Acute Aromatase Inhibition Impairs Neural and Behavioral Auditory Scene Analysis in Zebra Finches.

Auditory perception can be significantly disrupted by noise. To discriminate sounds from noise, auditory scene analysis (ASA) extracts the functionally relevant sounds from acoustic input. The zebra finch communicates in noisy environments. Neurons in their secondary auditory pallial cortex (caudomedial nidopallium, NCM) can encode song from background chorus, or scenes, and this capacity may aid behavioral ASA. Furthermore, song processing is modulated by the rapid synthesis of neuroestrogens when hearing conspecific song. To examine whether neuroestrogens support neural and behavioral ASA in both sexes, we retrodialyzed fadrozole (aromatase inhibitor, FAD) and recorded in vivo awake extracellular NCM responses to songs and scenes. We found that FAD affected neural encoding of songs by decreasing responsiveness and timing reliability in inhibitory (narrow-spiking), but not in excitatory (broad-spiking) neurons. Congruently, FAD decreased neural encoding of songs in scenes for both cell types, particularly in females. Behaviorally, we trained birds using operant conditioning and tested their ability to detect songs in scenes after administering FAD orally or injected bilaterally into NCM. Oral FAD increased response bias and decreased correct rejections in females, but not in males. FAD in NCM did not affect performance. Thus, FAD in the NCM impaired neuronal ASA but that did not lead to behavioral disruption suggesting the existence of resilience or compensatory responses. Moreover, impaired performance after systemic FAD suggests involvement of other aromatase-rich networks outside the auditory pathway in ASA. This work highlights how transient estrogen synthesis disruption can modulate higher-order processing in an animal model of vocal communication.

Differential encoding of temporally evolving color patterns across nearby V1 neurons.

Whereas studies of the V1 cortex have focused mainly on neural line orientation preference, color inputs are also known to have a strong presence among these neurons. Individual neurons typically respond to multiple colors and nearby neurons have different combinations of preferred color inputs. However, the computations performed by V1 neurons on such color inputs have not been extensively studied. Here we aimed to address this issue by studying how different V1 neurons encode different combinations of inputs composed of four basic colors. We quantified the decoding accuracy of individual neurons from multi-electrode array recordings, comparing multiple individual neurons located within 2 mm along the vertical axis of the V1 cortex of the anesthetized rat. We found essentially all V1 neurons to be good at decoding spatiotemporal patterns of color inputs and they did so by encoding them in different ways. Quantitative analysis showed that even adjacent neurons encoded the specific input patterns differently, suggesting a local cortical circuitry organization which tends to diversify rather than unify the neuronal responses to each given input. Using different pairs of monocolor inputs, we also found that V1 neocortical neurons had a diversified and rich color opponency across the four colors, which was somewhat surprising given the fact that rodent retina express only two different types of opsins. We propose that the processing of color inputs in V1 cortex is extensively composed of multiple independent circuitry components that reflect abstract functionalities resident in the internal cortical processing rather than the raw sensory information per se.

Electropolymerization processing of side-chain engineered EDOT for high performance microelectrode arrays.

Microelectrode Arrays (MEAs) are popular tools for in vitro extracellular recording. They are often optimized by surface engineering to improve affinity with neurons and guarantee higher recording quality and stability. Recently, PEDOT:PSS has been used to coat microelectrodes due to its good biocompatibility and low impedance, which enhances neural coupling. Herein, we investigate on electro-co-polymerization of EDOT with its triglymated derivative to control valence between monomer units and hydrophilic functions on a conducting polymer. Molecular packing, cation complexation, dopant stoichiometry are governed by the glycolation degree of the electro-active coating of the microelectrodes. Optimal monomer ratio allows fine-tuning the material hydrophilicity and biocompatibility without compromising the electrochemical impedance of microelectrodes nor their stability while interfaced with a neural cell culture. After incubation, sensing readout on the modified electrodes shows higher performances with respect to unmodified electropolymerized PEDOT, with higher signal-to-noise ratio (SNR) and higher spike counts on the same neural culture. Reported SNR values are superior to that of state-of-the-art PEDOT microelectrodes and close to that of state-of-the-art 3D microelectrodes, with a reduced fabrication complexity. Thanks to this versatile technique and its impact on the surface chemistry of the microelectrode, we show that electro-co-polymerization trades with many-compound properties to easily gather them into single macromolecular structures. Applied on sensor arrays, it holds great potential for the customization of neurosensors to adapt to environmental boundaries and to optimize extracted sensing features.

Dopaminergic and prefrontal dynamics co-determine mouse decisions in a spatial gambling task.

The neural mechanisms by which animals initiate goal-directed actions, choose between options, or explore opportunities remain unknown. Here, we develop a spatial gambling task in which mice, to obtain intracranial self-stimulation rewards, self-determine the initiation, direction, vigor, and pace of their actions based on their knowledge of the outcomes. Using electrophysiological recordings, pharmacology, and optogenetics, we identify a sequence of oscillations and firings in the ventral tegmental area (VTA), orbitofrontal cortex (OFC), and prefrontal cortex (PFC) that co-encodes and co-determines self-initiation and choices. This sequence appeared with learning as an uncued realignment of spontaneous dynamics. Interactions between the structures varied with the reward context, particularly the uncertainty associated with the different options. We suggest that self-generated choices arise from a distributed circuit based on an OFC-VTA core determining whether to wait for or initiate actions, while the PFC is specifically engaged by reward uncertainty in action selection and pace.

Electrophysiological Activity of Primary Cortical Neuron-Glia Mixed Cultures.

Neuroinflammation plays a central role in many neurological disorders, ranging from traumatic brain injuries to neurodegeneration. Electrophysiological activity is an essential measure of neuronal function, which is influenced by neuroinflammation. In order to study neuroinflammation and its electrophysiological fingerprints, there is a need for in vitro models that accurately capture the in vivo phenomena. In this study, we employed a new tri-culture of primary rat neurons, astrocytes, and microglia in combination with extracellular electrophysiological recording techniques using multiple electrode arrays (MEAs) to determine the effect of microglia on neural function and the response to neuroinflammatory stimuli. Specifically, we established the tri-culture and its corresponding neuron-astrocyte co-culture (lacking microglia) counterpart on custom MEAs and monitored their electrophysiological activity for 21 days to assess culture maturation and network formation. As a complementary assessment, we quantified synaptic puncta and averaged spike waveforms to determine the difference in excitatory to inhibitory neuron ratio (E/I ratio) of the neurons. The results demonstrate that the microglia in the tri-culture do not disrupt neural network formation and stability and may be a better representation of the in vivo rat cortex due to its more similar E/I ratio as compared to more traditional isolated neuron and neuron-astrocyte co-cultures. In addition, only the tri-culture displayed a significant decrease in both the number of active channels and spike frequency following pro-inflammatory lipopolysaccharide exposure, highlighting the critical role of microglia in capturing electrophysiological manifestations of a representative neuroinflammatory insult. We expect the demonstrated technology to assist in studying various brain disease mechanisms.

Electrophysiology as a Tool to Decipher the Network Mechanism of Visceral Pain in Functional Gastrointestinal Disorders.

Abdominal pain, including visceral pain, is prevalent in functional gastrointestinal (GI) disorders (FGIDs), affecting the overall quality of a patient's life. Neural circuits in the brain encode, store, and transfer pain information across brain regions. Ascending pain signals actively shape brain dynamics; in turn, the descending system responds to the pain through neuronal inhibition. Pain processing mechanisms in patients are currently mainly studied with neuroimaging techniques; however, these techniques have a relatively poor temporal resolution. A high temporal resolution method is warranted to decode the dynamics of the pain processing mechanisms. Here, we reviewed crucial brain regions that exhibited pain-modulatory effects in an ascending and descending manner. Moreover, we discussed a uniquely well-suited method, namely extracellular electrophysiology, that captures natural language from the brain with high spatiotemporal resolution. This approach allows parallel recording of large populations of neurons in interconnected brain areas and permits the monitoring of neuronal firing patterns and comparative characterization of the brain oscillations. In addition, we discussed the contribution of these oscillations to pain states. In summary, using innovative, state-of-the-art methods, the large-scale recordings of multiple neurons will guide us to better understanding of pain mechanisms in FGIDs.

3D pose estimation enables virtual head fixation in freely moving rats.

The impact of spontaneous movements on neuronal activity has created the need to quantify behavior. We present a versatile framework to directly capture the 3D motion of freely definable body points in a marker-free manner with high precision and reliability. Combining the tracking with neural recordings revealed multiplexing of information in the motor cortex neurons of freely moving rats. By integrating multiple behavioral variables into a model of the neural response, we derived a virtual head fixation for which the influence of specific body movements was removed. This strategy enabled us to analyze the behavior of interest (e.g., front paw movements). Thus, we unveiled an unexpectedly large fraction of neurons in the motor cortex with tuning to the paw movements, which was previously masked by body posture tuning. Once established, our framework can be efficiently applied to large datasets while minimizing the experimental workload caused by animal training and manual labeling.

High Fidelity Theta Phase Rolling of CA1 Neurons.

Single hippocampal cells encode the spatial position of an animal by increasing their firing rates within "place fields," and by shifting the phase of their spikes to earlier phases of the ongoing theta oscillations (theta phase precession). Whether other forms of spatial phase changes exist in the hippocampus is unknown. Here, we used high-density electrophysiological recordings in mice of either sex running back and forth on a 150-cm linear track. We found that the instantaneous phase of spikes shifts to progressively later theta phases as the animal traverses the place field. We term this shift theta "phase rolling." Phase rolling is opposite in direction to precession, faster than precession, and occurs between distinct theta cycles. Place fields that exhibit phase rolling are larger than nonrolling fields, and in-field spikes occur in distinct theta phases in rolling compared with nonrolling fields. As a phase change associated with position, theta phase rolling may be used to encode space.SIGNIFICANCE STATEMENT Theta phase precession is a well-known coding scheme in which neurons represent the position of the animal by the timing of their spikes with respect to the phase of ongoing theta oscillations. Here, we show that hippocampal neurons also undergo "theta phase rolling," a phase change faster and opposite in direction to precession. As the animal advances in space, spikes occur at progressively later phases of consecutive theta cycles. Future studies may reveal whether phase rolling constitutes a novel coding mechanism of space.

An automated method for precise axon reconstruction from recordings of high-density micro-electrode arrays.

Objective:Neurons communicate with each other by sending action potentials (APs) through their axons. The velocity of axonal signal propagation describes how fast electrical APs can travel. This velocity can be affected in a human brain by several pathologies, including multiple sclerosis, traumatic brain injury and channelopathies. High-density microelectrode arrays (HD-MEAs) provide unprecedented spatio-temporal resolution to extracellularly record neural electrical activity. The high density of the recording electrodes enables to image the activity of individual neurons down to subcellular resolution, which includes the propagation of axonal signals. However, axon reconstruction, to date, mainly relies on manual approaches to select the electrodes and channels that seemingly record the signals along a specific axon, while an automated approach to track multiple axonal branches in extracellular action-potential recordings is still missing.Approach:In this article, we propose a fully automated approach to reconstruct axons from extracellular electrical-potential landscapes, so-called 'electrical footprints' of neurons. After an initial electrode and channel selection, the proposed method first constructs a graph based on the voltage signal amplitudes and latencies. Then, the graph is interrogated to extract possible axonal branches. Finally, the axonal branches are pruned, and axonal action-potential propagation velocities are computed.Main results:We first validate our method using simulated data from detailed reconstructions of neurons, showing that our approach is capable of accurately reconstructing axonal branches. We then apply the reconstruction algorithm to experimental recordings of HD-MEAs and show that it can be used to determine axonal morphologies and signal-propagation velocities at high throughput.Significance:We introduce a fully automated method to reconstruct axonal branches and estimate axonal action-potential propagation velocities using HD-MEA recordings. Our method yields highly reliable and reproducible velocity estimations, which constitute an important electrophysiological feature of neuronal preparations.

Explorers of the cells: Toward cross-platform knowledge integration to evaluate neuronal function.

In this issue of Neuron, Petersen et al. (2021) introduce CellExplorer, an open-source tool to integrate neurophysiological metrics of neuronal activity from circuits to behavior. Together with other neuroinformatic resources, it may facilitate community-based multidisciplinary characterization of brain cell types.

A low-cost computational approach to analyze spiking activity in cockroach sensory neurons.

Undergraduates use a spike sorting routine developed in Octave to analyze the spiking activity generated from mechanical stimulation of spines of cockroach legs with the inexpensive SpikerBox amplifier and the free software Audacity. Students learn the procedures involved in handling the cockroaches and recording extracellular action potentials (spikes) with the SpikerBox apparatus as well as the importance of spike sorting for analysis in neuroscience. The spike sorting process requires students to choose the spike threshold and spike selection criteria and interact with the clustering process that forms the groups of similar spikes. Once the spike groups are identified, interspike intervals and neuron firing frequencies can be calculated and analyzed. A classic neurophysiology lab exercise is thus adapted to be interdisciplinary for underrepresented students in a small rural college.

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.

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 (dVmax) and minimum (dVmin) 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 (Vphase1) before depolarization. The percentage of the different types of mAPs was different for the nuclei addressed. The relationship between dVmax 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 Vphase1 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 Vphase1, is very common in thalamic APs. Moreover, subtle differences during repolarization are neuron-specific.

SpikeInterface, a unified framework for spike sorting.

Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.

Hidden Hearing Loss Impacts the Neural Representation of Speech in Background Noise.

Many individuals with seemingly normal hearing abilities struggle to understand speech in noisy backgrounds. To understand why this might be the case, we investigated the neural representation of speech in the auditory midbrain of gerbils with "hidden hearing loss" through noise exposure that increased hearing thresholds only temporarily. In noise-exposed animals, we observed significantly increased neural responses to speech stimuli, with a more pronounced increase at moderate than at high sound intensities. Noise exposure reduced discriminability of neural responses to speech in background noise at high sound intensities, with impairment most severe for tokens with relatively greater spectral energy in the noise-exposure frequency range (2-4 kHz). At moderate sound intensities, discriminability was surprisingly improved, which was unrelated to spectral content. A model combining damage to high-threshold auditory nerve fibers with increased response gain of central auditory neurons reproduced these effects, demonstrating that a specific combination of peripheral damage and central compensation could explain listening difficulties despite normal hearing thresholds.

Can We Put Aside Microelectrode Recordings in Deep Brain Stimulation Surgery?

Microelectrode recording (MER) in deep brain stimulation (DBS) surgery has long been a recognized and efficient method for defining a target. However, in recent decades, imaging techniques, including DBS surgery, have experienced significant growth. There is convincing evidence that imaging-guided surgery can be helpful for targeting anatomically well-defined nuclei (e.g., subthalamic nucleus (STN) or internal globus pallidus (GPi)), and reductions in secondary effects have also been claimed. It has even been proposed that MER is not necessary to perform DBS, identifying in this way asleep surgery and imaging-guided DBS. However, there are several reasons why this is not the case. Neurophysiological techniques can efficiently and safely help to identify neural structures even in sleeping patients (e.g., different types of evoked potentials or motor stimulation). Deep nuclei are not homogeneous structures (even STN), so it is important to identify different places inside the putative target. Evidently, this is more relevant in the case of thalamic or hypothalamic surgery. Moreover, it is important to remember that the clinical and scientific knowledge acquired during DBS surgery can be important to gain further insight into pathologies and develop more effective treatments. Finally, the cost/efficiency of medical technology should be considered.

Neural Correlates of Social Behavior in Mushroom Body Extrinsic Neurons of the Honeybee Apis mellifera.

The social behavior of honeybees (Apis mellifera) has been extensively investigated, but little is known about its neuronal correlates. We developed a method that allowed us to record extracellularly from mushroom body extrinsic neurons (MB ENs) in a freely moving bee within a small but functioning mini colony of approximately 1,000 bees. This study aimed to correlate the neuronal activity of multimodal high-order MB ENs with social behavior in a close to natural setting. The behavior of all bees in the colony was video recorded. The behavior of the recorded animal was compared with other hive mates and no significant differences were found. Changes in the spike rate appeared before, during or after social interactions. The time window of the strongest effect on spike rate changes ranged from 1 s to 2 s before and after the interaction, depending on the individual animal and recorded neuron. The highest spike rates occurred when the experimental animal was situated close to a hive mate. The variance of the spike rates was analyzed as a proxy for high order multi-unit processing. Comparing randomly selected time windows with those in which the recorded animal performed social interactions showed a significantly increased spike rate variance during social interactions. The experimental set-up employed for this study offers a powerful opportunity to correlate neuronal activity with intrinsically motivated behavior of socially interacting animals. We conclude that the recorded MB ENs are potentially involved in initiating and controlling social interactions in honeybees.

Lorcaserin bidirectionally regulates dopaminergic function site-dependently and disrupts dopamine brain area correlations in rats.

Lorcaserin, which is a selective agonist of serotonin2C receptors (5-HT2CRs), is a new FDA-approved anti-obesity drug that has also shown therapeutic promise in other brain disorders, such as addiction and epilepsy. The modulation of dopaminergic function might be critical in the therapeutic effect of lorcaserin, but its exact effect is unknown. Here, we studied the effect of the peripheral administration of lorcaserin on the ventral tegmental area (VTA), the substantia nigra pars compacta (SNc) dopaminergic neural activity, dopamine (DA) dialysis levels in the nucleus accumbens and striatum and on DA tissue levels in 29 different rat brain regions. Lorcaserin (5-640 µg/kg, i.v.) moderately inhibited only a subpopulation of VTA DA neurons, but had no effect on the SNc neurons. Lorcaserin (0.3, 3 mg/kg, i.p.) did not change VTA and SNc DA population neural activity but slightly decreased the firing rate and burst firing of the spontaneously active VTA neurons, without altering DA extracellular dialysate levels in both the nucleus accumbens and the striatum. Quantitative analysis of DA and metabolites tissue contents of the 29 areas studied revealed that lorcaserin (0.3 or 3 mg/kg, i.p.) only affected a few brain regions, i.e., increased DA in the central amygdala, ventral hypothalamus and nucleus accumbens core and decreased it in the ventromedial striatum. On the other hand, lorcaserin dramatically changed the direction and reduced the number of correlations of DA tissue content among several brain areas. These effects on DA terminal networks might be significant in the therapeutic mechanism of lorcaserin. This article is part of the special issue entitled 'Serotonin Research: Crossing Scales and Boundaries'.

Evidence for the origin of the binaural interaction component of the auditory brainstem response.

The binaural interaction component (BIC) represents the mismatch between auditory brainstem responses (ABR) obtained with binaural stimulation and the sum of ABRs obtained with monaural left and right stimulation. It is generally assumed that the BIC reflects binaural integration. Its potential use as a diagnostic tool, however, is hampered by the lack of direct evidence about its origin. While an origin at the initial site of binaural integration seems likely, there is no general agreement on the contribution of the two primary candidate nuclei, the lateral and medial superior olives (LSO and MSO, respectively). Here, we recorded local field potentials (LFP) and responses of units in the LSO and MSO of Mongolian gerbils (Meriones unguiculatus), presenting clicks with an interaural time or level difference (ITD and ILD, respectively), while simultaneously recording ABR. We determined the BIC from the ABR and, importantly, from LFP and responses of units in the LSO and MSO. If stimulus-induced changes in the ABR-derived BIC have their source in the LSO and/or MSO, we expect coherent changes in the unit-derived and the ABR-derived BIC. We find that BIC obtained from LSO units exhibits the same ITD and ILD dependence as the ABR-derived BIC. Neither BIC obtained from MSO units nor LFP-derived BIC recorded in either LSO or MSO did. The data thus strongly suggest that it is the activity of LSO units in the gerbil that is decisive for the generation of the ABR-derived BIC, determining its properties.

Optimizing the Yield of Multi-Unit Activity by Including the Entire Spiking Activity.

Neurophysiological data acquisition using multi-electrode arrays and/or (semi-) chronic recordings frequently has to deal with low signal-to-noise ratio (SNR) of neuronal responses and potential failure of detecting evoked responses within random background fluctuations. Conventional methods to extract action potentials (spikes) from background noise often apply thresholds to the recorded signal, usually allowing reliable detection of spikes when data exhibit a good SNR, but often failing when SNR is poor. We here investigate a threshold-independent, fast, and automated procedure for analysis of low SNR data, based on fullwave-rectification and low-pass filtering the signal as a measure of the entire spiking activity (ESA). We investigate the sensitivity and reliability of the ESA-signal for detecting evoked responses by applying an automated receptive field (RF) mapping procedure to semi-chronically recorded data from primary visual cortex (V1) of five macaque monkeys. For recording sites with low SNR, the usage of ESA improved the detection rate of RFs by a factor of 2.5 in comparison to MUA-based detection. For recording sites with medium and high SNR, ESA delivered 30% more RFs than MUA. This significantly higher yield of ESA-based RF-detection still hold true when using an iterative procedure for determining the optimal spike threshold for each MUA individually. Moreover, selectivity measures for ESA-based RFs were quite compatible with MUA-based RFs. Regarding RF size, ESA delivered larger RFs than thresholded MUA, but size difference was consistent over all SNR fractions. Regarding orientation selectivity, ESA delivered more sites with significant orientation-dependent responses but with somewhat lower orientation indexes than MUA. However, preferred orientations were similar for both signal types. The results suggest that ESA is a powerful signal for applications requiring automated, fast, and reliable response detection, as e.g., brain-computer interfaces and neuroprosthetics, due to its high sensitivity and its independence from user-dependent intervention. Because the full information of the spiking activity is preserved, ESA also constitutes a valuable alternative for offline analysis of data with limited SNR.