Propagating population activity patterns during spontaneous slow waves in the thalamus of rodents.

Slow waves (SWs) represent the most prominent electrophysiological events in the thalamocortical system under anesthesia and during deep sleep. Recent studies have revealed that SWs have complex spatiotemporal dynamics and propagate across neocortical regions. However, it is still unclear whether neuronal activity in the thalamus exhibits similar propagation properties during SWs. Here, we report propagating population activity in the thalamus of ketamine/xylazine-anesthetized rats and mice visualized by high-density silicon probe recordings. In both rodent species, propagation of spontaneous thalamic activity during up-states was most frequently observed in dorsal thalamic nuclei such as the higher order posterior (Po), lateral posterior (LP) or laterodorsal (LD) nuclei. The preferred direction of thalamic activity spreading was along the dorsoventral axis, with over half of the up-states exhibiting a gradual propagation in the ventral-to-dorsal direction. Furthermore, simultaneous neocortical and thalamic recordings collected under anesthesia demonstrated that there is a weak but noticeable interrelation between propagation patterns observed during cortical up-states and those displayed by thalamic population activity. In addition, using chronically implanted silicon probes, we detected propagating activity patterns in the thalamus of naturally sleeping rats during slow-wave sleep. However, in comparison to propagating up-states observed under anesthesia, these propagating patterns were characterized by a reduced rate of occurrence and a faster propagation speed. Our findings suggest that the propagation of spontaneous population activity is an intrinsic property of the thalamocortical network during synchronized brain states such as deep sleep or anesthesia. Additionally, our data implies that the neocortex may have partial control over the formation of propagation patterns within the dorsal thalamus under anesthesia.

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.

The medial septum controls hippocampal supra-theta oscillations.

Hippocampal theta oscillations orchestrate faster beta-to-gamma oscillations facilitating the segmentation of neural representations during navigation and episodic memory. Supra-theta rhythms of hippocampal CA1 are coordinated by local interactions as well as inputs from the entorhinal cortex (EC) and CA3 inputs. However, theta-nested gamma-band activity in the medial septum (MS) suggests that the MS may control supra-theta CA1 oscillations. To address this, we performed multi-electrode recordings of MS and CA1 activity in rodents and found that MS neuron firing showed strong phase-coupling to theta-nested supra-theta episodes and predicted changes in CA1 beta-to-gamma oscillations on a cycle-by-cycle basis. Unique coupling patterns of anatomically defined MS cell types suggested that indirect MS-to-CA1 pathways via the EC and CA3 mediate distinct CA1 gamma-band oscillations. Optogenetic activation of MS parvalbumin-expressing neurons elicited theta-nested beta-to-gamma oscillations in CA1. Thus, the MS orchestrates hippocampal network activity at multiple temporal scales to mediate memory encoding and retrieval.

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.

Adolescent ADHD and electrophysiological reward responsiveness: A machine learning approach to evaluate classification accuracy and prognosis.

We evaluated event-related potential (ERP) indices of reinforcement sensitivity as ADHD biomarkers by examining, in N=306 adolescents (Mage=15.78, SD=1.08), the extent to which ERP amplitude and latency variables measuring reward anticipation and response (1) differentiate, in age- and sex-matched subsamples, (i) youth with vs. without ADHD, (ii) youth at-risk for vs. not at-risk for ADHD, and, in the with ADHD subsample, (iii) youth with the inattentive vs. the hyperactive/impulsive (H/I) and combined presentations. We further examined the extent to which ERP variables (2) predict, in the ADHD subsample, substance use (i) concurrently and (ii) prospectively at 18-month follow-up. Linear support vector machine analyses indicated ERPs weakly differentiate youth with/without (65%) - and at-risk for/not at-risk for (63%) - ADHD but better differentiate ADHD presentations (78%). Regression analyses showed in adolescents with ADHD, ERPs explain a considerable proportion of variance (50%) in concurrent alcohol use and, controlling for concurrent marijuana and tobacco use, explain a considerable proportion of variance (87 and 87%) in, and predict later marijuana and tobacco use. Findings are consistent with the dual-pathway model of ADHD. Results also highlight limitations of a dichotomous, syndromic classification and indicate differences in neural reinforcement sensitivity are a promising ADHD prognostic biomarker.

Huygens synchronization of medial septal pacemaker neurons generates hippocampal theta oscillation.

Episodic learning and memory retrieval are dependent on hippocampal theta oscillation, thought to rely on the GABAergic network of the medial septum (MS). To test how this network achieves theta synchrony, we recorded MS neurons and hippocampal local field potential simultaneously in anesthetized and awake mice and rats. We show that MS pacemakers synchronize their individual rhythmicity frequencies, akin to coupled pendulum clocks as observed by Huygens. We optogenetically identified them as parvalbumin-expressing GABAergic neurons, while MS glutamatergic neurons provide tonic excitation sufficient to induce theta. In accordance, waxing and waning tonic excitation is sufficient to toggle between theta and non-theta states in a network model of single-compartment inhibitory pacemaker neurons. These results provide experimental and theoretical support to a frequency-synchronization mechanism for pacing hippocampal theta, which may serve as an inspirational prototype for synchronization processes in the central nervous system from Nematoda to Arthropoda to Chordate and Vertebrate phyla.

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.

Reliability of reward ERPs in middle-late adolescents using a custom and a standardized preprocessing pipeline.

Despite advantage of neuroimaging measures in translational research frameworks, less is known about the psychometric properties thereof, especially in middle-late adolescents. Earlier, we examined evidence of convergent and incremental validity of reward anticipation and response event-related potentials (ERPs) and here we examined, in the same sample of 43 adolescents (Mage  = 15.67 years; SD = 1.01; range: 14-18; 32.6% boys), data quality (signal-to-noise ratio [SNR]), stability (mean amplitude across trials), and internal consistency (Cronbach's α and split-half reliability) of the same ERPs. Further, because observed time course and peak amplitude of ERP grand averages and thus findings on SNR, stability, and internal consistency may depend on preprocessing method, we employed a custom and a standardized preprocessing pipeline and compared findings across those. Using our custom pipeline, reward anticipation components were stable by the 40th trial, achieved acceptable internal consistency by the 19th, and all (but the stimulus-preceding negativity [SPN]) achieved acceptable SNR by the 41st trial. Initial response to reward components were stable by the 20th trial and achieved acceptable internal consistency by the 11th and acceptable SNR by the 45th trial. Difference scores had worse psychometric properties than parent measures. Time course and peak amplitudes of ERPs and thus results on SNR, stability, and internal consistency were comparable across preprocessing pipelines. In case of reward anticipation ERPs examined here, 41 trials (+4 artifacted and removed) and, in case of reward response ERPs, 45 trials (+5 artifacted) yielded stable and internally consistent estimates with acceptable SNR. Results are robust across preprocessing methods.

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.

ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution.

Objective.The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the challenges generated by the increasing number of channels, highly automated signal processing tools are needed. Our goal was to build a spike sorting model that can perform as well as offline solutions while maintaining high efficiency, enabling high-performance online sorting.Approach.In this paper we present ELVISort, a deep learning method that combines the detection and clustering of different action potentials in an end-to-end fashion.Main results.The performance of ELVISort is comparable with other spike sorting methods that use manual or semi-manual techniques, while exceeding the methods which use an automatic approach: ELVISort has been tested on three independent datasets and yielded average F1scores of 0.96, 0.82 and 0.81, which comparable with the results of state-of-the-art algorithms on the same data. We show that despite the good performance, ELVISort is capable to process data in real-time: the time it needs to execute the necessary computations for a sample of given length is only 1/15.71 of its actual duration (i.e. the sampling time multiplied by the number of the sampling points).Significance.ELVISort, because of its end-to-end nature, can exploit the massively parallel processing capabilities of GPUs via deep learning frameworks by processing multiple batches in parallel, with the potential to be used on other cutting-edge AI-specific hardware such as TPUs, enabling the development of integrated, portable and real-time spike sorting systems with similar performance to offline sorters.

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.

Spatial Information Based OSort for Real-Time Spike Sorting Using FPGA.

OBJECTIVE: Spiking activity of individual neurons can be separated from the acquired multi-unit activity with spike sorting methods. Processing the recorded high-dimensional neural data can take a large amount of time when performed on general-purpose computers. METHODS: In this paper, an FPGA-based real-time spike sorting system is presented which takes into account the spatial correlation between the electrical signals recorded with closely-packed recording sites to cluster multi-channel neural data. The system uses a spatial window-based version of the Online Sorting algorithm, which uses unsupervised template-matching for clustering. RESULTS: The test results show that the proposed system can reach an average accuracy of 86% using simulated data (16-32 neurons, 4-10 dB Signal-to-Noise Ratio), while the single-channel clustering version achieves only 74% average accuracy in the same cases on a 128-channel electrode array. The developed system was also tested on in vivo cortical recordings obtained from an anesthetized rat. CONCLUSION: The proposed FPGA-based spike sorting system can process more than 11000 spikes/second, so it can be used during in vivo experiments providing real-time feedback on the location and electrophysiological properties of well-separable single units. SIGNIFICANCE: The proposed spike sorting system could be used to reduce the positioning error of the closely-packed recording site during a neural measurement.

Neural and self-reported reward responsiveness are associated with dispositional affectivity and emotion dysregulation in adolescents with evidence for convergent and incremental validity.

Adolescence is a developmental period characterized by heightened reward sensitivity which, in turn, confers risk for pertinent negative outcomes, underscoring the need to better understand biological bases and behavioral correlates of reward responsiveness during this developmental phase. Our goals in the current study were to examine, in a sample of 43 typically developing adolescents (Mage  = 15.67 years; SD = 1.01; 32.6% boys), (1) evidence of convergent validity between neural and self-report reward responsiveness, (2) associations between neural reward responsiveness and self-report dispositional affectivity and emotion dysregulation (ED) and (3) evidence of incremental validity of self-report beyond neural reward responsiveness in predicting affectivity and ED. During electroencephalography (EEG), adolescents completed two experimental paradigms probing event-related potential (ERP) indices of reward anticipation and initial responsiveness to reward attainment. Following EEG, they completed self-report measures of reward responsiveness, affectivity, and ED. Findings indicated some evidence of convergent validity between enhanced ERP indices of reward anticipation and initial response to reward and greater reinforcement sensitivity; that ERP indices of both reward responsiveness aspects predicted lower negative affectivity and less ED; and evidence of incremental validity of self-report beyond neural reward responsiveness in predicting outcomes. Results underscore the utility of a multi-method framework in assessing adolescent reward responsiveness and support the relevance of reward responsiveness in explaining individual differences in dispositional affectivity and ED.

A community-based transcriptomics classification and nomenclature of neocortical cell types.

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

The neural tissue around SU-8 implants: A quantitative in vivo biocompatibility study.

The use of SU-8 material in the production of neural sensors has grown recently. Despite its widespread application, a detailed systematic quantitative analysis concerning its biocompatibility in the central nervous system is lacking. In this immunohistochemical study, we quantified the neuronal preservation and the severity of astrogliosis around SU-8 devices implanted in the neocortex of rats, after a 2 months survival. We found that the density of neurons significantly decreased up to a distance of 20 µm from the implant, with an averaged density decrease to 24 ±â€¯28% of the control. At 20 to 40 µm distance from the implant, the majority of the neurons was preserved (74 ±â€¯39% of the control) and starting from 40 µm distance from the implant, the neuron density was control-like. The density of synaptic contacts - examined at the electron microscopic level - decreased in the close vicinity of the implant, but it recovered to the control level as close as 24 µm from the implant track. The intensity of the astroglial staining significantly increased compared to the control region, up to 560 µm and 480 µm distance from the track in the superficial and deep layers of the neocortex, respectively. Electron microscopic examination revealed that the thickness of the glial scar was around 5-10 µm thin, and the ratio of glial processes in the neuropil was not more than 16% up to a distance of 12 µm from the implant. Our data suggest that neuronal survival is affected only in a very small area around the implant. The glial scar surrounding the implant is thin, and the presence of glial elements is low in the neuropil, although the signs of astrogliosis could be observed up to about 500 µm from the track. Subsequently, the biocompatibility of the SU-8 material is high. Due to its low cost fabrication and more flexible nature, SU-8 based devices may offer a promising approach to experimental and clinical applications in the future.

Spike detection and sorting with deep learning.

OBJECTIVE: The extraction and identification of single-unit activities in intracortically recorded electric signals have a key role in basic neuroscience, but also in applied fields, like in the development of high-accuracy brain-computer interfaces. The purpose of this paper is to present our current results on the detection, classification and prediction of neural activities based on multichannel action potential recordings. APPROACH: Throughout our investigations, a deep learning approach utilizing convolutional neural networks and a combination of recurrent and convolutional neural networks was applied, with the latter used in case of spike detection and the former used for cases of sorting and predicting spiking activities. MAIN RESULTS: In our experience, the algorithms applied prove to be useful in accomplishing the tasks mentioned above: our detector could reach an average recall of 69%, while we achieved an average accuracy of 89% in classifying activities produced by more than 20 distinct neurons. SIGNIFICANCE: Our findings support the concept of creating real-time, high-accuracy action potential based BCIs in the future, providing a flexible and robust algorithmic background for further development.