Massively parallel recording of unit and local field potentials with silicon-based electrodes

Dynamics of spike transmission and suppression between principal cells and interneurons in the hippocampus and entorhinal cortex

Synaptic excitation and inhibition are essential for neuronal communication. However, the variables that regulate synaptic excitation and inhibition in the intact brain remain largely unknown. Here, we examined how spike transmission and suppression between principal cells (PCs) and interneurons (INTs) are modulated by activity history, brain state, cell type, and somatic distance between presynaptic and postsynaptic neurons by applying cross-correlogram analyses to datasets recorded from the dorsal hippocampus and medial entorhinal cortex (MEC) of 11 male behaving and sleeping Long Evans rats. The strength, temporal delay, and brain-state dependency of the spike transmission and suppression depended on the subregions/layers. The spike transmission probability of PC-INT excitatory pairs that showed short-term depression versus short-term facilitation was higher in CA1 and lower in CA3. Likewise, the intersomatic distance affected the proportion of PC-INT excitatory pairs that showed short-term depression and facilitation in the opposite manner in CA1 compared with CA3. The time constant of depression was longer, while that of facilitation was shorter in MEC than in CA1 and CA3. During sharp-wave ripples, spike transmission showed a larger gain in the MEC than in CA1 and CA3. The intersomatic distance affected the spike transmission gain during sharp-wave ripples differently in CA1 versus CA3. A subgroup of MEC layer 3 (EC3) INTs preferentially received excitatory inputs from and inhibited MEC layer 2 (EC2) PCs. The EC2 PC-EC3 INT excitatory pairs, most of which showed short-term depression, exhibited higher spike transmission probabilities than the EC2 PC-EC2 INT and EC3 PC-EC3 INT excitatory pairs. EC2 putative stellate cells exhibited stronger spike transmission to and received weaker spike suppression from EC3 INTs than EC2 putative pyramidal cells. This study provides detailed comparisons of monosynaptic interaction dynamics in the hippocampal-entorhinal loop, which may help to elucidate circuit operations.

Spatially Precise Genetic Engineering at the Electrode-Tissue Interface

The interface between electrodes and neural tissues plays a pivotal role in determining the efficacy and fidelity of neural activity recording and modulation. While considerable efforts have been made to improve the electrode-tissue interface, the majority of studies have primarily concentrated on the development of biocompatible neural electrodes through abiotic materials and structural engineering. In this study, an approach is presented that seamlessly integrates abiotic and biotic engineering principles into the electrode-tissue interface. Specifically, ultraflexible neural electrodes with short hairpin RNAs (shRNAs) designed to silence the expression of endogenous genes within neural tissues are combined. The system facilitates shRNA-mediated knockdown of phosphatase and tensin homolog deleted on chromosome 10 (PTEN) and polypyrimidine tract-binding protein 1 (PTBP1), two essential genes associated in neural survival/growth and neurogenesis, within specific cell populations located at the electrode-tissue interface. Additionally, it is demonstrated that the downregulation of PTEN in neurons can result in an enlargement of neuronal cell bodies at the electrode-tissue interface. Furthermore, the system enables long-term monitoring of neuronal activities following PTEN knockdown in a mouse model of Parkinson's disease and traumatic brain injury. The system provides a versatile approach for genetically engineering the electrode-tissue interface with unparalleled precision, paving the way for the development of regenerative electronics and next-generation brain-machine interfaces.

Transcranial electric stimulation modulates firing rate at clinically relevant intensities

BACKGROUND

Notwithstanding advances with low-intensity transcranial electrical stimulation (tES), there remain questions about the efficacy of clinically realistic electric fields on neuronal function.

OBJECTIVE

To measure electric fields magnitude and their effects on neuronal firing rate of hippocampal neurons in freely moving rats, and to establish calibrated computational models of current flow.

METHODS

Current flow models were calibrated on electric field measures in the motor cortex (n = 2 anesthetized rats) and hippocampus. A Neuropixels 2.0 probe with 384 channels was used in an in-vivo rat model of tES (n = 4 freely moving and 2 urethane anesthetized rats) to detect effects of weak fields on neuronal firing rate. High-density field mapping and computational models verified field intensity (1 V/m in hippocampus per 50 μA of applied skull currents).

RESULTS

Electric fields of as low as 0.35 V/m (0.25-0.47) acutely modulated average firing rate in the hippocampus. At these intensities, firing rate effects increased monotonically with electric field intensity at a rate of 11.5 % per V/m (7.2-18.3). For the majority of excitatory neurons, firing increased for soma-depolarizing stimulation and diminished for soma-hyperpolarizing stimulation. While more diverse, the response of inhibitory neurons followed a similar pattern on average, likely as a result of excitatory drive.

CONCLUSION

In awake animals, electric fields modulate spiking rate above levels previously observed in vitro. Firing rate effects are likely mediated by somatic polarization of pyramidal neurons. We recommend that all future rodent experiments directly measure electric fields to insure rigor and reproducibility.

Measuring excitation-inhibition balance through spectral components of local field potentials

The balance between excitation and inhibition is critical to brain functioning, and dysregulation of this balance is a hallmark of numerous psychiatric conditions. Measuring this excitation-inhibition (E:I) balance in vivo has remained difficult, but theoretical models have proposed that characteristics of local field potentials (LFP) may provide an accurate proxy. To establish a conclusive link between LFP and E:I balance, we recorded single units and LFP from the prefrontal cortex (mPFC) of rats during decision making. Dynamic measures of synaptic coupling strength facilitated direct quantification of E:I balance and revealed a strong inverse relationship to broadband spectral power of LFP. These results provide a critical link between LFP and underlying network properties, opening the door for non-invasive recordings to measure E:I balance in clinical settings.

In vivo recordings in freely behaving mice using independent silicon probes targeting multiple brain regions

In vivo recordings in freely behaving animals are crucial to understand the neuronal circuit basis of behavior. Although current multi-channel silicon probes provide unparalleled sampling density, the study of interacting neuronal populations requires the implantation of multiple probes across different regions of the brain. Ideally, these probes should be independently adjustable, to maximize the yield, and recoverable, to mitigate costs. In this work, we describe the implementation of a miniaturized 3D-printed headgear system for chronic in vivo recordings in mice using independently movable silicon probes targeting multiple brain regions. We successfully demonstrated the performance of the headgear by simultaneously recording the neuronal activity in the prelimbic cortex and dorsal hippocampus. The system proved to be sturdy, ensuring high-quality stable recordings and permitted reuse of the silicon probes, with no observable interference in mouse innate behaviors.

Stimulus-Induced Theta-Band LFP Oscillations Format Neuronal Representations of Social Chemosignals in the Mouse Accessory Olfactory Bulb

Social communication is crucial for the survival of many species. In most vertebrates, a dedicated chemosensory system, the vomeronasal system (VNS), evolved to process ethologically relevant chemosensory cues. The first central processing stage of the VNS is the accessory olfactory bulb (AOB), which sends information to downstream brain regions via AOB mitral cells (AMCs). Recent studies provided important insights about the functional properties of AMCs, but little is known about the principles that govern their coordinated activity. Here, we recorded local field potentials (LFPs) and single-unit activity in the AOB of adult male and female mice during presentation of natural stimuli. Our recordings reveal prominent LFP theta-band oscillatory episodes with a characteristic spatial pattern across the AOB. Throughout an experiment, the AOB network shows varying degrees of similarity to this pattern, in a manner that depends on the sensory stimulus. Analysis of LFP signal polarity and single-unit activity indicates that oscillatory episodes are generated locally within the AOB, likely representing a reciprocal interaction between AMCs and granule cells. Notably, spike times of many AMCs are constrained to the negative LFP oscillation phase in a manner that can drastically affect integration by downstream processing stages. Based on these observations, we propose that LFP oscillations may gate, bind, and organize outgoing signals from individual AOB neurons to downstream processing stages. Our findings suggest that, as in other neuronal systems and brain regions, population-level oscillations play a key role in organizing and enhancing transmission of socially relevant chemosensory information.SIGNIFICANCE STATEMENT The accessory olfactory bulb (AOB) is the first central stage of the vomeronasal system, a chemosensory system dedicated to processing cues from other organisms. Information from the AOB is conveyed to other brain regions via activity of its principal neurons, AOB mitral cells (AMCs). Here, we show that socially relevant sensory stimulation of the mouse vomeronasal system leads not only to changes in AMC activity, but also to distinct theta-band (∼5 Hz) oscillatory episodes in the local field potential. Notably AMCs favor the negative phase of these oscillatory events. Our findings suggest a novel mechanism for the temporal coordination of distributed patterns of neuronal activity, which can serve to efficiently activate downstream processing stages.

Transcranial electric stimulation modulates firing rate at clinically relevant intensities

Notwithstanding advances with low-intensity transcranial electrical stimulation (TES), there remain questions about the efficacy of clinically realistic electric fields on neuronal function. We used Neuropixels 2.0 probe with 384 channels in an in-vivo rat model of TES to detect effects of weak fields on neuronal firing rate. High-density field mapping and computational models verified field intensity (1 V/m in hippocampus per 50 μA of applied skull currents). We demonstrate that electric fields below 0.5 V/m acutely modulate firing rate in 5% of neurons recorded in the hippocampus. At these intensities, average firing rate effects increased monotonically with electric field intensity at a rate of 7 % per V/m. For the majority of excitatory neurons, firing increased for cathodal stimulation and diminished for anodal stimulation. While more diverse, the response of inhibitory neurons followed a similar pattern on average, likely as a result of excitatory drive. Our results indicate that responses to TES at clinically relevant intensities are driven by a fraction of high-responder excitatory neurons, with polarity-specific effects. We conclude that transcranial electric stimulation is an effective neuromodulator at clinically realistic intensities.

Application of deep reinforcement learning for spike sorting under multi-class imbalance

Spike sorting is the basis for analyzing spike firing patterns encoded in high-dimensional information spaces. With the fact that high-density microelectrode arrays record multiple neurons simultaneously, the data collected often suffers from two problems: a few overlapping spikes and different neuronal firing rates, which both belong to the multi-class imbalance problem. Since deep reinforcement learning (DRL) assign targeted attention to categories through reward functions, we propose ImbSorter to implement spike sorting under multi-class imbalance. We describe spike sorting as a Markov sequence decision and construct a dynamic reward function (DRF) to improve the sensitivity of the agent to minor classes based on the inter-class imbalance ratios. The agent is eventually guided by the optimal strategy to classify spikes. We consider the Wave_Clus dataset, which contains overlapping spikes and diverse noise levels, and the macaque dataset, which has a multi-scale imbalance. ImbSorter is compared with classical DRL architectures, traditional machine learning algorithms, and advanced overlapping spike sorting techniques on these two above datasets. ImbSorter obtained improved results on the Macro_F1. The results show ImbSorter has a promising ability to resist overlapping and noise interference. It has high stability and promising performance in processing spikes with different degrees of skewed distribution.

Power-Efficient LFP-Adaptive Dynamic Zoom-and-Track Incremental ΔΣ Front-End for Dual-Band Subcortical Recordings

We report a power-efficient analog front-end integrated circuit (IC) for multi-channel, dual-band subcortical recordings. In order to achieve high-resolution multi-channel recordings with low power consumption, we implemented an incremental ΔΣ ADC (IADC) with a dynamic zoom-and-track scheme. This scheme continuously tracks local field potential (LFP) and adaptively adjusts the input dynamic range (DR) into a zoomed sub-LFP range to resolve tiny action potentials. Thanks to the reduced DR, the oversampling rate of the IADC can be reduced by 64.3% compared to the conventional approach, leading to significant power reduction. In addition, dual-band recording can be easily attained because the scheme continuously tracks LFPs without additional on-chip hardware. A prototype four-channel front-end IC has been fabricated in 180 nm standard CMOS processes. The IADC achieved 11.3-bit ENOB at 6.8 μW, resulting in the best Walden and SNDR FoMs, 107.9 fJ/c-s and 162.1 dB, respectively, among two different comparison groups: the IADCs reported up to date in the state-of-the-art neural recording front-ends; and the recent brain recording ADCs using similar zooming or tracking techniques to this work. The intrinsic dual-band recording feature reduces the post-processing FPGA resources for subcortical signal band separation by >45.8%. The front-end IC with the zoom-and-track IADC showed an NEF of 5.9 with input-referred noise of 8.2 μVrms, sufficient for subcortical recording. The performance of the whole front-end IC was successfully validated through in vivo animal experiments.

Studying memory processes at different levels with simultaneous depth and surface EEG recordings

Investigating cognitive brain functions using non-invasive electrophysiology can be challenging due to the particularities of the task-related EEG activity, the depth of the activated brain areas, and the extent of the networks involved. Stereoelectroencephalographic (SEEG) investigations in patients with drug-resistant epilepsy offer an extraordinary opportunity to validate information derived from non-invasive recordings at macro-scales. The SEEG approach can provide brain activity with high spatial specificity during tasks that target specific cognitive processes (e.g., memory). Full validation is possible only when performing simultaneous scalp SEEG recordings, which allows recording signals in the exact same brain state. This is the approach we have taken in 12 subjects performing a visual memory task that requires the recognition of previously viewed objects. The intracranial signals on 965 contact pairs have been compared to 391 simultaneously recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. By analyzing a joint scalp and SEEG dataset, we investigated whether the two types of signals carry complementary information that might improve the machine-learning classifier performance. This joint analysis revealed that the results are driven by the modality exhibiting best individual performance, namely SEEG.

Differential processing of decision information in subregions of rodent medial prefrontal cortex

Decision-making involves multiple cognitive processes requiring different aspects of information about the situation at hand. The rodent medial prefrontal cortex (mPFC) has been hypothesized to be central to these abilities. Functional studies have sought to link specific processes to specific anatomical subregions, but past studies of mPFC have yielded controversial results, leaving the precise nature of mPFC function unclear. To settle this debate, we recorded from the full dorso-ventral extent of mPFC in each of 8 rats, as they performed a complex economic decision task. These data revealed four distinct functional domains within mPFC that closely mirrored anatomically identified subregions, including novel evidence to divide prelimbic cortex into dorsal and ventral components. We found that dorsal aspects of mPFC (ACC, dPL) were more involved in processing information about active decisions, while ventral aspects (vPL, IL) were more engaged in motivational factors.

Robotic multi-probe single-actuator inchworm neural microdrive

A wide range of techniques in neuroscience involve placing individual probes at precise locations in the brain. However, large-scale measurement and manipulation of the brain using such methods have been severely limited by the inability to miniaturize systems for probe positioning. Here, we present a fundamentally new, remote-controlled micropositioning approach composed of novel phase-change material-filled resistive heater micro-grippers arranged in an inchworm motor configuration. The microscopic dimensions, stability, gentle gripping action, individual electronic control, and high packing density of the grippers allow micrometer-precision independent positioning of many arbitrarily shaped probes using a single piezo actuator. This multi-probe single-actuator design significantly reduces the size and weight and allows for potential automation of microdrives. We demonstrate accurate placement of multiple electrodes into the rat hippocampus in vivo in acute and chronic preparations. Our robotic microdrive technology should therefore enable the scaling up of many types of multi-probe applications in neuroscience and other fields.

Spike-Gamma Phase Relationship in the Visual Cortex

Gamma oscillations (30-70 Hz) have been hypothesized to play a role in cortical function. Most of the proposed mechanisms involve rhythmic modulation of neuronal excitability at gamma frequencies, leading to modulation of spike timing relative to the rhythm. I first show that the gamma band could be more privileged than other frequencies in observing spike-field interactions even in the absence of genuine gamma rhythmicity and discuss several biases in spike-gamma phase estimation. I then discuss the expected spike-gamma phase according to several hypotheses. Inconsistent with the phase-coding hypothesis (but not with others), the spike-gamma phase does not change with changes in stimulus intensity or attentional state, with spikes preferentially occurring 2-4 ms before the trough, but with substantial variability. However, this phase relationship is expected even when gamma is a byproduct of excitatory-inhibitory interactions. Given that gamma occurs in short bursts, I argue that the debate over the role of gamma is a matter of semantics. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

HectoSTAR μLED Optoelectrodes for Large-Scale, High-Precision In Vivo Opto-Electrophysiology

Dynamic interactions within and across brain areas underlie behavioral and cognitive functions. To understand the basis of these processes, the activities of distributed local circuits inside the brain of a behaving animal must be synchronously recorded while the inputs to these circuits are precisely manipulated. Even though recent technological advances have enabled such large-scale recording capabilities, the development of the high-spatiotemporal-resolution and large-scale modulation techniques to accompany those recordings has lagged. A novel neural probe is presented in this work that enables simultaneous electrical monitoring and optogenetic manipulation of deep neuronal circuits at large scales with a high spatiotemporal resolution. The "hectoSTAR" micro-light-emitting-diode (μLED) optoelectrode features 256 recording electrodes and 128 stimulation μLEDs monolithically integrated on the surface of its four 30-µm thick silicon micro-needle shanks, covering a large volume with 1.3-mm × 0.9-mm cross-sectional area located as deep as 6 mm inside the brain. The use of this device in behaving mice for dissecting long-distance network interactions across cortical layers and hippocampal regions is demonstrated. The recording-and-stimulation capabilities hectoSTAR μLED optoelectrodes enables will open up new possibilities for the cellular and circuit-based investigation of brain functions in behaving animals.

Advancements in the Quest to Map, Monitor, and Manipulate Neural Circuitry

Neural circuits and the cells that comprise them represent the functional units of the brain. Circuits relay and process sensory information, maintain homeostasis, drive behaviors, and facilitate cognitive functions such as learning and memory. Creating a functionally-precise map of the mammalian brain requires anatomically tracing neural circuits, monitoring their activity patterns, and manipulating their activity to infer function. Advancements in cell-type-specific genetic tools allow interrogation of neural circuits with increased precision. This review provides a broad overview of recombination-based and activity-driven genetic targeting approaches, contemporary viral tracing strategies, electrophysiological recording methods, newly developed calcium, and voltage indicators, and neurotransmitter/neuropeptide biosensors currently being used to investigate circuit architecture and function. Finally, it discusses methods for acute or chronic manipulation of neural activity, including genetically-targeted cellular ablation, optogenetics, chemogenetics, and over-expression of ion channels. With this ever-evolving genetic toolbox, scientists are continuing to probe neural circuits with increasing resolution, elucidating the structure and function of the incredibly complex mammalian brain.

Modular microcircuit organization of the presubicular head-direction map

Our internal sense of direction is thought to rely on the activity of head-direction (HD) neurons. We find that the mouse dorsal presubiculum (PreS), a key structure in the cortical representation of HD, displays a modular "patch-matrix" organization, which is conserved across species (including human). Calbindin-positive layer 2 neurons within the "matrix" form modular recurrent microcircuits, while inputs from the anterodorsal and laterodorsal thalamic nuclei are non-overlapping and target the "patch" and "matrix" compartments, respectively. The apical dendrites of identified HD cells are largely restricted within the "matrix," pointing to a non-random sampling of patterned inputs and to a precise structure-function architecture. Optogenetic perturbation of modular recurrent microcircuits results in a drastic tonic suppression of firing only in a subpopulation of HD neurons. Altogether, our data reveal a modular microcircuit organization of the PreS HD map and point to the existence of cell-type-specific microcircuits that support the cortical HD representation.

Free-Standing Nanofilm Electrode Arrays for Long-Term Stable Neural Interfacings

Flexible neural electrodes integrated on micrometer-thick polymer substrates offer important opportunities for improving the stability of neuronal activity recordings during cognitive processes. However, the bending stiffness of micrometer-thick polymer substrates is typically two orders of magnitude higher than that of nanofilm electrodes, making it a limiting factor in electrode-tissue interfacings. Here, this limitation is overcome by developing self-assembled nanofilm electrode arrays (NEAs) that consist of high-density, free-standing gold nanofilm electrodes. Chronically implanted NEAs can form intimate and innervated interfaces with neural tissue, enabling stable neuronal activity recordings across multiple brain regions over several months. As an application example, the activities of the same neuronal populations are tracked across odor discrimination reversal learning and it is illustrated how dorsal striatal neurons represent and update stimulus-outcome associations across multiple timescales. The results underscore the potential of free-standing nanoscale materials for interfacing biological systems over long terms.

Temporal dynamics of affect in the brain: Evidence from human imaging and animal models

Emotions are time-varying internal states that promote survival in the face of dynamic environments and shifting homeostatic needs. Research in non-human organisms has recently afforded specific insights into the neural mechanisms that support the emergence, persistence, and decay of affective states. Concurrently, a separate affective neuroscience literature has begun to dissect the neural bases of affective dynamics in humans. However, the circuit-level mechanisms identified in animals lack a clear mapping to the human neuroscience literature. As a result, critical questions pertaining to the neural bases of affective dynamics in humans remain unanswered. To address these shortcomings, the present review integrates findings from humans and non-human organisms to highlight the neural mechanisms that govern the temporal features of emotional states. Using the theory of affective chronometry as an organizing framework, we describe the specific neural mechanisms and modulatory factors that arbitrate the rise-time, intensity, and duration of emotional states.

Breathing coordinates cortico-hippocampal dynamics in mice during offline states

Network dynamics have been proposed as a mechanistic substrate for the information transfer across cortical and hippocampal circuits. However, little is known about the mechanisms that synchronize and coordinate these processes across widespread brain regions during offline states. Here we address the hypothesis that breathing acts as an oscillatory pacemaker, persistently coupling distributed brain circuit dynamics. Using large-scale recordings from a number of cortical and subcortical brain regions in behaving mice, we uncover the presence of an intracerebral respiratory corollary discharge, that modulates neural activity across these circuits. During offline states, the respiratory modulation underlies the coupling of hippocampal sharp-wave ripples and cortical DOWN/UP state transitions, which mediates systems memory consolidation. These results highlight breathing, a perennial brain rhythm, as an oscillatory scaffold for the functional coordination of the limbic circuit that supports the segregation and integration of information flow across neuronal networks during offline states.

Using large-scale recordings from cortical and subcortical brain regions in behaving mice, the authors reveal the presence of a respiratory corollary discharge in mice, that modulates neural activity across these circuits and couples hippocampal sharp-wave ripples and cortical DOWN/UP state transitions.

Functional Characterization of Human Pluripotent Stem Cell-Derived Models of the Brain with Microelectrode Arrays

Human pluripotent stem cell (hPSC)-derived neuron cultures have emerged as models of electrical activity in the human brain. Microelectrode arrays (MEAs) measure changes in the extracellular electric potential of cell cultures or tissues and enable the recording of neuronal network activity. MEAs have been applied to both human subjects and hPSC-derived brain models. Here, we review the literature on the functional characterization of hPSC-derived two- and three-dimensional brain models with MEAs and examine their network function in physiological and pathological contexts. We also summarize MEA results from the human brain and compare them to the literature on MEA recordings of hPSC-derived brain models. MEA recordings have shown network activity in two-dimensional hPSC-derived brain models that is comparable to the human brain and revealed pathology-associated changes in disease models. Three-dimensional hPSC-derived models such as brain organoids possess a more relevant microenvironment, tissue architecture and potential for modeling the network activity with more complexity than two-dimensional models. hPSC-derived brain models recapitulate many aspects of network function in the human brain and provide valid disease models, but certain advancements in differentiation methods, bioengineering and available MEA technology are needed for these approaches to reach their full potential.

CellExplorer: A framework for visualizing and characterizing single neurons

The large diversity of neuron types provides the means by which cortical circuits perform complex operations. Neuron can be described by biophysical and molecular characteristics, afferent inputs, and neuron targets. To quantify, visualize, and standardize those features, we developed the open-source, MATLAB-based framework CellExplorer. It consists of three components: a processing module, a flexible data structure, and a powerful graphical interface. The processing module calculates standardized physiological metrics, performs neuron-type classification, finds putative monosynaptic connections, and saves them to a standardized, yet flexible, machine-readable format. The graphical interface makes it possible to explore the computed features at the speed of a mouse click. The framework allows users to process, curate, and relate their data to a growing public collection of neurons. CellExplorer can link genetically identified cell types to physiological properties of neurons collected across laboratories and potentially lead to interlaboratory standards of single-cell metrics.

Accurate Localization of Linear Probe Electrode Arrays across Multiple Brains

Recently developed probes for extracellular electrophysiological recordings have large numbers of electrodes on long linear shanks. Linear electrode arrays, such as Neuropixels probes, have hundreds of recording electrodes distributed over linear shanks that span several millimeters. Because of the length of the probes, linear probe recordings in rodents usually cover multiple brain areas. Typical studies collate recordings across several recording sessions and animals. Neurons recorded in different sessions and animals thus have to be aligned to each other and to a standardized brain coordinate system. Here, we evaluate two typical workflows for localization of individual electrodes in standardized coordinates. These workflows rely on imaging brains with fluorescent probe tracks and warping 3D image stacks to standardized brain atlases. One workflow is based on tissue clearing and selective plane illumination microscopy (SPIM), whereas the other workflow is based on serial block-face two-photon (SBF2P) microscopy. In both cases electrophysiological features are then used to anchor particular electrodes along the reconstructed tracks to specific locations in the brain atlas and therefore to specific brain structures. We performed groundtruth experiments, in which motor cortex outputs are labeled with ChR2 and a fluorescence protein. Light-evoked electrical activity and fluorescence can be independently localized. Recordings from brain regions targeted by the motor cortex reveal better than 0.1-mm accuracy for electrode localization, independent of workflow used.

Transcriptional characterization of the glial response due to chronic neural implantation of flexible microprobes

Long term implantation of (micro-)probes into neural tissue causes unique and disruptive responses. In this study, we investigate the transcriptional trajectory of glial cells responding to chronic implantation of 380 μm flexible micro-probes for up to 18 weeks. Transcriptomic analysis shows a rapid activation of microglial cells and a strong reactive astrocytic polarization, both of which are lost over the chronic of the implant duration. Animals that were implanted for 18 weeks show a transcriptional profile similar to non-implanted controls, with increased expression of genes associated with wound healing and angiogenesis, which raises hope of a normalization of the neuropil to the pre-injury state when using flexible probes. Nevertheless, our data shows that a subset of genes upregulated after 18 weeks belong to the family of immediate early genes, which indicates that structural and functional remodeling is not complete at this time point. Our results confirm and extend previous work on the molecular changes resulting from the presence of neural probes and provide a rational basis for developing interventional strategies to control them.

A Miniaturized 256-Channel Neural Recording Interface with Area-Efficient Hybrid Integration of Flexible Probes and CMOS Integrated Circuits

We report a miniaturized, minimally invasive high-density neural recording interface that occupies only a 1.53 mm2 footprint for hybrid integration of a flexible probe and a 256-channel integrated circuit chip. To achieve such a compact form factor, we developed a custom flip-chip bonding technique using anisotropic conductive film and analog circuit-under-pad in a tiny pitch of 75 m. To enhance signal-to-noise ratios, we applied a reference-replica topology that can provide the matched input impedance for signal and reference paths in low-noise aimpliers (LNAs). The analog front-end (AFE) consists of LNAs, buffers, programmable gain amplifiers, 10b ADCs, a reference generator, a digital controller, and serial-peripheral interfaces (SPIs). The AFE consumes 51.92 W from 1.2 V and 1.8 V supplies in an area of 0.0161 mm2 per channel, implemented in a 180 nm CMOS process. The AFE shows > 60 dB mid-band CMRR, 6.32 Vrms input-referred noise from 0.5 Hz to 10 kHz, and 48 M input impedance at 1 kHz. The fabricated AFE chip was directly flip-chip bonded with a 256-channel flexible polyimide neural probe and assembled in a tiny head-stage PCB. Full functionalities of the fabricated 256-channel interface were validated in both in vitro and in vivo experiments, demonstrating the presented hybrid neural recording interface is suitable for various neuroscience studies in the quest of large scale, miniaturized recording systems.

HTsort: Enabling Fast and Accurate Spike Sorting on Multi-Electrode Arrays

Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty. A fast and accurate spike sorting approach named HTsort is proposed for high-density multi-electrode arrays in this paper. Several improvements have been introduced to the traditional pipeline that is composed of threshold detection and clustering method. First, the divide-and-conquer method is employed to utilize electrode spatial information to achieve pre-clustering. Second, the clustering method HDBSCAN (hierarchical density-based spatial clustering of applications with noise) is used to classify spikes and detect overlapping events (multiple spikes firing simultaneously). Third, the template merging method is used to merge redundant exported templates according to the template similarity and the spatial distribution of electrodes. Finally, the template matching method is used to resolve overlapping events. Our approach is validated on simulation data constructed by ourselves and publicly available data and compared to other state-of-the-art spike sorters. We found that the proposed HTsort has a more favorable trade-off between accuracy and time consumption. Compared with MountainSort and SpykingCircus, the time consumption is reduced by at least 40% when the number of electrodes is 64 and below. Compared with HerdingSpikes, the classification accuracy can typically improve by more than 10%. Meanwhile, HTsort exhibits stronger robustness against background noise than other sorters. Our more sophisticated spike sorter would facilitate neurophysiologists to complete spike sorting more quickly and accurately.

Laser ablation of the pia mater for insertion of high-density microelectrode arrays in a translational sheep model

Objective. The safe insertion of high density intracortical electrode arrays has been a long-standing practical challenge for neural interface engineering and applications such as brain-computer interfaces (BCIs). However, the pia mater can be difficult to penetrate and causes deformation of underlying cortical tissue during insertion of high-density intracortical arrays. This can lead to neuron damage or failed insertions. The development of a method to ease insertion through the pia mater would represent a significant step toward inserting high density intracortical arrays.Approach. Here we describe a surgical procedure, inspired by laser corneal ablation, that can be used in translational models to thin the pia mater.Main results. We demonstrate that controlled pia removal with laser ablation over a small area of cortex allows for microelectrode arrays to be inserted into the cortex with less force, thus reducing deformation of underlying tissue during placement of the microelectrodes. This procedure allows for insertion of high-density electrode arrays and subsequent acute recordings of spiking neuron activity in sheep cortex. We also show histological and electrophysiological evidence that laser removal of the pia does not acutely affect neuronal viability in the region.Significance. Laser ablation of the pia reduces insertion forces of high-density arrays with minimal to no acute damage to cortical neurons. This approach suggests a promising new path for clinical BCI with high-density microelectrode arrays.

Metal microdrive and head cap system for silicon probe recovery in freely moving rodent

High-yield electrophysiological extracellular recording in freely moving rodents provides a unique window into the temporal dynamics of neural circuits. Recording from unrestrained animals is critical to investigate brain activity during natural behaviors. The use and implantation of high-channel-count silicon probes represent the largest cost and experimental complexity associated with such recordings making a recoverable and reusable system desirable. To address this, we have designed and tested a novel 3D printed head-gear system for freely moving mice and rats. The system consists of a recoverable microdrive printed in stainless steel and a plastic head cap system, allowing researchers to reuse the silicon probes with ease, decreasing the effective cost, and the experimental effort and complexity. The cap designs are modular and provide structural protection and electrical shielding to the implanted hardware and electronics. We provide detailed procedural instructions allowing researchers to adapt and flexibly modify the head-gear system.

Responsive manipulation of neural circuit pathology by fully implantable, front-end multiplexed embedded neuroelectronics

Responsive neurostimulation is increasingly required to probe neural circuit function and treat neuropsychiatric disorders. We introduce a multiplex-then-amplify (MTA) scheme that, in contrast to current approaches (which necessitate an equal number of amplifiers as number of channels), only requires one amplifier per multiplexer, significantly reducing the number of components and the size of electronics in multichannel acquisition systems. It also enables simultaneous stimulation of arbitrary waveforms on multiple independent channels. We validated the function of MTA by developing a fully implantable, responsive embedded system that merges the ability to acquire individual neural action potentials using conformable conducting polymer-based electrodes with real-time onboard processing, low-latency arbitrary waveform stimulation, and local data storage within a miniaturized physical footprint. We verified established responsive neurostimulation protocols and developed a network intervention to suppress pathological coupling between the hippocampus and cortex during interictal epileptiform discharges. The MTA design enables effective, self-contained, chronic neural network manipulation with translational relevance to the treatment of neuropsychiatric disease.

Hybrid Multisite Silicon Neural Probe with Integrated Flexible Connector for Interchangeable Packaging

Multisite neural probes are a fundamental tool to study brain function. Hybrid silicon/polymer neural probes combine rigid silicon and flexible polymer parts into one single device and allow, for example, the precise integration of complex probe geometries, such as multishank designs, with flexible biocompatible cabling. Despite these advantages and benefiting from highly reproducible fabrication methods on both silicon and polymer substrates, they have not been widely available. This paper presents the development, fabrication, characterization, and in vivo electrophysiological assessment of a hybrid multisite multishank silicon probe with a monolithically integrated polyimide flexible interconnect cable. The fabrication process was optimized at wafer level, and several neural probes with 64 gold electrode sites equally distributed along 8 shanks with an integrated 8 µm thick highly flexible polyimide interconnect cable were produced. The monolithic integration of the polyimide cable in the same fabrication process removed the necessity of the postfabrication bonding of the cable to the probe. This is the highest electrode site density and thinnest flexible cable ever reported for a hybrid silicon/polymer probe. Additionally, to avoid the time-consuming bonding of the probe to definitive packaging, the flexible cable was designed to terminate in a connector pad that can mate with commercial zero-insertion force (ZIF) connectors for electronics interfacing. This allows great experimental flexibility because interchangeable packaging can be used according to experimental demands. High-density distributed in vivo electrophysiological recordings were obtained from the hybrid neural probes with low intrinsic noise and high signal-to-noise ratio (SNR).

Efficient neural spike sorting using data subdivision and unification

Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activities of the brain at very high resolution, data rate and fidelity. However, the evolution in spike sorting algorithms to deal with the aforementioned technological advancement and capability to quantify higher density data sets is somewhat limited. Both supervised and unsupervised clustering algorithms do perform well when the data to quantify is small, however, their efficiency degrades with the increase in the data size in terms of processing time and quality of spike clusters being formed. This makes neural spike sorting an inefficient process to deal with large and dense electrophysiological data recorded from brain. The presented work aims to address this challenge by providing a novel data pre-processing framework, which can enhance the efficiency of the conventional spike sorting algorithms significantly. The proposed framework is validated by applying on ten widely used algorithms and six large feature sets. Feature sets are calculated by employing PCA and Haar wavelet features on three widely adopted large electrophysiological datasets for consistency during the clustering process. A MATLAB software of the proposed mechanism is also developed and provided to assist the researchers, active in this domain.

Biosensors for Studies on Adhesion-Mediated Cellular Responses to Their Microenvironment

Cells interact with their microenvironment by constantly sensing mechanical and chemical cues converting them into biochemical signals. These processes allow cells to respond and adapt to changes in their environment, and are crucial for most cellular functions. Understanding the mechanism underlying this complex interplay at the cell-matrix interface is of fundamental value to decipher key biochemical and mechanical factors regulating cell fate. The combination of material science and surface chemistry aided in the creation of controllable environments to study cell mechanosensing and mechanotransduction. Biologically inspired materials tailored with specific bioactive molecules, desired physical properties and tunable topography have emerged as suitable tools to study cell behavior. Among these materials, synthetic cell interfaces with built-in sensing capabilities are highly advantageous to measure biophysical and biochemical interaction between cells and their environment. In this review, we discuss the design of micro and nanostructured biomaterials engineered not only to mimic the structure, properties, and function of the cellular microenvironment, but also to obtain quantitative information on how cells sense and probe specific adhesive cues from the extracellular domain. This type of responsive biointerfaces provides a readout of mechanics, biochemistry, and electrical activity in real time allowing observation of cellular processes with molecular specificity. Specifically designed sensors based on advanced optical and electrochemical readout are discussed. We further provide an insight into the emerging role of multifunctional micro and nanosensors to control and monitor cell functions by means of material design.

Recent Advances in Electrical Neural Interface Engineering: Minimal Invasiveness, Longevity, and Scalability

Electrical neural interfaces serve as direct communication pathways that connect the nervous system with the external world. Technological advances in this domain are providing increasingly more powerful tools to study, restore, and augment neural functions. Yet, the complexities of the nervous system give rise to substantial challenges in the design, fabrication, and system-level integration of these functional devices. In this review, we present snapshots of the latest progresses in electrical neural interfaces, with an emphasis on advances that expand the spatiotemporal resolution and extent of mapping and manipulating brain circuits. We include discussions of large-scale, long-lasting neural recording; wireless, miniaturized implants; signal transmission, amplification, and processing; as well as the integration of interfaces with optical modalities. We outline the background and rationale of these developments and share insights into the future directions and new opportunities they enable.

Deciphering Brain Function by Miniaturized Fluorescence Microscopy in Freely Behaving Animals

Animal behavior is regulated by environmental stimuli and is shaped by the activity of neural networks, underscoring the importance of assessing the morpho-functional properties of different populations of cells in freely behaving animals. In recent years, a number of optical tools have been developed to monitor and modulate neuronal and glial activity at the protein, cellular, or network level and have opened up new avenues for studying brain function in freely behaving animals. Tools such as genetically encoded sensors and actuators are now commonly used for studying brain activity and function through their expression in different neuronal ensembles. In parallel, microscopy has also made major progress over the last decades. The advent of miniature microscopes (mini-microscopes also called mini-endoscopes) has become a method of choice for studying brain activity at the cellular and network levels in different brain regions of freely behaving mice. This technique also allows for longitudinal investigations while animals carrying the microscope on their head are performing behavioral tasks. In this review, we will discuss mini-endoscopic imaging and the advantages that these devices offer to research. We will also discuss current limitations of and potential future improvements in mini-endoscopic imaging.

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.

SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters

Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike sorting packages are available, there is little consensus about which are most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of ground-truth electrophysiological recordings, displaying results interactively on a continuously-updating website. With contributions from eleven laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include paired intracellular/extracellular recordings and state-of-the-art simulated recordings. Ten of the most popular spike sorting codes are wrapped in a Python package and evaluated on a compute cluster using an automated pipeline. SpikeForest documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.

Volumetric mapping of the functional neuroanatomy of the respiratory network in the perfused brainstem preparation of rats

KEY POINTS

The functional neuroanatomy of the mammalian respiratory network is far from being understood since experimental tools that measure neural activity across this brainstem-wide circuit are lacking. Here, we use silicon multi-electrode arrays to record respiratory local field potentials (rLFPs) from 196-364 electrode sites within 8-10 mm³ of brainstem tissue in single arterially perfused brainstem preparations with respect to the ongoing respiratory motor pattern of inspiration (I), post-inspiration (PI) and late-expiration (E2). rLFPs peaked specifically at the three respiratory phase transitions, E2-I, I-PI and PI-E2. We show, for the first time, that only the I-PI transition engages a brainstem-wide network, and that rLFPs during the PI-E2 transition identify a hitherto unknown role for the dorsal respiratory group. Volumetric mapping of pontomedullary rLFPs in single preparations could become a reliable tool for assessing the functional neuroanatomy of the respiratory network in health and disease.

ABSTRACT

While it is widely accepted that inspiratory rhythm generation depends on the pre-Bötzinger complex, the functional neuroanatomy of the neural circuits that generate expiration is debated. We hypothesized that the compartmental organization of the brainstem respiratory network is sufficient to generate macroscopic local field potentials (LFPs), and if so, respiratory (r) LFPs could be used to map the functional neuroanatomy of the respiratory network. We developed an approach using silicon multi-electrode arrays to record spontaneous LFPs from hundreds of electrode sites in a volume of brainstem tissue while monitoring the respiratory motor pattern on phrenic and vagal nerves in the perfused brainstem preparation. Our results revealed the expression of rLFPs across the pontomedullary brainstem. rLFPs occurred specifically at the three transitions between respiratory phases: (1) from late expiration (E2) to inspiration (I), (2) from I to post-inspiration (PI), and (3) from PI to E2. Thus, respiratory network activity was maximal at respiratory phase transitions. Spatially, the E2-I, and PI-E2 transitions were anatomically localized to the ventral and dorsal respiratory groups, respectively. In contrast, our data show, for the first time, that the generation of controlled expiration during the post-inspiratory phase engages a distributed neuronal population within ventral, dorsal and pontine network compartments. A group-wise independent component analysis demonstrated that all preparations exhibited rLFPs with a similar temporal structure and thus share a similar functional neuroanatomy. Thus, volumetric mapping of rLFPs could allow for the physiological assessment of global respiratory network organization in health and disease.

An investigation into the nonlinear coupling between CA1 layers and the dentate gyrus

Although the activity from the dentate gyrus is known to have strong connections with other hippocampal layers, the functionality of these connections, that is, the degree to which it drives activity in the downstream regions of the hippocampus, is not well understood. This question is particularly relevant for mesoscale localfield potential (LFP) rhythms such as gamma oscillations. Following the hypothesis that fundamental features of the LFP are consistent with turbulent dynamics, we investigate the crosslayer relationship between the CA1 layers and the dentate gyrus as a function of running speed. In agreement with previous studies, same-layer spectral and bispectral analyses show that increasing input (rat speed) results in an increase of power and nonlinearity (phase coupling) between theta and gamma. The effectiveness of the connection between the 2 regions is investigated using cross-bicoherence analysis. Based on the turbulence interpretation of the evolution of spectra and bispectra as a function of the power input rate, we propose a measure for estimating the strength of the cross-frequency, cross-layer nonlinear forcing, that compares the magnitude of bicoherence (same-layer) and cross-bicoherence (cross-layer). Our results suggest that at moderate speeds gamma in CA1 is mainly driven by local theta, while the coupling of the CA1 gamma to the dentate-gyrus gamma becomes significant. Overall, these data are consistent with the hypothesis of theta-to-gamma energy cascade model for the organization of hippocampal LFP, with theta playing the role of a global pacemaker across the entire hippocampus while gamma is a local oscillation generated by through local anatomical connections. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Chronically Implanted Microelectrodes Cause c-fos Expression Along Their Trajectory

When designing electrodes and probes for brain–machine interfaces, one of the challenges faced involves minimizing the brain-tissue response, which would otherwise create an environment that is detrimental for the accurate functioning of such probes. Following the implantation process, the brain reacts with a sterile inflammation response and resulting astrocytic glial scar formation, potentially resulting in neuronal cell loss around the implantation site. Such alterations in the naïve brain tissue can hinder both the quality of neuronal recordings, and the efficacy of deep-brain stimulation. In this study, we chronically implanted a glass-supported polyimide microelectrode in the dorsolateral striatum of Sprague–Dawley rats. The effect of high-frequency stimulation (HFS) was investigated using c-fos immunoreactivity techniques. GFAP and ED1 immunohistochemistry were used to analyze the brain-tissue response. No changes in c-fos expression were found for either the acute or chronic stimulus groups; although a c-fos expression was found along the length of the implantation trajectory, following chronic implantation of our stiffened polyimide microelectrode. Furthermore, we also observed the formation of a glial scar around the microelectrode, with an accompanying low number of inflammation cells. Histological and statistical analysis of NeuN-positive cells did not demonstrate a pronounced “kill zone” with accompanying neuronal cell death around the implantation site, neither on the polymer side, nor on the glass side of the PI-glass probe.

Understanding the circuit basis of cognitive functions using mouse models

Understanding how cognitive functions arise from computations occurring in the brain requires the ability to measure and perturb neural activity while the relevant circuits are engaged for specific cognitive processes. Rapid technical advances have led to the development of new approaches to transiently activate and suppress neuronal activity as well as to record simultaneously from hundreds to thousands of neurons across multiple brain regions during behavior. To realize the full potential of these approaches for understanding cognition, however, it is critical that behavioral conditions and stimuli are effectively designed to engage the relevant brain networks. Here, we highlight recent innovations that enable this combined approach. In particular, we focus on how to design behavioral experiments that leverage the ever-growing arsenal of technologies for controlling and measuring neural activity in order to understand cognitive functions.

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Neuroprosthetics and neuromodulation represent a promising field for several related applications in the central and peripheral nervous system, such as the treatment of neurological disorders, the control of external robotic devices, and the restoration of lost tactile functions. These actions are allowed by the neural interface, a miniaturized implantable device that most commonly exploits electrical energy to fulfill these operations. A neural interface must be biocompatible, stable over time, low invasive, and highly selective; the challenge is to develop a safe, compact, and reliable tool for clinical applications. In case of anatomical impairments, neuroprosthetics is bound to the need of exploring the surrounding environment by fast-responsive and highly sensitive artificial tactile sensors that mimic the natural sense of touch. Tactile sensors and neural interfaces are closely interconnected since the readouts from the first are required to convey information to the neural implantable apparatus. The role of these devices is pivotal hence technical improvements are essential to ensure a secure system to be eventually adopted in daily life. This review highlights the fundamental criteria for the design and microfabrication of neural interfaces and artificial tactile sensors, their use in clinical applications, and future enhancements for the release of a second generation of devices.

Localization of movable electrodes in a multi-electrode microdrive in nonhuman primates

BACKGROUND

Recently, large-scale semi-chronic recording systems have been developed, unique in their capability to record simultaneously from multiple individually moveable electrodes. As these recording systems can cover a large area, knowledge of the exact location of each individual electrode is crucial. Currently, the only method of keeping track of electrode depth and thus location is through detailed notebook keeping on neural activity.

NEW METHOD

We have improved the electrode localization by combining pre- and postoperative anatomical magnetic resonance imaging (MRI) scans with high resolution computed tomography (CT) scans throughout the experiment, and validated our method by comparing the resulting location estimates with traditional notebook-keeping. Finally, the actual location of a selection of electrodes was marked at the end of the experiment by creating small metallic depositions using electrical stimulation, and thereby made visible on MRI.

RESULTS

Combining CT scans with a high resolution, artefact reducing sequence during the experiment with a preoperative MRI scan provides crucial information about the exact electrode location of multielectrode arrays with individually moveable electrodes.

COMPARISON WITH EXISTING METHODS

The information obtained from the hybrid CT-MR image and the notes on spiking activity showed a similar pattern, with the clear advantage of the visualization of the exact position of the electrodes using our method.

CONCLUSIONS

The described technique allows for a precise anatomical identification of the recorded brain areas and thus to draw strong conclusions about the role of each targeted cortical area in the behavior under study.

Isolated cortical computations during delta waves support memory consolidation

Delta waves have been described as periods of generalized silence across the cortex, and their alternation with periods of endogenous activity results in the slow oscillation of slow-wave sleep. Despite evidence that delta waves are instrumental for memory consolidation, their specific role in reshaping cortical functional circuits remains puzzling. In a rat model, we found that delta waves are not periods of complete silence and that the residual activity is not mere neuronal noise. Instead, cortical cells involved in learning a spatial memory task subsequently formed cell assemblies during delta waves in response to transient reactivation of hippocampal ensembles during ripples. This process occurred selectively during endogenous or induced memory consolidation. Thus, delta waves represent isolated cortical computations tightly related to ongoing information processing underlying memory consolidation.

Is hippocampal remapping the physiological basis for context?

In 1980, Nadel and Wilner extended Richard Hirsh's notion that the hippocampus creates environmental representations, called "contexts," suggesting that the fundamental structure of context was the spatial representation proposed by O'Keefe and Nadel's landmark book, The Hippocampus as a Cognitive Map (1978). This book, in turn, derives from the discovery that individual hippocampal neurons act as place cells, with the complete set of place cells tiling an enclosure, forming a type of spatial map. It was found that unique environments had unique place cell representations. That is, if one takes the hippocampal map of a specific environment, this representation scrambles, or "remaps" when the animal is placed in a different environment. Several authors have speculated that "maps" and "remapping" form the physiological substrates for context and context shifting. One difficulty with this definition is that it is exclusively spatial; it can only be inferred when an animal locomotes in an enclosure. There are five aims for this article. The first is to give an historical overview of context as a variable that controls behavior. The second aim is to give an historical overview of concepts of place cell maps and remapping. The third aim is to propose an updated definition of a place cell map, based on temporal rather than spatial overlaps, which adds flexibility. The fourth aim is to address the issue of whether the biological phenomenon of hippocampal remapping, is, in fact, the substrate for shifts in the psychological phenomenon of context. The final aim is speculation of how contextual representations may contribute to effective behavior.

Inhibition of the cluster of differentiation 14 innate immunity pathway with IAXO-101 improves chronic microelectrode performance

OBJECTIVE

Neuroinflammatory mechanisms are hypothesized to contribute to intracortical microelectrode failures. The cluster of differentiation 14 (CD14) molecule is an innate immunity receptor involved in the recognition of pathogens and tissue damage to promote inflammation. The goal of the study was to investigate the effect of CD14 inhibition on intracortical microelectrode recording performance and tissue integration.

APPROACH

Mice implanted with intracortical microelectrodes in the motor cortex underwent electrophysiological characterization for 16 weeks, followed by endpoint histology. Three conditions were examined: (1) wildtype control mice, (2) knockout mice lacking CD14, and (3) wildtype control mice administered a small molecule inhibitor to CD14 called IAXO-101.

MAIN RESULTS

The CD14 knockout mice exhibited acute but not chronic improvements in intracortical microelectrode performance without significant differences in endpoint histology. Mice receiving IAXO-101 exhibited significant improvements in recording performance over the entire 16 week duration without significant differences in endpoint histology.

SIGNIFICANCE

Full removal of CD14 is beneficial at acute time ranges, but limited CD14 signaling is beneficial at chronic time ranges. Innate immunity receptor inhibition strategies have the potential to improve long-term intracortical microelectrode performance.

Multifunctional multi-shank neural probe for investigating and modulating long-range neural circuits in vivo

Investigation and modulation of neural circuits in vivo at the cellular level are very important for studying functional connectivity in a brain. Recently, neural probes with stimulation capabilities have been introduced, and they provided an opportunity for studying neural activities at a specific region in the brain using various stimuli. However, previous methods have a limitation in dissecting long-range neural circuits due to inherent limitations on their designs. Moreover, the large size of the previously reported probes induces more significant tissue damage. Herein, we present a multifunctional multi-shank MEMS neural probe that is monolithically integrated with an optical waveguide for optical stimulation, microfluidic channels for drug delivery, and microelectrode arrays for recording neural signals from different regions at the cellular level. In this work, we successfully demonstrated the functionality of our probe by confirming and modulating the functional connectivity between the hippocampal CA3 and CA1 regions in vivo.

Microelectromechanical neural probes can cause tissue damage and often cannot record from distant brain areas. Here the authors combine electrical recording, optical stimulation and microfluidic drug delivery in one multi-shank probe with thinner shanks to reduce damage and a flexible design to target long-range neural circuits.

Large-Scale, High-Resolution Microelectrode Arrays for Interrogation of Neurons and Networks

High-density microelectrode arrays (HD-MEAs) are increasingly being used for the observation and manipulation of neurons and networks in vitro. Large-scale electrode arrays allow for long-term extracellular recording of the electrical activity from thousands of neurons simultaneously. Beyond population activity, it has also become possible to extract information of single neurons at subcellular level (e.g., the propagation of action potentials along axons). In effect, HD-MEAs have become an electrical imaging platform for label-free extraction of the structure and activation of cells in cultures and tissues. The quality of HD-MEA data depends on the resolution of the electrode array and the signal-to-noise ratio. In this chapter, we begin with an introduction to HD-MEA signals. We provide an overview of the developments on complementary metal-oxide-semiconductor or CMOS-based HD-MEA technology. We also discuss the factors affecting the performance of HD-MEAs and the trending application requirements that drive the efforts for future devices. We conclude with an outlook on the potential of HD-MEAs for advancing basic neuroscience and drug discovery.

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

Spike sorting is the process of detecting and clustering action potential waveforms of putative single neurons from extracellular voltage recordings. Typically, spike detection uses a fixed voltage threshold and shadow period, but this approach often misses spikes during high firing rate epochs or noisy conditions. We developed a simple, data-driven spike detection method using a scaled form of template matching, based on the sliding cosine similarity between the extracellular voltage signal and mean spike waveforms of candidate single units. Performance was tested in whisker somatosensory cortex (S1) of anesthetized mice in vivo. The method consistently detected whisker-evoked spikes that were missed by the standard fixed threshold. Detection was improved most for spikes evoked by strong stimuli (40–70% increase), improved less for weaker stimuli, and unchanged for spontaneous spiking. This represents improved detection during spatiotemporally dense spiking, and yielded sharper sensory tuning estimates. We also benchmarked performance using computationally generated voltage data. Template matching detected ~85–90% of spikes compared to ~70% for the standard fixed threshold method, and was more tolerant to high firing rates and simulated recording noise. Thus, a simple template matching approach substantially improves detection of single-unit spiking for cortical physiology.

Cracking the Function of Layers in the Sensory Cortex

Understanding how cortical activity generates sensory perceptions requires a detailed dissection of the function of cortical layers. Despite our relatively extensive knowledge of their anatomy and wiring, we have a limited grasp of what each layer contributes to cortical computation. We need to develop a theory of cortical function that is rooted solidly in each layer's component cell types and fine circuit architecture and produces predictions that can be validated by specific perturbations. Here we briefly review the progress toward such a theory and suggest an experimental road map toward this goal. We discuss new methods for the all-optical interrogation of cortical layers, for correlating in vivo function with precise identification of transcriptional cell type, and for mapping local and long-range activity in vivo with synaptic resolution. The new technologies that can crack the function of cortical layers are finally on the immediate horizon.