT-07:00 Tarik S Bel-Bahar <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
> manner.
>
>
>
> 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.
> 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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-- 

Johanna Wagner, PhD

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

<http://sccn.ucsd.edu/>
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