Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification

Unsupervised spike sorting for multielectrode arrays based on spike shape features and location methods

Microelectrode arrays (MEAs) enable simultaneous measurement of spike trains from numerous neurons, owing to advancements in microfabrication technology. These probes are highly valuable for comprehending the intricate dynamics of neuronal networks. Spike sorting is a pivotal step in comprehensively analyzing the activity of neuronal networks from extracellular recordings. However, the accuracy of spike sorting is relatively low due to the dense sampling of spikes in MEAs. Here, we propose an unsupervised pipeline named UMAP-COM method, which utilizes combined features to address this problem. These combined features comprise dominant spike shape features extracted by the uniform manifold approximation and projection (UMAP), as well as spike locations estimated by the center of mass (COM). We validate the UMAP-COM method on publicly available datasets from different kinds of probes, demonstrating that it is more accurate than other spike sorting methods. Furthermore, we conduct separate evaluations of spike shape feature extraction methods and spike localization methods. In this comparison, UMAP emerges as the superior feature extraction method, demonstrating its effectiveness in accurately representing spike shapes. Additionally, we find that the COM method outperforms other spike localization methods, highlighting its ability to enhance the accuracy of spike sorting.

Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density (~10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron's electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to ~3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

Discovering optimal features for neuron-type identification from extracellular recordings

Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.

A Modular Implementation to Handle and Benchmark Drift Correction for High-Density Extracellular Recordings

High-density neural devices are now offering the possibility to record from neuronal populations in vivo at unprecedented scale. However, the mechanical drifts often observed in these recordings are currently a major issue for "spike sorting," an essential analysis step to identify the activity of single neurons from extracellular signals. Although several strategies have been proposed to compensate for such drifts, the lack of proper benchmarks makes it hard to assess the quality and effectiveness of motion correction. In this paper, we present a benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in the literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying a motion correction algorithm as a preprocessing step in the spike sorting pipeline. We show how important it is to properly estimate the positions of the neurons from extracellular traces in order to correctly estimate the probe motion, compare several interpolation procedures, and highlight what are the current limits for motion correction approaches.

DREDge: robust motion correction for high-density extracellular recordings across species

High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.

Positive and biphasic extracellular waveforms correspond to return currents and axonal spikes

Multiple biophysical mechanisms may generate non-negative extracellular waveforms during action potentials, but the origin and prevalence of positive spikes and biphasic spikes in the intact brain are unknown. Using extracellular recordings from densely-connected cortical networks in freely-moving mice, we find that a tenth of the waveforms are non-negative. Positive phases of non-negative spikes occur in synchrony or just before wider same-unit negative spikes. Narrow positive spikes occur in isolation in the white matter. Isolated biphasic spikes are narrower than negative spikes, occurring right after spikes of verified inhibitory units. In CA1, units with dominant non-negative spikes exhibit place fields, phase precession, and phase-locking to ripples. Thus, near-somatic narrow positive extracellular potentials correspond to return currents, and isolated non-negative spikes correspond to axonal potentials. Identifying non-negative extracellular waveforms that correspond to non-somatic compartments during spikes can enhance the understanding of physiological and pathological neural mechanisms in intact animals.

Compression strategies for large-scale electrophysiology data

Objective.With the rapid adoption of high-density electrode arrays for recording neural activity, electrophysiology data volumes within labs and across the field are growing at unprecedented rates. For example, a one-hour recording with a 384-channel Neuropixels probe generates over 80 GB of raw data. These large data volumes carry a high cost, especially if researchers plan to store and analyze their data in the cloud. Thus, there is a pressing need for strategies that can reduce the data footprint of each experiment.Approach.Here, we establish a set of benchmarks for comparing the performance of various compression algorithms on experimental and simulated recordings from Neuropixels 1.0 (NP1) and 2.0 (NP2) probes.Main results.For lossless compression, audio codecs (FLACandWavPack) achieve compression ratios (CRs) 6% higher for NP1 and 10% higher for NP2 than the best general-purpose codecs, at the expense of decompression speed. For lossy compression, theWavPackalgorithm in 'hybrid mode' increases the CR from 3.59 to 7.08 for NP1 and from 2.27 to 7.04 for NP2 (compressed file size of ∼14% for both types of probes), without adverse effects on spike sorting accuracy or spike waveforms.Significance.Along with the tools we have developed to make compression easier to deploy, these results should encourage all electrophysiologists to apply compression as part of their standard analysis workflows.

An efficient deep learning approach to identify dynamics in in vitro neural networks

Understanding and discriminating the spatiotemporal patterns of activity generated by in vitro and in vivo neuronal networks is a fundamental task in neuroscience and neuroengineering. The state-of-the-art algorithms to describe the neuronal activity mostly rely on global and local well-established spike and burst-related parameters. However, they are not able to capture slight differences in the activity patterns. In this work, we introduce a deep-learning-based algorithm to automatically infer the dynamics exhibited by different neuronal populations. Specifically, we demonstrate that our algorithm is able to discriminate with high accuracy the dynamics of five different populations of in vitro human-derived neural networks with an increasing inhibitory to excitatory neurons ratio.

Precision of neuronal localization in 2D cell cultures by using high-performance electropolymerized microelectrode arrays correlated with optical imaging

Recently, the development of electronic devices to extracellularly record the simultaneous electrical activities of numerous neurons has been blooming, opening new possibilities to interface and decode neuronal activity. In this work, we tested how the use of EDOT electropolymerization to tune post-fabrication materials could optimize the cell/electrode interface of such devices. Our results showed an improved signal-to-noise ratio, better biocompatibility, and a higher number of neurons detected in comparison with gold electrodes. Then, using such enhanced recordings with 2D neuronal cultures combined with fluorescent optical imaging, we checked the extent to which the positions of the recorded neurons could be estimated solely via their extracellular signatures. Our results showed that assuming neurons behave as monopoles, positions could be estimated with a precision of approximately tens of micrometers.

Computational cardiac physiology for new modelers: Origins, foundations, and future

Mathematical models of the cardiovascular system have come a long way since they were first introduced in the early 19th century. Driven by a rapid development of experimental techniques, numerical methods, and computer hardware, detailed models that describe physical scales from the molecular level up to organs and organ systems have been derived and used for physiological research. Mathematical and computational models can be seen as condensed and quantitative formulations of extensive physiological knowledge and are used for formulating and testing hypotheses, interpreting and directing experimental research, and have contributed substantially to our understanding of cardiovascular physiology. However, in spite of the strengths of mathematics to precisely describe complex relationships and the obvious need for the mathematical and computational models to be informed by experimental data, there still exist considerable barriers between experimental and computational physiological research. In this review, we present a historical overview of the development of mathematical and computational models in cardiovascular physiology, including the current state of the art. We further argue why a tighter integration is needed between experimental and computational scientists in physiology, and point out important obstacles and challenges that must be overcome in order to fully realize the synergy of experimental and computational physiological research.

Identification of adult spinal Shox2 neuronal subpopulations based on unbiased computational clustering of electrophysiological properties

Spinal cord neurons integrate sensory and descending information to produce motor output. The expression of transcription factors has been used to dissect out the neuronal components of circuits underlying behaviors. However, most of the canonical populations of interneurons are heterogeneous and require additional criteria to determine functional subpopulations. Neurons expressing the transcription factor Shox2 can be subclassified based on the co-expression of the transcription factor Chx10 and each subpopulation is proposed to have a distinct connectivity and different role in locomotion. Adult Shox2 neurons have recently been shown to be diverse based on their firing properties. Here, in order to subclassify adult mouse Shox2 neurons, we performed multiple analyses of data collected from whole-cell patch clamp recordings of visually-identified Shox2 neurons from lumbar spinal slices. A smaller set of Chx10 neurons was included in the analyses for validation. We performed k-means and hierarchical unbiased clustering approaches, considering electrophysiological variables. Unlike the categorizations by firing type, the clusters displayed electrophysiological properties that could differentiate between clusters of Shox2 neurons. The presence of clusters consisting exclusively of Shox2 neurons in both clustering techniques suggests that it is possible to distinguish Shox2+Chx10- neurons from Shox2+Chx10+ neurons by electrophysiological properties alone. Computational clusters were further validated by immunohistochemistry with accuracy in a small subset of neurons. Thus, unbiased cluster analysis using electrophysiological properties is a tool that can enhance current interneuronal subclassifications and can complement groupings based on transcription factor and molecular expression.

Using multielectrode arrays to investigate neurodegenerative effects of the amyloid-beta peptide

BACKGROUND

Multielectrode arrays are widely used to analyze the effects of potentially toxic compounds, as well as to evaluate neuroprotective agents upon the activity of neural networks in short- and long-term cultures. Multielectrode arrays provide a way of non-destructive analysis of spontaneous and evoked neuronal activity, allowing to model neurodegenerative diseases in vitro. Here, we provide an overview on how these devices are currently used in research on the amyloid-β peptide and its role in Alzheimer's disease, the most common neurodegenerative disorder.

MAIN BODY

Most of the studies analysed here indicate fast responses of neuronal cultures towards aggregated forms of amyloid-β, leading to increases of spike frequency and impairments of long-term potentiation. This in turn suggests that this peptide might play a crucial role in causing the typical neuronal dysfunction observed in patients with Alzheimer's disease.

CONCLUSIONS

Although the number of studies using multielectrode arrays to examine the effect of the amyloid-β peptide onto neural cultures or whole compartments is currently limited, they still show how this technique can be used to not only investigate the interneuronal communication in neural networks, but also making it possible to examine the effects onto synaptic currents. This makes multielectrode arrays a powerful tool in future research on neurodegenerative diseases.

Dataset of cortical activity recorded with high spatial resolution from anesthetized rats

Publicly available neural recordings obtained with high spatial resolution are scarce. Here, we present an electrophysiological dataset recorded from the neocortex of twenty rats anesthetized with ketamine/xylazine. The wideband, spontaneous recordings were acquired with a single-shank silicon-based probe having 128 densely-packed recording sites arranged in a 32 × 4 array. The dataset contains the activity of a total of 7126 sorted single units extracted from all layers of the cortex. Here, we share raw neural recordings, as well as spike times, extracellular spike waveforms and several properties of units packaged in a standardized electrophysiological data format. For technical validation of our dataset, we provide the distributions of derived single unit properties along with various spike sorting quality metrics. This large collection of in vivo data enables the investigation of the high-resolution electrical footprint of cortical neurons which in turn may aid their electrophysiology-based classification. Furthermore, the dataset might be used to study the laminar-specific neuronal activity during slow oscillation, a brain rhythm strongly involved in neural mechanisms underlying memory consolidation and sleep.

MEArec: A Fast and Customizable Testbench Simulator for Ground-truth Extracellular Spiking Activity

When recording neural activity from extracellular electrodes, both in vivo and in vitro, spike sorting is a required and very important processing step that allows for identification of single neurons’ activity. Spike sorting is a complex algorithmic procedure, and in recent years many groups have attempted to tackle this problem, resulting in numerous methods and software packages. However, validation of spike sorting techniques is complicated. It is an inherently unsupervised problem and it is hard to find universal metrics to evaluate performance. Simultaneous recordings that combine extracellular and patch-clamp or juxtacellular techniques can provide ground-truth data to evaluate spike sorting methods. However, their utility is limited by the fact that only a few cells can be measured at the same time. Simulated ground-truth recordings can provide a powerful alternative mean to rank the performance of spike sorters. We present here MEArec, a Python-based software which permits flexible and fast simulation of extracellular recordings. MEArec allows users to generate extracellular signals on various customizable electrode designs and can replicate various problematic aspects for spike sorting, such as bursting, spatio-temporal overlapping events, and drifts. We expect MEArec will provide a common testbench for spike sorting development and evaluation, in which spike sorting developers can rapidly generate and evaluate the performance of their algorithms.

Electrophysiological Phenotype Characterization of Human iPSC-Derived Neuronal Cell Lines by Means of High-Density Microelectrode Arrays

Recent advances in the field of cellular reprogramming have opened a route to studying the fundamental mechanisms underlying common neurological disorders. High-density microelectrode-arrays (HD-MEAs) provide unprecedented means to study neuronal physiology at different scales, ranging from network through single-neuron to subcellular features. In this work, HD-MEAs are used in vitro to characterize and compare human induced-pluripotent-stem-cell-derived dopaminergic and motor neurons, including isogenic neuronal lines modeling Parkinson's disease and amyotrophic lateral sclerosis. Reproducible electrophysiological network, single-cell and subcellular metrics are used for phenotype characterization and drug testing. Metrics, such as burst shape and axonal velocity, enable the distinction of healthy and diseased neurons. The HD-MEA metrics can also be used to detect the effects of dosing the drug retigabine to human motor neurons. Finally, it is shown that the ability to detect drug effects and the observed culture-to-culture variability critically depend on the number of available recording electrodes.

High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification

Different neuron types serve distinct roles in neural processing. Extracellular electrical recordings are extensively used to study brain function but are typically blind to cell identity. Morphoelectrical properties of neurons measured on spatially dense electrode arrays have the potential to distinguish neuron types. We used high-density silicon probes to record from cortical and subcortical regions of the mouse brain. Extracellular waveforms of each neuron were detected across many channels and showed distinct spatiotemporal profiles among brain regions. Classification of neurons by brain region was improved with multichannel compared with single-channel waveforms. In visual cortex, unsupervised clustering identified the canonical regular-spiking (RS) and fast-spiking (FS) classes but also indicated a subclass of RS units with unidirectional backpropagating action potentials (BAPs). Moreover, BAPs were observed in many hippocampal RS cells. Overall, waveform analysis of spikes from high-density probes aids neuron identification and can reveal dendritic backpropagation. NEW & NOTEWORTHY It is challenging to identify neuron types with extracellular electrophysiology in vivo. We show that spatiotemporal action potentials measured on high-density electrode arrays can capture cell type-specific morphoelectrical properties, allowing classification of neurons across brain structures and within the cortex. Moreover, backpropagating action potentials are reliably detected in vivo from subpopulations of cortical and hippocampal neurons. Together, these results enhance the utility of dense extracellular electrophysiology for cell-type interrogation of brain network function.

How does the presence of neural probes affect extracellular potentials?

OBJECTIVE

Mechanistic modeling of neurons is an essential component of computational neuroscience that enables scientists to simulate, explain, and explore neural activity. The conventional approach to simulation of extracellular neural recordings first computes transmembrane currents using the cable equation and then sums their contribution to model the extracellular potential. This two-step approach relies on the assumption that the extracellular space is an infinite and homogeneous conductive medium, while measurements are performed using neural probes. The main purpose of this paper is to assess to what extent the presence of the neural probes of varying shape and size impacts the extracellular field and how to correct for them.

APPROACH

We apply a detailed modeling framework allowing explicit representation of the neuron and the probe to study the effect of the probes and thereby estimate the effect of ignoring it. We use meshes with simplified neurons and different types of probe and compare the extracellular action potentials with and without the probe in the extracellular space. We then compare various solutions to account for the probes' presence and introduce an efficient probe correction method to include the probe effect in modeling of extracellular potentials.

MAIN RESULTS

Our computations show that microwires hardly influence the extracellular electric field and their effect can therefore be ignored. In contrast, multi-electrode arrays (MEAs) significantly affect the extracellular field by magnifying the recorded potential. While MEAs behave similarly to infinite insulated planes, we find that their effect strongly depends on the neuron-probe alignment and probe orientation.

SIGNIFICANCE

Ignoring the probe effect might be deleterious in some applications, such as neural localization and parameterization of neural models from extracellular recordings. Moreover, the presence of the probe can improve the interpretation of extracellular recordings, by providing a more accurate estimation of the extracellular potential generated by neuronal models.