A normalized template matching method for improving spike detection in extracellular voltage recordings

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

DualSort: online spike sorting with a running neural network

Objective.Spike sorting, i.e. the detection and separation of measured action potentials from different extracellularly recorded neurons, remains one of the bottlenecks in deciphering the brain. In recent years, the application of neural networks (NNs) for spike sorting has garnered significant attention. Most methods focus on specific sub-problems within the conventional spike sorting pipeline, such as spike detection or feature extraction, and attempt to solve them with complex network architectures. This paper presents DualSort, a simple NN that gets combined with downstream post-processing for real-time spike sorting. It shows high efficiency, low complexity, and requires a comparatively small amount of human interaction.Approach.Synthetic and experimentally obtained extracellular single-channel recordings were utilized to train and evaluate the proposed NN. For training, spike waveforms were labeled with respect to their associated neuron and position in the signal, allowing the detection and categorization of spikes in unison. DualSort classifies a single spike multiple times in succession, as it runs over the signal in a step-by-step manner and uses a post-processing algorithm that transmits the network output into spike trains. Main results.With the used datasets, DualSort was able to detect and distinguish different spike waveforms and separate them from background activity. The post-processing algorithm significantly strengthened the overall performance of the model, making the system more robust as a whole. Although DualSort is an end-to-end solution that efficiently transforms filtered signals into spike trains, it competes with contemporary state-of-the-art technologies that exclusively target single sub-problems in the conventional spike sorting pipeline.Significance.This work demonstrates that even under high noise levels, complex NNs are not necessary by any means to achieve high performance in spike detection and sorting. The utilization of data augmentation on a limited quantity of spikes could substantially decrease hand-labeling compared to other studies. Furthermore, the proposed framework can be utilized without human interaction when combined with an unsupervised technique that provides pseudo labels for DualSort. Due to the low complexity of our network, it works efficiently and enables real-time processing on basic hardware. The proposed approach is not limited to spike sorting, as it may also be used to process different signals, such as electroencephalogram (EEG), which needs to be investigated in future research.

Thermal neuromodulation using pulsed and continuous infrared illumination in a penicillin-induced acute epilepsy model

Infrared neuromodulation (INM) is a promising neuromodulation tool that utilizes pulsed or continuous-wave near-infrared (NIR) laser light to produce an elevation of the background temperature of the neural tissue. The INM-based cortical heating has been proven as an effective modality to induce changes in neuronal activities. In this paper, we investigate the effect of INM-based cortical heating on the characteristics of interictal epileptiform discharges (IEDs) induced by penicillin in anesthetized rats. Cortical heating was conducted using a NIR laser light guided through a needle-like silicon-based waveguide probe. We detected penicillin-induced cortical IEDs from preprocessed micro-electrocorticography ([Formula: see text]ECoG) recordings, then we assessed changes in various temporal and spectral features of IEDs due to INM. Our findings show that the fast cortical heating phase obtained with continuous-wave NIR light is highly associated with a reduction of IED amplitudes, small but significant changes in the negative amplitude of IEDs compared with the baseline, and a proportional increase in the power of frequency bands related to delta/theta (2-8 Hz) and gamma (28-80 Hz) oscillations. Furthermore, a low rate of cortical heating with pulsed NIR illumination has a more inhibitory impact on the sharp negative polarity of IEDs. Our findings do not indicate a clear reduction in the frequency of IEDs in anesthetized rodents. In contrast, 2-4 min of continuous laser illumination leads to a notable increase in IED frequency. This effect of INM could potentially restrict its use in therapeutic applications related to epilepsy. However, the thermal effect of INM on cortical neurons induces changes in other characteristics of IEDs, which could prove beneficial for future applications.

Audiovisual task switching rapidly modulates sound encoding in mouse auditory cortex

In everyday behavior, sensory systems are in constant competition for attentional resources, but the cellular and circuit-level mechanisms of modality-selective attention remain largely uninvestigated. We conducted translaminar recordings in mouse auditory cortex (AC) during an audiovisual (AV) attention shifting task. Attending to sound elements in an AV stream reduced both pre-stimulus and stimulus-evoked spiking activity, primarily in deep-layer neurons and neurons without spectrotemporal tuning. Despite reduced spiking, stimulus decoder accuracy was preserved, suggesting improved sound encoding efficiency. Similarly, task-irrelevant mapping stimuli during inter-trial intervals evoked fewer spikes without impairing stimulus encoding, indicating that attentional modulation generalized beyond training stimuli. Importantly, spiking reductions predicted trial-to-trial behavioral accuracy during auditory attention, but not visual attention. Together, these findings suggest auditory attention facilitates sound discrimination by filtering sound-irrelevant background activity in AC, and that the deepest cortical layers serve as a hub for integrating extramodal contextual information.

Intertwined-pulse modulation for compressive data telemetry

This paper presents a novel approach for anisochronous pulse-based modulation. In the proposed approach, referred to as the intertwined-pulse modulation (IPM), every pair of consecutive symbols overlap in time. This allows for shortening the time allocated for the transmission of the symbols, hence achieving temporal compaction while the data goes through the line encoding step in a digital communication system. The IPM is also uniquely superior to other existing anisochronous pulse-based modulation schemes in the fact that it exhibits robust symbol error rate against unwanted variations in both rise/fall times of the pulses in the modulated waveform, and in the threshold level used for data detection on the receiver side. An experimental setup was developed to implement an IPM encoder using standard digital hardware, and an IPM decoder as a part of the receiver system in software. According to the experimental results (supported by simulation results and theoretical studies), for the data mean value of mid-full-scale range, the proposed IPM scheme exhibits a time-domain compaction rate of up to 209.2%.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

Fast parametric curve matching (FPCM) for automatic spike detection

Epilepsy is a widely spread neurological disease, whose treatment often requires resection of the pathological cortical tissue. Interictal spike analysis observed in the non-invasively collected EEG or MEG data offers a way to localize epileptogenic cortical structures for surgery planning purposes. While a plethora of automatic spike detection techniques have been developed each with its own assumptions and limitations, non of them is ideal and the best results are achieved when the output of several automatic spike detectors are combined. This is especially true in the low signal-to-noise ratio conditions. To this end we propose a novel biomimetic approach for automatic spike detection based on a constrained mixed spline machinery that we dub as fast parametric curve matching (FPCM). Using the peak-wave shape parametrization, the constrained parametric morphological model is constructed and convolved with the observed multichannel data to very efficiently determine mixed spline parameters corresponding to each time-point in the dataset. Then the logical predicates that directly map to the expected interictal event morphology allow us to accomplish the spike detection task. The results of simulations mimicking typical low SNR scenario show the robustness and high ROC AUC values of the FPCM method as compared to the spike detection performed by the means of more conventional approaches such as wavelet decomposition, template matching or simple amplitude thresholding. Applied to the real MEG and EEG data from the human patients and to ECoG data from the rat, the FPCM technique demonstrates reliable detection of the interictal events and localization of epileptogenic zones concordant with independent conclusions made by the epileptologist. Since the FPCM is computationally light, tolerant to high amplitude artifacts and flexible to accommodate verbalized descriptions of the arbitrary target morphology, it may complement the existing arsenal of means for analysis of noisy interictal datasets.

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.

Recording site placement on planar silicon-based probes affects signal quality in acute neuronal recordings

Multisite, silicon-based probes are widely used tools to record the electrical activity of neuronal populations. Several physical features of these devices are designed to improve their recording performance. Here, our goal was to investigate whether the position of recording sites on the silicon shank might affect the quality of the recorded neural signal in acute experiments. Neural recordings obtained with five different types of high-density, single-shank, planar silicon probes from anesthetized rats were analyzed. Wideband data were filtered to extract spiking activity, then the amplitude distribution of samples and quantitative properties of the recorded brain activity (single unit yield, spike amplitude and isolation distance) were compared between sites located at different positions of the silicon shank, focusing particularly on edge and center sites. Edge sites outperformed center sites: for all five probe types there was a significant difference in the signal power computed from the amplitude distributions, and edge sites recorded significantly more large amplitude samples both in the positive and negative range. Although the single unit yield was similar between site positions, the difference in spike amplitudes was noticeable in the range corresponding to high-amplitude spikes. Furthermore, the advantage of edge sites slightly decreased with decreasing shank width. Our results might aid the design of novel neural implants in enhancing their recording performance by identifying more efficient recording site placements.

Recording site placement on planar silicon-based probes affects signal quality in acute neuronal recordings

Multisite, silicon-based probes are widely used tools to record the electrical activity of neuronal populations. Several physical features of these devices are designed to improve their recording performance. Here, our goal was to investigate whether the position of recording sites on the silicon shank might affect the quality of the recorded neural signal in acute experiments. Neural recordings obtained with five different types of high-density, single-shank, planar silicon probes from anesthetized rats were analyzed. Wideband data were filtered to extract spiking activity, then the amplitude distribution of samples and quantitative properties of the recorded brain activity (single unit yield, spike amplitude and isolation distance) were compared between sites located at different positions of the silicon shank, focusing particularly on edge and center sites. Edge sites outperformed center sites: for all five probe types there was a significant difference in the signal power computed from the amplitude distributions, and edge sites recorded significantly more large amplitude samples both in the positive and negative range. Although the single unit yield was similar between site positions, the difference in spike amplitudes was noticeable in the range corresponding to high-amplitude spikes. Furthermore, the advantage of edge sites slightly decreased with decreasing shank width. Our results might aid the design of novel neural implants in enhancing their recording performance by identifying more efficient recording site placements.

Features of Action Potentials from Identified Thalamic Nuclei in Anesthetized Patients

Our objective was to describe the electrophysiological properties of the extracellular action potential (AP) picked up through microelectrode recordings (MERs). Five patients were operated under general anesthesia for centromedian deep brain stimulation (DBS). APs from the same cell were pooled to obtain a mean AP (mAP). The amplitudes and durations for all 2/3 phases were computed from the mAP, together with the maximum (dV_max) and minimum (dV_min) values of the first derivative, as well as the slopes of different phases during repolarization. The mAPs are denominated according to the phase polarity (P/N for positive/negative). We obtained a total of 1109 mAPs, most of the positive (98.47%) and triphasic (93.69%) with a small P/N deflection (V_phase1) before depolarization. The percentage of the different types of mAPs was different for the nuclei addressed. The relationship between dV_max and the depolarizing phase is specific. The descending phase of the first derivative identified different phases during the repolarizing period. We observed a high correlation between V_phase1 and the amplitudes of either depolarization or repolarization phases. Human thalamic nuclei differ in their electrophysiological properties of APs, even under general anesthesia. Capacitive current, which is probably responsible for V_phase1, is very common in thalamic APs. Moreover, subtle differences during repolarization are neuron-specific.

Elementary motion sequence detectors in whisker somatosensory cortex

How somatosensory cortex (S1) encodes complex patterns of touch, as occur during tactile exploration, is poorly understood. In mouse whisker S1, temporally dense stimulation of local whisker pairs revealed that most neurons are not classical single-whisker feature detectors, but instead are strongly tuned to 2-whisker sequences involving the columnar whisker (CW) and one, specific surround whisker (SW), usually in SW-leading-CW order. Tuning was spatiotemporally precise and diverse across cells, generating a rate code for local motion vectors defined by SW-CW combinations. Spatially asymmetric, sublinear suppression for suboptimal combinations and near-linearity for preferred combinations sharpened combination tuning relative to linearly predicted tuning. This resembles computation of motion direction selectivity in vision. SW-tuned neurons, misplaced in the classical whisker map, had the strongest combination tuning. Thus, each S1 column contains a rate code for local motion sequences involving the CW, providing a basis for higher-order feature extraction.