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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal>These are sound ideas and I agree that there is much research that can come out of these conversations.<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>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.<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>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.<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Please follow this link for more information about this special issue and how to submit abstracts/articles:<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><a href="http://journal.frontiersin.org/researchtopic/5265/computational-methodologies-in-brain-imaging-and-connectivity-eeg-and-meg-applications">http://journal.frontiersin.org/researchtopic/5265/computational-methodologies-in-brain-imaging-and-connectivity-eeg-and-meg-applications</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal style='margin-bottom:18.75pt;mso-line-height-alt:18.0pt;background:white'><span style='font-size:19.5pt;color:#020202'>About this Research Topic<o:p></o:p></span></p><p class=MsoNormal style='line-height:18.0pt'><span style='font-size:11.5pt;font-family:"Trebuchet MS",sans-serif;color:#555555'>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.<br><br>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.<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>If anyone has any questions please let me know,<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Rob<o:p></o:p></p><p class=MsoNormal><a name="_MailEndCompose"><o:p> </o:p></a></p><span style='mso-bookmark:_MailEndCompose'></span><p class=MsoNormal><b>From:</b> eeglablist [mailto:eeglablist-bounces@sccn.ucsd.edu] <b>On Behalf Of </b>Tarik S Bel-Bahar<br><b>Sent:</b> Friday, July 28, 2017 1:24 PM<br><b>To:</b> Eeglablist <eeglablist@sccn.ucsd.edu><br><b>Subject:</b> Re: [Eeglablist] FW: Beyond good and evil of ICA<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><div><div><p class=MsoNormal><span style='color:black'>Greetings, it's great to see a more congenial future-focused discussion <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>building<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'> here.<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'> 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". <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>Overall, kudos for keeping the <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>human <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>conversation going<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>, it may eventually lead to positive impacts for the EEG field.<o:p></o:p></span></p></div></div><div><div><p class=MsoNormal><o:p> </o:p></p></div></div><div><p class=MsoNormal><span style='color:black'>From my perspective,<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'> <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>argument<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>s<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>or point of view, whether based on theory<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>, math,<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>or experience,</span><o:p></o:p></p></div><div><p class=MsoNormal><span style='color:black'>will <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>remain<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'> <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>un<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>satisfactory until th<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>ey<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'> are <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>in 1) <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>multiple published new high-quality<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'> empirical<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='color:black'> <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>papers <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>that 2) examine a <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>range of topics related to the discussion here about ICA, EEG, <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>phase metrics, <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>artifacts, etc... from a <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>3) <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>data-and assessment-focused point of view. <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>It's important to have conversations, but it's new peer-reviewed publications that will truly clarify things.<o:p></o:p></span></p></div></div><div><div><p class=MsoNormal><span style='color:black'><o:p> </o:p></span></p></div></div><div><div><p class=MsoNormal><span style='color:black'>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. <o:p></o:p></span></p></div></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><span style='color:black'>One thing that has struck me is that the conversation has reflected <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>1) <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>a divide between low-density and high-density EEG systems users, <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>2) the differences in researcher and clinician values, <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>and <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>that 3) <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>there is enough data <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>already <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>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. </span><o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><span style='color:black'>Below, a resend of my earlier list of topics that future articles can consider<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'> or that we can add to/clarify<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>, and a list of articles (and authors) that <o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>can<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'> be consulted regarding current use/opinions of ICA, EEG, phase, and con<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>n<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>ectivity<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'> analyses<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>.</span><o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><b><span style='color:black'>Possible dimensions/constraints to consider or compare?</span></b><o:p></o:p></p></div><p class=MsoNormal><span style='color:black'><br>Channel density and relative total-head coverage</span><o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p><div><div><p class=MsoNormal><span style='color:black'>Low-density vs. moderate-density vs high-density</span><o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><span style='color:black'>With and without EOG and/or EMG recording ?</span><o:p></o:p></p></div><p class=MsoNormal><span style='color:black'><br>Number/type of artifact<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>ual detected, number/type of<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>ICs removed</span><o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><div><div><p class=MsoNormal><span style='color:black'>IC-based vs. non-IC based artifact removal</span><o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><span style='color:black'>Number/type of artifact visible in channel record<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'><o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'><br><br>Clarity/robustness of artifact<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>ual ICs <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>(e.g., ICs that are mixed vs. ICs that<br>are mixed (containing both artifact and neural info)<br><br>Channel-level vs. ICA-level vs. source<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'>-estimated<o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'> connectivity metrics<br><br>Length of epochs/trials<br><br>Type of source analysis<br><br>Type of reference (e.g., Chella et al., 2016)<br><br>Type of ICA/blind source separation (e.g., Bridwell et al., 2016;<br>Brain Topography)<br><br>Event-related or resting data<br><br>Signal quality (e.g., gel versus saline, very noisy vs. quite clean)<br><br>Reliability of particular metric<br><br>Type of connectivity metric (e.g., various kinds of phase measures,<br>and the plethora of other connectivity and graph theoretical measures)<br><br>MEG vs. EEG ?<br><br>Sampling rate?</span><o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><span style='color:black'>Type of population ?<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'> Rate of artifacts per population/sample?<o:p></o:p></span></p></div></div><div><div><p class=MsoNormal><span style='color:black'><o:p> </o:p></span></p></div></div><div><div><p class=MsoNormal><span style='color:black'>Individual differences in cleanness or clarity of EEG signal?<o:p></o:p></span></p></div></div><div><div><p class=MsoNormal><span style='color:black'><o:p> </o:p></span></p></div></div><div><p class=MsoNormal><span style='color:black'><br><br><br><br>Sample articles that seem to use ICA in relation to connectivity<br>metrics are listed below.<o:p></o:p></span></p><div><p class=MsoNormal><span style='color:black'> <o:p></o:p></span></p></div><p class=MsoNormal><span style='color:black'>Moving forward, it may be beneficial to survey these authors and their findings.</span><o:p></o:p></p></div><div><p class=MsoNormal><span style='color:black'><br>de Pasquale, F., Della Penna, S., Sporns, O., Romani, G. L., &<br>Corbetta, M. (2015). A dynamic core network and global efficiency in<br>the resting human brain. Cerebral Cortex, bhv185.<br><br>Lai, M., Demuru, M., Hillebrand, A., & Fraschini, M. (2017). A<br>Comparison Between Scalp-And Source-Reconstructed EEG Networks.<br>bioRxiv, 121764.<br><br>Colclough, G. L., Woolrich, M. W., Tewarie, P. K., Brookes, M. J.,<br>Quinn, A. J., & Smith, S. M. (2016). How reliable are MEG<br>resting-state connectivity metrics?. NeuroImage, 138, 284-293.<br><br>Kuntzelman, K., & Miskovic, V. (2017). Reliability of graph metrics<br>derived from resting‐state human EEG. Psychophysiology, 54(1), 51-61.<br><br>Siems, M., Pape, A. A., Hipp, J. F., & Siegel, M. (2016). Measuring<br>the cortical correlation structure of spontaneous oscillatory activity<br>with EEG and MEG. NeuroImage, 129, 345-355.<br><br>Toppi, J., Astolfi, L., Poudel, G. R., Innes, C. R., Babiloni, F., &<br>Jones, R. D. (2016). Time-varying effective connectivity of the<br>cortical neuroelectric activity associated with behavioural<br>microsleeps. NeuroImage, 124, 421-432.<br><br>Farahibozorg, S. R., Henson, R. N., & Hauk, O. (2017). Adaptive<br>Cortical Parcellations for Source Reconstructed EEG/MEG Connectomes.<br>bioRxiv, 097774.<br><br>Rueda-Delgado, L. M., Solesio-Jofre, E., Mantini, D., Dupont, P.,<br>Daffertshofer, A., & Swinnen, S. P. (2016). Coordinative task<br>difficulty and behavioural errors are associated with increased<br>long-range beta band synchronization, NeuroImage.<br><br>Cooper, P. S., Wong, A. S., Fulham, W. R., Thienel, R., Mansfield, E.,<br>Michie, P. T., & Karayanidis, F. (2015). Theta frontoparietal<br>connectivity associated with proactive and reactive cognitive control<br>processes. Neuroimage, 108, 354-363.<br><br>Nayak, C. S., Bhowmik, A., Prasad, P. D., Pati, S., Choudhury, K. K.,<br>& Majumdar, K. K. (2017). Phase Synchronization Analysis of Natural<br>Wake and Sleep States in Healthy Individuals Using a Novel Ensemble<br>Phase Synchronization Measure. Journal of Clinical Neurophysiology,<br>34(1), 77-83.<br><br>Vecchio, F., Miraglia, F., Piludu, F., Granata, G., Romanello, R.,<br>Caulo, M., ... & Rossini, P. M. (2017). “Small World” architecture in<br>brain connectivity and hippocampal volume in Alzheimer’s disease: a<br>study via graph theory from EEG data. Brain imaging and behavior,<br>11(2), 473-485.<br><br>Ranzi, P., Freund, J. A., Thiel, C. M., & Herrmann, C. S. (2016).<br>Encephalography Connectivity on Sources in Male Nonsmokers after<br>Nicotine Administration during the Resting State. Neuropsychobiology,<br>74(1), 48-59.<br><br>Vecchio, F., Miraglia, F., Curcio, G., Della Marca, G., Vollono, C.,<br>Mazzucchi, E., ... & Rossini, P. M. (2015). Cortical connectivity in<br>fronto-temporal focal epilepsy from EEG analysis: a study via graph<br>theory. Clinical Neurophysiology, 126(6), 1108-1116.<br><br>Smit, D. J., de Geus, E. J., Boersma, M., Boomsma, D. I., & Stam, C.<br>J. (2016). Life-span development of brain network integration assessed<br>with phase lag index connectivity and minimum spanning tree graphs.<br>Brain connectivity, 6(4), 312-325.<br><br>Chung, Jae W., Edward Ofori, Gaurav Misra, Christopher W. Hess, and<br>David E. Vaillancourt. "Beta-band activity and connectivity in<br>sensorimotor and parietal cortex are important for accurate motor<br>performance." NeuroImage 144 (2017): 164-173.<br><br>Shou, G., & Ding, L. (2015). Detection of EEG<br>spatial–spectral–temporal signatures of errors: A comparative study of<br>ICA-based and channel-based methods. Brain topography, 28(1), 47-61.<br><br>Kline, J. E., Huang, H. J., Snyder, K. L., & Ferris, D. P. (2016).<br>Cortical Spectral Activity and Connectivity during Active and Viewed<br>Arm and Leg Movement. Frontiers in neuroscience, 10.<br><br>van Driel, J., Gunseli, E., Meeter, M., & Olivers, C. N. (2017). Local<br>and interregional alpha EEG dynamics dissociate between memory for<br>search and memory for recognition. NeuroImage, 149, 114-128.<br><br>Castellanos, N.P., Makarov, V.A., 2006. Recovering EEG brain signals:<br>Artifact suppression with wavelet enhanced independent component<br>analysis. J. Neurosci. Methods 158, 300–312.<br>doi:10.1016/j.jneumeth.2006.05.033<br><br>Mehrkanoon, S., Breakspear, M., Britz, J., & Boonstra, T. W. (2014).<br>Intrinsic coupling modes in source-reconstructed<br>electroencephalography. Brain connectivity, 4(10), 812-825.</span><o:p></o:p></p></div></div></div></div></div></div></div></body></html>