[Eeglablist] ICA misinformation

[Tarik S Bel-Bahar]

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.