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

Authors :
Rogers, Nicholas
Hermiz, John
Ganji, Mehran
Kaestner, Erik
Kılıç, Kıvılcım
Hossain, Lorraine
Thunemann, Martin
Cleary, Daniel R.
Carter, Bob S.
Barba, David
Devor, Anna
Halgren, Eric
Dayeh, Shadi A.
Gilja, Vikash
Source :
PLoS Computational Biology. 2/11/2019, Vol. 15 Issue 2, p1-21. 21p. 1 Diagram, 5 Graphs.
Publication Year :
2019

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1553734X
Volume :
15
Issue :
2
Database :
Academic Search Index
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
134634330
Full Text :
https://doi.org/10.1371/journal.pcbi.1006769