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.

FEATURES ANALYSIS AND SYSTEM IDENTIFICATION OF MECHANOMYOGRAPHY AND ELECTROMYOGRAPHY UNDER TRANSCRANIAL MAGNETIC STIMULATION

Transcranial magnetic stimulation (TMS) is an electrophysiological technique that uses alternating magnetic fields to deliver electric current and stimulate the cerebral cortex. When TMS is used for the evaluation of brain diseases, it is necessary to detect the contraction of the corresponding muscles in the cerebral cortex stimulated by TMS, and the muscle activity referred to as motor evoked potential (MEP). This study simultaneously recorded the mechanomyography (MMG) and electromyography (EMG) from the right abductor pollicis brevis muscle during TMS with different intensities in order to observe whether the MEP parameters from MMG signals showed similar trait of EMG recordings. Moreover, the subspace method (N4SID) and transfer function were used to identify the TMS–MMG system. In this system, the input was a pulse signal of TMS, and the output was the MMG signal detected from the target muscle. The TMS–MMG system was identified as a fourth-order model. This study also analyzed the internal features of the system and demonstrated that the poles of healthy subjects were distributed in a range, and the gain increased with the increase of the TMS intensity. It was found that MMG signals can be used as diagnostic indicators of TMS, and the TMS–MMG model can be used to further explore the details of how TMS generates responses measured with MMG.

Electroencephalography Patterns in Children with First Unprovoked Seizure

Background First unprovoked seizure (FUS) is a neurological health problem that occurs in an estimated 2% of children aged 16 years or younger. Electroencephalography (EEG) is an electrophysiological technique to record electrical activities arising from the brain; this technique can be used to evaluate patients with suspected seizures, epilepsy, and unusual concomitants. The objective of this study is to describe the EEG patterns in children with FUS and the factors associated with these EEG results. Method A retrospective analytic study was conducted in the Neuropaediatric Clinic, Dr Soetomo General Academic Hospital. The medical record data were obtained from January 2018 to December 2019. Children aged one month to 18 years with FUS and their complete EEG records were included. Descriptive statistics and the chi-square test with Cochran's Q test and Mantel–Haenszel tests were used for statistical calculations. Results One hundred participants enrolled the study. The majority (54%) showed abnormal EEG, which was dominated by epileptiform discharges (68.5%) consisting of benign epileptiform with centro-temporal spikes (BECTS), focal and generalized sharp waves, focal and generalized spikes, and EEG seizures. Factors associated with abnormal EEG results were children aged ≥ 5 years (p = 0.07, OR = 3.093, 95% CI = 1.361–7.030), focal seizure type (p = 0.021, OR = 6.286, 95% CI = 1.327–29,779), and long seizure duration ≥ 5 minutes (p < 0.001, OR = 8.333, 95% CI = 3.029–22.929). Conclusion Children with abnormal EEG were at risk for recurrent seizures. Over 50% of children with FUS had abnormal EEG results. In the present study, abnormal EEG results were frequently found in children with FUS, especially in older children (≥ 5 years old), those with focal seizures, and those with long seizure durations (≥ 5 minutes).

openMNGlab: Data Analysis Framework for Microneurography – A Technical Report

openMNGlab is an open-source software framework for data analysis, tailored for the specific needs of microneurography – a type of electrophysiological technique particularly important for research on peripheral neural fibers coding. Currently, openMNGlab loads data from Spike2 and Dapsys, which are two major data acquisition solutions. By building on top of the Neo software, openMNGlab can be easily extended to handle the most common electrophysiological data formats. Furthermore, it provides methods for data visualization, fiber tracking, and a modular feature database to extract features for data analysis and machine learning.

Development of novel potent ligands for GPR85, an orphan G protein-coupled receptor expressed in the brain

GPR85 is a member of the G protein-coupled receptor and is a super-conserved receptor expressed in the brain sub-family (SREB) with GPR27 and GPR173. These three receptors are orphan receptors; however, their endogenous ligands have not been identified. SREB has garnered the interest of many scientists because it is expressed in the central nervous system and is evolutionarily conserved. In particular, brain mass is reported to be increased and learning and memory are improved in GPR85 knockout mice (Matsumoto et al., 2008). In this study, we characterized newly synthesized compounds using a GPR85-Gsα fusion protein and the [35S]GTPδS binding assay and identified novel GPR85 inverse-agonists with IC50 values of approximately 1 μM. To analyze the neurochemical character of the compounds and investigate the physiological significance of GPR85, we used cerebellar Purkinje cells expressing GPR85 and an electrophysiological technique. Based on the results, the inverse-agonist compound for GPR85 modulated potassium channel opening. Together with the results of previous gene analysis of GPR85, we expect that the development of the GPR85 ligand will provide new insights into a few types of neurological disorders.

Framework for acquisition and automated sorting of neural spikes from multielectrode recordings in freely-moving mice

BackgroundExtracellular 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 are able to 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. The purpose of this study is to provide a simple-to-execute framework for using MountainSort, an automated spike sorting pipeline, in conjunction with MATLAB and the acquisition system (Cheetah, Neuralynx). We validate this automated framework with neural recordings from the hippocampus and prelimbic cortex. MethodsMultielectrode recordings of freely-moving mice are obtained from the CA1 region of the hippocampus as they navigate a linear track. Multielectrode 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. ResultsWe 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. ConclusionsAutomated spike sorting is a necessity for medium and large-scale extracellular neural recordings. Here, we have smoothly integrated MountainSort-based spike sorting into a framework for acquisition and analysis of multielectrode brain recordings in mice.

Continuous Facial Nerve Stimulation During Cochlear Implantation – An Electrophysiological Technique to Control the Intracochlear Electrode Contact Position

Purpose: This proof of concept describes the use of evoked electromyographic (EMG) activation of the facial nerve for intraoperative monitoring of the electrode insertion during cochlear implantation (CI).Methods: Intraoperative EMG measurements from the facial nerve were conducted in nine patients undergoing CI implantation. Electric pulses were emitted from contacts on the CI array during and immediately after electrode insertion. For control, the results of EMG measurements were compared to postoperative flat panel volume computed tomography scans with secondary reconstruction (fpVCTSECO).Results: During insertion, the EMG response evoked by the electrical stimulation from the CI was growing with the stimulating contact approaching the facial nerve and declined with increasing distance. After full insertion, contacts on the apical half of the CI array stimulated higher EMG responses compared with those on the basal half. Comparison with postoperative imaging demonstrated that electrode contacts stimulating high EMG responses had the shortest distances to the facial nerve. Conclusion: It could be demonstrated that electrically evoked EMG activation of the facial nerve can be used to monitor the progress during CI electrode insertion and to control the intracochlear electrode position after full insertion.

Determining 5HT7R’s Involvement in Modifying the Antihyperalgesic Effects of Electroacupuncture on Rats With Recurrent Migraine

Electroacupuncture (EA) is widely used in clinical practice to relieve migraine pain. 5-HT7 receptor (5-HT7R) has been reported to play an excitatory role in neuronal systems and regulate hyperalgesic pain and neurogenic inflammation. 5-HT7R could influence phosphorylation of protein kinase A (PKA)- or extracellular signal-regulated kinase1/2 (ERK1/2)-mediated signaling pathways, which mediate sensitization of nociceptive neurons via interacting with cyclic adenosine monophosphate (cAMP). In this study, we evaluated the role of 5-HT7R in the antihyperalgesic effects of EA and the underlying mechanism through regulation of PKA and ERK1/2 in trigeminal ganglion (TG) and trigeminal nucleus caudalis (TNC). Hyperalgesia was induced in rats with dural injection of inflammatory soup (IS) to cause meningeal neurogenic inflammatory pain. Electroacupuncture was applied for 15 min every other day before IS injection. Von Frey filaments, tail-flick, hot-plate, and cold-plated tests were used to evaluate the mechanical and thermal hyperalgesia. Neuronal hyperexcitability in TNC was studied by an electrophysiological technique. The 5-HT7R antagonist (SB269970) or 5-HT7R agonist (AS19) was administered intrathecally before each IS application at 2-day intervals during the 7-day injection protocol. The changes in 5-HT7R and 5-HT7R-associated signaling pathway were examined by real-time polymerase chain reaction (RT-PCR), Western blot, immunofluorescence, and enzyme-linked immunosorbent assay (ELISA) analyses. When compared with IS group, mechanical and thermal pain thresholds of the IS + EA group were significantly increased. Furthermore, EA prevented the enhancement of both spontaneous activity and evoked responses of second-order trigeminovascular neurons in TNC. Remarkable decreases in 5-HT7R mRNA expression and protein levels were detected in the IS + EA group. More importantly, 5-HT7R agonist AS19 impaired the antihyperalgesic effects of EA on p-PKA and p-ERK1/2. Injecting 5-HT7R antagonist SB-269970 into the intrathecal space of IS rats mimicked the effects of EA antihyperalgesia and inhibited p-PKA and p-ERK1/2. Our findings indicate that 5-HT7R mediates the antihyperalgesic effects of EA on IS-induced migraine pain by regulating PKA and ERK1/2 in TG and TNC.