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      Correlation Structure in Micro-ECoG Recordings is Described by Spatially Coherent Components

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          Abstract

          Electrocorticography (ECoG) is becoming more prevalent due to improvements in fabrication and recording technology as well as its ease of implantation compared to intracortical electrophysiology, larger cortical coverage, and potential advantages for use in long term chronic implantation. Given the flexibility in the design of ECoG grids, which is only increasing, it remains an open question what geometry of the electrodes is optimal for an application. Conductive polymer, PEDOT:PSS, coated microelectrodes have an advantage that they can be made very small without losing low impedance. This makes them suitable for evaluating the required granularity of ECoG recording in humans and experimental animals. We used two-dimensional (2D) micro-ECoG grids to record intra-operatively in humans and during acute implantations in mouse with separation distance between neighboring electrodes (i.e., pitch) of 0.4 mm and 0.2/0.25 mm respectively. To assess the spatial properties of the signals, we used the average correlation between electrodes as a function of the pitch. In agreement with prior studies, we find a strong frequency dependence in the spatial scale of correlation. By applying independent component analysis (ICA), we find that the spatial pattern of correlation is largely due to contributions from multiple spatially extended, time-locked sources present at any given time. Our analysis indicates the presence of spatially structured activity down to the sub-millimeter spatial scale in ECoG despite the effects of volume conduction, justifying the use of dense micro-ECoG grids.

          Author summary

          Electrocorticography (ECoG) is a type of electrophysiological monitoring that uses electrodes placed directly on the exposed surface of the brain. ECoG is a promising technique for studying the brain, and EcoG signals can be used to control brain-computer interfaces. Advances have made it possible to record simultaneously with an increasing number of smaller, and more closely spaced electrodes. However, a property of electrical recording from outside the brain is that common signals appear on different electrodes at different locations, and this affects decisions about how to best distribute a limited number of electrodes to maximize the information that can be gathered. Large spacing of electrodes around one centimeter apart on the brain’s surface has proven useful for clinical and research use, but how much benefit there is to recording from more locations in a smaller area remains to be answered. We found that we can explain the commonality between the different locations as the combination of different patterns of brain activity that are present at multiple electrode locations, and that signals recorded from very closely spaced electrodes, around a millimeter or less apart, are able to identify patterns that are at this small scale.

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          Most cited references39

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          Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources.

          To address the problem of volume conduction and active reference electrodes in the assessment of functional connectivity, we propose a novel measure to quantify phase synchronization, the phase lag index (PLI), and compare its performance to the well-known phase coherence (PC), and to the imaginary component of coherency (IC). The PLI is a measure of the asymmetry of the distribution of phase differences between two signals. The performance of PLI, PC, and IC was examined in (i) a model of 64 globally coupled oscillators, (ii) an EEG with an absence seizure, (iii) an EEG data set of 15 Alzheimer patients and 13 control subjects, and (iv) two MEG data sets. PLI and PC were more sensitive than IC to increasing levels of true synchronization in the model. PC and IC were influenced stronger than PLI by spurious correlations because of common sources. All measures detected changes in synchronization during the absence seizure. In contrast to PC, PLI and IC were barely changed by the choice of different montages. PLI and IC were superior to PC in detecting changes in beta band connectivity in AD patients. Finally, PLI and IC revealed a different spatial pattern of functional connectivity in MEG data than PC. The PLI performed at least as well as the PC in detecting true changes in synchronization in model and real data but, at the same token and like-wise the IC, it was much less affected by the influence of common sources and active reference electrodes. Copyright 2007 Wiley-Liss, Inc.
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            Spectral changes in cortical surface potentials during motor movement.

            In the first large study of its kind, we quantified changes in electrocorticographic signals associated with motor movement across 22 subjects with subdural electrode arrays placed for identification of seizure foci. Patients underwent a 5-7 d monitoring period with array placement, before seizure focus resection, and during this time they participated in the study. An interval-based motor-repetition task produced consistent and quantifiable spectral shifts that were mapped on a Talairach-standardized template cortex. Maps were created independently for a high-frequency band (HFB) (76-100 Hz) and a low-frequency band (LFB) (8-32 Hz) for several different movement modalities in each subject. The power in relevant electrodes consistently decreased in the LFB with movement, whereas the power in the HFB consistently increased. In addition, the HFB changes were more focal than the LFB changes. Sites of power changes corresponded to stereotactic locations in sensorimotor cortex and to the results of individual clinical electrical cortical mapping. Sensorimotor representation was found to be somatotopic, localized in stereotactic space to rolandic cortex, and typically followed the classic homunculus with limited extrarolandic representation.
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              EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales.

              Several methodological issues which impact experimental design and physiological interpretations in EEG coherence studies are considered, including reference electrode and volume conduction contributions to erroneous coherence estimates. A new measure, 'reduced coherency', is introduced as the difference between measured coherency and the coherency expected from uncorrelated neocortical sources, based on simulations and analytic-statistical studies with a volume conductor model. The concept of reduced coherency is shown to be in semi-quantitative agreement with experimental EEG data. The impact of volume conduction on statistical confidence intervals for coherence estimates is discussed. Conventional reference, average reference, bipolar, Laplacian, and cortical image coherencies are shown to be partly independent measures of neocortical dynamic function at different spatial scales, due to each method's unique spatial filtering of intracranial source activity.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: SoftwareRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: SoftwareRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                11 February 2019
                February 2019
                : 15
                : 2
                : e1006769
                Affiliations
                [1 ] Physics, University of California San Diego, La Jolla, CA, United States of America
                [2 ] Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States of America
                [3 ] Department of Neurosciences, University of California San Diego, La Jolla, CA, United States of America
                [4 ] Materials Science and Engineering, NanoEngineering University of California San Diego, La Jolla, CA, United States of America
                [5 ] Department of Radiology, University of California San Diego, La Jolla, CA, United States of America
                [6 ] Department of Neurosurgery, University of California San Diego, La Jolla, CA, United States of America
                [7 ] Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States of America
                Harvard University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-0571-4291
                http://orcid.org/0000-0002-3450-0738
                http://orcid.org/0000-0003-3252-5365
                http://orcid.org/0000-0001-7112-501X
                http://orcid.org/0000-0003-4139-079X
                http://orcid.org/0000-0001-5926-6872
                http://orcid.org/0000-0002-0619-2686
                Article
                PCOMPBIOL-D-18-01132
                10.1371/journal.pcbi.1006769
                6386410
                30742605
                ed004733-3e97-4b46-9a73-0ab568432fad
                © 2019 Rogers et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 29 June 2018
                : 3 January 2019
                Page count
                Figures: 6, Tables: 0, Pages: 21
                Funding
                This work was supported by the Center for Brain Activity Mapping ( https://cbam.ucsd.edu) at UC San Diego. S.A.D. and V.G. received faculty start-up support from the Department of Electrical and Computer Engineering at UC San Diego. S.A.D. acknowledges partial support from NSF ( https://www.nsf.gov) No. ECCS-1351980. V.G. acknowledges partial support from University of California Multicampus Research Programs and Initiatives ( https://www.ucop.edu/research-initiatives/programs/mrpi/index.html) No. MR-15-328909. E.H. acknowledges partial support from the Office of Naval Research ( https://www.onr.navy.mil) No. N00014-13-1-0672. A.D. acknowledges support from BRAIN Initiative ( https://www.braininitiative.nih.gov/) R01MH111359, and from the National Institutes of Health ( https://www.nih.gov/) R01NS057198 and S10RR029050. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Mathematics
                Probability Theory
                Random Variables
                Covariance
                Physical Sciences
                Chemistry
                Electrochemistry
                Electrode Potentials
                Biology and Life Sciences
                Neuroscience
                Brain Mapping
                Electrocorticography
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Electrocorticography
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Electrocorticography
                Research and Analysis Methods
                Bioassays and Physiological Analysis
                Electrophysiological Techniques
                Membrane Electrophysiology
                Electrode Recording
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Engineering and Technology
                Electronics
                Electrodes
                Reference Electrodes
                Biology and Life Sciences
                Physiology
                Electrophysiology
                Medicine and Health Sciences
                Physiology
                Electrophysiology
                Engineering and Technology
                Electronics
                Electrodes
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-02-22
                The windowed form of the data used in this study is available at https://doi.org/10.6084/m9.figshare.7633418.v4.

                Quantitative & Systems biology
                Quantitative & Systems biology

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