Dataset Information

Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites.

ABSTRACT: Axons are traditionally considered stable transmission cables, but evidence of the regulation of action potential propagation demonstrates that axons may have more important roles. However, their small diameters render intracellular recordings challenging, and low-magnitude extracellular signals are difficult to detect and assign. Better experimental access to axonal function would help to advance this field. Here we report methods to electrically visualize action potential propagation and network topology in cortical neurons grown over custom arrays, which contain 11,011 microelectrodes and are fabricated using complementary metal oxide semiconductor technology. Any neuron lying on the array can be recorded at high spatio-temporal resolution, and simultaneously precisely stimulated with little artifact. We find substantial velocity differences occurring locally within single axons, suggesting that the temporal control of a neuron's output may contribute to neuronal information processing.

SUBMITTER: Bakkum DJ

PROVIDER: S-EPMC5419423 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites.

Bakkum Douglas J DJ Frey Urs U Radivojevic Milos M Russell Thomas L TL Müller Jan J Fiscella Michele M Takahashi Hirokazu H Hierlemann Andreas A

Nature communications 20130101

Axons are traditionally considered stable transmission cables, but evidence of the regulation of action potential propagation demonstrates that axons may have more important roles. However, their small diameters render intracellular recordings challenging, and low-magnitude extracellular signals are difficult to detect and assign. Better experimental access to axonal function would help to advance this field. Here we report methods to electrically visualize action potential propagation and netwo ...[more]

PMID: 23867868

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Project description:Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as "spike sorting." For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.

Project description:Despite being composed of highly plastic neurons with extensive positive feedback, the nervous system maintains stable overall function. To keep activity within bounds, it relies on a set of negative feedback mechanisms that can induce stabilizing adjustments and that are collectively termed "homeostatic plasticity." Recently, a highly excitable microdomain, located at the proximal end of the axon-the axon initial segment (AIS)-was found to exhibit structural modifications in response to activity perturbations. Though AIS plasticity appears to serve a homeostatic purpose, many aspects governing its expression and its functional role in regulating neuronal excitability remain elusive. A central challenge in studying the phenomenon is the rich heterogeneity of its expression (distal/proximal relocation, shortening, lengthening) and the variability of its functional role. A potential solution is to track AISs of a large number of neurons over time and attempt to induce structural plasticity in them. To this end, a promising approach is to use extracellular electrophysiological readouts to track a large number of neurons at high spatiotemporal resolution by means of high-density microelectrode arrays (HD-MEAs). However, an analysis framework that reliably identifies specific activity signatures that uniquely map on to underlying microstructural changes is missing. In this study, we assessed the feasibility of such a task and used the distal relocation of the AIS as an exemplary problem. We used sophisticated computational models to systematically explore the relationship between incremental changes in AIS positions and the specific consequences observed in simulated extracellular field potentials. An ensemble of feature changes in the extracellular fields that reliably characterize AIS plasticity was identified. We trained models that could detect these signatures with remarkable accuracy. Based on these findings, we propose a hybrid analysis framework that could potentially enable high-throughput experimental studies of activity-dependent AIS plasticity using HD-MEAs.

Dataset Information

Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites.

Publications

Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites.

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