Improving data quality in neuronal population recordings

Peeking into the sleeping brain: Using in vivo imaging in rodents to understand the relationship between sleep and cognition

Sleep is well known to benefit cognitive function. In particular, sleep has been shown to enhance learning and memory in both humans and animals. While the underlying mechanisms are not fully understood, it has been suggested that brain activity during sleep modulates neuronal communication through synaptic plasticity. These insights were mostly gained using electrophysiology to monitor ongoing large scale and single cell activity. While these efforts were instrumental in the characterisation of important network and cellular activity during sleep, several aspects underlying cognition are beyond the reach of this technology. Neuronal circuit activity is dynamically regulated via the precise interaction of different neuronal and non-neuronal cell types and relies on subtle modifications of individual synapses. In contrast to established electrophysiological approaches, recent advances in imaging techniques, mainly applied in rodents, provide unprecedented access to these aspects of neuronal function in vivo. In this review, we describe various techniques currently available for in vivo brain imaging, from single synapse to large scale network activity. We discuss the advantages and limitations of these approaches in the context of sleep research and describe which particular aspects related to cognition lend themselves to this kind of investigation. Finally, we review the few studies that used in vivo imaging in rodents to investigate the sleeping brain and discuss how the results have already significantly contributed to a better understanding on the complex relation between sleep and plasticity across development and adulthood.

AutonoMouse: High throughput operant conditioning reveals progressive impairment with graded olfactory bulb lesions

Operant conditioning is a crucial tool in neuroscience research for probing brain function. While molecular, anatomical and even physiological techniques have seen radical increases in throughput, efficiency, and reproducibility in recent years, behavioural tools have somewhat lagged behind. Here we present a fully automated, high-throughput system for self-initiated conditioning of up to 25 group-housed, radio-frequency identification (RFID) tagged mice over periods of several months and >106 trials. We validate this "AutonoMouse" system in a series of olfactory behavioural tasks and show that acquired data is comparable to previous semi-manual approaches. Furthermore, we use AutonoMouse to systematically probe the impact of graded olfactory bulb lesions on olfactory behaviour, demonstrating that while odour discrimination in general is robust to even most extensive disruptions, small olfactory bulb lesions already impair odour detection. Discrimination learning of similar mixtures as well as learning speed are in turn reliably impacted by medium lesion sizes. The modular nature and open-source design of AutonoMouse should allow for similar robust and systematic assessments across neuroscience research areas.

Elastocapillary self-assembled neurotassels for stable neural activity recordings

Implantable neural probes that are mechanically compliant with brain tissue offer important opportunities for stable neural interfaces in both basic neuroscience and clinical applications. Here, we developed a Neurotassel consisting of an array of flexible and high-aspect ratio microelectrode filaments. A Neurotassel can spontaneously assemble into a thin and implantable fiber through elastocapillary interactions when withdrawn from a molten, tissue-dissolvable polymer. Chronically implanted Neurotassels elicited minimal neuronal cell loss in the brain and enabled stable activity recordings of the same population of neurons in mice learning to perform a task. Moreover, Neurotassels can be readily scaled up to 1024 microelectrode filaments, each with a neurite-scale cross-sectional footprint of 3 × 1.5 μm², to form implantable fibers with a total diameter of ~100 μm. With their ultrasmall sizes, high flexibility, and scalability, Neurotassels offer a new approach for stable neural activity recording and neuroprosthetics.

Parallel pathways for sound processing and functional connectivity among layer 5 and 6 auditory corticofugal neurons

Cortical layers (L) 5 and 6 are populated by intermingled cell-types with distinct inputs and downstream targets. Here, we made optogenetically guided recordings from L5 corticofugal (CF) and L6 corticothalamic (CT) neurons in the auditory cortex of awake mice to discern differences in sensory processing and underlying patterns of functional connectivity. Whereas L5 CF neurons showed broad stimulus selectivity with sluggish response latencies and extended temporal non-linearities, L6 CTs exhibited sparse selectivity and rapid temporal processing. L5 CF spikes lagged behind neighboring units and imposed weak feedforward excitation within the local column. By contrast, L6 CT spikes drove robust and sustained activity, particularly in local fast-spiking interneurons. Our findings underscore a duality among sub-cortical projection neurons, where L5 CF units are canonical broadcast neurons that integrate sensory inputs for transmission to distributed downstream targets, while L6 CT neurons are positioned to regulate thalamocortical response gain and selectivity.

Leveraging calcium imaging to illuminate circuit dysfunction in addiction

Alcohol and drug use can dysregulate neural circuit function to produce a wide range of neuropsychiatric disorders, including addiction. To understand the neural circuit computations that mediate behavior, and how substances of abuse may transform them, we must first be able to observe the activity of circuits. While many techniques have been utilized to measure activity in specific brain regions, these regions are made up of heterogeneous sub-populations, and assessing activity from neuronal populations of interest has been an ongoing challenge. To fully understand how neural circuits mediate addiction-related behavior, we must be able to reveal the cellular granularity within brain regions and circuits by overlaying functional information with the genetic and anatomical identity of the cells involved. The development of genetically encoded calcium indicators, which can be targeted to populations of interest, allows for in vivo visualization of calcium dynamics, a proxy for neuronal activity, thus providing an avenue for real-time assessment of activity in genetically and anatomically defined populations during behavior. Here, we highlight recent advances in calcium imaging technology, compare the current technology with other state-of-the-art approaches for in vivo monitoring of neural activity, and discuss the strengths, limitations, and practical concerns for observing neural circuit activity in preclinical addiction models.

Accurate signal-source localization in brain slices by means of high-density microelectrode arrays

Extracellular recordings by means of high-density microelectrode arrays (HD-MEAs) have become a powerful tool to resolve subcellular details of single neurons in active networks grown from dissociated cells. To extend the application of this technology to slice preparations, we developed models describing how extracellular signals, produced by neuronal cells in slices, are detected by microelectrode arrays. The models help to analyze and understand the electrical-potential landscape in an in vitro HD-MEA-recording scenario based on point-current sources. We employed two modeling schemes, (i) a simple analytical approach, based on the method of images (MoI), and (ii) an approach, based on finite-element methods (FEM). We compared and validated the models with large-scale, high-spatiotemporal-resolution recordings of slice preparations by means of HD-MEAs. We then developed a model-based localization algorithm and compared the performance of MoI and FEM models. Both models provided accurate localization results and a comparable and negligible systematic error, when the point source was in saline, a condition similar to cell-culture experiments. Moreover, the relative random error in the x-y-z-localization amounted only up to 4.3% for z-distances up to 200 μm from the HD-MEA surface. In tissue, the systematic errors of both, MoI and FEM models were significantly higher, and a pre-calibration was required. Nevertheless, the FEM values proved to be closer to the tissue experimental results, yielding 5.2 μm systematic mean error, compared to 22.0 μm obtained with MoI. These results suggest that the medium volume or "saline height", the brain slice thickness and anisotropy, and the location of the reference electrode, which were included in the FEM model, considerably affect the extracellular signal and localization performance, when the signal source is at larger distance to the array. After pre-calibration, the relative random error of the z-localization in tissue was only 3% for z-distances up to 200 μm. We then applied the model and related detailed understanding of extracellular recordings to achieve an electrically-guided navigation of a stimulating micropipette, solely based on the measured HD-MEA signals, and managed to target spontaneously active neurons in an acute brain slice for electroporation.

Slow insertion of silicon probes improves the quality of acute neuronal recordings

Neural probes designed for extracellular recording of brain electrical activity are traditionally implanted with an insertion speed between 1 µm/s and 1 mm/s into the brain tissue. Although the physical effects of insertion speed on the tissue are well studied, there is a lack of research investigating how the quality of the acquired electrophysiological signal depends on the speed of probe insertion. In this study, we used four different insertion speeds (0.002 mm/s, 0.02 mm/s, 0.1 mm/s, 1 mm/s) to implant high-density silicon probes into deep layers of the somatosensory cortex of ketamine/xylazine anesthetized rats. After implantation, various qualitative and quantitative properties of the recorded cortical activity were compared across different speeds in an acute manner. Our results demonstrate that after the slowest insertion both the signal-to-noise ratio and the number of separable single units were significantly higher compared with those measured after inserting probes at faster speeds. Furthermore, the amplitude of recorded spikes as well as the quality of single unit clusters showed similar speed-dependent differences. Post hoc quantification of the neuronal density around the probe track showed a significantly higher number of NeuN-labelled cells after the slowest insertion compared with the fastest insertion. Our findings suggest that advancing rigid probes slowly (~1 µm/s) into the brain tissue might result in less tissue damage, and thus in neuronal recordings of improved quality compared with measurements obtained after inserting probes with higher speeds.

Stability of representational geometry across a wide range of fMRI activity levels

Fine-grained activity patterns, as measured with functional magnetic resonance imaging (fMRI), are thought to reflect underlying neural representations. Multivariate analysis techniques, such as representational similarity analysis (RSA), can be used to test models of brain representation by quantifying the representational geometry (the collection of pair-wise dissimilarities between activity patterns). One important caveat, however, is that non-linearities in the coupling between neural activity and the fMRI signal may lead to significant distortions in the representational geometry estimated from fMRI activity patterns. Here we tested the stability of representational dissimilarity measures in primary sensory-motor (S1 and M1) and early visual regions (V1/V2) across a large range of activation levels. Participants were visually cued with different letters to perform single finger presses with one of the 5 fingers at a rate of 0.3-2.6 Hz. For each stimulation frequency, we quantified the difference between the 5 activity patterns in M1, S1, and V1/V2. We found that the representational geometry remained relatively stable, even though the average activity increased over a large dynamic range. These results indicate that the representational geometry of fMRI activity patterns can be reliably assessed, largely independent of the average activity in the region. This has important methodological implications for RSA and other multivariate analysis approaches that use the representational geometry to make inferences about brain representations.

A novel and fully automatic spike-sorting implementation with variable number of features

The most widely used spike-sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike-sorting algorithm that can capture multiple clusters of different sizes and densities. In addition, we introduce an improved feature selection method, by using a variable number of wavelet coefficients, based on the degree of non-Gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single-channel recordings, in ~95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single-channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared with previous implementations and also compared with recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real tetrode recordings. NEW & NOTEWORTHY We propose a new fully automatic spike-sorting algorithm, including several steps that allow the selection of multiple clusters of different sizes and densities. Moreover, it defines the dimensionality of the feature space in an unsupervised way. We evaluated the performance of the algorithm with real and simulated data, from both single-channel and tetrode recordings. The proposed algorithm was able to outperform manual sorting from experts and other recent unsupervised algorithms.

Utilizing Miniature Fluorescence Microscopy to Image Hippocampal Place Cell Ensemble Function in Thy1.GCaMP6f Transgenic Mice

Imaging neuronal activity in awake behaving mice with miniature fluorescence microscopes requires the implementation of a variety of procedures. Surgeries are performed to gain access to the cell population of interest and to implant microscope components. After a recovery period, mice are trained to exhibit a desired behavior. Finally, neuronal activity is imaged and synchronized with that behavior. To take full advantage of the technology, selection of the calcium indicator and experimental design must be carefully considered. In this article, we explain the procedures and considerations that are critical for obtaining high-quality calcium imaging data. As an example, we describe how to utilize miniature fluorescence microscopy to image hippocampal place cell activity during linear track running in Thy1.GCaMP6f transgenic mice. © 2018 by John Wiley & Sons, Inc.

A Guide to Emerging Technologies for Large-Scale and Whole-Brain Optical Imaging of Neuronal Activity

The mammalian brain is a densely interconnected network that consists of millions to billions of neurons. Decoding how information is represented and processed by this neural circuitry requires the ability to capture and manipulate the dynamics of large populations at high speed and high resolution over a large area of the brain. Although the use of optical approaches by the neuroscience community has rapidly increased over the past two decades, most microscopy approaches are unable to record the activity of all neurons comprising a functional network across the mammalian brain at relevant temporal and spatial resolutions. In this review, we survey the recent development in optical technologies for Ca2+ imaging in this regard and provide an overview of the strengths and limitations of each modality and its potential for scalability. We provide guidance from the perspective of a biological user driven by the typical biological applications and sample conditions. We also discuss the potential for future advances and synergies that could be obtained through hybrid approaches or other modalities.

Neural Coding With Bursts-Current State and Future Perspectives

Neuronal action potentials or spikes provide a long-range, noise-resistant means of communication between neurons. As point processes single spikes contain little information in themselves, i.e., outside the context of spikes from other neurons. Moreover, they may fail to cross a synapse. A burst, which consists of a short, high frequency train of spikes, will more reliably cross a synapse, increasing the likelihood of eliciting a postsynaptic spike, depending on the specific short-term plasticity at that synapse. Both the number and the temporal pattern of spikes in a burst provide a coding space that lies within the temporal integration realm of single neurons. Bursts have been observed in many species, including the non-mammalian, and in brain regions that range from subcortical to cortical. Despite their widespread presence and potential relevance, the uncertainties of how to classify bursts seems to have limited the research into the coding possibilities for bursts. The present series of research articles provides new insights into the relevance and interpretation of bursts across different neural circuits, and new methods for their analysis. Here, we provide a succinct introduction to the history of burst coding and an overview of recent work on this topic.

Tracking the Same Neurons across Multiple Days in Ca2+ Imaging Data

Ca²⁺ imaging techniques permit time-lapse recordings of neuronal activity from large populations over weeks. However, without identifying the same neurons across imaging sessions (cell registration), longitudinal analysis of the neural code is restricted to population-level statistics. Accurate cell registration becomes challenging with increased numbers of cells, sessions, and inter-session intervals. Current cell registration practices, whether manual or automatic, do not quantitatively evaluate registration accuracy, possibly leading to data misinterpretation. We developed a probabilistic method that automatically registers cells across multiple sessions and estimates the registration confidence for each registered cell. Using large-scale Ca²⁺ imaging data recorded over weeks from the hippocampus and cortex of freely behaving mice, we show that our method performs more accurate registration than previously used routines, yielding estimated error rates <5%, and that the registration is scalable for many sessions. Thus, our method allows reliable longitudinal analysis of the same neurons over long time periods.

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A method for tracking neurons across days (cell registration) in Ca²⁺ imaging data

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The method is probabilistic and quantitatively evaluates registration accuracy

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The method is applicable to various imaging techniques and cell detection algorithms

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Registration accuracy remains high with an increased number of registered sessions

Challenges and opportunities for large-scale electrophysiology with Neuropixels probes

Electrophysiological methods are the gold standard in neuroscience because they reveal the activity of individual neurons at high temporal resolution and in arbitrary brain locations. Microelectrode arrays based on complementary metal-oxide semiconductor (CMOS) technology, such as Neuropixels probes, look set to transform these methods. Neuropixels probes provide ∼1000 recording sites on an extremely narrow shank, with on-board amplification, digitization, and multiplexing. They deliver low-noise recordings from hundreds of neurons, providing a step change in the type of data available to neuroscientists. Here we discuss the opportunities afforded by these probes for large-scale electrophysiology, the challenges associated with data processing and anatomical localization, and avenues for further improvements of the technology.

Mesh electronics: a new paradigm for tissue-like brain probes

Existing implantable neurotechnologies for understanding the brain and treating neurological diseases have intrinsic properties that have limited their capability to achieve chronically-stable brain interfaces with single-neuron spatiotemporal resolution. These limitations reflect what has been dichotomy between the structure and mechanical properties of living brain tissue and non-living neural probes. To bridge the gap between neural and electronic networks, we have introduced the new concept of mesh electronics probes designed with structural and mechanical properties such that the implant begins to 'look and behave' like neural tissue. Syringe-implanted mesh electronics have led to the realization of probes that are neuro-attractive and free of the chronic immune response, as well as capable of stable long-term mapping and modulation of brain activity at the single-neuron level. This review provides a historical overview of a 10-year development of mesh electronics by highlighting the tissue-like design, syringe-assisted delivery, seamless neural tissue integration, and single-neuron level chronic recording stability of mesh electronics. We also offer insights on unique near-term opportunities and future directions for neuroscience and neurology that now are available or expected for mesh electronics neurotechnologies.

Multiple Approaches to the Investigation of Cell Assembly in Memory Research-Present and Future

In this review article we focus on research methodologies for detecting the actual activity of cell assemblies, which are populations of functionally connected neurons that encode information in the brain. We introduce and discuss traditional and novel experimental methods and those currently in development and briefly discuss their advantages and disadvantages for the detection of cell-assembly activity. First, we introduce the electrophysiological method, i.e., multineuronal recording, and review former and recent examples of studies showing models of dynamic coding by cell assemblies in behaving rodents and monkeys. We also discuss how the firing correlation of two neurons reflects the firing synchrony among the numerous surrounding neurons that constitute cell assemblies. Second, we review the recent outstanding studies that used the novel method of optogenetics to show causal relationships between cell-assembly activity and behavioral change. Third, we review the most recently developed method of live-cell imaging, which facilitates the simultaneous observation of firings of a large number of neurons in behaving rodents. Currently, all these available methods have both advantages and disadvantages, and no single measurement method can directly and precisely detect the actual activity of cell assemblies. The best strategy is to combine the available methods and utilize each of their advantages with the technique of operant conditioning of multiple-task behaviors in animals and, if necessary, with brain-machine interface technology to verify the accuracy of neural information detected as cell-assembly activity.

Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders

Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem in neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction (HD) system, we present evidence demonstrating how gradient boosted trees, a non-linear and supervised Machine Learning tool, can learn the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. We show how the method, unlike linear analysis, reveals that the coordination in thalamo-cortical circuits is qualitatively the same during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine Learning tools thus open new avenues for benchmarking model-based characterization of spike trains.

A review on cluster estimation methods and their application to neural spike data

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons-'spike sorting'-is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.

Dynamical networks: Finding, measuring, and tracking neural population activity using network science

Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.

A very large-scale microelectrode array for cellular-resolution electrophysiology

In traditional electrophysiology, spatially inefficient electronics and the need for tissue-to-electrode proximity defy non-invasive interfaces at scales of more than a thousand low noise, simultaneously recording channels. Using compressed sensing concepts and silicon complementary metal-oxide-semiconductors (CMOS), we demonstrate a platform with 65,536 simultaneously recording and stimulating electrodes in which the per-electrode electronics consume an area of 25.5 μm by 25.5 μm. Application of this platform to mouse retinal studies is achieved with a high-performance processing pipeline with a 1 GB/s data rate. The platform records from 65,536 electrodes concurrently with a ~10 µV r.m.s. noise; senses spikes from more than 34,000 electrodes when recording across the entire retina; automatically sorts and classifies greater than 1700 neurons following visual stimulation; and stimulates individual neurons using any number of the 65,536 electrodes while observing spikes over the entire retina. The approaches developed here are applicable to other electrophysiological systems and electrode configurations.

Large electronics limit low-noise, non-invasive electrophysiological measurements to a thousand simultaneously recording channels. Here the authors build an array of 65k simultaneously recording and stimulating electrodes and use it to sort and classify single neurons across the entire mouse retina.

Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology

Implantable electrical probes have led to advances in neuroscience, brain-machine interfaces, and treatment of neurological diseases, yet they remain limited in several key aspects. Ideally, an electrical probe should be capable of recording from large numbers of neurons across multiple local circuits and, importantly, allow stable tracking of the evolution of these neurons over the entire course of study. Silicon probes based on microfabrication can yield large-scale, high-density recording but face challenges of chronic gliosis and instability due to mechanical and structural mismatch with the brain. Ultraflexible mesh electronics, on the other hand, have demonstrated negligible chronic immune response and stable long-term brain monitoring at single-neuron level, although, to date, it has been limited to 16 channels. Here, we present a scalable scheme for highly multiplexed mesh electronics probes to bridge the gap between scalability and flexibility, where 32 to 128 channels per probe were implemented while the crucial brain-like structure and mechanics were maintained. Combining this mesh design with multisite injection, we demonstrate stable 128-channel local field potential and single-unit recordings from multiple brain regions in awake restrained mice over 4 mo. In addition, the newly integrated mesh is used to validate stable chronic recordings in freely behaving mice. This scalable scheme for mesh electronics together with demonstrated long-term stability represent important progress toward the realization of ideal implantable electrical probes allowing for mapping and tracking single-neuron level circuit changes associated with learning, aging, and neurodegenerative diseases.

Long-Term Optical Access to an Estimated One Million Neurons in the Live Mouse Cortex

A major technological goal in neuroscience is to enable the interrogation of individual cells across the live brain. By creating a curved glass replacement to the dorsal cranium and surgical methods for its installation, we developed a chronic mouse preparation providing optical access to an estimated 800,000-1,100,000 individual neurons across the dorsal surface of neocortex. Post-surgical histological studies revealed comparable glial activation as in control mice. In behaving mice expressing a Ca²⁺ indicator in cortical pyramidal neurons, we performed Ca²⁺ imaging across neocortex using an epi-fluorescence macroscope and estimated that 25,000-50,000 individual neurons were accessible per mouse across multiple focal planes. Two-photon microscopy revealed dendritic morphologies throughout neocortex, allowed time-lapse imaging of individual cells, and yielded estimates of >1 million accessible neurons per mouse by serial tiling. This approach supports a variety of optical techniques and enables studies of cells across >30 neocortical areas in behaving mice.

Acquisition of Bioelectrical Signals with Small Electrodes

Although the mechanisms of recording bioelectrical signals from different types of electrogenic cells (neurons, cardiac cells etc.) by means of planar metal electrodes have been extensively studied, the recording characteristics and conditions for very small electrode sizes are not yet established. Here, we present a combined experimental and computational approach to elucidate, how the electrode size influences the recorded signals, and how inherent properties of the electrode, such as impedance, noise, and transmission characteristics shape the signal. We demonstrate that good quality recordings can be achieved with electrode diameters of less than 10 µm, provided that impedance reduction measures have been implemented and provided that a set of requirements for signal amplification has been met.

Q&A: How can advances in tissue clearing and optogenetics contribute to our understanding of normal and diseased biology?

Mammalian organs comprise a variety of cells that interact with each other and have distinct biological roles. Access to evaluate and perturb intact biological systems at the cellular and molecular levels is essential to fully understand their functioning in normal and diseased conditions, yet technical limitations have constrained most research to small pieces of tissue. Tissue clearing and optogenetics can help overcome this hurdle: tissue clearing affords optical interrogation of whole organs at the molecular level, and optogenetics enables the scalable control and measurement of cellular activity with light. In this Q&A, we delineate recent advances and practical challenges associated with these two techniques when applied body-wide.

Uncorrelated Neural Firing in Mouse Visual Cortex during Spontaneous Retinal Waves

Synchronous firing among the elements of forming circuits is critical for stabilization of synapses. Understanding the nature of these local network interactions during development can inform models of circuit formation. Within cortex, spontaneous activity changes throughout development. Unlike the adult, early spontaneous activity occurs in discontinuous population bursts separated by long silent periods, suggesting a high degree of local synchrony. However, whether the micro-patterning of activity within early bursts is unique to this early age and specifically tuned for early development is poorly understood, particularly within the column. To study this we used single-shank multi-electrode array recordings of spontaneous activity in the visual cortex of non-anesthetized neonatal mice to quantify single-unit firing rates, and applied multiple measures of network interaction and synchrony throughout the period of map formation and immediately after eye-opening. We find that despite co-modulation of firing rates on a slow time scale (hundreds of ms), the number of coactive neurons, as well as pair-wise neural spike-rate correlations, are both lower before eye-opening. In fact, on post-natal days (P)6–9 correlated activity was lower than expected by chance, suggesting active decorrelation of activity during early bursts. Neurons in lateral geniculate nucleus developed in an opposite manner, becoming less correlated after eye-opening. Population coupling, a measure of integration in the local network, revealed a population of neurons with particularly strong local coupling present at P6–11, but also an adult-like diversity of coupling at all ages, suggesting that a neuron’s identity as locally or distally coupled is determined early. The occurrence probabilities of unique neuronal “words” were largely similar at all ages suggesting that retinal waves drive adult-like patterns of co-activation. These findings suggest that the bursts of spontaneous activity during early visual development do not drive hyper-synchronous activity within columns. Rather, retinal waves provide windows of potential activation during which neurons are active but poorly correlated, adult-like patterns of correlation are achieved soon after eye-opening.

A Fully Automated Approach to Spike Sorting

Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible.

Model-based spike sorting with a mixture of drifting t-distributions

BACKGROUND

Chronic extracellular recordings are a powerful tool for systems neuroscience, but spike sorting remains a challenge. A common approach is to fit a generative model, such as a mixture of Gaussians, to the observed spike data. Even if non-parametric methods are used for spike sorting, such generative models provide a quantitative measure of unit isolation quality, which is crucial for subsequent interpretation of the sorted spike trains.

NEW METHOD

We present a spike sorting strategy that models the data as a mixture of drifting t-distributions. This model captures two important features of chronic extracellular recordings-cluster drift over time and heavy tails in the distribution of spikes-and offers improved robustness to outliers.

RESULTS

We evaluate this model on several thousand hours of chronic tetrode recordings and show that it fits the empirical data substantially better than a mixture of Gaussians. We also provide a software implementation that can re-fit long datasets in a few seconds, enabling interactive clustering of chronic recordings.

COMPARISON WITH EXISTING METHODS

We identify three common failure modes of spike sorting methods that assume stationarity and evaluate their impact given the empirically-observed cluster drift in chronic recordings. Using hybrid ground truth datasets, we also demonstrate that our model-based estimate of misclassification error is more accurate than previous unit isolation metrics.

CONCLUSIONS

The mixture of drifting t-distributions model enables efficient spike sorting of long datasets and provides an accurate measure of unit isolation quality over a wide range of conditions.

Particle Tracking Facilitates Real Time Capable Motion Correction in 2D or 3D Two-Photon Imaging of Neuronal Activity

The application of 2-photon laser scanning microscopy (TPLSM) techniques to measure the dynamics of cellular calcium signals in populations of neurons is an extremely powerful technique for characterizing neural activity within the central nervous system. The use of TPLSM on awake and behaving subjects promises new insights into how neural circuit elements cooperatively interact to form sensory perceptions and generate behavior. A major challenge in imaging such preparations is unavoidable animal and tissue movement, which leads to shifts in the imaging location (jitter). The presence of image motion can lead to artifacts, especially since quantification of TPLSM images involves analysis of fluctuations in fluorescence intensities for each neuron, determined from small regions of interest (ROIs). Here, we validate a new motion correction approach to compensate for motion of TPLSM images in the superficial layers of auditory cortex of awake mice. We use a nominally uniform fluorescent signal as a secondary signal to complement the dynamic signals from genetically encoded calcium indicators. We tested motion correction for single plane time lapse imaging as well as multiplane (i.e., volume) time lapse imaging of cortical tissue. Our procedure of motion correction relies on locating the brightest neurons and tracking their positions over time using established techniques of particle finding and tracking. We show that our tracking based approach provides subpixel resolution without compromising speed. Unlike most established methods, our algorithm also captures deformations of the field of view and thus can compensate e.g., for rotations. Object tracking based motion correction thus offers an alternative approach for motion correction, one that is well suited for real time spike inference analysis and feedback control, and for correcting for tissue distortions.

Partially Mixed Selectivity in Human Posterior Parietal Association Cortex

To clarify the organization of motor representations in posterior parietal cortex, we test how three motor variables (body side, body part, cognitive strategy) are coded in the human anterior intraparietal cortex. All tested movements were encoded, arguing against strict anatomical segregation of effectors. Single units coded for diverse conjunctions of variables, with different dimensions anatomically overlapping. Consistent with recent studies, neurons encoding body parts exhibited mixed selectivity. This mixed selectivity resulted in largely orthogonal coding of body parts, which "functionally segregate" the effector responses despite the high degree of anatomical overlap. Body side and strategy were not coded in a mixed manner as effector determined their organization. Mixed coding of some variables over others, what we term "partially mixed coding," argues that the type of functional encoding depends on the compared dimensions. This structure is advantageous for neuroprosthetics, allowing a single array to decode movements of a large extent of the body.

Improving voltage-sensitive dye imaging: with a little help from computational approaches

Voltage-sensitive dye imaging (VSDI) is a key neurophysiological recording tool because it reaches brain scales that remain inaccessible to other techniques. The development of this technique from in vitro to the behaving nonhuman primate has only been made possible thanks to the long-lasting, visionary work of Amiram Grinvald. This work has opened new scientific perspectives to the great benefit to the neuroscience community. However, this unprecedented technique remains largely under-utilized, and many future possibilities await for VSDI to reveal new functional operations. One reason why this tool has not been used extensively is the inherent complexity of the signal. For instance, the signal reflects mainly the subthreshold neuronal population response and is not linked to spiking activity in a straightforward manner. Second, VSDI gives access to intracortical recurrent dynamics that are intrinsically complex and therefore nontrivial to process. Computational approaches are thus necessary to promote our understanding and optimal use of this powerful technique. Here, we review such approaches, from computational models to dissect the mechanisms and origin of the recorded signal, to advanced signal processing methods to unravel new neuronal interactions at mesoscopic scale. Only a stronger development of interdisciplinary approaches can bridge micro- to macroscales.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca2+-imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca2+ imaging datasets, particularly when these have been acquired at different spatial scales.

An integrated calcium imaging processing toolbox for the analysis of neuronal population dynamics

The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a comprehensive computational workflow for the analysis of neuronal population calcium dynamics. The toolbox includes newly developed algorithms and interactive tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters' features against surrogate control datasets. The modules are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain. We demonstrate the suitability of the toolbox for a variety of calcium imaging datasets. The toolbox open-source code, a step-by-step tutorial and a case study dataset are available at https://github.com/zebrain-lab/Toolbox-Romano-et-al.

Rate and Temporal Coding Convey Multisensory Information in Primary Sensory Cortices

Optimal behavior and survival result from integration of information across sensory systems. Modulation of network activity at the level of primary sensory cortices has been identified as a mechanism of cross-modal integration, yet its cellular substrate is still poorly understood. Here, we uncover the mechanisms by which individual neurons in primary somatosensory (S1) and visual (V1) cortices encode visual-tactile stimuli. For this, simultaneous extracellular recordings were performed from all layers of the S1 barrel field and V1 in Brown Norway rats in vivo and units were clustered and assigned to pyramidal neurons (PYRs) and interneurons (INs). We show that visual-tactile stimulation modulates the firing rate of a relatively low fraction of neurons throughout all cortical layers. Generally, it augments the firing of INs and decreases the activity of PYRs. Moreover, bimodal stimulation shapes the timing of neuronal firing by strengthening the phase-coupling between neuronal discharge and theta–beta band network oscillations as well as by modulating spiking onset. Sparse direct axonal projections between neurons in S1 and V1 seem to time the spike trains between the two cortical areas and, thus, may act as a substrate of cross-modal modulation. These results indicate that few cortical neurons mediate multisensory effects in primary sensory areas by directly encoding cross-modal information by their rate and timing of firing.