Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex

Plasticity in inhibitory networks improves pattern separation in early olfactory processing

Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and varying ratios in complex mixtures. Changes in nectar production throughout the day and over the animal's lifetime add to the complexity. The honeybee olfactory system, containing fewer than 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity in the AL circuits, but its role in odor learning remains poorly understood. Using a biophysical computational model, tuned by in vivo electrophysiological data, and live imaging of the honeybee's AL, we explored the neural mechanisms of plasticity in the AL. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses responses to shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our calcium imaging data support these predictions. Analysis of a graph convolutional neural network performing an odor categorization task revealed a similar mechanism for contrast enhancement. Our study provides insights into how inhibitory plasticity in the early olfactory network reshapes the coding for efficient learning of complex odors.

Interfacing with the Brain: How Nanotechnology Can Contribute

Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain-machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain-machine interfaces and look forward in discussing perspectives and limitations based on the authors' expertise across a range of complementary disciplines─from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.

Upconversion Nanoparticle-Delivery Flexible Optrodes for Long-Lasting Multichannel Electrophysiology and Transcranial NIR Optogenetics

Near-infrared (NIR) activatable nanoparticles enable remote, cell-type-specific manipulation of neuronal activity, whereas flexible microelectrode arrays (FMAs) facilitate long-term, multichannel recording of neural signals. Despite the recent development of multifunctional neural probes, integrating these techniques into a single, minimally invasive device remains challenging. Here, we present a novel optrode that combines NIR-activatable upconversion nanoparticles (UCNPs) with FMAs. The UCNPs and FMAs are coencapsulated in a nanoliter-scale polymer carrier and delivered into the same brain regions through a single surgery, ensuring highly spatial congruence between the manipulated and recorded neuronal populations. Chronically implanted devices enable simultaneous multichannel recording and transcranial modulation of opsin-defined neuronal populations over extended periods. The flexibility and minimal invasiveness of our optrodes provide a powerful tool for the long-term and spatially precise interrogation of neural circuit functions.

Fast activity chirp patterns in focal seizures from patients and animal models

Time-frequency analysis of focal seizure electroencephalographic signals performed with depth electrodes in human temporal lobe structures has revealed the occurrence at onset of oscillations at approximately 30-100 Hz that feature a monotonic rapid decay in frequency content. This seizure onset pattern, referred to as chirp, has been identified as a highly specific and sensitive marker of focal seizures that are characterized by low-voltage fast activity. We report that this chirp pattern is also observed in animal models of temporal lobe epilepsy in both in vivo and in vitro preparations. We propose here that chirps mirror the involvement of synchronous interneuron firing that is known to represent a specific cellular mechanism leading to the initiation of focal seizures, in particular those characterized by low-voltage fast activity.

A central role for acetylcholine in entorhinal cortex function and dysfunction with age in humans and mice

Structural and functional changes in the entorhinal cortex (EC) are among the earliest signs of cognitive aging. Here, we ask whether a compromised cholinergic system influences early EC impairments and plays a primary role in EC cognition. We evaluated the relationship between loss of integrity of cholinergic inputs to the EC and cognitive deficits in otherwise healthy humans and mice. Using in vivo imaging (PET/MRI) in older humans and high-resolution imaging in wild-type mice and mice with genetic susceptibility to Alzheimer's disease pathology, we establish that loss of cholinergic input to the EC is, in fact, an early feature in cognitive aging. Through mechanistic studies in mice, we find a central role for EC-projecting cholinergic neurons in the expression of EC-related behaviors. Our data demonstrate that alterations to the cholinergic EC are an early, conserved feature of cognitive aging across species and may serve as an early predictor of cognitive status.

Multi-channel microelectrode arrays for detection of single-cell level neural information in the hippocampus CA1 under general anesthesia induced by low-dose isoflurane

Timely monitoring of anesthesia status during surgery is important to prevent an overdose of isoflurane anesthesia. Therefore, in-depth studies of the neural mechanisms of anesthetics are warranted. Hippocampal CA1 plays an important role during anesthesia. Currently, a high spatiotemporal resolution microdevice technology for the accurate detection of deep brain nuclei is lacking. In this research, four-shank 32-channel implantable microelectrode arrays (MEAs) were developed for the real-time recording of single-cell level neural information in rat hippocampal CA1. Platinum nanoparticles were modified onto the microelectrodes to substantially enhance the electrical properties of the microelectrode arrays. The modified MEAs exhibited low impedance (11.5 ± 1 kΩ) and small phase delay (-18.5° ± 2.54°), which enabled the MEAs to record single-cell level neural information with a high signal-to-noise ratio. The MEAs were implanted into the CA1 nuclei of the anesthetized rats, and the electrophysiological signals were recorded under different degrees of anesthesia mediated by low-dose concentrations of isoflurane. The recorded signals were analyzed in depth. Isoflurane caused an inhibition of spike firing rate in hippocampal CA1 neurons, while inducing low-frequency oscillations in CA1, thus enhancing the low-frequency power of local field potentials. In this manner, the spike firing rate and the power of local field potentials in CA1 could characterize the degree of isoflurane anesthesia. The present study provides a technical tool to study the neural mechanisms of isoflurane anesthesia and a research method for monitoring the depth of isoflurane anesthesia in clinical practice.

Multifunctional Tetrode-like Drug delivery, Optical stimulation, and Electrophysiology (Tetro-DOpE) probes

Having reliable tools for recording and manipulating circuit activity are essential to understand the complex patterns of neural dynamics that underlie brain function. We present Tetro-DOpE (Tetrode-like Drug delivery, Optical stimulation, and Electrophysiology) probes that can simultaneously record and manipulate neural activity in behaving rodents. We fabricated thin multifunctional fibers (<50 μm) using the scalable convergence thermal drawing process. Then, the thin fibers are bundled, similar to tetrode fabrication, to produce Tetro-DOpE probes. We demonstrated the multifunctionality (i.e., electrophysiology, optical stimulation, and drug delivery) of our probe in head-fixed behaving mice. Furthermore, we assembled a six-shank probe mounted on a microdrive which enabled stable recordings of over months when chronically implanted in freely behaving mice. These in vivo experiments demonstrate the potential of customizable, low cost, and accessible multifunctional Tetro-DOpE probes for investigation of neural circuitry in behaving animals.

Flexible multimaterial fibers in modern biomedical applications

Biomedical devices are indispensable in modern healthcare, significantly enhancing patients' quality of life. Recently, there has been a drastic increase in innovations for the fabrication of biomedical devices. Amongst these fabrication methods, the thermal drawing process has emerged as a versatile and scalable process for the development of advanced biomedical devices. By thermally drawing a macroscopic preform, which is meticulously designed and integrated with functional materials, hundreds of meters of multifunctional fibers are produced. These scalable flexible multifunctional fibers are embedded with functionalities such as electrochemical sensing, drug delivery, light delivery, temperature sensing, chemical sensing, pressure sensing, etc. In this review, we summarize the fabrication method of thermally drawn multifunctional fibers and highlight recent developments in thermally drawn fibers for modern biomedical application, including neural interfacing, chemical sensing, tissue engineering, cancer treatment, soft robotics and smart wearables. Finally, we discuss the existing challenges and future directions of this rapidly growing field.

Recording Quality Is Systematically Related to Electrode Impedance

Extracellular recordings with planar microelectrodes are the gold standard technique for recording the fast action potentials of neurons in the intact brain. The introduction of microfabrication techniques has revolutionized the in vivo recording of neuronal activity and introduced high-density, multi-electrode arrays that increase the spatial resolution of recordings and the number of neurons that can be simultaneously recorded. Despite these innovations, there is still debate about the ideal electrical transfer characteristics of extracellular electrodes. This uncertainty is partly due to the lack of systematic studies comparing electrodes with different characteristics, particularly for chronically implanted arrays over extended time periods. Here a high-density, flexible, and thin-film array is fabricated and tested, containing four distinct electrode types differing in surface material and surface topology and, thus, impedance. It is found that recording quality is strongly related to electrode impedance with signal amplitude and unit yield negatively correlated to impedance. Electrode impedances are stable for the duration of the experiment (up to 12 weeks) and recording quality does not deteriorate. The findings support the expectation from the theory that recording quality will increase as impedance decreases.

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.

Neuronal functional connectivity is impaired in a layer dependent manner near chronically implanted intracortical microelectrodes in C57BL6 wildtype mice

Objective.This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near chronically implanted microelectrodes. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how the FBR affects the functional stability of neural circuits near implanted brain-computer interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.Approach.This study utilized single-shank, 16-channel,100µm site-spacing Michigan-style microelectrodes (3 mm length, 703µm2 site area) that span all cortical layers and the hippocampal CA1 region. Sex balanced C57BL6 wildtype mice (11-13 weeks old) received perpendicularly implanted microelectrode in left primary visual cortex. Electrophysiological recordings were performed during both spontaneous activity and visual sensory stimulation. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of local field potential (LFP) and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.Main results. The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.Significance. This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

Double Electrode Experiments Reveal the Processes Occurring at PEDOT-Coated Neural Electrode Arrays

Neural electrodes have recently been developed with surface modifications of conductive polymers, in particular poly(3,4-ethylenedioxythiophene) (PEDOT), and extensively studied for their roles in recording and stimulation, aiming to improve their biocompatibility. In this work, the implications for the design of practical neural sensors are clarified, and systematic procedures for their preparation are reported. In particular, this study introduces the use of in vitro double electrode experiments to mimic the responses of neural electrodes with a focus on signal-recording electrodes modified with PEDOT. Specifically, potential steps on one unmodified electrode in an array are used to identify the responses for PEDOT doped with different anions and compared with that of a bare platinum (Pt) electrode. The response is shown to be related to the rearrangement of ions in solution near the detector electrode resulting from the potential step, with a current transient seen at the detector electrode. A rapid response for PEDOT doped with chloride (ca. 0.04 s) ions was observed and attributed to the fast movement of chloride ions in and out of the polymer film. In contrast, PEDOT doped with poly(styrenesulfonate) (PSS) responds much slower (ca. 2.2 s), and the essential immobility of polyanion constrains the direction of current flow.

Automatical Spike Sorting With Low-Rank and Sparse Representation

Spikesorting is crucial in studying neural individually and synergistically encoding and decoding behaviors. However, existent spike sorting algorithms perform unsatisfactorily in real scenarios where heavy noises and overlapping samples are commonly in the spikes, and the spikes from different neurons are similar. To address such challenging scenarios, we propose an automatic spike sporting method in this paper, which integrally combines low-rank and sparse representation (LRSR) into a unified model. In particular, LRSR models spikes through low-rank optimization, uncovering global data structure for handling similar and overlapped samples. To eliminate the influence of the embedded noises, LRSR uses a sparse constraint, effectively separating spikes from noise. The optimization is solved using alternate augmented Lagrange multipliers methods. Moreover, we conclude with an automatic spike-sorting framework that employs the spectral clustering theorem to estimate the number of neurons. Extensive experiments over various simulated and real-world datasets demonstrate that our proposed method, LRSR, can handle spike sorting effectively and efficiently.

Comparison of head direction cell firing characteristics across thalamo-parahippocampal circuitry

Head direction (HD) cells, which fire persistently when an animal's head is pointed in a particular direction, are widely thought to underlie an animal's sense of spatial orientation and have been identified in several limbic brain regions. Robust HD cell firing is observed throughout the thalamo-parahippocampal system, although recent studies report that parahippocampal HD cells exhibit distinct firing properties, including conjunctive aspects with other spatial parameters, which suggest they play a specialized role in spatial processing. Few studies, however, have quantified these apparent differences. Here, we performed a comparative assessment of HD cell firing characteristics across the anterior dorsal thalamus (ADN), postsubiculum (PoS), parasubiculum (PaS), medial entorhinal (MEC), and postrhinal (POR) cortices. We report that HD cells with a high degree of directional specificity were observed in all five brain regions, but ADN HD cells display greater sharpness and stability in their preferred directions, and greater anticipation of future headings compared to parahippocampal regions. Additional analysis indicated that POR HD cells were more coarsely modulated by other spatial parameters compared to PoS, PaS, and MEC. Finally, our analyses indicated that the sharpness of HD tuning decreased as a function of laminar position and conjunctive coding within the PoS, PaS, and MEC, with cells in the superficial layers along with conjunctive firing properties showing less robust directional tuning. The results are discussed in relation to theories of functional organization of HD cell tuning in thalamo-parahippocampal circuitry.

Differential clustering of visual and choice- and saccade-related activity in macaque V3A and CIP

Neurons in sensory and motor cortices tend to aggregate in clusters with similar functional properties. Within the primate dorsal ("where") pathway, an important interface between three-dimensional (3-D) visual processing and motor-related functions consists of two hierarchically organized areas: V3A and the caudal intraparietal (CIP) area. In these areas, 3-D visual information, choice-related activity, and saccade-related activity converge, often at the single-neuron level. Characterizing the clustering of functional properties in areas with mixed selectivity, such as these, may help reveal organizational principles that support sensorimotor transformations. Here we quantified the clustering of visual feature selectivity, choice-related activity, and saccade-related activity by performing correlational and parametric comparisons of the responses of well-isolated, simultaneously recorded neurons in macaque monkeys. Each functional domain showed statistically significant clustering in both areas. However, there were also domain-specific differences in the strength of clustering across the areas. Visual feature selectivity and saccade-related activity were more strongly clustered in V3A than in CIP. In contrast, choice-related activity was more strongly clustered in CIP than in V3A. These differences in clustering may reflect the areas' roles in sensorimotor processing. Stronger clustering of visual and saccade-related activity in V3A may reflect a greater role in within-domain processing, as opposed to cross-domain synthesis. In contrast, stronger clustering of choice-related activity in CIP may reflect a greater role in synthesizing information across functional domains to bridge perception and action.NEW & NOTEWORTHY The occipital and parietal cortices of macaque monkeys are bridged by hierarchically organized areas V3A and CIP. These areas support 3-D visual transformations, carry choice-related activity during 3-D perceptual tasks, and possess saccade-related activity. This study quantifies the functional clustering of neuronal response properties within V3A and CIP for each of these domains. The findings reveal domain-specific cross-area differences in clustering that may reflect the areas' roles in sensorimotor processing.

Loss of cholinergic input to the entorhinal cortex is an early indicator of cognitive impairment in natural aging of humans and mice

In a series of translational experiments using fully quantitative positron emission tomography (PET) imaging with a new tracer specific for the vesicular acetylcholine transporter ([18F]VAT) in vivo in humans, and genetically targeted cholinergic markers in mice, we evaluated whether changes to the cholinergic system were an early feature of age-related cognitive decline. We found that deficits in cholinergic innervation of the entorhinal cortex (EC) and decline in performance on behavioral tasks engaging the EC are, strikingly, early features of the aging process. In human studies, we recruited older adult volunteers that were physically healthy and without prior clinical diagnosis of cognitive impairment. Using [18F]VAT PET imaging, we demonstrate that there is measurable loss of cholinergic inputs to the EC that can serve as an early signature of decline in EC cognitive performance. These deficits are specific to the cholinergic circuit between the medial septum and vertical limb of the diagonal band (MS/vDB; CH1/2) to the EC. Using diffusion imaging, we further demonstrate impaired structural connectivity in the tracts between the MS/vDB and EC in older adults with mild cognitive impairment. Experiments in mouse, designed to parallel and extend upon the human studies, used high resolution imaging to evaluate cholinergic terminal density and immediate early gene (IEG) activity of EC neurons in healthy aging mice and in mice with genetic susceptibility to accelerated accumulation amyloid beta plaques and hyperphosphorylated mouse tau. Across species and aging conditions, we find that the integrity of cholinergic projections to the EC directly correlates with the extent of EC activation and with performance on EC-related object recognition memory tasks. Silencing EC-projecting cholinergic neurons in young, healthy mice during the object-location memory task impairs object recognition performance, mimicking aging. Taken together we identify a role for acetylcholine in normal EC function and establish loss of cholinergic input to the EC as an early, conserved feature of age-related cognitive decline in both humans and rodents.

Waveform-based classification of dentate spikes

Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimer's disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing.

Discovering optimal features for neuron-type identification from extracellular recordings

Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.

Hierarchical computation of 3D motion across macaque areas MT and FST

Computing behaviorally relevant representations of three-dimensional (3D) motion from two-dimensional (2D) retinal signals is critical for survival. To ascertain where and how the primate visual system performs this computation, we recorded from the macaque middle temporal (MT) area and its downstream target, the fundus of the superior temporal sulcus (area FST). Area MT is a key site of 2D motion processing, but its role in 3D motion processing is controversial. The functions of FST remain highly underexplored. To distinguish representations of 3D motion from those of 2D retinal motion, we contrast responses to multiple motion cues during a motion discrimination task. The results reveal a hierarchical transformation whereby many FST but not MT neurons are selective for 3D motion. Modeling results further show how generalized, cue-invariant representations of 3D motion in FST may be created by selectively integrating the output of 2D motion selective MT neurons.

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.

Waveform-based classification of dentate spikes

Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimer's disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing.

Neuronal functional connectivity is impaired in a layer dependent manner near the chronically implanted microelectrodes

Objective

This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near the chronically implanted microelectrode. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how does the FBR impact affect the functional stability of neural circuits near implanted Brain-Computer Interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.

Approach

This study utilized multisite Michigan-style microelectrodes that span all cortical layers and the hippocampal CA1 region to collect spontaneous and visually-evoked electrophysiological activity. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of LFP and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.

Main Results

The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.

Significance

This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

Network size affects the complexity of activity in human iPSC-derived neuronal populations

Multi-electrode recording of neural activity in cultures offer opportunities for understanding how the structure of a network gives rise to function. Although it is hypothesized that network size is critical for determining the dynamics of activity, this relationship in human neural cultures remains largely unexplored. By applying new methods for analyzing neural activity to human iPSC derived cultures at either low-densities or high-densities, we uncovered the significant impacts that neuron number has on the individual neurophysiological properties of cells (such as firing rates), the collective behavior of the networks these cultures formed (as measured by entropy), and the relationship between the two. As a result, simply changing the densities of neurons generated dynamics and network behavior that differed not just in degree, but in kind. Beyond revealing the relationship between network structure and function, our findings provide a novel analytical framework to study diseases where network level activity is affected.

Coherent mapping of position and head direction across auditory and visual cortex

Neurons in primary visual cortex (V1) may not only signal current visual input but also relevant contextual information such as reward expectancy and the subject's spatial position. Such contextual representations need not be restricted to V1 but could participate in a coherent mapping throughout sensory cortices. Here, we show that spiking activity coherently represents a location-specific mapping across auditory cortex (AC) and lateral, secondary visual cortex (V2L) of freely moving rats engaged in a sensory detection task on a figure-8 maze. Single-unit activity of both areas showed extensive similarities in terms of spatial distribution, reliability, and position coding. Importantly, reconstructions of subject position based on spiking activity displayed decoding errors that were correlated between areas. Additionally, we found that head direction, but not locomotor speed or head angular velocity, was an important determinant of activity in AC and V2L. By contrast, variables related to the sensory task cues or to trial correctness and reward were not markedly encoded in AC and V2L. We conclude that sensory cortices participate in coherent, multimodal representations of the subject's sensory-specific location. These may provide a common reference frame for distributed cortical sensory and motor processes and may support crossmodal predictive processing.

Bioactive polymer-enabled conformal neural interface and its application strategies

Neural interface is a powerful tool to control the varying neuron activities in the brain, where the performance can directly affect the quality of recording neural signals and the reliability of in vivo connection between the brain and external equipment. Recent advances in bioelectronic innovation have provided promising pathways to fabricate flexible electrodes by integrating electrodes on bioactive polymer substrates. These bioactive polymer-based electrodes can enable the conformal contact with irregular tissue and result in low inflammation when compared to conventional rigid inorganic electrodes. In this review, we focus on the use of silk fibroin and cellulose biopolymers as well as certain synthetic polymers to offer the desired flexibility for constructing electrode substrates for a conformal neural interface. First, the development of a neural interface is reviewed, and the signal recording methods and tissue response features of the implanted electrodes are discussed in terms of biocompatibility and flexibility of corresponding neural interfaces. Following this, the material selection, structure design and integration of conformal neural interfaces accompanied by their effective applications are described. Finally, we offer our perspectives on the evolution of desired bioactive polymer-enabled neural interfaces, regarding the biocompatibility, electrical properties and mechanical softness.

Working memory and reward increase the accuracy of animal location encoding in the medial prefrontal cortex

The ability to perceive spatial environments and locate oneself during navigation is crucial for the survival of animals. Mounting evidence suggests a role of the medial prefrontal cortex (mPFC) in spatially related behaviors. However, the properties of mPFC spatial encoding and how it is influenced by animal behavior are poorly defined. Here, we train the mice to perform 3 tasks differing in working memory and reward-seeking: a delayed non-match to place (DNMTP) task, a passive alternation (PA) task, and a free-running task. Single-unit recording in the mPFC shows that although individual mPFC neurons exhibit spatially selective firing, they do not reliably represent the animal location. The population activity of mPFC neurons predicts the animal location. Notably, the population coding of animal locations by the mPFC is modulated by animal behavior in that the coding accuracy is higher in tasks involved in working memory and reward-seeking. This study reveals an approach whereby the mPFC encodes spatial positions and the behavioral variables affecting it.

Place cells are nonrandomly clustered by field location in CA1 hippocampus

A challenge in both modern and historic neuroscience has been achieving an understanding of neuron circuits, and determining the computational and organizational principles that underlie these circuits. Deeper understanding of the organization of brain circuits and cell types, including in the hippocampus, is required for advances in behavioral and cognitive neuroscience, as well as for understanding principles governing brain development and evolution. In this manuscript, we pioneer a new method to analyze the spatial clustering of active neurons in the hippocampus. We use calcium imaging and a rewarded navigation task to record from 100 s of place cells in the CA1 of freely moving rats. We then use statistical techniques developed for and in widespread use in geographic mapping studies, global Moran's I, and local Moran's I to demonstrate that cells that code for similar spatial locations tend to form small spatial clusters. We present evidence that this clustering is not the result of artifacts from calcium imaging, and show that these clusters are primarily formed by cells that have place fields around previously rewarded locations. We go on to show that, although cells with similar place fields tend to form clusters, there is no obvious topographic mapping of environmental location onto the hippocampus, such as seen in the visual cortex. Insights into hippocampal organization, as in this study, can elucidate mechanisms underlying motivational behaviors, spatial navigation, and memory formation.

Improved space breakdown method - A robust clustering technique for spike sorting

Space Breakdown Method (SBM) is a clustering algorithm that was developed specifically for low-dimensional neuronal spike sorting. Cluster overlap and imbalance are common characteristics of neuronal data that produce difficulties for clustering methods. SBM is able to identify overlapping clusters through its design of cluster centre identification and the expansion of these centres. SBM's approach is to divide the distribution of values of each feature into chunks of equal size. In each of these chunks, the number of points is counted and based on this number the centres of clusters are found and expanded. SBM has been shown to be a contender for other well-known clustering algorithms especially for the particular case of two dimensions while being too computationally expensive for high-dimensional data. Here, we present two main improvements to the original algorithm in order to increase its ability to deal with high-dimensional data while preserving its performance: the initial array structure was substituted with a graph structure and the number of partitions has been made feature-dependent, denominating this improved version as the Improved Space Breakdown Method (ISBM). In addition, we propose a clustering validation metric that does not punish overclustering and such obtains more suitable evaluations of clustering for spike sorting. Extracellular data recorded from the brain is unlabelled, therefore we have chosen simulated neural data, to which we have the ground truth, to evaluate more accurately the performance. Evaluations conducted on synthetic data indicate that the proposed improvements reduce the space and time complexity of the original algorithm, while simultaneously leading to an increased performance on neural data when compared with other state-of-the-art algorithms.

Code available at

https://github.com/ArdeleanRichard/Space-Breakdown-Method.

The policy of testing hypotheses in Chilean science. The role of a hypothesis-driven research funding programme in the installation of a hypothesis-driven experimental system in visual neuroscience

O'Malley et al. (2009) and Haufe (2013) suggest that the philosophical idea of science as hypothesis testing generates a pernicious bias towards hypothesis-driven research and against exploratory research in the review process of research proposals and the allocation of resources. This paper addresses a conceptual objection to the argument by O'Malley et al. (2009) and Haufe (2013). We argue that the funding agencies' concepts of good science do not belong to epistemological or philosophical contexts but to political and institutional contexts. This means that correcting (potential) biases in research funding does not entail correcting funding agencies' (supposed) philosophies of science. To illustrate this point, we provide an in-depth historical case study: the granting of funds to neuroscientist Pedro Maldonado by the Chilean funding programme FONDECYT. This is a relevant comparison as FONDECYT's guidelines explicitly promote hypothesis-driven research and endorse a view of "good science" as hypothesis testing. However, we will see that the overall influence of the philosophical idea of science as hypothesis testing over this funding programme, the research project, and the actual practice of hypothesis testing is somewhat limited. The concept of science as hypothesis testing seems to play a crucial institutional or political (not philosophical) role in allowing the conceptual articulation of social expectations and researchers' expectations.

Longitudinal neural and vascular recovery following ultraflexible neural electrode implantation in aged mice

Flexible neural electrodes improve the recording longevity and quality of individual neurons by promoting tissue-electrode integration. However, the intracortical implantation of flexible electrodes inevitably induces tissue damage. Understanding the longitudinal neural and vascular recovery following the intracortical implantation is critical for the ever-growing applications of flexible electrodes in both healthy and disordered brains. Aged animals are of particular interest because they play a key role in modeling neurological disorders, but their tissue-electrode interface remains mostly unstudied. Here we integrate in-vivo two-photon imaging and electrophysiological recording to determine the time-dependent neural and vascular dynamics after the implantation of ultraflexible neural electrodes in aged mice. We find heightened angiogenesis and vascular remodeling in the first two weeks after implantation, which coincides with the rapid increase in local field potentials and unit activities detected by electrophysiological recordings. Vascular remodeling in shallow cortical layers preceded that in deeper layers, which often lasted longer than the recovery of neural signals. By six weeks post-implantation vascular abnormalities had subsided, resulting in normal vasculature and microcirculation. Putative cell classification based on firing pattern and waveform shows similar recovery time courses in fast-spiking interneurons and pyramidal neurons. These results elucidate how structural damages and remodeling near implants affecting recording efficacy, and support the application of ultraflexible electrodes in aged animals at minimal perturbations to endogenous neurophysiology.

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.

A silk-based self-adaptive flexible opto-electro neural probe

The combination of optogenetics and electrophysiological recording enables high-precision bidirectional interactions between neural interfaces and neural circuits, which provides a promising approach for the study of progressive neurophysiological phenomena. Opto-electrophysiological neural probes with sufficient flexibility and biocompatibility are desirable to match the low mechanical stiffness of brain tissue for chronic reliable performance. However, lack of rigidity poses challenges for the accurate implantation of flexible neural probes with less invasiveness. Herein, we report a hybrid probe (Silk-Optrode) consisting of a silk protein optical fiber and multiple flexible microelectrode arrays. The Silk-Optrode can be accurately inserted into the brain and perform synchronized optogenetic stimulation and multichannel recording in freely behaving animals. Silk plays an important role due to its high transparency, excellent biocompatibility, and mechanical controllability. Through the hydration of the silk optical fiber, the Silk-Optrode probe enables itself to actively adapt to the environment after implantation and reduce its own mechanical stiffness to implant into the brain with high fidelity while maintaining mechanical compliance with the surrounding tissue. The probes with 128 recording channels can detect high-yield well-isolated single units while performing intracranial light stimulation with low optical losses, surpassing previous work of a similar type. Two months of post-surgery results suggested that as-reported Silk-Optrode probes exhibit better implant-neural interfaces with less immunoreactive glial responses and tissue lesions. A silk optical fiber-based Silk-Optrode probe consisting of a natural silk optical fiber and a flexible micro/nano electrode array is reported. The multifunctional soft probe can modify its own Young's modulus through hydration to achieve accurate implantation into the brain. The low optical loss and single-unit recording abilities allow simultaneous optogenetic stimulation and multichannel readout, which expands the applications in the operation and parsing of neural circuits in behavioral animals.

GRINtrode: a neural implant for simultaneous two-photon imaging and extracellular electrophysiology in freely moving animals

Significance

In vivo imaging and electrophysiology are powerful tools to explore neuronal function that each offer unique complementary information with advantages and limitations. Capturing both data types from the same neural population in the freely moving animal would allow researchers to take advantage of the capabilities of both modalities and further understand how they relate to each other.

Aim

Here, we present a head-mounted neural implant suitable for in vivo two-photon imaging of neuronal activity with simultaneous extracellular electrical recording in head-fixed or fiber-coupled freely moving animals.

Approach

A gradient refractive index (GRIN) lens-based head-mounted neural implant with extracellular electrical recording provided by tetrodes on the periphery of the GRIN lens was chronically implanted. The design of the neural implant allows for recording from head-fixed animals, as well as freely moving animals by coupling the imaging system to a coherent imaging fiber bundle.

Results

We demonstrate simultaneous two-photon imaging of GCaMP and extracellular electrophysiology of neural activity in awake head-fixed and freely moving mice. Using the collected information, we perform correlation analysis to reveal positive correlation between optical and local field potential recordings.

Conclusion

Simultaneously recording neural activity using both optical and electrical methods provides complementary information from each modality. Designs that can provide such bi-modal recording in freely moving animals allow for the investigation of neural activity underlying a broader range of behavioral paradigms.

A Review of Neurologgers for Extracellular Recording of Neuronal Activity in the Brain of Freely Behaving Wild Animals

Simultaneous monitoring of animal behavior and neuronal activity in the brain enables us to examine the neural underpinnings of behaviors. Conventionally, the neural activity data are buffered, amplified, multiplexed, and then converted from analog to digital in the head-stage amplifier, following which they are transferred to a storage server via a cable. Such tethered recording systems, intended for indoor use, hamper the free movement of animals in three-dimensional (3D) space as well as in large spaces or underwater, making it difficult to target wild animals active under natural conditions; it also presents challenges in realizing its applications to humans, such as the Brain-Machine Interfaces (BMI). Recent advances in micromachine technology have established a wireless logging device called a neurologger, which directly stores neural activity on ultra-compact memory media. The advent of the neurologger has triggered the examination of the neural correlates of 3D flight, underwater swimming of wild animals, and translocation experiments in the wild. Examples of the use of neurologgers will provide an insight into understanding the neural underpinnings of behaviors in the natural environment and contribute to the practical application of BMI. Here we outline the monitoring of the neural underpinnings of flying and swimming behaviors using neurologgers. We then focus on neuroethological findings and end by discussing their future perspectives.

A robust spike sorting method based on the joint optimization of linear discrimination analysis and density peaks

Spike sorting is a fundamental step in extracting single-unit activity from neural ensemble recordings, which play an important role in basic neuroscience and neurotechnologies. A few algorithms have been applied in spike sorting. However, when noise level or waveform similarity becomes relatively high, their robustness still faces a big challenge. In this study, we propose a spike sorting method combining Linear Discriminant Analysis (LDA) and Density Peaks (DP) for feature extraction and clustering. Relying on the joint optimization of LDA and DP: DP provides more accurate classification labels for LDA, LDA extracts more discriminative features to cluster for DP, and the algorithm achieves high performance after iteration. We first compared the proposed LDA-DP algorithm with several algorithms on one publicly available simulated dataset and one real rodent neural dataset with different noise levels. We further demonstrated the performance of the LDA-DP method on a real neural dataset from non-human primates with more complex distribution characteristics. The results show that our LDA-DP algorithm extracts a more discriminative feature subspace and achieves better cluster quality than previously established methods in both simulated and real data. Especially in the neural recordings with high noise levels or waveform similarity, the LDA-DP still yields a robust performance with automatic detection of the number of clusters. The proposed LDA-DP algorithm achieved high sorting accuracy and robustness to noise, which offers a promising tool for spike sorting and facilitates the following analysis of neural population activity.

Local neuronal excitation and global inhibition during epileptic fast ripples in humans

Understanding the neuronal basis of epileptic activity is a major challenge in neurology. Cellular integration into larger scale networks is all the more challenging. In the local field potential, interictal epileptic discharges can be associated with fast ripples (200-600 Hz), which are a promising marker of the epileptogenic zone. Yet, how neuronal populations in the epileptogenic zone and in healthy tissue are affected by fast ripples remain unclear. Here, we used a novel 'hybrid' macro-micro depth electrode in nine drug-resistant epileptic patients, combining classic depth recording of local field potentials (macro-contacts) and two or three tetrodes (four micro-wires bundled together) enabling up to 15 neurons in local circuits to be simultaneously recorded. We characterized neuronal responses (190 single units) with the timing of fast ripples (2233 fast ripples) on the same hybrid and other electrodes that target other brain regions. Micro-wire recordings reveal signals that are not visible on macro-contacts. While fast ripples detected on the closest macro-contact to the tetrodes were always associated with fast ripples on the tetrodes, 82% of fast ripples detected on tetrodes were associated with detectable fast ripples on the nearest macro-contact. Moreover, neuronal recordings were taken in and outside the epileptogenic zone of implanted epileptic subjects and they revealed an interlay of excitation and inhibition across anatomical scales. While fast ripples were associated with increased neuronal activity in very local circuits only, they were followed by inhibition in large-scale networks (beyond the epileptogenic zone, even in healthy cortex). Neuronal responses to fast ripples were homogeneous in local networks but differed across brain areas. Similarly, post-fast ripple inhibition varied across recording locations and subjects and was shorter than typical inter-fast ripple intervals, suggesting that this inhibition is a fundamental refractory process for the networks. These findings demonstrate that fast ripples engage local and global networks, including healthy tissue, and point to network features that pave the way for new diagnostic and therapeutic strategies. They also reveal how even localized pathological brain dynamics can affect a broad range of cognitive functions.

Low and high frequency intracranial neural signals match in the human associative cortex

In vivo intracranial recordings of neural activity offer a unique opportunity to understand human brain function. Intracranial electrophysiological (iEEG) activity related to sensory, cognitive or motor events manifests mostly in two types of signals: event-related local field potentials in lower frequency bands (<30 Hz, LF) and broadband activity in the higher end of the frequency spectrum (>30 Hz, High frequency, HF). While most current studies rely exclusively on HF, thought to be more focal and closely related to spiking activity, the relationship between HF and LF signals is unclear, especially in human associative cortex. Here, we provide a large-scale in-depth investigation of the spatial and functional relationship between these 2 signals based on intracranial recordings from 121 individual brains (8000 recording sites). We measure category-selective responses to complex ecologically salient visual stimuli - human faces - across a wide cortical territory in the ventral occipito-temporal cortex (VOTC), with a frequency-tagging method providing high signal-to-noise ratio (SNR) and the same objective quantification of signal and noise for the two frequency ranges. While LF face-selective activity has higher SNR across the VOTC, leading to a larger number of significant electrode contacts especially in the anterior temporal lobe, LF and HF display highly similar spatial, functional, and timing properties. Specifically, and contrary to a widespread assumption, our results point to nearly identical spatial distribution and local spatial extent of LF and HF activity at equal SNR. These observations go a long way towards clarifying the relationship between the two main iEEG signals and reestablish the informative value of LF iEEG to understand human brain function.

Semi-Implantable Bioelectronics

Developing techniques to effectively and real-time monitor and regulate the interior environment of biological objects is significantly important for many biomedical engineering and scientific applications, including drug delivery, electrophysiological recording and regulation of intracellular activities. Semi-implantable bioelectronics is currently a hot spot in biomedical engineering research area, because it not only meets the increasing technical demands for precise detection or regulation of biological activities, but also provides a desirable platform for externally incorporating complex functionalities and electronic integration. Although there is less definition and summary to distinguish it from the well-reviewed non-invasive bioelectronics and fully implantable bioelectronics, semi-implantable bioelectronics have emerged as highly unique technology to boost the development of biochips and smart wearable device. Here, we reviewed the recent progress in this field and raised the concept of "Semi-implantable bioelectronics", summarizing the principle and strategies of semi-implantable device for cell applications and in vivo applications, discussing the typical methodologies to access to intracellular environment or in vivo environment, biosafety aspects and typical applications. This review is meaningful for understanding in-depth the design principles, materials fabrication techniques, device integration processes, cell/tissue penetration methodologies, biosafety aspects, and applications strategies that are essential to the development of future minimally invasive bioelectronics.

Neural Substrates of Homing Pigeon Spatial Navigation: Results From Electrophysiology Studies

Over many centuries, the homing pigeon has been selectively bred for returning home from a distant location. As a result of this strong selective pressure, homing pigeons have developed an excellent spatial navigation system. This system passes through the hippocampal formation (HF), which shares many striking similarities to the mammalian hippocampus; there are a host of shared neuropeptides, interconnections, and its role in the storage and manipulation of spatial maps. There are some notable differences as well: there are unique connectivity patterns and spatial encoding strategies. This review summarizes the comparisons between the avian and mammalian hippocampal systems, and the responses of single neurons in several general categories: (1) location and place cells responding in specific areas, (2) path and goal cells responding between goal locations, (3) context-dependent cells that respond before or during a task, and (4) pattern, grid, and boundary cells that increase firing at stable intervals. Head-direction cells, responding to a specific compass direction, are found in mammals and other birds but not to date in pigeons. By studying an animal that evolved under significant adaptive pressure to quickly develop a complex and efficient spatial memory system, we may better understand the comparative neurology of neurospatial systems, and plot new and potentially fruitful avenues of comparative research in the future.

Novelty detection in early olfactory processing of the honey bee, Apis mellifera

Animals are constantly bombarded with stimuli, which presents a fundamental problem of sorting among pervasive uninformative stimuli and novel, possibly meaningful stimuli. We evaluated novelty detection behaviorally in honey bees as they position their antennae differentially in an air stream carrying familiar or novel odors. We then characterized neuronal responses to familiar and novel odors in the first synaptic integration center in the brain-the antennal lobes. We found that the neurons that exhibited stronger initial responses to the odor that was to be familiarized are the same units that later distinguish familiar and novel odors, independently of chemical identities. These units, including both tentative projection neurons and local neurons, showed a decreased response to the familiar odor but an increased response to the novel odor. Our results suggest that the antennal lobe may represent familiarity or novelty to an odor stimulus in addition to its chemical identity code. Therefore, the mechanisms for novelty detection may be present in early sensory processing, either as a result of local synaptic interaction or via feedback from higher brain centers.

Toroidal topology of population activity in grid cells

The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment1. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations2, and are organized in modules3 that collectively form a population code for the animal's allocentric position1. The invariance of the correlation structure of this population code across environments4,5 and behavioural states6,7, independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern1,8-11. However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models12. This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.

Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice

BACKGROUND

Extracellular recording represents a crucial electrophysiological technique in neuroscience for studying the activity of single neurons and neuronal populations. The electrodes capture voltage traces that, with the help of analytical tools, reveal action potentials ('spikes') as well as local field potentials. The process of spike sorting is used for the extraction of action potentials generated by individual neurons. Until recently, spike sorting was performed with manual techniques, which are laborious and unreliable due to inherent operator bias. As neuroscientists add multiple electrodes to their probes, the high-density devices can record hundreds to thousands of neurons simultaneously, making the manual spike sorting process increasingly difficult. The advent of automated spike sorting software has offered a compelling solution to this issue and, in this study, we present a simple-to-execute framework for running an automated spike sorter.

METHODS

Tetrode recordings of freely-moving mice are obtained from the CA1 region of the hippocampus as they navigate a linear track. Tetrode recordings are also acquired from the prelimbic cortex, a region of the medial prefrontal cortex, while the mice are tested in a T maze. All animals are implanted with custom-designed, 3D-printed microdrives that carry 16 electrodes, which are bundled in a 4-tetrode geometry.

RESULTS

We provide an overview of a framework for analyzing single-unit data in which we have concatenated the acquisition system (Cheetah, Neuralynx) with analytical software (MATLAB) and an automated spike sorting pipeline (MountainSort). We give precise instructions on how to implement the different steps of the framework, as well as explanations of our design logic. We validate this framework by comparing manually-sorted spikes against automatically-sorted spikes, using neural recordings of the hippocampus and prelimbic cortex in freely-moving mice.

CONCLUSIONS

We have efficiently integrated the MountainSort spike sorter with Neuralynx-acquired neural recordings. Our framework is easy to implement and provides a high-throughput solution. We predict that within the broad field of bioelectronic medicine, those teams that incorporate high-density neural recording devices to their armamentarium might find our framework quite valuable as they expand their analytical footprint.

Self-assembled multifunctional neural probes for precise integration of optogenetics and electrophysiology

Optogenetics combined with electrical recording has emerged as a powerful tool for investigating causal relationships between neural circuit activity and function. However, the size of optogenetically manipulated tissue is typically 1-2 orders of magnitude larger than that can be electrically recorded, rendering difficulty for assigning functional roles of recorded neurons. Here we report a viral vector-delivery optrode (VVD-optrode) system for precise integration of optogenetics and electrophysiology in the brain. Our system consists of flexible microelectrode filaments and fiber optics that are simultaneously self-assembled in a nanoliter-scale, viral vector-delivery polymer carrier. The highly localized delivery and neuronal expression of opsin genes at microelectrode-tissue interfaces ensure high spatial congruence between optogenetically manipulated and electrically recorded neuronal populations. We demonstrate that this multifunctional system is capable of optogenetic manipulation and electrical recording of spatially defined neuronal populations for three months, allowing precise and long-term studies of neural circuit functions.

The authors present a viral vector-delivery optrode system to integrate optogenetics and electrophysiology. The flexible microelectrode filaments and fiber optics self-assemble in a nanoliter-scale, viral vector-delivery polymer carrier for localized delivery and expression of opsin genes at microelectrode-tissue interfaces.

Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo

How information in the nervous system is encoded by patterns of action potentials (i.e. spikes) remains an open question. Multi-neuron patterns of single spikes are a prime candidate for spike time encoding but their temporal variability requires further characterisation. Here we show how known sources of spike count variability affect stimulus-evoked spike time patterns between neurons separated over multiple layers and columns of adult rat somatosensory cortex in vivo. On subsets of trials (clusters) and after controlling for stimulus-response adaptation, spike time differences between pairs of neurons are “time-warped” (compressed/stretched) by trial-to-trial changes in shared excitability, explaining why fixed spike time patterns and noise correlations are seldom reported. We show that predicted cortical state is correlated between groups of 4 neurons, introducing the possibility of spike time pattern modulation by population-wide trial-to-trial changes in excitability (i.e. cortical state). Under the assumption of state-dependent coding, we propose an improved potential encoding capacity.

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.

Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings

Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality.

Inferring thalamocortical monosynaptic connectivity in vivo

As the tools to simultaneously record electrophysiological signals from large numbers of neurons within and across brain regions become increasingly available, this opens up for the first time the possibility of establishing the details of causal relationships between monosynaptically connected neurons and the patterns of neural activation that underlie perception and behavior. Although recorded activity across synaptically connected neurons has served as the cornerstone for much of what we know about synaptic transmission and plasticity, this has largely been relegated to ex vivo preparations that enable precise targeting under relatively well-controlled conditions. Analogous studies in vivo, where image-guided targeting is often not yet possible, rely on indirect, data-driven measures, and as a result such studies have been sparse and the dependence upon important experimental parameters has not been well studied. Here, using in vivo extracellular single-unit recordings in the topographically aligned rodent thalamocortical pathway, we sought to establish a general experimental and computational framework for inferring synaptic connectivity. Specifically, attacking this problem within a statistical signal detection framework utilizing experimentally recorded data in the ventral-posterior medial (VPm) region of the thalamus and the homologous region in layer 4 of primary somatosensory cortex (S1) revealed a trade-off between network activity levels needed for the data-driven inference and synchronization of nearby neurons within the population that results in masking of synaptic relationships. Here, we provide a framework for establishing connectivity in multisite, multielectrode recordings based on statistical inference, setting the stage for large-scale assessment of synaptic connectivity within and across brain structures.NEW & NOTEWORTHY Despite the fact that all brain function relies on the long-range transfer of information across different regions, the tools enabling us to measure connectivity across brain structures are lacking. Here, we provide a statistical framework for identifying and assessing potential monosynaptic connectivity across neuronal circuits from population spiking activity that generalizes to large-scale recording technologies that will help us to better understand the signaling within networks that underlies perception and behavior.

Light Modulation of Brain and Development of Relevant Equipment

Light modulation plays an important role in understanding the pathology of brain disorders and improving brain function. Optogenetic techniques can activate or silence targeted neurons with high temporal and spatial accuracy and provide precise control, and have recently become a method for quick manipulation of genetically identified types of neurons. Photobiomodulation (PBM) is light therapy that utilizes non-ionizing light sources, including lasers, light emitting diodes, or broadband light. It provides a safe means of modulating brain activity without any irreversible damage and has established optimal treatment parameters in clinical practice. This manuscript reviews 1) how optogenetic approaches have been used to dissect neural circuits in animal models of Alzheimer's disease, Parkinson's disease, and depression, and 2) how low level transcranial lasers and LED stimulation in humans improves brain activity patterns in these diseases. State-of-the-art brain machine interfaces that can record neural activity and stimulate neurons with light have good prospects in the future.

Inflammatory Foreign Body Response Induced by Neuro-Implants in Rat Cortices Depleted of Resident Microglia by a CSF1R Inhibitor and Its Implications

Inflammatory encapsulation of implanted cortical-neuro-probes [the foreign body response (FBR)] severely limits their use in basic brain research and in clinical applications. A better understanding of the inflammatory FBR is needed to effectively mitigate these critical limitations. Combining the use of the brain permeant colony stimulating factor 1 receptor inhibitor PLX5622 and a perforated polyimide-based multielectrode array platform (PPMP) that can be sectioned along with the surrounding tissue, we examined the contribution of microglia to the formation of inflammatory FBR. To that end, we imaged the inflammatory processes induced by PPMP implantations after eliminating 89-94% of the cortical microglia by PLX5622 treatment. The observations showed that: (I) inflammatory encapsulation of implanted PPMPs proceeds by astrocytes in microglia-free cortices. The activated astrocytes adhered to the PPMP's surfaces. This suggests that the roles of microglia in the FBR might be redundant. (II) PPMP implantation into control or continuously PLX5622-treated rats triggered a localized surge of microglia mitosis. The daughter cells that formed a "cloud" of short-lived (T 1 / 2 ≤ 14 days) microglia around and in contact with the implant surfaces were PLX5622 insensitive. (III) Neuron degeneration by PPMP implantation and the ensuing recovery in time, space, and density progressed in a similar manner in the cortices following 89-94% depletion of microglia. This implies that microglia do not serve a protective role with respect to the neurons. (IV) Although the overall cell composition and dimensions of the encapsulating scar in PLX5622-treated rats differed from the controls, the recorded field potential (FP) qualities and yield were undistinguishable. This is accounted for by assuming that the FP amplitudes in the control and PLX5622-treated rats were related to the seal resistance formed at the interface between the adhering microglia and/or astrocytes and the PPMP platform rather than across the scar tissue. These observations suggest that the prevention of both astrocytes and microglia adhesion to the electrodes is required to improve FP recording quality and yield.

Detecting time lag between a pair of time series using visibility graph algorithm

Estimating the time lag between a pair of time series is of significance in many practical applications. In this article, we introduce a method to quantify such lags by adapting the visibility graph algorithm, which converts time series into a mathematical graph. Currently widely used method to detect such lags is based on cross-correlations, which has certain limitations. We present simulated examples where the new method identifies the lag correctly and unambiguously while as the cross-correlation method does not. The article includes results from an extensive simulation study conducted to better understand the scenarios where the new method performed better or worse than the existing approach. We also present a likelihood based parametric modeling framework and consider frameworks for quantifying uncertainty and hypothesis testing for the new approach. We apply the current and new methods to two case studies, one from neuroscience and the other from environmental epidemiology, to illustrate the methods further.