Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice

Background Extracellular recording represents a crucial electrophysiological technique in neuroscience for studying the activity of single neurons and neuronal populations. The electrodes capture voltage traces that, with the help of analytical tools, reveal action potentials (‘spikes’) as well as local field potentials. The process of spike sorting is used for the extraction of action potentials generated by individual neurons. Until recently, spike sorting was performed with manual techniques, which are laborious and unreliable due to inherent operator bias. As neuroscientists add multiple electrodes to their probes, the high-density devices can record hundreds to thousands of neurons simultaneously, making the manual spike sorting process increasingly difficult. The advent of automated spike sorting software has offered a compelling solution to this issue and, in this study, we present a simple-to-execute framework for running an automated spike sorter. Methods Tetrode recordings of freely-moving mice are obtained from the CA1 region of the hippocampus as they navigate a linear track. Tetrode recordings are also acquired from the prelimbic cortex, a region of the medial prefrontal cortex, while the mice are tested in a T maze. All animals are implanted with custom-designed, 3D-printed microdrives that carry 16 electrodes, which are bundled in a 4-tetrode geometry. Results We provide an overview of a framework for analyzing single-unit data in which we have concatenated the acquisition system (Cheetah, Neuralynx) with analytical software (MATLAB) and an automated spike sorting pipeline (MountainSort). We give precise instructions on how to implement the different steps of the framework, as well as explanations of our design logic. We validate this framework by comparing manually-sorted spikes against automatically-sorted spikes, using neural recordings of the hippocampus and prelimbic cortex in freely-moving mice. Conclusions We have efficiently integrated the MountainSort spike sorter with Neuralynx-acquired neural recordings. Our framework is easy to implement and provides a high-throughput solution. We predict that within the broad field of bioelectronic medicine, those teams that incorporate high-density neural recording devices to their armamentarium might find our framework quite valuable as they expand their analytical footprint.

KCC2 antagonism increases neuronal network excitability but disrupts ictogenesis in vitro

The potassium-chloride cotransporter 2 (KCC2) plays a role in epileptiform synchronization, but it remains unclear how it influences such a process. Here, we used tetrode recordings in the in vitro rat entorhinal cortex (EC) to analyze the effects of the KCC2 antagonist VU0463271 on 4-aminopyridine (4AP)-induced ictal and interictal activity. During 4AP application, ictal events were associated with significant increases in interneurons and principal cells activities. VU0463271 application transformed ictal discharges to shorter ictal-like events that were not accompanied by significant increases in interneuron or principal cell firing. Interictal events persisted during VU0463271 application at an accelerated frequency of occurrence with significant increases in interneuron and principal cell activity. Further analysis revealed that interneuron and principal cell firing rate during 4AP-induced interictal events were increased after VU0463271 application without changes in synchronicity. Overall, our results demonstrate that in the EC, KCC2 antagonism enhances both interneuron and principal cell excitability, while paradoxically decreasing the ability of neuronal networks to generate structured ictal events. NEW & NOTEWORTHY We are the first to use tetrode recordings in the entorhinal cortex to demonstrate that antagonizing potassium-chloride cotransporter 2 (KCC2) function abolishes ictal discharges and the associated, dynamic changes in single-unit firing in the in vitro 4-aminopyrine model of epileptiform synchronization. Interictal discharges were, however, shorter and more frequent during KCC2 antagonism, while the associated single-unit activity increased, suggesting augmented neuronal excitability. Our findings highlight the complex role of KCC2 in disease pathology.

An easy-to-assemble, robust, and lightweight drive implant for chronic tetrode recordings in freely moving animals

Tetrode arrays are the gold-standard method for neuronal recordings in many studies with behaving animals, especially for deep structures and chronic recordings. Here we outline an improved drive design for use in freely behaving animals. Our design makes use of recently developed technologies to reduce the complexity and build time of the drive while maintaining a low weight. The design also presents an improvement over many existing designs in terms of robustness and ease of use. We describe two variants: a 16 tetrode implant weighing ∼2 g for mice, bats, tree shrews and similar animals, and a 64 tetrode implant weighing ∼16 g for rats, and similar animals.These designs were co-developed and optimized alongside a new class of drive-mounted feature-rich amplifier boards with ultra-thin RF tethers, as described in an upcoming paper (Newman, Zhang et al., in prep). This design significantly improves the data yield of chronic electrophysiology experiments.

A novel and fully automatic spike-sorting implementation with variable number of features

The most widely used spike-sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike-sorting algorithm that can capture multiple clusters of different sizes and densities. In addition, we introduce an improved feature selection method, by using a variable number of wavelet coefficients, based on the degree of non-Gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single-channel recordings, in ~95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single-channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared with previous implementations and also compared with recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real tetrode recordings. NEW & NOTEWORTHY We propose a new fully automatic spike-sorting algorithm, including several steps that allow the selection of multiple clusters of different sizes and densities. Moreover, it defines the dimensionality of the feature space in an unsupervised way. We evaluated the performance of the algorithm with real and simulated data, from both single-channel and tetrode recordings. The proposed algorithm was able to outperform manual sorting from experts and other recent unsupervised algorithms.

A Detailed and Fast Model of Extracellular Recordings

We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. To reduce the computational load, the contributions of neurons further away were simulated using previously recorded spikes with their amplitude normalized by the distance to the recording electrode. For making the simulations more realistic, we also considered a model of a finite-size electrode by averaging the potential along the electrode surface and modeling the electrode-tissue interface with a capacitive filter. This model allowed studying the effect of the electrode diameter on the quality of the recordings and how it affects the number of identified neurons after spike sorting. Given that not all neurons are active at a time, we also generated simulations with different ratios of active neurons and estimated the ratio that matches the signal-to-noise values observed in real data. Finally, we used the model to simulate tetrode recordings.