Benchmarking Spike Rate Inference in Population Calcium Imaging

Spike rate inference from mouse spinal cord calcium imaging data

Calcium imaging is a key method to record the spiking activity of identified and genetically targeted neurons. However, the observed calcium signals are only an indirect readout of the underlying electrophysiological events (single spikes or bursts of spikes) and require dedicated algorithms to recover the spike rate. These algorithms for spike inference can be optimized using ground truth data from combined electrical and optical recordings, but it is not clear how such optimized algorithms perform on cell types and brain regions for which ground truth does not exist. Here, we use a state-of-the-art algorithm based on supervised deep learning (CASCADE) and a non-supervised algorithm based on non-negative deconvolution (OASIS) to test spike rate inference in spinal cord neurons. To enable these tests, we recorded specific ground truth from glutamatergic and GABAergic somatosensory neurons in the superficial dorsal horn of spinal cord in mice of both sexes. We find that CASCADE and OASIS algorithms that were designed for cortical excitatory neurons generalize well to both spinal cord cell types. However, CASCADE models re-trained on our ground truth further improved the performance, resulting in a more accurate inference of spiking activity from spinal cord neurons. We openly provide re-trained models that can be applied to spinal cord data with variable noise levels and frame rates. Together, our ground-truth recordings and analyses provide a solid foundation for the interpretation of calcium imaging data from spinal cord dorsal horn and showcase how spike rate inference can generalize between different regions of the nervous system.Significance Statement Calcium imaging is a powerful method for measuring the activity of genetically identified neurons. However, accurate interpretation of calcium transients depends on having a detailed understanding of how neuronal activity correlates with fluorescence. Such calibration recordings have been performed for cerebral cortex but not yet for most other CNS regions and neuron types. Here, we obtained ground truth data in spinal cord by conducting simultaneous calcium and electrophysiology recordings in excitatory and inhibitory neurons. We systematically investigated the transferability of cortical algorithms to spinal neuron subpopulations and generated inference algorithms optimized to excitatory and inhibitory neurons. Our study provides a foundation for the rigorous interpretation of calcium imaging data from spinal cord.

A deep learning framework for automated and generalized synaptic event analysis

Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring synaptic events carry fundamental information about synaptic function and plasticity. However, their stochastic nature and low signal-to-noise ratio present major challenges for the reliable and consistent analysis. Here, we introduce miniML, a supervised deep learning-based method for accurate classification and automated detection of spontaneous synaptic events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event analysis methods in terms of both precision and recall. miniML enables precise detection and quantification of synaptic events in electrophysiological recordings. We demonstrate that the deep learning approach generalizes easily to diverse synaptic preparations, different electrophysiological and optical recording techniques, and across animal species. miniML provides not only a comprehensive and robust framework for automated, reliable, and standardized analysis of synaptic events, but also opens new avenues for high-throughput investigations of neural function and dysfunction.

Window into the Brain: In Vivo Multiphoton Imaging

Decoding the principles underlying neuronal information processing necessitates the emergence of techniques and methodologies to monitor multiscale brain networks in behaving animals over long periods of time. Novel advances in biophotonics, specifically progress in multiphoton microscopy, combined with the development of optical indicators for neuronal activity have provided the possibility to concurrently track brain functions at scales ranging from individual neurons to thousands of neurons across connected brain regions. This Review presents state-of-the-art multiphoton imaging modalities and optical indicators for in vivo brain imaging, highlighting recent advancements and current challenges in the field.

Spike rate inference from mouse spinal cord calcium imaging data

Calcium imaging is a key method to record the spiking activity of identified and genetically targeted neurons. However, the observed calcium signals are only an indirect readout of the underlying electrophysiological events (single spikes or bursts of spikes) and require dedicated algorithms to recover the spike rate. These algorithms for spike inference can be optimized using ground truth data from combined electrical and optical recordings, but it is not clear how such optimized algorithms perform on cell types and brain regions for which ground truth does not exist. Here, we use a state-of-the-art algorithm based on supervised deep learning (CASCADE) and a non-supervised algorithm based on non-negative deconvolution (OASIS) to test spike rate inference in spinal cord neurons. To enable these tests, we recorded specific ground truth from glutamatergic and GABAergic somatosensory neurons in the superficial dorsal horn of spinal cord in mice of both sexes. We find that CASCADE and OASIS algorithms that were designed for cortical excitatory neurons generalize well to both spinal cord cell types. However, CASCADE models re-trained on our ground truth further improved the performance, resulting in a more accurate inference of spiking activity from spinal cord neurons. We openly provide re-trained models that can be applied to spinal cord data of variable noise levels and frame rates. Together, our ground-truth recordings and analyses provide a solid foundation for the interpretation of calcium imaging data from spinal cord dorsal horn and showcase how spike rate inference can generalize between different regions of the nervous system.

Multimodal Characterization of Cortical Neuron Response to Permanent Magnetic Field Induced Nanomagnetic Force Maps

Nanomagnetic forces deliver precise mechanical cues to biological systems through the remote pulling of magnetic nanoparticles under a permanent magnetic field. Cortical neurons respond to nanomagnetic forces with cytosolic calcium influx and event rate shifts. However, the underlying consequences of nanomagnetic force modulation on cortical neurons remain to be elucidated. Here, we integrate electrophysiological and optical recording modalities with nanomagnetic forces to characterize the in vitro functional response to mechanical cues. Neurons exposed to chitosan functionalized magnetic nanoparticles for 24 h and then exposed to magnetic fields capable of generating forces of 2-160 pN present elevated cytosolic calcium in neurons and a time-dynamic electrophysiological spike rate and magnitude response. Extracellular recordings with microelectrode arrays revealed a 2-8 pN force-specific increase in electrophysiological spiking with a trend in reduced activity following 2 min of continuous force exposure. Nanomagnetic forces in the 16-160 pN range produced increased electrophysiological activity and remained excited for up to 4 h under continuous stimulation before silencing. Furthermore, the neuronal response to nanomagnetic forces at 16-160 pN can be electrophysiologically mediated without calcium influx by altering the magnetic nanoparticle-neuron interactions. These results demonstrate that low pN nanomagnetic forces mediate neuronal function and suggest that magnetic nanoparticle interactions and force magnitudes can be harnessed to provoke different responses in cortical neurons.

A novel rhodopsin-based voltage indicator for simultaneous two-photon optical recording with GCaMP in vivo

Genetically encoded voltage indicators (GEVIs) allow optical recording of membrane potential from targeted cells in vivo. However, red GEVIs that are compatible with two-photon microscopy and that can be multiplexed in vivo with green reporters like GCaMP, are currently lacking. To address this gap, we explored diverse rhodopsin proteins as GEVIs and engineered a novel GEVI, 2Photron, based on a rhodopsin from the green algae Klebsormidium nitens. 2Photron, combined with two photon ultrafast local volume excitation (ULoVE), enabled multiplexed readout of spiking and subthreshold voltage simultaneously with GCaMP calcium signals in visual cortical neurons of awake, behaving mice. These recordings revealed the cell-specific relationship of spiking and subthreshold voltage dynamics with GCaMP responses, highlighting the challenges of extracting underlying spike trains from calcium imaging.

A chromatic feature detector in the retina signals visual context changes

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

Long-wavelength traveling waves of vasomotion modulate the perfusion of cortex

Brain arterioles are active, multicellular complexes whose diameters oscillate at ∼ 0.1 Hz. We assess the physiological impact and spatiotemporal dynamics of vaso-oscillations in the awake mouse. First, vaso-oscillations in penetrating arterioles, which source blood from pial arterioles to the capillary bed, profoundly impact perfusion throughout neocortex. The modulation in flux during resting-state activity exceeds that of stimulus-induced activity. Second, the change in perfusion through arterioles relative to the change in their diameter is weak. This implies that the capillary bed dominates the hydrodynamic resistance of brain vasculature. Lastly, the phase of vaso-oscillations evolves slowly along arterioles, with a wavelength that exceeds the span of the cortical mantle and sufficient variability to establish functional cortical areas as parcels of uniform phase. The phase-gradient supports traveling waves in either direction along both pial and penetrating arterioles. This implies that waves along penetrating arterioles can mix, but not directionally transport, interstitial fluids.

Nonlinear super-resolution signal processing allows intracellular tracking of calcium dynamics

Objective.Traditional quantification of fluorescence signals, such asΔF/F, relies on ratiometric measures that necessitate a baseline for comparison, limiting their applicability in dynamic analyses. Our goal here is to develop a baseline-independent method for analyzing fluorescence data that fully exploits temporal dynamics to introduce a novel approach for dynamical super-resolution analysis, including in subcellular resolution.Approach.We introduce ARES (Autoregressive RESiduals), a novel method that leverages the temporal aspect of fluorescence signals. By focusing on the quantification of residuals following linear autoregression, ARES obviates the need for a predefined baseline, enabling a more nuanced analysis of signal dynamics.Main result.We delineate the foundational attributes of ARES, illustrating its capability to enhance both spatial and temporal resolution of calcium fluorescence activity beyond the conventional ratiometric measure (ΔF/F). Additionally, we demonstrate ARES's utility in elucidating intracellular calcium dynamics through the detailed observation of calcium wave propagation within a dendrite.Significance.ARES stands out as a robust and precise tool for the quantification of fluorescence signals, adept at analyzing both spontaneous and evoked calcium dynamics. Its ability to facilitate the subcellular localization of calcium signals and the spatiotemporal tracking of calcium dynamics-where traditional ratiometric measures falter-underscores its potential to revolutionize baseline-independent analyses in the field.

Automatic monitoring of neural activity with single-cell resolution in behaving Hydra

The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.

A Semi-supervised Pipeline for Accurate Neuron Segmentation with Fewer Ground Truth Labels

Recent advancements in two-photon calcium imaging have enabled scientists to record the activity of thousands of neurons with cellular resolution. This scope of data collection is crucial to understanding the next generation of neuroscience questions, but analyzing these large recordings requires automated methods for neuron segmentation. Supervised methods for neuron segmentation achieve state of-the-art accuracy and speed but currently require large amounts of manually generated ground truth training labels. We reduced the required number of training labels by designing a semi-supervised pipeline. Our pipeline used neural network ensembling to generate pseudolabels to train a single shallow U-Net. We tested our method on three publicly available datasets and compared our performance to three widely used segmentation methods. Our method outperformed other methods when trained on a small number of ground truth labels and could achieve state-of-the-art accuracy after training on approximately a quarter of the number of ground truth labels as supervised methods. When trained on many ground truth labels, our pipeline attained higher accuracy than that of state-of-the-art methods. Overall, our work will help researchers accurately process large neural recordings while minimizing the time and effort needed to generate manual labels.

Automatic monitoring of whole-body neural activity in behaving Hydra

The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.

All-optical recreation of naturalistic neural activity with a multifunctional transgenic reporter mouse

Determining which features of the neural code drive behavior requires the ability to simultaneously read out and write in neural activity patterns with high precision across many neurons. All-optical systems that combine two-photon calcium imaging and targeted photostimulation enable the activation of specific, functionally defined groups of neurons. However, these techniques are unable to test how patterns of activity across a population contribute to computation because of an inability to both read and write cell-specific firing rates. To overcome this challenge, we make two advances: first, we introduce a genetic line of mice for Cre-dependent co-expression of a calcium indicator and a potent soma-targeted microbial opsin. Second, using this line, we develop a method for read-out and write-in of precise population vectors of neural activity by calibrating the photostimulation to each cell. These advances offer a powerful and convenient platform for investigating the neural codes of computation and behavior.

Effective and efficient neural networks for spike inference from in vivo calcium imaging

Calcium imaging provides advantages in monitoring large populations of neuronal activities simultaneously. However, it lacks the signal quality provided by neural spike recording in traditional electrophysiology. To address this issue, we developed a supervised data-driven approach to extract spike information from calcium signals. We propose the ENS2 (effective and efficient neural networks for spike inference from calcium signals) system for spike-rate and spike-event predictions using ΔF/F0 calcium inputs based on a U-Net deep neural network. When testing on a large, ground-truth public database, it consistently outperformed state-of-the-art algorithms in both spike-rate and spike-event predictions with reduced computational load. We further demonstrated that ENS2 can be applied to analyses of orientation selectivity in primary visual cortex neurons. We conclude that it would be a versatile inference system that may benefit diverse neuroscience studies.

Microscale Neuronal Activity Collectively Drives Chaotic and Inflexible Dynamics at the Macroscale in Seizures

Neuronal activity propagates through the network during seizures, engaging brain dynamics at multiple scales. Such propagating events can be described through the avalanches framework, which can relate spatiotemporal activity at the microscale with global network properties. Interestingly, propagating avalanches in healthy networks are indicative of critical dynamics, where the network is organized to a phase transition, which optimizes certain computational properties. Some have hypothesized that the pathologic brain dynamics of epileptic seizures are an emergent property of microscale neuronal networks collectively driving the brain away from criticality. Demonstrating this would provide a unifying mechanism linking microscale spatiotemporal activity with emergent brain dysfunction during seizures. Here, we investigated the effect of drug-induced seizures on critical avalanche dynamics, using in vivo whole-brain two-photon imaging of GCaMP6s larval zebrafish (males and females) at single neuron resolution. We demonstrate that single neuron activity across the whole brain exhibits a loss of critical statistics during seizures, suggesting that microscale activity collectively drives macroscale dynamics away from criticality. We also construct spiking network models at the scale of the larval zebrafish brain, to demonstrate that only densely connected networks can drive brain-wide seizure dynamics away from criticality. Importantly, such dense networks also disrupt the optimal computational capacities of critical networks, leading to chaotic dynamics, impaired network response properties and sticky states, thus helping to explain functional impairments during seizures. This study bridges the gap between microscale neuronal activity and emergent macroscale dynamics and cognitive dysfunction during seizures.SIGNIFICANCE STATEMENT Epileptic seizures are debilitating and impair normal brain function. It is unclear how the coordinated behavior of neurons collectively impairs brain function during seizures. To investigate this we perform fluorescence microscopy in larval zebrafish, which allows for the recording of whole-brain activity at single-neuron resolution. Using techniques from physics, we show that neuronal activity during seizures drives the brain away from criticality, a regime that enables both high and low activity states, into an inflexible regime that drives high activity states. Importantly, this change is caused by more connections in the network, which we show disrupts the ability of the brain to respond appropriately to its environment. Therefore, we identify key neuronal network mechanisms driving seizures and concurrent cognitive dysfunction.

Quantifying uncertainty in spikes estimated from calcium imaging data

In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty associated with these estimated spikes remains an open problem. We consider a simple and well-studied model for calcium imaging data, which states that calcium decays exponentially in the absence of a spike, and instantaneously increases when a spike occurs. We wish to test the null hypothesis that the neuron did not spike-i.e., that there was no increase in calcium-at a particular timepoint at which a spike was estimated. In this setting, classical hypothesis tests lead to inflated Type I error, because the spike was estimated on the same data used for testing. To overcome this problem, we propose a selective inference approach. We describe an efficient algorithm to compute finite-sample $p$-values that control selective Type I error, and confidence intervals with correct selective coverage, for spikes estimated using a recent proposal from the literature. We apply our proposal in simulation and on calcium imaging data from the $\texttt{spikefinder}$ challenge.

Serotonergic modulation of local network processing in V1 mirrors previously reported signatures of local network modulation by spatial attention

Sensory processing is influenced by neuromodulators such as serotonin, thought to relay behavioural state. Recent work has shown that the modulatory effect of serotonin itself differs with the animal's behavioural state. In primates, including humans, the serotonin system is anatomically important in the primary visual cortex (V1). We previously reported that in awake fixating macaques, serotonin reduces the spiking activity by decreasing response gain in V1. But the effect of serotonin on the local network is unknown. Here, we simultaneously recorded single-unit activity and local field potentials (LFPs) while iontophoretically applying serotonin in V1 of alert monkeys fixating on a video screen for juice rewards. The reduction in spiking response we observed previously is the opposite of the known increase of spiking activity with spatial attention. Conversely, in the local network (LFP), the application of serotonin resulted in changes mirroring the local network effects of previous reports in macaques directing spatial attention to the receptive field. It reduced the LFP power and the spike-field coherence, and the LFP became less predictive of spiking activity, consistent with reduced functional connectivity. We speculate that together, these effects may reflect the sensory side of a serotonergic contribution to quiet vigilance: The lower gain reduces the salience of stimuli to suppress an orienting reflex to novel stimuli, whereas at the network level, visual processing is in a state comparable to that of spatial attention.

CaV3.1 T-type calcium channels are important for spatial memory processing in the dorsal subiculum

The dorsal subiculum (dSub) is one of the key structures responsible for the formation of hippocampal memory traces but the contribution of individual ionic currents to its cognitive function is not well studied. Although we recently reported that low-voltage-activated T-type calcium channels (T-channels) are crucial for the burst firing pattern regulation in the dSub pyramidal neurons, their potential role in learning and memory remains unclear. Here we used in vivo local field potential recordings and miniscope calcium imaging in freely behaving mice coupled with pharmacological and genetic tools to address this gap in knowledge. We show that the CaV3.1 isoform of T-channels is critically involved in controlling neuronal activity in the dSub in vivo. Altering neuronal excitability by inhibiting T-channel activity markedly affects calcium dynamics, synaptic plasticity, neuronal oscillations and phase-amplitude coupling in the dSub, thereby disrupting spatial learning. These results provide an important causative link between the CaV3.1 channels, burst firing of dSub neurons and memory formation, thus further supporting the notion that changes in neuronal excitability regulate memory processing. We posit that subicular CaV3.1 T-channels could be a promising novel drug target for cognitive disorders.

Dimensionality reduction of calcium-imaged neuronal population activity

Calcium imaging has been widely adopted for its ability to record from large neuronal populations. To summarize the time course of neural activity, dimensionality reduction methods, which have been applied extensively to population spiking activity, may be particularly useful. However, it is unclear if the dimensionality reduction methods applied to spiking activity are appropriate for calcium imaging. We thus carried out a systematic study of design choices based on standard dimensionality reduction methods. We also developed a method to perform deconvolution and dimensionality reduction simultaneously (Calcium Imaging Linear Dynamical System, CILDS). CILDS most accurately recovered the single-trial, low-dimensional time courses from simulated calcium imaging data. CILDS also outperformed the other methods on calcium imaging recordings from larval zebrafish and mice. More broadly, this study represents a foundation for summarizing calcium imaging recordings of large neuronal populations using dimensionality reduction in diverse experimental settings.

Could respiration-driven blood oxygen changes modulate neural activity?

Oxygen is critical for neural metabolism, but under most physiological conditions, oxygen levels in the brain are far more than are required. Oxygen levels can be dynamically increased by increases in respiration rate that are tied to the arousal state of the brain and cognition, and not necessarily linked to exertion by the body. Why these changes in respiration occur when oxygen is already adequate has been a long-standing puzzle. In humans, performance on cognitive tasks can be affected by very high or very low oxygen levels, but whether the physiological changes in blood oxygenation produced by respiration have an appreciable effect is an open question. Oxygen has direct effects on potassium channels, increases the degradation rate of nitric oxide, and is rate limiting for the synthesis of some neuromodulators. We discuss whether oxygenation changes due to respiration contribute to neural dynamics associated with attention and arousal.

Reduced excitatory neuron activity and interneuron-type-specific deficits in a mouse model of Alzheimer's disease

Alzheimer's disease (AD) is characterized by progressive memory loss and cognitive decline. These impairments correlate with early alterations in neuronal network activity in AD patients. Disruptions in the activity of individual neurons have been reported in mouse models of amyloidosis. However, the impact of amyloid pathology on the spontaneous activity of distinct neuronal types remains unexplored in vivo. Here we use in vivo calcium imaging with multiphoton microscopy to monitor and compare the activity of excitatory and two types of inhibitory interneurons in the cortices of APP/PS1 and control mice under isoflurane anesthesia. We also determine the relationship between amyloid accumulation and the deficits in spontaneous activity in APP/PS1 mice. We show that somatostatin-expressing (SOM) interneurons are hyperactive, while parvalbumin-expressing interneurons are hypoactive in APP/PS1 mice. Only SOM interneuron hyperactivity correlated with proximity to amyloid plaque. These inhibitory deficits were accompanied by decreased excitatory neuron activity in APP/PS1 mice. Our study identifies cell-specific neuronal firing deficits in APP/PS1 mice driven by amyloid pathology. These findings highlight the importance of addressing the complexity of neuron-specific deficits to ameliorate circuit dysfunction in Alzheimer's disease.

Dimensionality reduction and recurrence analysis reveal hidden structures of striatal pathological states

A pipeline is proposed here to describe different features to study brain microcircuits on a histological scale using multi-scale analyses, including the uniform manifold approximation and projection (UMAP) dimensional reduction technique and modularity algorithm to identify neuronal ensembles, Runs tests to show significant ensembles activation, graph theory to show trajectories between ensembles, and recurrence analyses to describe how regular or chaotic ensembles dynamics are. The data set includes ex-vivo NMDA-activated striatal tissue in control conditions as well as experimental models of disease states: decorticated, dopamine depleted, and L-DOPA-induced dyskinetic rodent samples. The goal was to separate neuronal ensembles that have correlated activity patterns. The pipeline allows for the demonstration of differences between disease states in a brain slice. First, the ensembles were projected in distinctive locations in the UMAP space. Second, graphs revealed functional connectivity between neurons comprising neuronal ensembles. Third, the Runs test detected significant peaks of coactivity within neuronal ensembles. Fourth, significant peaks of coactivity were used to show activity transitions between ensembles, revealing recurrent temporal sequences between them. Fifth, recurrence analysis shows how deterministic, chaotic, or recurrent these circuits are. We found that all revealed circuits had recurrent activity except for the decorticated circuits, which tended to be divergent and chaotic. The Parkinsonian circuits exhibit fewer transitions, becoming rigid and deterministic, exhibiting a predominant temporal sequence that disrupts transitions found in the controls, thus resembling the clinical signs of rigidity and paucity of movements. Dyskinetic circuits display a higher recurrence rate between neuronal ensembles transitions, paralleling clinical findings: enhancement in involuntary movements. These findings confirm that looking at neuronal circuits at the histological scale, recording dozens of neurons simultaneously, can show clear differences between control and diseased striatal states: "fingerprints" of the disease states. Therefore, the present analysis is coherent with previous ones of striatal disease states, showing that data obtained from the tissue are robust. At the same time, it adds heuristic ways to interpret circuitry activity in different states.

Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches

Vision plays a peculiar role in intelligence. Visual information, forming a large part of the sensory information, is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents. Recent advances have led to the development of brain-inspired algorithms and models for machine vision. One of the key components of these methods is the utilization of the computational principles underlying biological neurons. Additionally, advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information. Thus, there is a high demand for mapping out functional models for reading out visual information from neural signals. Here, we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals, from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography (EEG) and functional magnetic resonance imaging recordings of brain signals.

Review of data processing of functional optical microscopy for neuroscience

Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging

Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.

Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey

Calcium imaging has rapidly become a methodology of choice for real-time in vivo neuron analysis. Its application to large sets of data requires automated tools to annotate and segment cells, allowing scalable image segmentation under reproducible criteria. In this paper, we review and summarize the most recent methods for computational segmentation of calcium imaging. The contributions of the paper are three-fold: we provide an overview of the main algorithms taxonomized in three categories (signal processing, matrix factorization and machine learning-based approaches), we highlight the main advantages and disadvantages of each category and we provide a summary of the performance of the methods that have been tested on public benchmarks (with links to the public code when available).

Geometric framework to predict structure from function in neural networks

Neural computation in biological and artificial networks relies on the nonlinear summation of many inputs. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function, but quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of threshold-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate the solution space of all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. A generalization accounting for noise further reveals that the solution space geometry can undergo topological transitions as the allowed error increases, which could provide insight into both neuroscience and machine learning. We ultimately use this geometric characterization to derive certainty conditions guaranteeing a nonzero synapse between neurons. Our theoretical framework could thus be applied to neural activity data to make rigorous anatomical predictions that follow generally from the model architecture.

Assessing Local and Branch-specific Activity in Dendrites

Dendrites are elaborate neural processes which integrate inputs from various sources in space and time. While decades of work have suggested an independent role for dendrites in driving nonlinear computations for the cell, only recently have technological advances enabled us to capture the variety of activity in dendrites and their coupling dynamics with the soma. Under certain circumstances, activity generated in a given dendritic branch remains isolated, such that the soma or even sister dendrites are not privy to these localized signals. Such branch-specific activity could radically increase the capacity and flexibility of coding for the cell as a whole. Here, we discuss these forms of localized and branch-specific activity, their functional relevance in plasticity and behavior, and their supporting biophysical and circuit-level mechanisms. We conclude by showcasing electrical and optical approaches in hippocampal area CA3, using original experimental data to discuss experimental and analytical methodology and key considerations to take when investigating the functional relevance of independent dendritic activity.

Motor cortical output for skilled forelimb movement is selectively distributed across projection neuron classes

The interaction of descending neocortical outputs and subcortical premotor circuits is critical for shaping skilled movements. Two broad classes of motor cortical output projection neurons provide input to many subcortical motor areas: pyramidal tract (PT) neurons, which project throughout the neuraxis, and intratelencephalic (IT) neurons, which project within the cortex and subcortical striatum. It is unclear whether these classes are functionally in series or whether each class carries distinct components of descending motor control signals. Here, we combine large-scale neural recordings across all layers of motor cortex with cell type-specific perturbations to study cortically dependent mouse motor behaviors: kinematically variable manipulation of a joystick and a kinematically precise reach-to-grasp. We find that striatum-projecting IT neuron activity preferentially represents amplitude, whereas pons-projecting PT neurons preferentially represent the variable direction of forelimb movements. Thus, separable components of descending motor cortical commands are distributed across motor cortical projection cell classes.

Reliable Sensory Processing in Mouse Visual Cortex through Cooperative Interactions between Somatostatin and Parvalbumin Interneurons

Intrinsic neuronal variability significantly limits information encoding in the primary visual cortex (V1). However, under certain conditions, neurons can respond reliably with highly precise responses to the same visual stimuli from trial to trial. This suggests that there exists intrinsic neural circuit mechanisms that dynamically modulate the intertrial variability of visual cortical neurons. Here, we sought to elucidate the role of different inhibitory interneurons (INs) in reliable coding in mouse V1. To study the interactions between somatostatin-expressing interneurons (SST-INs) and parvalbumin-expressing interneurons (PV-INs), we used a dual-color calcium imaging technique that allowed us to simultaneously monitor these two neural ensembles while awake mice, of both sexes, passively viewed natural movies. SST neurons were more active during epochs of reliable pyramidal neuron firing, whereas PV neurons were more active during epochs of unreliable firing. SST neuron activity lagged that of PV neurons, consistent with a feedback inhibitory SST→PV circuit. To dissect the role of this circuit in pyramidal neuron activity, we used temporally limited optogenetic activation and inactivation of SST and PV interneurons during periods of reliable and unreliable pyramidal cell firing. Transient firing of SST neurons increased pyramidal neuron reliability by actively suppressing PV neurons, a proposal that was supported by a rate-based model of V1 neurons. These results identify a cooperative functional role for the SST→PV circuit in modulating the reliability of pyramidal neuron activity.SIGNIFICANCE STATEMENT Cortical neurons often respond to identical sensory stimuli with large variability. However, under certain conditions, the same neurons can also respond highly reliably. The circuit mechanisms that contribute to this modulation remain unknown. Here, we used novel dual-wavelength calcium imaging and temporally selective optical perturbation to identify an inhibitory neural circuit in visual cortex that can modulate the reliability of pyramidal neurons to naturalistic visual stimuli. Our results, supported by computational models, suggest that somatostatin interneurons increase pyramidal neuron reliability by suppressing parvalbumin interneurons via the inhibitory SST→PV circuit. These findings reveal a novel role of the SST→PV circuit in modulating the fidelity of neural coding critical for visual perception.

A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging

Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.

Eavesdropping wires: Recording activity in axons using genetically encoded calcium indicators

Neurons broadcast electrical signals to distal brain regions through extensive axonal arbors. Genetically encoded calcium sensors permit the direct observation of action potential activity at axonal terminals, providing unique insights on the organization and function of neural projections. Here, we consider what information can be gleaned from axonal recordings made at scales ranging from the summed activity extracted from multi-cell axon projections to single boutons. In particular, we discuss the application of different recently developed multi photon and fiber photometry methods for recording neural activity in axons of rodents. We define experimental difficulties associated with imaging approaches in the axonal compartment and highlight the latest methodological advances for addressing these issues. Finally, we reflect on ways in which new technologies can be used in conjunction with axon calcium imaging to address current questions in neurobiology.

Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology

Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of cortical neurons. While each of these two modalities has distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging of genetically expressed GCaMP6f or electrophysiology with silicon probes. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging, which was partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could only reconcile differences in responsiveness when restricted to neurons with low contamination and an event rate above a minimum threshold. This work established how the biases of these two modalities impact functional metrics that are fundamental for characterizing sensory-evoked responses.

Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods

BACKGROUND

The past decade has seen a multitude of new in vivo functional imaging methodologies. However, the lack of ground-truth comparisons or evaluation metrics makes the large-scale, systematic validation vital to the continued development and use of optical microscopy impossible.

NEW-METHOD

We provide a new framework for evaluating two-photon microscopy methods via in silico Neural Anatomy and Optical Microscopy (NAOMi) simulation. Our computationally efficient model generates large anatomical volumes of mouse cortex, simulates neural activity, and incorporates optical propagation and scanning to create realistic calcium imaging datasets.

RESULTS

We verify NAOMi simulations against in vivo two-photon recordings from mouse cortex. We leverage this in silico ground truth to directly compare different segmentation algorithms and optical designs. We find modern segmentation algorithms extract strong neural time-courses comparable to estimation using oracle spatial information, but with an increase in the false positive rate. Comparison between optical setups demonstrate improved resilience to motion artifacts in sparsely labeled samples using Bessel beams, increased signal-to-noise ratio and cell-count using low numerical aperture Gaussian beams and nuclear GCaMP, and more uniform spatial sampling with temporal focusing versus multi-plane imaging.

COMPARISON WITH EXISTING METHODS

NAOMi is a first-of-its kind framework for assessing optical imaging modalities. Existing methods are either anatomical simulations or do not address functional imaging. Thus there is no competing method for simulating realistic functional optical microscopy data.

CONCLUSIONS

By leveraging the rich accumulated knowledge of neural anatomy and optical physics, we provide a powerful new tool to assess and develop important methods in neural imaging.

High-resolution light-field microscopy with patterned illumination

Light-field fluorescence microscopy can record large-scale population activity of neurons expressing genetically-encoded fluorescent indicators within volumes of tissue. Conventional light-field microscopy (LFM) suffers from poor lateral resolution when using wide-field illumination. Here, we demonstrate a structured-illumination light-field microscopy (SI-LFM) modality that enhances spatial resolution over the imaging volume. This modality increases resolution by illuminating sample volume with grating patterns that are invariant over the axial direction. The size of the SI-LFM point-spread-function (PSF) was approximately half the size of the conventional LFM PSF when imaging fluorescent beads. SI-LFM also resolved fine spatial features in lens tissue samples and fixed mouse retina samples. Finally, SI-LFM reported neural activity with approximately three times the signal-to-noise ratio of conventional LFM when imaging live zebrafish expressing a genetically encoded calcium sensor.

Direct extraction of signal and noise correlations from two-photon calcium imaging of ensemble neuronal activity

Neuronal activity correlations are key to understanding how populations of neurons collectively encode information. While two-photon calcium imaging has created a unique opportunity to record the activity of large populations of neurons, existing methods for inferring correlations from these data face several challenges. First, the observations of spiking activity produced by two-photon imaging are temporally blurred and noisy. Secondly, even if the spiking data were perfectly recovered via deconvolution, inferring network-level features from binary spiking data is a challenging task due to the non-linear relation of neuronal spiking to endogenous and exogenous inputs. In this work, we propose a methodology to explicitly model and directly estimate signal and noise correlations from two-photon fluorescence observations, without requiring intermediate spike deconvolution. We provide theoretical guarantees on the performance of the proposed estimator and demonstrate its utility through applications to simulated and experimentally recorded data from the mouse auditory cortex.

Inferring spikes from calcium imaging in dopamine neurons

Calcium imaging has led to discoveries about neural correlates of behavior in subcortical neurons, including dopamine (DA) neurons. However, spike inference methods have not been tested in most populations of subcortical neurons. To address this gap, we simultaneously performed calcium imaging and electrophysiology in DA neurons in brain slices and applied a recently developed spike inference algorithm to the GCaMP fluorescence. This revealed that individual spikes can be inferred accurately in this population. Next, we inferred spikes in vivo from calcium imaging from these neurons during Pavlovian conditioning, as well as during navigation in virtual reality. In both cases, we quantitatively recapitulated previous in vivo electrophysiological observations. Our work provides a validated approach to infer spikes from calcium imaging in DA neurons and implies that aspects of both tonic and phasic spike patterns can be recovered.

MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo

BACKGROUND

To understand information coding in single neurons, it is necessary to analyze subthreshold synaptic events, action potentials (APs), and their interrelation in different behavioral states. However, detecting excitatory postsynaptic potentials (EPSPs) or currents (EPSCs) in behaving animals remains challenging, because of unfavorable signal-to-noise ratio, high frequency, fluctuating amplitude, and variable time course of synaptic events.

NEW METHOD

We developed a method for synaptic event detection, termed MOD (Machine-learning Optimal-filtering Detection-procedure), which combines concepts of supervised machine learning and optimal Wiener filtering. Experts were asked to manually score short epochs of data. The algorithm was trained to obtain the optimal filter coefficients of a Wiener filter and the optimal detection threshold. Scored and unscored data were then processed with the optimal filter, and events were detected as peaks above threshold.

RESULTS

We challenged MOD with EPSP traces in vivo in mice during spatial navigation and EPSC traces in vitro in slices under conditions of enhanced transmitter release. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was, on average, 0.894 for in vivo and 0.969 for in vitro data sets, indicating high detection accuracy and efficiency.

COMPARISON WITH EXISTING METHODS

When benchmarked using a (1 - AUC)^-1 metric, MOD outperformed previous methods (template-fit, deconvolution, and Bayesian methods) by an average factor of 3.13 for in vivo data sets, but showed comparable (template-fit, deconvolution) or higher (Bayesian) computational efficacy.

CONCLUSIONS

MOD may become an important new tool for large-scale, real-time analysis of synaptic activity.

Relationship between simultaneously recorded spiking activity and fluorescence signal in GCaMP6 transgenic mice

Fluorescent calcium indicators are often used to investigate neural dynamics, but the relationship between fluorescence and action potentials (APs) remains unclear. Most APs can be detected when the soma almost fills the microscope’s field of view, but calcium indicators are used to image populations of neurons, necessitating a large field of view, generating fewer photons per neuron, and compromising AP detection. Here, we characterized the AP-fluorescence transfer function in vivo for 48 layer 2/3 pyramidal neurons in primary visual cortex, with simultaneous calcium imaging and cell-attached recordings from transgenic mice expressing GCaMP6s or GCaMP6f. While most APs were detected under optimal conditions, under conditions typical of population imaging studies, only a minority of 1 AP and 2 AP events were detected (often <10% and ~20–30%, respectively), emphasizing the limits of AP detection under more realistic imaging conditions.

Neurons, the cells that make up the nervous system, transmit information using electrical signals known as action potentials or spikes. Studying the spiking patterns of neurons in the brain is essential to understand perception, memory, thought, and behaviour. One way to do that is by recording electrical activity with microelectrodes. Another way to study neuronal activity is by using molecules that change how they interact with light when calcium binds to them, since changes in calcium concentration can be indicative of neuronal spiking. That change can be observed with specialized microscopes know as two-photon fluorescence microscopes. Using calcium indicators, it is possible to simultaneously record hundreds or even thousands of neurons. However, calcium fluorescence and spikes do not translate one-to-one.

In order to interpret fluorescence data, it is important to understand the relationship between the fluorescence signals and the spikes associated with individual neurons. The only way to directly measure this relationship is by using calcium imaging and electrical recording simultaneously to record activity from the same neuron. However, this is extremely challenging experimentally, so this type of data is rare.

To shed some light on this, Huang, Ledochowitsch et al. used mice that had been genetically modified to produce a calcium indicator in neurons of the visual cortex and simultaneously obtained both fluorescence measurements and electrical recordings from these neurons. These experiments revealed that, while the majority of time periods containing multi-spike neural activity could be identified using calcium imaging microscopy, on average, less than 10% of isolated single spikes were detectable. This is an important caveat that researchers need to take into consideration when interpreting calcium imaging results.

These findings are intended to serve as a guide for interpreting calcium imaging studies that look at neurons in the mammalian brain at the population level. In addition, the data provided will be useful as a reference for the development of activity sensors, and to benchmark and improve computational approaches for detecting and predicting spikes.

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson's correlation coefficient, Spearman's rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators.

Calcium Imaging and the Curse of Negativity

The imaging of neuronal activity using calcium indicators has become a staple of modern neuroscience. However, without ground truths, there is a real risk of missing a significant portion of the real responses. Here, we show that a common assumption, the non-negativity of the neuronal responses as detected by calcium indicators, biases all levels of the frequently used analytical methods for these data. From the extraction of meaningful fluorescence changes to spike inference and the analysis of inferred spikes, each step risks missing real responses because of the assumption of non-negativity. We first show that negative deviations from baseline can exist in calcium imaging of neuronal activity. Then, we use simulated data to test three popular algorithms for image analysis, CaImAn, suite2p, and CellSort, finding that suite2p may be the best suited to large datasets. We also tested the spike inference algorithms included in CaImAn, suite2p, and Cellsort, as well as the dedicated inference algorithms MLspike and CASCADE, and found each to have limitations in dealing with inhibited neurons. Among these spike inference algorithms, FOOPSI, from CaImAn, performed the best on inhibited neurons, but even this algorithm inferred spurious spikes upon the return of the fluorescence signal to baseline. As such, new approaches will be needed before spikes can be sensitively and accurately inferred from calcium data in inhibited neurons. We further suggest avoiding data analysis approaches that, by assuming non-negativity, ignore inhibited responses. Instead, we suggest a first exploratory step, using k-means or PCA for example, to detect whether meaningful negative deviations are present. Taking these steps will ensure that inhibition, as well as excitation, is detected in calcium imaging datasets.

Evolution of in vivo dopamine monitoring techniques

The brain dopamine system is central to numerous behavioral processes, including movement, learning, and motivation. Accordingly, disruptions of this neural system underlie numerous neurological and psychiatric disorders. Current understanding of how dopamine neurotransmission contributes to behavior and its dysfunction has been driven by technological advancements that permit spatiotemporally-defined measurements of dopaminergic signaling in behaving animals. In this review, we will discuss the evolution of in vivo neural monitoring technologies for measuring dopamine neuron function. We focus on the dopamine system for two reasons: (1) the central role of dopamine neurotransmission in normal behavior and disease, and (2) dopamine neuron measurements have long been at the forefront of in vivo neural monitoring technologies. We will provide a brief overview of standard techniques for monitoring dopamine function, including electrophysiology, microdialysis, and voltammetry. Then, we will discuss recent advancements in optical technologies using genetically-encoded fluorescent proteins (GEFPs), including a critical evaluation of their advantages and limitations.

Neurovascular coupling and bilateral connectivity during NREM and REM sleep

To understand how arousal state impacts cerebral hemodynamics and neurovascular coupling, we monitored neural activity, behavior, and hemodynamic signals in un-anesthetized, head-fixed mice. Mice frequently fell asleep during imaging, and these sleep events were interspersed with periods of wake. During both NREM and REM sleep, mice showed large increases in cerebral blood volume ([HbT]) and arteriole diameter relative to the awake state, two to five times larger than those evoked by sensory stimulation. During NREM, the amplitude of bilateral low-frequency oscillations in [HbT] increased markedly, and coherency between neural activity and hemodynamic signals was higher than the awake resting and REM states. Bilateral correlations in neural activity and [HbT] were highest during NREM, and lowest in the awake state. Hemodynamic signals in the cortex are strongly modulated by arousal state, and changes during sleep are substantially larger than sensory-evoked responses.

Improved hyperacuity estimation of spike timing from calcium imaging

Two-photon imaging is a major recording technique used in neuroscience. However, it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low SNR, all of which severely limit the potential of two-photon imaging to elucidate neuronal dynamics with high temporal resolution. We developed a hyperacuity algorithm (HA_time) based on an approach that combines a generative model and machine learning to improve spike detection and the precision of spike time inference. Bayesian inference was performed to estimate the calcium spike model, assuming constant spike shape and size. A support vector machine using this information and a jittering method maximizing the likelihood of estimated spike times enhanced spike time estimation precision approximately fourfold (range, 2–7; mean, 3.5–4.0; 2SEM, 0.1–0.25) compared to the sampling interval. Benchmark scores of HA_time for biological data from three different brain regions were among the best of the benchmark algorithms. Simulation of broader data conditions indicated that our algorithm performed better than others with high firing rate conditions. Furthermore, HA_time exhibited comparable performance for conditions with and without ground truths. Thus HA_time is a useful tool for spike reconstruction from two-photon imaging.

Spontaneous Activity of Neuronal Ensembles in Mouse Motor Cortex: Changes after GABAergic Blockade

The mouse motor cortex exhibits spontaneous activity in the form of temporal sequences of neuronal ensembles in vitro without the need of tissue stimulation. These neuronal ensembles are defined as groups of neurons with a strong correlation between its firing patterns, generating what appears to be a predetermined neural conduction mode that needs study. Each ensemble is commonly accompanied by one or more parvalbumin expressing neurons (PV+) or fast spiking interneurons. Many of these interneurons have functional connections between them, helping to form a circuit configuration similar to a small-world network. However, rich club metrics show that most connected neurons are neurons not expressing parvalbumin, mainly pyramidal neurons (PV-) suggesting feed-forward propagation through pyramidal cells. Ensembles with PV+ neurons are connected to these hubs. When ligand-gated fast GABAergic transmission is blocked, temporal sequences of ensembles collapse into a unique synchronous and recurrent ensemble, showing the need of inhibition for coding cortical spontaneous activity. This new ensemble has a duration and electrophysiological characteristics of brief recurrent interictal epileptiform discharges (IEDs) composed by the coactivity of both PV- and PV+ neurons, demonstrating that GABA transmission impedes its occurrence. Synchronous ensembles are clearly divided into two clusters one of them lasting longer and mainly composed by PV+ neurons. Because an ictal-like event was not recorded after several minutes of IEDs recording, it is inferred that an external stimulus and/or fast GABA transmission are necessary for its appearance, making this preparation ideal to study both the neuronal machinery to encode cortical spontaneous activity and its transformation into brief non-ictal epileptiform discharges.

nNOS-expressing interneurons control basal and behaviorally evoked arterial dilation in somatosensory cortex of mice

Cortical neural activity is coupled to local arterial diameter and blood flow. However, which neurons control the dynamics of cerebral arteries is not well understood. We dissected the cellular mechanisms controlling the basal diameter and evoked dilation in cortical arteries in awake, head-fixed mice. Locomotion drove robust arterial dilation, increases in gamma band power in the local field potential (LFP), and increases calcium signals in pyramidal and neuronal nitric oxide synthase (nNOS)-expressing neurons. Chemogenetic or pharmocological modulation of overall neural activity up or down caused corresponding increases or decreases in basal arterial diameter. Modulation of pyramidal neuron activity alone had little effect on basal or evoked arterial dilation, despite pronounced changes in the LFP. Modulation of the activity of nNOS-expressing neurons drove changes in the basal and evoked arterial diameter without corresponding changes in population neural activity.

Stability of motor cortex network states during learning-associated neural reorganizations

A substantial reorganization of neural activity and neuron-to-movement relationship in motor cortical circuits accompanies the emergence of reproducible movement patterns during motor learning. Little is known about how this tempest of neural activity restructuring impacts the stability of network states in recurrent cortical circuits. To investigate this issue, we reanalyzed data in which we recorded for 14 days via population calcium imaging the activity of the same neural populations of pyramidal neurons in layer 2/3 and layer 5 of forelimb motor and premotor cortex in mice during the daily learning of a lever-press task. We found that motor cortex network states remained stable with respect to the critical network state during the extensive reorganization of both neural population activity and its relation to lever movement throughout learning. Specifically, layer 2/3 cortical circuits unceasingly displayed robust evidence for operating at the critical network state, a regime that maximizes information capacity and transmission and provides a balance between network robustness and flexibility. In contrast, layer 5 circuits operated away from the critical network state for all 14 days of recording and learning. In conclusion, this result indicates that the wide-ranging malleability of synapses, neurons, and neural connectivity during learning operates within the constraint of a stable and layer-specific network state regarding dynamic criticality, and suggests that different cortical layers operate under distinct constraints because of their specialized goals.NEW & NOTEWORTHY The neural activity reorganizes throughout motor learning, but how this reorganization impacts the stability of network states is unclear. We used two-photon calcium imaging to investigate how the network states in layer 2/3 and layer 5 of forelimb motor and premotor cortex are modulated by motor learning. We show that motor cortex network states are layer-specific and constant regarding criticality during neural activity reorganization, and suggests that layer-specific constraints could be motivated by different functions.

A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology

Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and low-pass filtering, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, including changes in single-neuron selectivity and population decoding. These were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data.

Scalable surrogate deconvolution for identification of partially-observable systems and brain modeling

OBJECTIVE

For many biophysical systems, direct measurement of all state-variables, in - vivo is not feasible. Thus, a key challenge in biological modeling and signal processing is to reconstruct the activity and structure of interesting biological systems from indirect measurements. These measurements are often generated by approximately linear time-invariant dynamical interactions with the hidden system and may therefore be described as a convolution of hidden state-variables with an unknown kernel.

APPROACH

In the current work, we present an approach termed surrogate deconvolution, to directly identify such coupled systems (i.e. parameterize models). Surrogate deconvolution reframes certain non linear partially-observable identification problems, which are common in neuroscience/biology, as analytical objectives that are compatible with almost any user-chosen optimization procedure.

MAIN RESULTS

We show that the proposed technique is highly scalable, low in computational complexity, and performs competitively with the current gold-standard in partially-observable system estimation: the joint Kalman Filters (Unscented and Extended). We show the benefits of surrogate deconvolution for model identification when applied to simulations of the Local Field Potential and blood oxygen level dependent (BOLD) signal. Lastly, we demonstrate the empirical stability of Hemodynamic Response Function (HRF) kernel estimates for Mesoscale Individualized NeuroDynamic (MINDy) models of individual human brains. The recovered HRF parameters demonstrate reliable individual variation as well as a stereotyped spatial distribution, on average.

SIGNIFICANCE

These results demonstrate that surrogate deconvolution promises to enhance brain-modeling approaches by simultaneously and rapidly fitting large-scale models of brain networks and the physiological processes which generate neuroscientific measurements (e.g. hemodynamics for BOLD fMRI).

Reduced activity of GAD67 expressing cells in the reticular thalamus enhance thalamic excitatory activity and varicella zoster virus associated pain

Within the reticular thalamic nucleus neurons express gamma aminobutyric acid (GABA) and these cells project to the ventral posteromedial thalamic nucleus. When GABA activity decreases the activity of excitatory cells in the ventral posteromedial nucleus would be expected to increase. In this study, we addressed the hypothesis that attenuating GABAergic cells in the reticular thalamic nucleus increases excitatory activity in the ventral posteromedial nucleus increasing varicella zoster virus (VZV) associated pain in the orofacial region. Adeno-associated virus (AAV) was infused in the reticular thalamic nucleus of Gad1-Cre rats. This virus transduced a G inhibitory designer receptor exclusively activated by designer drugs (DREADD) gene that was Cre dependent. A dose of estradiol that was previously shown to reduce VZV pain and increase GABAergic activity was administered to castrated and ovariectomized rats. Previous studies suggest that estradiol attenuates herpes zoster pain by increasing the activity of inhibitory neurons and decreasing the activity of excitatory cells within the lateral thalamic region. The ventral posteromedial nucleus was infused with AAV containing a GCaMP6f expression construct. A glass lens was implanted for miniscope imaging. Our results show that the activity of GABA cells within the reticular thalamic region decreased with clozapine N-oxide treatment concomitant with increased calcium activity of excitatory cells in the ventral posteromedial nucleus and an increased orofacial pain response. The results suggest that estradiol attenuates herpes zoster pain by increasing the activity of inhibitory neurons within the reticular thalamus that then inhibit excitatory activity in ventral posteromedial nucleus causing a reduction in orofacial pain.