Challenges and opportunities for large-scale electrophysiology with Neuropixels probes

Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number

The brain's remarkable properties arise from the collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, can such low-dimensional representations truly explain the vast range of brain activity, and if not, what is the appropriate resolution and scale of recording to capture them? Imaging neural activity at cellular resolution and near-simultaneously across the mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number in populations up to 1 million neurons. Although half of the neural variance is contained within sixteen dimensions correlated with behavior, our discovered scaling of dimensionality corresponds to an ever-increasing number of neuronal ensembles without immediate behavioral or sensory correlates. The activity patterns underlying these higher dimensions are fine grained and cortex wide, highlighting that large-scale, cellular-resolution recording is required to uncover the full substrates of neuronal computations.

Neuroanatomical distribution of fluorophores within adult RXFP3 Cre-tdTomato/YFP mouse brain

Relaxin-family peptide 3 receptor (RXFP3) is activated by relaxin-3 in the brain to influence arousal and related functions, such as feeding and stress responses. Two transgenic mouse lines have recently been developed that co-express different fluorophores within RXFP3-expressing neurons: either yellow fluorescent protein (YFP; RXFP3-Cre/YFP mice) or tdTomato (RXFP3-Cre/tdTomato mice). To date, the characteristics of neurons that express RXFP3-associated fluorophores in these mice have only been investigated in the bed nucleus of the stria terminalis and the hypothalamic arcuate nucleus. To better determine the utility of these fluorophore-expressing mice for further research, we characterised the neuroanatomical distribution of fluorophores throughout the brain of these mice and compared this to the published distribution of Rxfp3 mRNA (detected by in situ hybridisation) in wildtype mice. Coronal sections of RXFP3-Cre/YFP (n = 8) and RXFP3-Cre/tdTomato (n = 8) mouse brains were imaged, and the density of fluorophore-expressing cells within various brain regions/nuclei was qualitatively assessed. Comparisons with our previously reported RXFP3 mRNA distribution revealed that of 212 brain regions that contained either fluorophore or RXFP3 mRNA, approximately half recorded densities that were within two qualitative measurements of each other (on a 9-point scale), including hippocampal dentate gyrus and amygdala subregions. However, many brain areas with likely non-authentic, false-positive, or false-negative fluorophore expression were also detected, including the cerebellum. Therefore, this study provides a guide to which brain regions should be prioritized for future study of RXFP3 in these mice, to better understand the neuroanatomy and function of this intriguing, neuronal peptide receptor.

Acute Neuropixels Recordings in the Marmoset Monkey

High-density linear probes, such as Neuropixels, provide an unprecedented opportunity to understand how neural populations within specific laminar compartments contribute to behavior. Marmoset monkeys, unlike macaque monkeys, have a lissencephalic (smooth) cortex that enables recording perpendicular to the cortical surface, thus making them an ideal animal model for studying laminar computations. Here we present a method for acute Neuropixels recordings in the common marmoset (Callithrix jacchus). The approach replaces the native dura with an artificial silicon-based dura that grants visual access to the cortical surface, which is helpful in avoiding blood vessels, ensures perpendicular penetrations, and could be used in conjunction with optical imaging or optogenetic techniques. The chamber housing the artificial dura is simple to maintain with minimal risk of infection and could be combined with semichronic microdrives and wireless recording hardware. This technique enables repeated acute penetrations over a period of several months. With occasional removal of tissue growth on the pial surface, recordings can be performed for a year or more. The approach is fully compatible with Neuropixels probes, enabling the recording of hundreds of single neurons distributed throughout the cortical column.

Topological structure of population activity in mouse visual cortex encodes densely sampled stimulus rotations

The primary visual cortex is one of the most well understood regions supporting the processing involved in sensory computation. Following the popularization of high-density neural recordings, it has been observed that the activity of large neural populations is often constrained to low dimensional manifolds. In this work, we quantify the structure of such neural manifolds in the visual cortex. We do this by analyzing publicly available two-photon optical recordings of mouse primary visual cortex in response to visual stimuli with a densely sampled rotation angle. Using a geodesic metric along with persistent homology, we discover that population activity in response to such stimuli generates a circular manifold, encoding the angle of rotation. Furthermore, we observe that this circular manifold is expressed differently in subpopulations of neurons with differing orientation and direction selectivity. Finally, we discuss some of the obstacles to reliably retrieving the truthful topology generated by a neural population.

Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density (~10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron's electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to ~3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

The direction of theta and alpha travelling waves modulates human memory processing

To support a range of behaviours, the brain must flexibly coordinate neural activity across widespread brain regions. One potential mechanism for this coordination is a travelling wave, in which a neural oscillation propagates across the brain while organizing the order and timing of activity across regions. Although travelling waves are present across the brain in various species, their potential functional relevance has remained unknown. Here, using rare direct human brain recordings, we demonstrate a distinct functional role for travelling waves of theta- and alpha-band (2-13 Hz) oscillations in the cortex. Travelling waves propagate in different directions during separate cognitive processes. In episodic memory, travelling waves tended to propagate in a posterior-to-anterior direction during successful memory encoding and in an anterior-to-posterior direction during recall. Because travelling waves of oscillations correspond to local neuronal spiking, these patterns indicate that rhythmic pulses of activity move across the brain in different directions for separate behaviours. More broadly, our results suggest a fundamental role for travelling waves and oscillations in dynamically coordinating neural connectivity, by flexibly organizing the timing and directionality of network interactions across the cortex to support cognition and behaviour.

A paradigm for ethanol consumption in head-fixed mice during prefrontal cortical two-photon calcium imaging

The prefrontal cortex (PFC) is a hub for cognitive behaviors and is a key target for neuroadaptations in alcohol use disorders. Recent advances in genetically encoded sensors and functional microscopy allow multimodal in vivo PFC activity recordings at subcellular and cellular scales. While these methods could enable a deeper understanding of the relationship between alcohol and PFC function/dysfunction, they typically require animals to be head-fixed. Here, we present a method in mice for binge-like ethanol consumption during head-fixation. Male and female mice were first acclimated to ethanol by providing home cage access to 20% ethanol (v/v) for 4 or 8 days. After home cage drinking, mice consumed ethanol from a lick spout during head-fixation. We used two-photon calcium imaging during the head-fixed drinking paradigm to record from a large population of PFC neurons (>1000) to explore how acute ethanol affects their activity. Drinking exerted temporally heterogeneous effects on PFC activity at single neuron and population levels. Intoxication modulated the tonic activity of some neurons while others showed phasic responses around ethanol receipt. Population level activity did not show tonic or phasic modulation but tracked ethanol consumption over the minute-timescale. Network level interactions assessed through between-neuron pairwise correlations were largely resilient to intoxication at the population level while neurons with increased tonic activity showed higher synchrony by the end of the drinking period. By establishing a method for binge-like drinking in head-fixed mice, we lay the groundwork for leveraging advanced microscopy technologies to study alcohol-induced neuroadaptations in PFC and other brain circuits. This article is part of the Special Issue on "PFC circuit function in psychiatric disease and relevant models".

AI-organoid integrated systems for biomedical studies and applications

In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.

Printable devices for neurotechnology

Printable electronics for neurotechnology is a rapidly emerging field that leverages various printing techniques to fabricate electronic devices, offering advantages in rapid prototyping, scalability, and cost-effectiveness. These devices have promising applications in neurobiology, enabling the recording of neuronal signals and controlled drug delivery. This review provides an overview of printing techniques, materials used in neural device fabrication, and their applications. The printing techniques discussed include inkjet, screen printing, flexographic printing, 3D printing, and more. Each method has its unique advantages and challenges, ranging from precise printing and high resolution to material compatibility and scalability. Selecting the right materials for printable devices is crucial, considering factors like biocompatibility, flexibility, electrical properties, and durability. Conductive materials such as metallic nanoparticles and conducting polymers are commonly used in neurotechnology. Dielectric materials, like polyimide and polycaprolactone, play a vital role in device fabrication. Applications of printable devices in neurotechnology encompass various neuroprobes, electrocorticography arrays, and microelectrode arrays. These devices offer flexibility, biocompatibility, and scalability, making them cost-effective and suitable for preclinical research. However, several challenges need to be addressed, including biocompatibility, precision, electrical performance, long-term stability, and regulatory hurdles. This review highlights the potential of printable electronics in advancing our understanding of the brain and treating neurological disorders while emphasizing the importance of overcoming these challenges.

Inserting a Neuropixels probe into awake monkey cortex: two probes, two methods

BACKGROUND

Neuropixels probes have revolutionized neurophysiological studies in the rodent, but inserting these probes through the much thicker primate dura remains a challenge.

NEW METHODS

Here we describe two methods we have developed for the insertion of two types of Neuropixels probes acutely into the awake macaque monkey cortex. For the fine rodent probe (Neuropixels 1.0, IMEC), which is unable to pierce native primate dura, we developed a dural-eyelet method to insert the probe repeatedly without breakage. For the thicker short NHP probe (Neuropixels NP1010), we developed an artificial dura system to insert the probe.

RESULTS AND COMPARISON WITH EXISTING METHODS

We have now conducted successful experiments in 3 animals across 7 recording chambers with the procedures described here and have achieved recordings with similar yields over several months in each case.

CONCLUSION

We hope that our hardware, surgical preparation, methods for insertion and methods for removal of broken probe parts are of value to primate physiologists everywhere.

Interpretable deep learning for deconvolutional analysis of neural signals

The widespread adoption of deep learning to build models that capture the dynamics of neural populations is typically based on "black-box" approaches that lack an interpretable link between neural activity and function. Here, we propose to apply algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We characterize our method, referred to as deconvolutional unrolled neural learning (DUNL), and show its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. To exemplify use cases of our decomposition method, we uncover multiplexed salience and reward prediction error signals from midbrain dopamine neurons in an unbiased manner, perform simultaneous event detection and characterization in somatosensory thalamus recordings, and characterize the responses of neurons in the piriform cortex. Our work leverages the advances in interpretable deep learning to gain a mechanistic understanding of neural dynamics.

Simultaneous, cortex-wide and cellular-resolution neuronal population dynamics reveal an unbounded scaling of dimensionality with neuron number

The brain's remarkable properties arise from collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, what would be the biological utility of such a redundant and metabolically costly encoding scheme and what is the appropriate resolution and scale of neural recording to understand brain function? Imaging the activity of one million neurons at cellular resolution and near-simultaneously across mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number. While half of the neural variance lies within sixteen behavior-related dimensions, we find this unbounded scaling of dimensionality to correspond to an ever-increasing number of internal variables without immediate behavioral correlates. The activity patterns underlying these higher dimensions are fine-grained and cortex-wide, highlighting that large-scale recording is required to uncover the full neural substrates of internal and potentially cognitive processes.

Myomatrix arrays for high-definition muscle recording

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ('Myomatrix arrays') that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a 'motor unit,' during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and identifying pathologies of the motor system.

Recovery of a learned behavior despite partial restoration of neuronal dynamics after chronic inactivation of inhibitory neurons

Maintaining motor skills is crucial for an animal's survival, enabling it to endure diverse perturbations throughout its lifespan, such as trauma, disease, and aging. What mechanisms orchestrate brain circuit reorganization and recovery to preserve the stability of behavior despite the continued presence of a disturbance? To investigate this question, we chronically silenced a fraction of inhibitory neurons in a brain circuit necessary for singing in zebra finches. Song in zebra finches is a complex, learned motor behavior and central to reproduction. This manipulation altered brain activity and severely perturbed song for around two months, after which time it was precisely restored. Electrophysiology recordings revealed abnormal offline dynamics, resulting from chronic inhibition loss, some aspects of which returned to normal as the song recovered. However, even after the song had fully recovered, the levels of neuronal firing in the premotor and motor areas did not return to a control-like state. Single-cell RNA sequencing revealed that chronic silencing of interneurons led to elevated levels of microglia and MHC I, which were also observed in normal juveniles during song learning. These experiments demonstrate that the adult brain can overcome extended periods of abnormal activity, and precisely restore a complex behavior, without recovering normal neuronal dynamics. These findings suggest that the successful functional recovery of a brain circuit after a perturbation can involve more than mere restoration to its initial configuration. Instead, the circuit seems to adapt and reorganize into a new state capable of producing the original behavior despite the persistence of some abnormal neuronal dynamics.

Recent strategies for neural dynamics observation at a larger scale and wider scope

The tremendous technical progress in neuroscience offers opportunities to observe a more minor or/and broader dynamic picture of the brain. Moreover, the large-scale neural activity of individual neurons enables the dissection of detailed mechanistic links between neural populations and behaviors. To measure neural activity in-vivo, multi-neuron recording, and neuroimaging techniques are employed and developed to acquire more neurons. The tools introduced concurrently recorded dozens to hundreds of neurons in the coordinated brain regions and elucidated the neuronal ensembles from a massive population perspective of diverse neurons at cellular resolution. In particular, the increasing spatiotemporal resolution of neuronal monitoring across the whole brain dramatically facilitates our understanding of additional nervous system functions in health and disease. Here, we will introduce state-of-the-art neuroscience tools involving large-scale neural population recording and the long-range connections spanning multiple brain regions. Their synergic effects provide to clarify the controversial circuitry underlying neuroscience. These challenging neural tools present a promising outlook for the fundamental dynamic interplay across levels of synaptic cellular, circuit organization, and brain-wide. Hence, more observations of neural dynamics will provide more clues to elucidate brain functions and push forward innovative technology at the intersection of neural engineering disciplines. We hope this review will provide insight into the use or development of recent neural techniques considering spatiotemporal scales of brain observation.

Decoding the Spike-Band Subthreshold Motor Cortical Activity

Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.

Putting early sensory neurons to sleep

Neurons that transmit information from the retina to other parts of the brain are more affected by anesthesia than previously thought.

A selection and targeting framework of cortical locations for line-scanning fMRI

Depth-resolved functional magnetic resonance imaging (fMRI) is an emerging field growing in popularity given the potential of separating signals from different computational processes in cerebral cortex. Conventional acquisition schemes suffer from low spatial and temporal resolutions. Line-scanning methods allow depth-resolved fMRI by sacrificing spatial coverage to sample blood oxygenated level-dependent (BOLD) responses at ultra-high temporal and spatial resolution. For neuroscience applications, it is critical to be able to place the line accurately to (1) sample the right neural population and (2) target that neural population with tailored stimuli or tasks. To this end, we devised a multi-session framework where a target cortical location is selected based on anatomical and functional properties. The line is then positioned according to this information in a separate second session, and we tailor the experiment to focus on the target location. Anatomically, the precision of the line placement was confirmed by projecting a nominal representation of the acquired line back onto the surface. Functional estimates of neural selectivities in the line, as quantified by a visual population-receptive field model, resembled the target selectivities well for most subjects. This functional precision was quantified in detail by estimating the distance between the visual field location of the targeted vertex and the location in visual cortex (V1) that most closely resembled the line-scanning estimates; this distance was on average ~5.5 mm. Given the dimensions of the line, differences in acquisition, session, and stimulus design, this validates that line-scanning can be used to probe local neural sensitivities across sessions. In summary, we present an accurate framework for line-scanning MRI; we believe such a framework is required to harness the full potential of line-scanning and maximize its utility. Furthermore, this approach bridges canonical fMRI experiments with electrophysiological experiments, which in turn allows novel avenues for studying human physiology non-invasively.

DREDge: robust motion correction for high-density extracellular recordings across species

High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.

Subcortical coding of predictable and unsupervised sound-context associations

Our environment is made of a myriad of stimuli present in combinations often patterned in predictable ways. For example, there is a strong association between where we are and the sounds we hear. Like many environmental patterns, sound-context associations are learned implicitly, in an unsupervised manner, and are highly informative and predictive of normality. Yet, we know little about where and how unsupervised sound-context associations are coded in the brain. Here we measured plasticity in the auditory midbrain of mice living over days in an enriched task-less environment in which entering a context triggered sound with different degrees of predictability. Plasticity in the auditory midbrain, a hub of auditory input and multimodal feedback, developed over days and reflected learning of contextual information in a manner that depended on the predictability of the sound-context association and not on reinforcement. Plasticity manifested as an increase in response gain and tuning shift that correlated with a general increase in neuronal frequency discrimination. Thus, the auditory midbrain is sensitive to unsupervised predictable sound-context associations, revealing a subcortical engagement in the detection of contextual sounds. By increasing frequency resolution, this detection might facilitate the processing of behaviorally relevant foreground information described to occur in cortical auditory structures.

Silicon microfabrication technologies for biology integrated advance devices and interfaces

Miniaturization is the trend to manufacture ever smaller devices and this process requires knowledge, experience, understanding of materials, manufacturing techniques and scaling laws. The fabrication techniques used in semiconductor industry deliver an exceptionally high yield of devices and provide a well-established platform. Today, these miniaturized devices are manufactured with high reproducibility, design flexibility, scalability and multiplexed features to be used in several applications including micro-, nano-fluidics, implantable chips, diagnostics/biosensors and neural probes. We here provide a review on the microfabricated devices used for biology driven science. We will describe the ubiquity of the use of micro-nanofabrication techniques in biology and biotechnology through the fabrication of high-aspect-ratio devices for cell sensing applications, intracellular devices, probes developed for neuroscience-neurotechnology and biosensing of the certain biomarkers. Recently, the research on micro and nanodevices for biology has been progressing rapidly. While the understanding of the unknown biological fields -such as human brain- has been requiring more research with advanced materials and devices, the development protocols of desired devices has been advancing in parallel, which finally meets with some of the requirements of biological sciences. This is a very exciting field and we aim to highlight the impact of micro-nanotechnologies that can shed light on complex biological questions and needs.

Myomatrix arrays for high-definition muscle recording

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ("Myomatrix arrays") that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a "motor unit", during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and in identifying pathologies of the motor system.

Identification of interacting neural populations: methods and statistical considerations

As improved recording technologies have created new opportunities for neurophysiological investigation, emphasis has shifted from individual neurons to multiple populations that form circuits, and it has become important to provide evidence of cross-population coordinated activity. We review various methods for doing so, placing them in six major categories while avoiding technical descriptions and instead focusing on high-level motivations and concerns. Our aim is to indicate what the methods can achieve and the circumstances under which they are likely to succeed. Toward this end, we include a discussion of four cross-cutting issues: the definition of neural populations, trial-to-trial variability and Poisson-like noise, time-varying dynamics, and causality.

Larger and Denser: An Optimal Design for Surface Grids of EMG Electrodes to Identify Greater and More Representative Samples of Motor Units

The spinal motor neurons are the only neural cells whose individual activity can be noninvasively identified. This is usually done using grids of surface electromyographic (EMG) electrodes and source separation algorithms; an approach called EMG decomposition. In this study, we combined computational and experimental analyses to assess how the design parameters of grids of electrodes influence the number and the properties of the identified motor units. We first computed the percentage of motor units that could be theoretically discriminated within a pool of 200 simulated motor units when decomposing EMG signals recorded with grids of various sizes and interelectrode distances (IEDs). Increasing the density, the number of electrodes, and the size of the grids, increased the number of motor units that our decomposition algorithm could theoretically discriminate, i.e., up to 83.5% of the simulated pool (range across conditions: 30.5-83.5%). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). The configuration with the largest number of electrodes and the shortest IED maximized the number of identified motor units (56 ± 14; range: 39-79) and the percentage of early recruited motor units within these samples (29 ± 14%). Finally, the number of identified motor units further increased with a prototyped grid of 256 electrodes and an IED of 2 mm. Taken together, our results showed that larger and denser surface grids of electrodes allow to identify a more representative pool of motor units than currently reported in experimental studies.

Neuroscience Needs Network Science

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.

Statistical inference on representational geometries

Neuroscience has recently made much progress, expanding the complexity of both neural activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here, we introduce new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison with simultaneous generalization to both new subjects and new conditions). We validate the inference methods on data where the ground-truth model is known, by simulating data with deep neural networks and by resampling of calcium-imaging and functional MRI data. Results demonstrate that the methods are valid and conclusions generalize correctly. These data analysis methods are available in an open-source Python toolbox (rsatoolbox.readthedocs.io).

Neural Decoding for Intracortical Brain-Computer Interfaces

Brain-computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural activity either by noninvasive or intracortical neural recording. Intracortical recording is an invasive way of measuring neural electrical activity with high temporal and spatial resolution. Herein, we review recent developments in neural signal decoding methods for intracortical brain-computer interfaces. These methods have achieved good performance in analyzing neural activity and controlling robots and prostheses in nonhuman primates and humans. For more complex paradigms in motor rehabilitation or other clinical applications, there remains more space for further improvements of decoders.

A paradigm for ethanol consumption in head-fixed mice during prefrontal cortical two-photon calcium imaging

The prefrontal cortex (PFC) is a hub for higher-level cognitive behaviors and is a key target for neuroadaptations in alcohol use disorders. Preclinical models of ethanol consumption are instrumental for understanding how acute and repeated drinking affects PFC structure and function. Recent advances in genetically encoded sensors of neuronal activity and neuromodulator release combined with functional microscopy (multiphoton and one-photon widefield imaging) allow multimodal in-vivo PFC recordings at subcellular and cellular scales. While these methods could enable a deeper understanding of the relationship between alcohol and PFC function/dysfunction, they require animals to be head-fixed. Here, we present a method in mice for binge-like ethanol consumption during head-fixation. Male and female mice were first acclimated to ethanol by providing home cage access to 20% ethanol (v/v) for 4 or 8 days. After home cage drinking, mice consumed ethanol from a lick spout during head-fixation. We used two-photon calcium imaging during the head-fixed drinking paradigm to record from a large population of PFC neurons (>1000) to explore how acute ethanol affects their activity. Drinking modulated activity rates in a subset of neurons on slow (minutes) and fast (seconds) time scales but the majority of neurons were unaffected. Moreover, ethanol intake did not significantly affect network level interactions in the PFC as assessed through inter-neuronal pairwise correlations. By establishing a method for binge-like drinking in head-fixed mice, we lay the groundwork for leveraging advanced microscopy technologies to study alcohol-induced neuroadaptations in PFC and other brain circuits.

Inserting a Neuropixels probe into awake monkey cortex: two probes, two methods

Neuropixels probes have revolutionized neurophysiological studies in the rodent, but inserting these probes through the much thicker primate dura remains a challenge. Here we describe two methods we have developed for the insertion of two types of Neuropixels probes acutely into the awake monkey cortex. For the fine rodent probe, which is unable to pierce native primate dura, we developed a dural-eyelet method to insert the probe repeatedly without breakage. For the thicker NHP probe, we developed an artificial dura system to insert the probe. We have now conducted successful experiments in 3 animals across 7 recording chambers with the procedures described here and have achieved stable recordings over several months in each case. Here we describe our hardware, surgical preparation, methods for insertion and methods for removal of broken probe parts. We hope that our methods are of value to primate physiologists everywhere.

ROBUST ONLINE MULTIBAND DRIFT ESTIMATION IN ELECTROPHYSIOLOGY DATA

High-density electrophysiology probes have opened new possibilities for systems neuroscience in human and non-human animals, but probe motion poses a challenge for downstream analyses, particularly in human recordings. We improve on the state of the art for tracking this motion with four major contributions. First, we extend previous decentralized methods to use multiband information, leveraging the local field potential (LFP) in addition to spikes. Second, we show that the LFP-based approach enables registration at sub-second temporal resolution. Third, we introduce an efficient online motion tracking algorithm, enabling the method to scale up to longer and higher-resolution recordings, and possibly facilitating real-time applications. Finally, we improve the robustness of the approach by introducing a structure-aware objective and simple methods for adaptive parameter selection. Together, these advances enable fully automated scalable registration of challenging datasets from human and mouse.

A unifying perspective on neural manifolds and circuits for cognition

Two different perspectives have informed efforts to explain the link between the brain and behaviour. One approach seeks to identify neural circuit elements that carry out specific functions, emphasizing connectivity between neurons as a substrate for neural computations. Another approach centres on neural manifolds - low-dimensional representations of behavioural signals in neural population activity - and suggests that neural computations are realized by emergent dynamics. Although manifolds reveal an interpretable structure in heterogeneous neuronal activity, finding the corresponding structure in connectivity remains a challenge. We highlight examples in which establishing the correspondence between low-dimensional activity and connectivity has been possible, unifying the neural manifold and circuit perspectives. This relationship is conspicuous in systems in which the geometry of neural responses mirrors their spatial layout in the brain, such as the fly navigational system. Furthermore, we describe evidence that, in systems in which neural responses are heterogeneous, the circuit comprises interactions between activity patterns on the manifold via low-rank connectivity. We suggest that unifying the manifold and circuit approaches is important if we are to be able to causally test theories about the neural computations that underlie behaviour.

Neuroscience needs Network Science

The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions.

Multilayer Arrays for Neurotechnology Applications (MANTA): Chronically Stable Thin-Film Intracortical Implants

Flexible implantable neurointerfaces show great promise in addressing one of the major challenges of implantable neurotechnology, namely the loss of signal connected to unfavorable probe tissue interaction. The authors here show how multilayer polyimide probes allow high-density intracortical recordings to be combined with a reliable long-term stable tissue interface, thereby progressing toward chronic stability of implantable neurotechnology. The probes could record 10-60 single units over 5 months with a consistent peak-to-peak voltage at dimensions that ensure robust handling and insulation longevity. Probes that remain in intimate contact with the signaling tissue over months to years are a game changer for neuroscience and, importantly, open up for broader clinical translation of systems relying on neurotechnology to interface the human brain.

Stitching Flexible Electronics into the Brain

Understanding complex neuronal networks requires monitoring long-term neuronal activity in various regions of the brain. Significant progress has been made in multisite implantations of well-designed probes, such as multisite implantation of Si-based and polymer-based probes. However, these multiprobe strategies are limited by the sizes and weights of interfaces to the multiple probes and the inability to track the activity of the same neurons and changes in neuronal activity over longer time periods. Here, a long single flexible probe that can be implanted by stitching into multiple regions of the mouse brain and subsequently transmit chronically stable neuronal signals from the multiple sites via a single low-mass interface is reported. The probe at four different sites is implemented using a glass capillary needle or two sites using an ultrathin metal needle. In vitro tests in brain-mimicking hydrogel show that multisite probe implantations achieve a high connection yield of >86%. In vivo histological images at each site of probes, implanted by stitching using either glass capillary or ultrathin metal insertion needles exhibit seamless tissue-probe interfaces with negligible chronic immune response. In addition, electrophysiology studies demonstrate the ability to track single neuron activities at every injection site with chronic stability over at least one month. Notably, the measured spike amplitudes and signal-to-noise ratios at different implantation sites show no statistically significant differences. Multisite stitching implantation of flexible electronics in the brain opens up new opportunities for both fundamental neuroscience research and electrotherapeutic applications.

Translational opportunities and challenges of invasive electrodes for neural interfaces

Invasive brain-machine interfaces can restore motor, sensory and cognitive functions. However, their clinical adoption has been hindered by the surgical risk of implantation and by suboptimal long-term reliability. In this Review, we highlight the opportunities and challenges of invasive technology for clinically relevant electrophysiology. Specifically, we discuss the characteristics of neural probes that are most likely to facilitate the clinical translation of invasive neural interfaces, describe the neural signals that can be acquired or produced by intracranial electrodes, the abiotic and biotic factors that contribute to their failure, and emerging neural-interface architectures.

Inferring Pyramidal Neuron Morphology using EAP Data

We report a computational algorithm that uses an inverse modeling scheme to infer neuron position and morphology of cortical pyramidal neurons using spatio-temporal extracellular action potential recordings.. We first develop a generic pyramidal neuron model with stylized morphology and active channels that could mimic the realistic electrophysiological dynamics of pyramidal cells from different cortical layers. The generic stylized single neuron model has adjustable parameters for soma location, and morphology and orientation of the dendrites. The ranges for the parameters were selected to include morphology of the pyramidal neuron types in the rodent primary motor cortex. We then developed a machine learning approach that uses the local field potential simulated from the stylized model for training a convolutional neural network that predicts the parameters of the stylized neuron model. Preliminary results suggest that the proposed methodology can reliably infer the key position and morphology parameters using the simulated spatio-temporal profile of EAP waveforms. We also provide partial support to validate the inference algorithm using in vivo data. Finally, we highlight the issues involved and ongoing work to develop a pipeline to automate the scheme.

Calcium imaging and analysis of the jugular-nodose ganglia enables identification of distinct vagal sensory neuron subsets

Objective.Sensory nerves of the peripheral nervous system (PNS) transmit afferent signals from the body to the brain. These peripheral nerves are composed of distinct subsets of fibers and associated cell bodies, which reside in peripheral ganglia distributed throughout the viscera and along the spinal cord. The vagus nerve (cranial nerve X) is a complex polymodal nerve that transmits a wide array of sensory information, including signals related to mechanical, chemical, and noxious stimuli. To understand how stimuli applied to the vagus nerve are encoded by vagal sensory neurons in the jugular-nodose ganglia, we developed a framework for micro-endoscopic calcium imaging and analysis.Approach.We developed novel methods forin vivoimaging of the intact jugular-nodose ganglion using a miniature microscope (Miniscope) in transgenic mice with the genetically-encoded calcium indicator GCaMP6f. We adapted the Python-based analysis package Calcium Imaging Analysis (CaImAn) to process the resulting one-photon fluorescence data into calcium transients for subsequent analysis. Random forest classification was then used to identify specific types of neuronal responders.Results.We demonstrate that recordings from the jugular-nodose ganglia can be accomplished through careful surgical dissection and ganglia stabilization. Using a customized acquisition and analysis pipeline, we show that subsets of vagal sensory neurons respond to different chemical stimuli applied to the vagus nerve. Successful classification of the responses with a random forest model indicates that certain calcium transient features, such as amplitude and duration, are important for encoding these stimuli by sensory neurons.Significance.This experimental approach presents a new framework for investigating how individual vagal sensory neurons encode various stimuli on the vagus nerve. Our surgical and analytical approach can be applied to other PNS ganglia in rodents and other small animal species to elucidate previously unexplored roles for peripheral neurons in a diverse set of physiological functions.

Significantly reduced inflammatory foreign-body-response to neuroimplants and improved recording performance in young compared to adult rats

The multicellular inflammatory encapsulation of implanted intracortical multielectrode arrays (MEA) is associated with severe deterioration of their field potentials' (FP) recording performance, which thus limits the use of brain implants in basic research and clinical applications. Therefore, extensive efforts have been made to identify the conditions in which the inflammatory foreign body response (FBR) is alleviated, or to develop methods to mitigate the formation of the inflammatory barrier. Here, for the first time, we show that (1) in young rats (74±8 gr, 4 weeks old at the onset of the experiments), cortical tissue recovery following MEA implantation proceeds with ameliorated inflammatory scar as compared to adult rats (242 ± 18 gr, 9 weeks old at the experimental onset); (2) in contrast to adult rats in which the Colony Stimulating factor 1 Receptor (CSF1R) antagonist chow eliminated ∼95% of the cortical microglia but not microglia adhering to the implant surfaces, in young rats the microglia adhering to the implant were eliminated along with the parenchymal microglia population. The removal of microglia adhering to the implant surfaces was correlated with improved recording performance by in-house fabricated Perforated Polyimide MEA Platforms (PPMP). These results support the hypothesis that microglia adhering to the surface of the electrodes, rather than the multicellular inflammatory scar, is the major underlying mechanism that deteriorates implant recording performance, and that young rats provide an advantageous model to study months-long, multisite electrophysiology in freely behaving rats. STATEMENT OF SIGNIFICANCE: Multisite electrophysiological recordings and stimulation devices play central roles in basic brain research and medical applications. The insertion of multielectrode-array platforms into the brain's parenchyma unavoidably injures the tissue, and initiates a multicellular inflammatory cascade culminating in the formation of an encapsulating scar tissue (the foreign body response-FBR). The dominant view, which directs most current research efforts to mitigate the FBR, holds that the FBR is the major hurdle to effective electrophysiological use of neuroprobes. By contrast, this report demonstrates that microglia adhering to the surface of a neuroimplants, rather than the multicellular FBR, underlie the performance deterioration of neuroimplants. These findings pave the way to the development of novel and focused strategies to overcome the functional deterioration of neuroimplants.

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

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

Multi-area recordings and optogenetics in the awake, behaving marmoset

The common marmoset has emerged as a key model in neuroscience. Marmosets are small in size, show great potential for genetic modification and exhibit complex behaviors. Thus, it is necessary to develop technology that enables monitoring and manipulation of the underlying neural circuits. Here, we describe a novel approach to record and optogenetically manipulate neural activity in awake, behaving marmosets. Our design utilizes a light-weight, 3D printed titanium chamber that can house several high-density silicon probes for semi-chronic recordings, while enabling simultaneous optogenetic stimulation. We demonstrate the application of our method in male marmosets by recording multi- and single-unit data from areas V1 and V6 with 192 channels simultaneously, and show that optogenetic activation of excitatory neurons in area V6 can influence behavior in a detection task. This method may enable future studies to investigate the neural basis of perception and behavior in the marmoset.

Acute head-fixed recordings in awake mice with multiple Neuropixels probes

Multi-electrode arrays such as Neuropixels probes enable electrophysiological recordings from large populations of single neurons with high temporal resolution. By using such probes, the activity from functionally interacting, yet distinct, brain regions can be measured simultaneously by inserting multiple probes into the same subject. However, the use of multiple probes in small animals such as mice requires the removal of a sizable fraction of the skull, while also minimizing tissue damage and keeping the brain stable during the recordings. Here, we describe a step-by-step process designed to facilitate reliable recordings from up to six Neuropixels probes simultaneously in awake, head-fixed mice. The procedure involves four stages: the implantation of a headframe and a removable glass coverslip, the precise positioning of the Neuropixels probes at targeted points on the brain surface, the placement of a perforated plastic imaging window and the insertion of the probes into the brain of an awake mouse. The approach provides access to multiple brain regions and has been successfully applied across hundreds of mice. The procedure has been optimized for dense recordings from the mouse visual system, but it can be adapted for alternative recording configurations to target multiple probes in other brain areas. The protocol is suitable for users with experience in stereotaxic surgery in mice.

Spikebench: An open benchmark for spike train time-series classification

Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks. The wide range of algorithms that can be utilized to learn from neural spike trains, which are essentially time-series data, results in the need for diverse and challenging benchmarks for neural decoding, similar to the ones in the fields of computer vision and natural language processing. In this work, we propose a spike train classification benchmark, based on open-access neural activity datasets and consisting of several learning tasks such as stimulus type classification, animal's behavioral state prediction, and neuron type identification. We demonstrate that an approach based on hand-crafted time-series feature engineering establishes a strong baseline performing on par with state-of-the-art deep learning-based models for neural decoding. We release the code allowing to reproduce the reported results.

Neuroprosthetics: from sensorimotor to cognitive disorders

Neuroprosthetics is a multidisciplinary field at the interface between neurosciences and biomedical engineering, which aims at replacing or modulating parts of the nervous system that get disrupted in neurological disorders or after injury. Although neuroprostheses have steadily evolved over the past 60 years in the field of sensory and motor disorders, their application to higher-order cognitive functions is still at a relatively preliminary stage. Nevertheless, a recent series of proof-of-concept studies suggest that electrical neuromodulation strategies might also be useful in alleviating some cognitive and memory deficits, in particular in the context of dementia. Here, we review the evolution of neuroprosthetics from sensorimotor to cognitive disorders, highlighting important common principles such as the need for neuroprosthetic systems that enable multisite bidirectional interactions with the nervous system.

Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications

Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively large electrodes, allowing for only coarse modulation of circuit function. Because of the dysfunction in distributed neural circuits - each requiring fine, tailored modulation - that characterizes most neuropsychiatric disorders, this approach holds limited promise. To develop the next generation of neuromodulation therapies, we will have to achieve fine-grained, closed-loop control over multiple neural circuits. Recent work has demonstrated spatial and frequency selectivity using microstimulation with many small, closely-spaced contacts, mimicking endogenous neural dynamics. Using custom electrode design and stimulation parameters, it should be possible to achieve bidirectional control over behavioral outcomes, such as increasing or decreasing arousal during central thalamic stimulation. Here, we discuss one possible approach, which we term microscale multicircuit brain stimulation (MMBS). We discuss how machine learning leverages behavioral and neural data to find optimal stimulation parameters across multiple contacts, to drive the brain towards desired states associated with behavioral goals. We expound a mathematical framework for MMBS, where behavioral and neural responses adjust the model in real-time, allowing us to adjust stimulation in real-time. These technologies will be critical to the development of the next generation of neurostimulation therapies, which will allow us to treat problems like disorders of consciousness and cognition.

Data management strategy for a collaborative research center

The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.

Structural and functional imaging of brains

Analyzing the complex structures and functions of brain is the key issue to understanding the physiological and pathological processes. Although neuronal morphology and local distribution of neurons/blood vessels in the brain have been known, the subcellular structures of cells remain challenging, especially in the live brain. In addition, the complicated brain functions involve numerous functional molecules, but the concentrations, distributions and interactions of these molecules in the brain are still poorly understood. In this review, frontier techniques available for multiscale structure imaging from organelles to the whole brain are first overviewed, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), serial-section electron microscopy (ssEM), light microscopy (LM) and synchrotron-based X-ray microscopy (XRM). Specially, XRM for three-dimensional (3D) imaging of large-scale brain tissue with high resolution and fast imaging speed is highlighted. Additionally, the development of elegant methods for acquisition of brain functions from electrical/chemical signals in the brain is outlined. In particular, the new electrophysiology technologies for neural recordings at the single-neuron level and in the brain are also summarized. We also focus on the construction of electrochemical probes based on dual-recognition strategy and surface/interface chemistry for determination of chemical species in the brain with high selectivity and long-term stability, as well as electrochemophysiological microarray for simultaneously recording of electrochemical and electrophysiological signals in the brain. Moreover, the recent development of brain MRI probes with high contrast-to-noise ratio (CNR) and sensitivity based on hyperpolarized techniques and multi-nuclear chemistry is introduced. Furthermore, multiple optical probes and instruments, especially the optophysiological Raman probes and fiber Raman photometry, for imaging and biosensing in live brain are emphasized. Finally, a brief perspective on existing challenges and further research development is provided.

A Multilevel Synchronized Optical Pulsed Modulation for High Efficiency Biotelemetry

The paper describes the design, implementation, and characterization of a novel multilevel synchronized pulse position modulation paradigm for high efficiency optical biotelemetry links. The entire optoelectronic architecture has been designed with the aim to improve the efficiency of the data transmission and decrease the overall power consumption that are key factors for the fabrication of implantable and wearable medical devices. By employing specially designed digital architectures, the proposed modulation technique automatically transmits more than one bit per symbol together with the reference clock signal enabling the decoding process of the received coded data. In the present case, the paper demonstrates the capability of the modulation technique to transmit symbols composed by 3 and 4 bits. This has been achieved by developing a prototype of an optical biotelemetry system implemented on an FPGA board that, making use of 500 ps laser pulses, operates under the following two working conditions: (i) 40 MHz clock signal corresponding to a baud rate of 40 Mega symbol per second for symbols composed by 3 bits; (ii) 30 MHz clock signal corresponding to a baud rate of 30 Mega symbol per second for symbols composed by 4 bits. Thus, for both these two configurations the transmission data rate is 120 Mbps and the measured BER was lower than 10^-10. Finally, the power consumption was found to be 1.95 and 1.8 mW and the resulting energy efficiencies were 16.25 and 15 pJ/bit for transmitted symbols composed by 3 and 4 bits/symbol, respectively.

Decoding cognition in real-time

How can we study unobservable cognitive processes that cannot be measured directly? This has been an enduring challenge for cognitive scientists. In this essay we discuss advances in neurotechnology that could allow cognitive processes to be decoded in real-time and the implications that this may have for cognitive science and the treatment of neuropsychiatric disease.

Verification of multi-structure targeting in chronic microelectrode brain recordings from CT scans

BACKGROUND

Large-scale microelectrode recordings offer a unique opportunity to study neurophysiological processes at the network level with single cell resolution. However, in the small brains of many experimental animals, it is often technically challenging to verify the correct targeting of the intended structures, which inherently limits the reproducibility of acquired data.

NEW METHOD

To mitigate this problem, we have developed a method to programmatically segment the trajectory of electrodes arranged in larger arrays from acquired CT-images and thereby determine the position of individual recording tips with high spatial resolution, while also allowing for coregistration with an anatomical atlas, without pre-processing of the animal samples or post-imaging histological analyses.

RESULTS

Testing the technical limitations of the developed method, we found that the choice of scanning angle influences the achievable spatial resolution due to shadowing effects caused by the electrodes. However, under optimal acquisition conditions, individual electrode tip locations within arrays with 250 µm inter-electrode spacing were possible to reliably determine.

COMPARISON TO EXISTING METHODS

Comparison to a histological verification method suggested that, under conditions where individual wires are possible to track in slices, a 90% correspondence could be achieved in terms of the number of electrodes groups that could be reliably assigned to the same anatomical structure.

CONCLUSIONS

The herein reported semi-automated procedure to verify anatomical targeting of brain structures in the rodent brain could help increasing the quality and reproducibility of acquired neurophysiological data by reducing the risk of assigning recorded brain activity to incorrectly identified anatomical locations.

DATA AVAILABILITY

The tools developed in this study are freely available as a software package at: https://github.com/NRC-Lund/ct-tools.

In Vivo Penetrating Microelectrodes for Brain Electrophysiology

In recent decades, microelectrodes have been widely used in neuroscience to understand the mechanisms behind brain functions, as well as the relationship between neural activity and behavior, perception and cognition. However, the recording of neuronal activity over a long period of time is limited for various reasons. In this review, we briefly consider the types of penetrating chronic microelectrodes, as well as the conductive and insulating materials for microelectrode manufacturing. Additionally, we consider the effects of penetrating microelectrode implantation on brain tissue. In conclusion, we review recent advances in the field of in vivo microelectrodes.