ArticleNeuroinformatics2015
Modelling and Analysis of Electrical Potentials Recorded in Microelectrode Arrays (MEAs).
Article in Neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed.
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- Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Improving morphological and functional properties of enteric neuronal networks in vitro using a novel upside-down culture approach.American journal of physiology. Gastrointestinal and liver physiology · 2024Article
- kCSD-python, reliable current source density estimation with quality control.PLoS computational biology · 2024Article
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- Discovering optimal features for neuron-type identification from extracellular recordings.Frontiers in neuroinformatics · 2024Article
- Adaptive octree meshes for simulation of extracellular electrophysiology.Journal of neural engineering · 2023Article
- An automated method for precise axon reconstruction from recordings of high-density micro-electrode arrays.Journal of neural engineering · 2022Article
- Nonhomogeneous volume conduction effects affecting needle electromyography: an analytical and simulation study.Physiological measurement · 2021Article
- Cross-population coupling of neural activity based on Gaussian process current source densities.PLoS computational biology · 2021Article
- Using multielectrode arrays to investigate neurodegenerative effects of the amyloid-beta peptide.Bioelectronic medicine · 2021Review
- What we can and what we cannot see with extracellular multielectrodes.PLoS computational biology · 2021Article
- MEArec: A Fast and Customizable Testbench Simulator for Ground-truth Extracellular Spiking Activity.Neuroinformatics · 2021Article
- Adaptive virtual referencing for the extraction of extracellularly recorded action potentials in noisy environments.Journal of neural engineering · 2020Article
- Fast simulation of extracellular action potential signatures based on a morphological filtering approximation.Journal of computational neuroscience · 2020Article
- Impact of Brain Surface Boundary Conditions on Electrophysiology and Implications for Electrocorticography.Frontiers in neuroscience · 2020Article
- Bayesian Modelling of Induced Responses and Neuronal Rhythms.Brain topography · 2019Article
- Accurate signal-source localization in brain slices by means of high-density microelectrode arrays.Scientific reports · 2019Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microelectrode arrays (MEAs), substrate-integrated planar arrays of up to thousands of closely spaced metal electrode contacts, have long been used to record neuronal activity in in vitro brain slices with high spatial and temporal resolution. However, the analysis of the MEA potentials has generally been mainly qualitative. Here we use a biophysical forward-modelling formalism based on the finite element method (FEM) to establish quantitatively accurate links between neural activity in the slice and potentials recorded in the MEA set-up. Then we develop a simpler approach based on the method of images (MoI) from electrostatics, which allows for computation of MEA potentials by simple formulas similar to what is used for homogeneous volume conductors. As we find MoI to give accurate results in most situations of practical interest, including anisotropic slices covered with highly conductive saline and MEA-electrode contacts of sizable physical extensions, a Python software package (ViMEAPy) has been developed to facilitate forward-modelling of MEA potentials generated by biophysically detailed multicompartmental neurons. We apply our scheme to investigate the influence of the MEA set-up on single-neuron spikes as well as on potentials generated by a cortical network comprising more than 3000 model neurons. The generated MEA potentials are substantially affected by both the saline bath covering the brain slice and a (putative) inadvertent saline layer at the interface between the MEA chip and the brain slice. We further explore methods for estimation of current-source density (CSD) from MEA potentials, and find the results to be much less sensitive to the experimental set-up.
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