Fast, cell-resolution, contiguous-wide two-photon imaging to reveal functional network architectures across multi-modal cortical areas

Probing neuronal activity with genetically encoded calcium and voltage fluorescent indicators

Monitoring neural activity in individual neurons is crucial for understanding neural circuits and brain functions. The emergence of optical imaging technologies has dramatically transformed the field of neuroscience, enabling detailed observation of large-scale neuronal populations with both cellular and subcellular resolution. This transformation will be further accelerated by the integration of these imaging technologies and advanced big data analysis. Genetically encoded fluorescent indicators to detect neural activity with high signal-to-noise ratios are pivotal in this advancement. In recent years, these indicators have undergone significant developments, greatly enhancing the understanding of neural dynamics and networks. This review highlights the recent progress in genetically encoded calcium and voltage indicators and discusses the future direction of imaging techniques with big data analysis that deepens our understanding of the complexities of the brain.

Inference of monosynaptic connections from parallel spike trains: A review

This article presents a mini-review about the progress in inferring monosynaptic connections from spike trains of multiple neurons over the past twenty years. First, we explain a variety of meanings of "neuronal connectivity" in different research areas of neuroscience, such as structural connectivity, monosynaptic connectivity, and functional connectivity. Among these, we focus on the methods used to infer the monosynaptic connectivity from spike data. We then summarize the inference methods based on two main approaches, i.e., correlation-based and model-based approaches. Finally, we describe available source codes for connectivity inference and future challenges. Although inference will never be perfect, the accuracy of identifying the monosynaptic connections has improved dramatically in recent years due to continuous efforts.

Bioengineered nanomaterials for dynamic diagnostics in vivo

In vivo diagnostics obtains real-time physiological information directly from the site of interest in a patient's body, providing more accurate disease diagnosis compared with ex vivo diagnostics. Particularly, in vivo dynamic diagnostics allows the continuous monitoring of physiological signals over a period of time, offering deeper insights into disease pathogenesis and progression. However, achieving in situ dynamic diagnostics in deep tissues presents challenges related to energy and signal penetration as well as dynamic monitoring. Bioengineered nanomaterials serve as an ideal platform for in vivo dynamic diagnostics, leveraging energy conversion and biofunctionalization to enable continuous acquisition of physiological information across temporal and spatial scales. In this review, with reference to the studies from the last five years, we summarize the fundamental components that are essential for dynamic diagnosis in vivo. Firstly, an input energy source with high tissue penetration is needed, such as near-infrared (NIR) light, X-rays, magnetic field and ultrasound. Secondly, a nanomaterial class that is responsive to such an energy source to provide a readable output signal is chosen. Thirdly, bioengineered nanoprobes are designed to exhibit spatial, temporal or spatiotemporal changes in the output signal. Finally, different methods are used to analyse the output signal of nanoprobes, such as detecting changes in optical, radiation, magnetic and ultrasound signals. This review also discusses the obstacles and potential solutions for advancing these bioengineered nanomaterials toward clinical translational applications.

The future of neurotechnology: From big data to translation

Advances in neurotechnologies, including molecular tools, neural sensors, and large-scale recording, are transforming neuroscience and generating vast datasets. A recent meeting highlighted the resulting challenges in global collaboration, data management, and effective translation, emphasizing the need for innovative strategies to harness big data for diagnosing and treating brain disorders.

A large field of view 2- and 3-photon microscope

A new multiphoton fluorescence microscope has been developed, offering cellular resolution across a large field of view deep within biological tissues. This opens new possibilities across a range of biological sciences, particularly within neuroscience where optical approaches can reveal signaling in real time throughout an extended network of cells distributed through the brain of an awake, behaving mouse.

Volumetric trans-scale imaging of massive quantity of heterogeneous cell populations in centimeter-wide tissue and embryo

We established a volumetric trans-scale imaging system with an ultra-large field-of-view (FOV) that enables simultaneous observation of millions of cellular dynamics in centimeter-wide three-dimensional (3D) tissues and embryos. Using a custom-made giant lens system with a magnification of ×2 and a numerical aperture (NA) of 0.25, and a CMOS camera with more than 100 megapixels, we built a trans-scale scope AMATERAS-2, and realized fluorescence imaging with a transverse spatial resolution of approximately 1.1 µm across an FOV of approximately 1.5×1.0 cm2. The 3D resolving capability was realized through a combination of optical and computational sectioning techniques tailored for our low-power imaging system. We applied the imaging technique to 1.2 cm-wide section of mouse brain, and successfully observed various regions of the brain with sub-cellular resolution in a single FOV. We also performed time-lapse imaging of a 1-cm-wide vascular network during quail embryo development for over 24 hr, visualizing the movement of over 4.0×105 vascular endothelial cells and quantitatively analyzing their dynamics. Our results demonstrate the potential of this technique in accelerating production of comprehensive reference maps of all cells in organisms and tissues, which contributes to understanding developmental processes, brain functions, and pathogenesis of disease, as well as high-throughput quality check of tissues used for transplantation medicine.

A Sensitive Soma-localized Red Fluorescent Calcium Indicator for Multi-Modality Imaging of Neuronal Populations In Vivo

Recent advancements in genetically encoded calcium indicators, particularly those based on green fluorescent proteins, have optimized their performance for monitoring neuronal activities in a variety of model organisms. However, progress in developing red-shifted GECIs, despite their advantages over green indicators, has been slower, resulting in fewer options for end-users. In this study, we explored topological inversion and soma-targeting strategies, which are complementary to conventional mutagenesis, to re-engineer a red genetically encoded calcium indicator, FRCaMP, for enhanced in vivo performance. The resulting sensors, FRCaMPi and soma-targeted FRCaMPi (SomaFRCaMPi), exhibit up to 2-fold higher dynamic range and peak ΔF/F0 per single AP compared to widely used jRGECO1a in neurons in culture and in vivo. Compared to jRGECO1a and FRCaMPi, SomaFRCaMPi reduces erroneous correlation of neuronal activity in the brains of mice and zebrafish by two- to four-fold due to diminished neuropil contamination without compromising the signal-to-noise ratio. Under wide-field imaging in primary somatosensory and visual cortex in mice with high labeling density (80-90%), SomaFRCaMPi exhibits up to 40% higher SNR and decreased artifactual correlation across neurons. Altogether, SomaFRCaMPi improves the accuracy and scale of neuronal activity imaging at single-neuron resolution in densely labeled brain tissues due to a 2-3-fold enhanced automated neuronal segmentation, 50% higher fraction of responsive cells, up to 2-fold higher SNR compared to jRGECO1a. Our findings highlight the potential of SomaFRCaMPi, comparable to the most sensitive soma-targeted GCaMP, for precise spatial recording of neuronal populations using popular imaging modalities in model organisms such as zebrafish and mice.

Moculus: an immersive virtual reality system for mice incorporating stereo vision

Due to technical roadblocks, it is unclear how visual circuits represent multiple features or how behaviorally relevant representations are selected for long-term memory. Here we developed Moculus, a head-mounted virtual reality platform for mice that covers the entire visual field, and allows binocular depth perception and full visual immersion. This controllable environment, with three-dimensional (3D) corridors and 3D objects, in combination with 3D acousto-optical imaging, affords rapid visual learning and the uncovering of circuit substrates in one measurement session. Both the control and reinforcement-associated visual cue coding neuronal assemblies are transiently expanded by reinforcement feedback to near-saturation levels. This increases computational capability and allows competition among assemblies that encode behaviorally relevant information. The coding assemblies form partially orthogonal and overlapping clusters centered around hub cells with higher and earlier ramp-like responses, as well as locally increased functional connectivity.

Neuronal Electrical Activity in Neuronal Networks Induced by a Focused Femtosecond Laser

The spatial propagation of neuronal activity within neuronal circuits, which is associated with brain functions, such as memory and learning, is regulated by external stimuli. Conventional external stimuli, such as electrical inputs, pharmacological treatments, and optogenetic modifications, have been used to modify neuronal activity. However, these methods are tissue invasive, have insufficient spatial resolution, and cause irreversible gene modifications. To establish neuronal stimulation with less invasiveness and higher spatial resolution, we propose and demonstrate single-neuron stimulation using a focused femtosecond laser. Fluorescence Ca2+ imaging and microelectrode array recordings of extracellular potentials revealed Ca2+ influx into the target neuron and high-frequency electrical responses after laser irradiation. These results indicate that femtosecond laser-induced neuronal electrical activity has a greater number of electrical spikes, lasts longer after stimulation, and propagates to a region more distant from the target neuron compared with the properties evoked by electrical stimulation. Focused femtosecond laser-induced stimulation can be a promising tool for stimulating single neurons in neuronal networks.

Optical sectioning methods in three-dimensional bioimaging

In recent advancements in life sciences, optical microscopy has played a crucial role in acquiring high-quality three-dimensional structural and functional information. However, the quality of 3D images is often compromised due to the intense scattering effect in biological tissues, compounded by several issues such as limited spatiotemporal resolution, low signal-to-noise ratio, inadequate depth of penetration, and high phototoxicity. Although various optical sectioning techniques have been developed to address these challenges, each method adheres to distinct imaging principles for specific applications. As a result, the effective selection of suitable optical sectioning techniques across diverse imaging scenarios has become crucial yet challenging. This paper comprehensively overviews existing optical sectioning techniques and selection guidance under different imaging scenarios. Specifically, we categorize the microscope design based on the spatial relationship between the illumination and detection axis, i.e., on-axis and off-axis. This classification provides a unique perspective to compare the implementation and performances of various optical sectioning approaches. Lastly, we integrate selected optical sectioning methods on a custom-built off-axis imaging system and present a unique perspective for the future development of optical sectioning techniques.

Noisy neuronal populations effectively encode sound localization in the dorsal inferior colliculus of awake mice

Sound location coding has been extensively studied at the central nucleus of the mammalian inferior colliculus (CNIC), supporting a population code. However, this population code has not been extensively characterized on the single-trial level with simultaneous recordings or at other anatomical regions like the dorsal cortex of inferior colliculus (DCIC), which is relevant for learning-induced experience dependent plasticity. To address these knowledge gaps, here we made in two complementary ways large-scale recordings of DCIC populations from awake mice in response to sounds delivered from 13 different frontal horizontal locations (azimuths): volumetric two-photon calcium imaging with ~700 cells simultaneously recorded at a relatively low temporal resolution, and high-density single-unit extracellular recordings with ~20 cells simultaneously recorded at a high temporal resolution. Independent of the method, the recorded DCIC population responses revealed substantial trial-to-trial variation (neuronal noise) which was significantly correlated across pairs of neurons (noise correlations) in the passively listening condition. Nevertheless, decoding analysis supported that these noisy response patterns encode sound location on the single-trial basis, reaching errors that match the discrimination ability of mice. The detected noise correlations contributed to minimize the error of the DCIC population code of sound azimuth. Altogether these findings point out that DCIC can encode sound location in a similar format to what has been proposed for CNIC, opening exciting questions about how noise correlations could shape this code in the context of cortico-collicular input and experience-dependent plasticity.

Dynamics of directional motor tuning in the primate premotor and primary motor cortices during sensorimotor learning

Sensorimotor learning requires reorganization of neuronal activity in the premotor cortex (PM) and primary motor cortex (M1). To reveal PM- and M1-specific reorganization in a primate, we conducted calcium imaging in common marmosets while they learned a two-target reaching (pull/push) task after mastering a one-target reaching (pull) task. Throughout learning of the two-target reaching task, the dorsorostral PM (PMdr) showed peak activity earlier than the dorsocaudal PM (PMdc) and M1. During learning, the reaction time in pull trials increased and correlated strongly with the peak timing of PMdr activity. PMdr showed decreasing representation of newly introduced (push) movement, whereas PMdc and M1 maintained high representation of pull and push movements. Many task-related neurons in PMdc and M1 exhibited a strong preference to either movement direction. PMdc neurons dynamically switched their preferred direction depending on their performance in push trials in the early learning stage, whereas M1 neurons stably retained their preferred direction and high similarity of preferred direction between neighbors. These results suggest that in primate sensorimotor learning, dynamic directional motor tuning in PMdc converts the sensorimotor association formed in PMdr to the stable and specific motor representation of M1.

Multiphoton imaging of hippocampal neural circuits: techniques and biological insights into region-, cell-type-, and pathway-specific functions

Significance

The function of the hippocampus in behavior and cognition has long been studied primarily through electrophysiological recordings from freely moving rodents. However, the application of optical recording methods, particularly multiphoton fluorescence microscopy, in the last decade or two has dramatically advanced our understanding of hippocampal function. This article provides a comprehensive overview of techniques and biological findings obtained from multiphoton imaging of hippocampal neural circuits.

Aim

This review aims to summarize and discuss the recent technical advances in multiphoton imaging of hippocampal neural circuits and the accumulated biological knowledge gained through this technology.

Approach

First, we provide a brief overview of various techniques of multiphoton imaging of the hippocampus and discuss its advantages, drawbacks, and associated key innovations and practices. Then, we review a large body of findings obtained through multiphoton imaging by region (CA1 and dentate gyrus), cell type (pyramidal neurons, inhibitory interneurons, and glial cells), and cellular compartment (dendrite and axon).

Results

Multiphoton imaging of the hippocampus is primarily performed under head-fixed conditions and can reveal detailed mechanisms of circuit operation owing to its high spatial resolution and specificity. As the hippocampus lies deep below the cortex, its imaging requires elaborate methods. These include imaging cannula implantation, microendoscopy, and the use of long-wavelength light sources. Although many studies have focused on the dorsal CA1 pyramidal cells, studies of other local and inter-areal circuitry elements have also helped provide a more comprehensive picture of the information processing performed by the hippocampal circuits. Imaging of circuit function in mouse models of Alzheimer's disease and other brain disorders such as autism spectrum disorder has also contributed greatly to our understanding of their pathophysiology.

Conclusions

Multiphoton imaging has revealed much regarding region-, cell-type-, and pathway-specific mechanisms in hippocampal function and dysfunction in health and disease. Future technological advances will allow further illustration of the operating principle of the hippocampal circuits via the large-scale, high-resolution, multimodal, and minimally invasive imaging.

A multicolor suite for deciphering population coding of calcium and cAMP in vivo

cAMP is a universal second messenger regulated by various upstream pathways including Ca2+ and G-protein-coupled receptors (GPCRs). To decipher in vivo cAMP dynamics, we rationally designed cAMPinG1, a sensitive genetically encoded green cAMP indicator that outperformed its predecessors in both dynamic range and cAMP affinity. Two-photon cAMPinG1 imaging detected cAMP transients in the somata and dendritic spines of neurons in the mouse visual cortex on the order of tens of seconds. In addition, multicolor imaging with a sensitive red Ca2+ indicator RCaMP3 allowed simultaneous measurement of population patterns in Ca2+ and cAMP in hundreds of neurons. We found Ca2+-related cAMP responses that represented specific information, such as direction selectivity in vision and locomotion, as well as GPCR-related cAMP responses. Overall, our multicolor suite will facilitate analysis of the interaction between the Ca2+, GPCR and cAMP signaling at single-cell resolution both in vitro and in vivo.

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

Strength in numbers: Unleashing the potential of trans-scale scope AMATERAS for massive cell quantification

Singularity biology is a scientific field that targets drastic state changes in multicellular systems, aiming to discover the key cells that induce the state change and investigate the mechanisms behind them. To achieve this goal, we developed a trans-scale optical imaging system (trans-scale scope), that is capable of capturing both macroscale changes across the entire system and the micro-scale behavior of individual cells, surpassing the cell observation capabilities of traditional microscopes. We developed two units of the trans-scale scope, named AMATERAS-1 and -2, which demonstrated the ability to observe multicellular systems consisting of over one million cells in a single field of view with sub-cellular resolution. This flagship instrument has been used to observe the dynamics of various cell species, with the advantage of being able to observe a large number of cells, allowing the detection and analysis of rare events and cells such as leader cells in multicellular pattern formation and cells that spontaneously initiate calcium waves. In this paper, we present the design concept of AMATERAS, the optical configuration, and several examples of observations, and demonstrate how the strength-in-numbers works in life sciences.

Large-scale cranial window for in vivo mouse brain imaging utilizing fluoropolymer nanosheet and light-curable resin

Two-photon microscopy enables in vivo imaging of neuronal activity in mammalian brains at high resolution. However, two-photon imaging tools for stable, long-term, and simultaneous study of multiple brain regions in same mice are lacking. Here, we propose a method to create large cranial windows covering such as the whole parietal cortex and cerebellum in mice using fluoropolymer nanosheets covered with light-curable resin (termed the 'Nanosheet Incorporated into light-curable REsin' or NIRE method). NIRE method can produce cranial windows conforming the curved cortical and cerebellar surfaces, without motion artifacts in awake mice, and maintain transparency for >5 months. In addition, we demonstrate that NIRE method can be used for in vivo two-photon imaging of neuronal ensembles, individual neurons and subcellular structures such as dendritic spines. The NIRE method can facilitate in vivo large-scale analysis of heretofore inaccessible neural processes, such as the neuroplastic changes associated with maturation, learning and neural pathogenesis.

Ensemble dynamics and information flow deduction from whole-brain imaging data

The recent advancements in large-scale activity imaging of neuronal ensembles offer valuable opportunities to comprehend the process involved in generating brain activity patterns and understanding how information is transmitted between neurons or neuronal ensembles. However, existing methodologies for extracting the underlying properties that generate overall dynamics are still limited. In this study, we applied previously unexplored methodologies to analyze time-lapse 3D imaging (4D imaging) data of head neurons of the nematode Caenorhabditis elegans. By combining time-delay embedding with the independent component analysis, we successfully decomposed whole-brain activities into a small number of component dynamics. Through the integration of results from multiple samples, we extracted common dynamics from neuronal activities that exhibit apparent divergence across different animals. Notably, while several components show common cooperativity across samples, some component pairs exhibited distinct relationships between individual samples. We further developed time series prediction models of synaptic communications. By combining dimension reduction using the general framework, gradient kernel dimension reduction, and probabilistic modeling, the overall relationships of neural activities were incorporated. By this approach, the stochastic but coordinated dynamics were reproduced in the simulated whole-brain neural network. We found that noise in the nervous system is crucial for generating realistic whole-brain dynamics. Furthermore, by evaluating synaptic interaction properties in the models, strong interactions within the core neural circuit, variable sensory transmission and importance of gap junctions were inferred. Virtual optogenetics can be also performed using the model. These analyses provide a solid foundation for understanding information flow in real neural networks.

Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity

Many cognitive functions are represented as cell assemblies. In the case of spatial navigation, the population activity of place cells in the hippocampus and grid cells in the entorhinal cortex represents self-location in the environment. The brain cannot directly observe self-location information in the environment. Instead, it relies on sensory information and memory to estimate self-location. Therefore, estimating low-dimensional dynamics, such as the movement trajectory of an animal exploring its environment, from only the high-dimensional neural activity is important in deciphering the information represented in the brain. Most previous studies have estimated the low-dimensional dynamics (i.e., latent variables) behind neural activity by unsupervised learning with Bayesian population decoding using artificial neural networks or gaussian processes. Recently, persistent cohomology has been used to estimate latent variables from the phase information (i.e., circular coordinates) of manifolds created by neural activity. However, the advantages of persistent cohomology over Bayesian population decoding are not well understood. We compared persistent cohomology and Bayesian population decoding in estimating the animal location from simulated and actual grid cell population activity. We found that persistent cohomology can estimate the animal location with fewer neurons than Bayesian population decoding and robustly estimate the animal location from actual noisy data.

Chromophore-assisted light inactivation of target proteins for singularity biology

Singularity phenomena are rare events that occur only with a probability of one in tens of thousands and yet play an important role in the fate of the entire system. Recently, an ultra-wide-field microscopy imaging systems, AMATERAS, have been developed to reliably capture singularity phenomena. However, to determine whether a rare phenomenon captured by microscopy is a true singularity phenomenon-one with a significant impact on the entire system-, causal analysis is required. In this section, we introduce the CALI method, which uses light to inactivate molecules as one of the techniques enabling causal analysis. In addition, we discuss the technical innovations of the CALI method that are required to contribute to the future development of singularity biology.

Modern Methods for Unraveling Cell- and Circuit-Level Mechanisms of Neurophysiological Biomarkers in Psychiatry

Methods for studying the mammalian brain in vivo have advanced dramatically in the past two decades. State-of-the-art optical and electrophysiological techniques allow direct recordings of the functional dynamics of thousands of neurons across distributed brain circuits with single-cell resolution. With transgenic tools, specific neuron types, pathways, and/or neurotransmitters can be targeted in user-determined brain areas for precise measurement and manipulation. In this chapter, we catalog these advancements. We emphasize that the impact of this methodological revolution on neuropsychiatry remains uncertain. This stems from the fact that these tools remain mostly limited to research in mice. And while translational paradigms are needed, recapitulations of human psychiatric disease states (e.g., schizophrenia) in animal models are inherently challenging to validate and may have limited utility in heterogeneous disease populations. Here we focus on an alternative strategy aimed at the study of neurophysiological biomarkers-the subject of this volume-translated to animal models, where precision neuroscience tools can be applied to provide molecular, cellular, and circuit-level insights and novel therapeutic targets. We summarize several examples of this approach throughout the chapter and emphasize the importance of careful experimental design and choice of dependent measures.

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

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

Emergence of cortical network motifs for short-term memory during learning

Learning of adaptive behaviors requires the refinement of coordinated activity across multiple brain regions. However, how neural communications develop during learning remains poorly understood. Here, using two-photon calcium imaging, we simultaneously recorded the activity of layer 2/3 excitatory neurons in eight regions of the mouse dorsal cortex during learning of a delayed-response task. Across learning, while global functional connectivity became sparser, there emerged a subnetwork comprising of neurons in the anterior lateral motor cortex (ALM) and posterior parietal cortex (PPC). Neurons in this subnetwork shared a similar choice code during action preparation and formed recurrent functional connectivity across learning. Suppression of PPC activity disrupted choice selectivity in ALM and impaired task performance. Recurrent neural networks reconstructed from ALM activity revealed that PPC-ALM interactions rendered choice-related attractor dynamics more stable. Thus, learning constructs cortical network motifs by recruiting specific inter-areal communication channels to promote efficient and robust sensorimotor transformation.

Large-scale recording of neuronal activity in freely-moving mice at cellular resolution

Current methods for recording large-scale neuronal activity from behaving mice at single-cell resolution require either fixing the mouse head under a microscope or attachment of a recording device to the animal's skull. Both of these options significantly affect the animal behavior and hence also the recorded brain activity patterns. Here, we introduce a different method to acquire snapshots of single-cell cortical activity maps from freely-moving mice using a calcium sensor called CaMPARI. CaMPARI has a unique property of irreversibly changing its color from green to red inside active neurons when illuminated with 400 nm light. We capitalize on this property to demonstrate cortex-wide activity recording without any head fixation, tethering, or attachment of a miniaturized device to the mouse's head. Multiple cortical regions were recorded while the mouse was performing a battery of behavioral and cognitive tests. We identified task-dependent activity patterns across motor and somatosensory cortices, with significant differences across sub-regions of the motor cortex and correlations across several activity patterns and task parameters. This CaMPARI-based recording method expands the capabilities of recording neuronal activity from freely-moving and behaving mice under minimally-restrictive experimental conditions and provides large-scale volumetric data that are currently not accessible otherwise.

Social circuits and their dysfunction in autism spectrum disorder

Social behaviors, how individuals act cooperatively and competitively with conspecifics, are widely seen across species. Rodents display various social behaviors, and many different behavioral paradigms have been used for investigating their neural circuit bases. Social behavior is highly vulnerable to brain network dysfunction caused by neurological and neuropsychiatric conditions such as autism spectrum disorders (ASDs). Studying mouse models of ASD provides a promising avenue toward elucidating mechanisms of abnormal social behavior and potential therapeutic targets for treatment. In this review, we outline recent progress and key findings on neural circuit mechanisms underlying social behavior, with particular emphasis on rodent studies that monitor and manipulate the activity of specific circuits using modern systems neuroscience approaches. Social behavior is mediated by a distributed brain-wide network among major cortical (e.g., medial prefrontal cortex (mPFC), anterior cingulate cortex, and insular cortex (IC)) and subcortical (e.g., nucleus accumbens, basolateral amygdala (BLA), and ventral tegmental area) structures, influenced by multiple neuromodulatory systems (e.g., oxytocin, dopamine, and serotonin). We particularly draw special attention to IC as a unique cortical area that mediates multisensory integration, encoding of ongoing social interaction, social decision-making, emotion, and empathy. Additionally, a synthesis of studies investigating ASD mouse models demonstrates that dysfunctions in mPFC-BLA circuitry and neuromodulation are prominent. Pharmacological rescues by local or systemic (e.g., oral) administration of various drugs have provided valuable clues for developing new therapeutic agents for ASD. Future efforts and technological advances will push forward the next frontiers in this field, such as the elucidation of brain-wide network activity and inter-brain neural dynamics during real and virtual social interactions, and the establishment of circuit-based therapy for disorders affecting social functions.

Generalization of generative model for neuronal ensemble inference method

Various brain functions that are necessary to maintain life activities materialize through the interaction of countless neurons. Therefore, it is important to analyze functional neuronal network. To elucidate the mechanism of brain function, many studies are being actively conducted on functional neuronal ensemble and hub, including all areas of neuroscience. In addition, recent study suggests that the existence of functional neuronal ensembles and hubs contributes to the efficiency of information processing. For these reasons, there is a demand for methods to infer functional neuronal ensembles from neuronal activity data, and methods based on Bayesian inference have been proposed. However, there is a problem in modeling the activity in Bayesian inference. The features of each neuron's activity have non-stationarity depending on physiological experimental conditions. As a result, the assumption of stationarity in Bayesian inference model impedes inference, which leads to destabilization of inference results and degradation of inference accuracy. In this study, we extend the range of the variable for expressing the neuronal state, and generalize the likelihood of the model for extended variables. By comparing with the previous study, our model can express the neuronal state in larger space. This generalization without restriction of the binary input enables us to perform soft clustering and apply the method to non-stationary neuroactivity data. In addition, for the effectiveness of the method, we apply the developed method to multiple synthetic fluorescence data generated from the electrical potential data in leaky integrated-and-fire model.

In vivo two-photon calcium imaging of cortical neurons in neonatal mice

In vivo calcium imaging is essential to elucidate unique synchronous activities observed in the developing brain. Here, we present a protocol to image and analyze activity patterns in neonatal mouse neocortex in a single-cell level. We describe steps for in utero electroporation, cranial window surgery, two-photon imaging, and activity correlation analysis. This protocol facilitates the understanding of neuronal activities and activity-dependent circuit formation during development. For complete details on the use and execution of this protocol, please refer to Mizuno et al. (2014),¹ Mizuno et al. (2018a),² and Mizuno et al. (2018b).³.

Large-scale interrogation of retinal cell functions by 1-photon light-sheet microscopy

Visual processing in the retina depends on the collective activity of large ensembles of neurons organized in different layers. Current techniques for measuring activity of layer-specific neural ensembles rely on expensive pulsed infrared lasers to drive 2-photon activation of calcium-dependent fluorescent reporters. We present a 1-photon light-sheet imaging system that can measure the activity in hundreds of neurons in the ex vivo retina over a large field of view while presenting visual stimuli. This allows for a reliable functional classification of different retinal cell types. We also demonstrate that the system has sufficient resolution to image calcium entry at individual synaptic release sites across the axon terminals of dozens of simultaneously imaged bipolar cells. The simple design, large field of view, and fast image acquisition make this a powerful system for high-throughput and high-resolution measurements of retinal processing at a fraction of the cost of alternative approaches.

Pupil Dynamics-derived Sleep Stage Classification of a Head-fixed Mouse Using a Recurrent Neural Network

The standard method for sleep state classification is thresholding the amplitudes of electroencephalography (EEG) and electromyography (EMG) data, followed by manual correction by an expert. Although popular, this method has some shortcomings: (1) the time-consuming manual correction by human experts is sometimes a bottleneck hindering sleep studies, (2) EEG electrodes on the skull interfere with wide-field imaging of the cortical activity of a head-fixed mouse under a microscope, (3) invasive surgery to fix the electrodes on the thin mouse skull risks brain tissue injury, and (4) metal electrodes for EEG and EMG recording are difficult to apply to some experimental apparatus such as that for functional magnetic resonance imaging. To overcome these shortcomings, we propose a pupil dynamics-based vigilance state classification method for a head-fixed mouse using a long short-term memory (LSTM) model, a variant of a recurrent neural network, for multi-class labeling of NREM, REM, and WAKE states. For supervisory hypnography, EEG and EMG recording were performed on head-fixed mice. This setup was combined with left eye pupillometry using a USB camera and a markerless tracking toolbox, DeepLabCut. Our open-source LSTM model with feature inputs of pupil diameter, pupil location, pupil velocity, and eyelid opening for 10 s at a 10 Hz sampling rate achieved vigilance state estimation with a higher classification performance (macro F1 score, 0.77; accuracy, 86%) than a feed-forward neural network. Findings from a diverse range of pupillary dynamics implied possible subdivision of the vigilance states defined by EEG and EMG. Pupil dynamics-based hypnography can expand the scope of alternatives for sleep stage scoring of head-fixed mice.

Neural circuit plasticity for complex non-declarative sensorimotor memory consolidation during sleep

Evidence is accumulating that the brain actively consolidates long-term memory during sleep. Motor skill memory is a form of non-declarative procedural memory and can be coordinated with multi-sensory processing such as visual, tactile, and, auditory. Conversely, perception is affected by body movement signal from motor brain regions. Although both cortical and subcortical brain regions are involved in memory consolidation, cerebral cortex activity can be recorded and manipulated noninvasively or minimally invasively in humans and animals. NREM sleep, which is important for non-declarative memory consolidation, is characterized by slow and spindle waves representing thalamo-cortical population activity. In animals, electrophysiological recording, optical imaging, and manipulation approaches have revealed multi-scale cortical dynamics across learning and sleep. In the sleeping cortex, neural activity is affected by prior learning and neural circuits are continually reorganized. Here I outline how sensorimotor coordination is formed through awake learning and subsequent sleep.

Wide-Field Calcium Imaging of Neuronal Network Dynamics In Vivo

A central tenet of neuroscience is that sensory, motor, and cognitive behaviors are generated by the communications and interactions among neurons, distributed within and across anatomically and functionally distinct brain regions. Therefore, to decipher how the brain plans, learns, and executes behaviors requires characterizing neuronal activity at multiple spatial and temporal scales. This includes simultaneously recording neuronal dynamics at the mesoscale level to understand the interactions among brain regions during different behavioral and brain states. Wide-field Ca2+ imaging, which uses single photon excitation and improved genetically encoded Ca2+ indicators, allows for simultaneous recordings of large brain areas and is proving to be a powerful tool to study neuronal activity at the mesoscopic scale in behaving animals. This review details the techniques used for wide-field Ca2+ imaging and the various approaches employed for the analyses of the rich neuronal-behavioral data sets obtained. Also discussed is how wide-field Ca2+ imaging is providing novel insights into both normal and altered neural processing in disease. Finally, we examine the limitations of the approach and new developments in wide-field Ca2+ imaging that are bringing new capabilities to this important technique for investigating large-scale neuronal dynamics.

A genetically encoded far-red fluorescent calcium ion biosensor derived from a biliverdin-binding protein

Far-red and near-infrared (NIR) genetically encoded calcium ion (Ca2+ ) indicators (GECIs) are powerful tools for in vivo and multiplexed imaging of neural activity and cell signaling. Inspired by a previous report to engineer a far-red fluorescent protein (FP) from a biliverdin (BV)-binding NIR FP, we have developed a far-red fluorescent GECI, designated iBB-GECO1, from a previously reported NIR GECI. iBB-GECO1 exhibits a relatively high molecular brightness, an inverse response to Ca2+ with ΔF/Fmin  = -13, and a near-optimal dissociation constant (Kd ) for Ca2+ of 105 nM. We demonstrate the utility of iBB-GECO1 for four-color multiplexed imaging in MIN6 cells and five-color imaging in HEK293T cells. Like other BV-binding GECIs, iBB-GECO1 did not give robust signals during in vivo imaging of neural activity in mice, but did provide promising results that will guide future engineering efforts. SIGNIFICANCE: Genetically encoded calcium ion (Ca2+ ) indicators (GECIs) compatible with common far-red laser lines (~630-640 nm) on commercial microscopes are of critical importance for their widespread application to deep-tissue multiplexed imaging of neural activity. In this study, we engineered a far-red excitable fluorescent GECI, designated iBB-GECO1, that exhibits a range of preferable specifications such as high brightness, large fluorescence response to Ca2+ , and compatibility with multiplexed imaging in mammalian cells.

Toward an integrative neurovascular framework for studying brain networks

Brain functional connectivity based on the measure of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals has become one of the most widely used measurements in human neuroimaging. However, the nature of the functional networks revealed by BOLD fMRI can be ambiguous, as highlighted by a recent series of experiments that have suggested that typical resting-state networks can be replicated from purely vascular or physiologically driven BOLD signals. After going through a brief review of the key concepts of brain network analysis, we explore how the vascular and neuronal systems interact to give rise to the brain functional networks measured with BOLD fMRI. This leads us to emphasize a view of the vascular network not only as a confounding element in fMRI but also as a functionally relevant system that is entangled with the neuronal network. To study the vascular and neuronal underpinnings of BOLD functional connectivity, we consider a combination of methodological avenues based on multiscale and multimodal optical imaging in mice, used in combination with computational models that allow the integration of vascular information to explain functional connectivity.

Versatile software and hardware combo enabling photon counting acquisition and real-time display for multiplexing, 2D and continuous 3D two-photon imaging applications

Significance: rPySight brings a flexible and highly customizable open-software platform built around a powerful multichannel digitizer; combined, it enables performing complex photon counting-based experiments. We exploited advanced programming technology to share the photon counting stream with the graphical processing unit (GPU), making possible real-time display of two-dimensional (2D) and three-dimensional (3D) experiments and paving the road for other real-time applications. Aim: Photon counting improves multiphoton imaging by providing better signal-to-noise ratio in photon-deprived applications and is becoming more widely implemented, as indicated by its increasing presence in many microscopy vendor portfolios. Despite the relatively easy access to this technology offered in commercial systems, these remain limited to one or two channels of data and might not enable highly tailored experiments, forcing most researchers to develop their own electronics and code. We set to develop a flexible and open-source interface to a cutting-edge multichannel fast digitizer that can be easily integrated into existing imaging systems. Approach: We selected an advanced multichannel digitizer capable of generating 70M tags/s and wrote an open software application, based on Rust and Python languages, to share the stream of detected events with the GPU, enabling real-time data processing. Results: rPySight functionality was showcased in real-time monitoring of 2D imaging, improved calcium imaging, multiplexing, and 3D imaging through a varifocal lens. We provide a detailed protocol for implementing out-of-the-box rPySight and its related hardware. Conclusions: Applying photon-counting approaches is becoming a fundamental component in recent technical developments that push well beyond existing acquisition speed limitations of classical multiphoton approaches. Given the performance of rPySight, we foresee its use to capture, among others, the joint dynamics of hundreds (if not thousands) of neuronal and vascular elements across volumes, as is likely required to uncover in a much broader sense the hemodynamic transform function.

In utero electroporation and cranial window implantation for in vivo wide-field two-photon calcium imaging using G-CaMP9a transgenic mice

We present a protocol to prepare mouse cranial window implantation for in vivo two-photon wide-field calcium imaging. This protocol uses G-CaMP9a transgenic mice, which express a genetically encoded calcium indicator with high signal-to-noise ratio. We describe in utero electroporation, followed by headplate fixation and cranial window implantation. This protocol can be used for measuring neural activity and is suitable for long-term imaging in large populations. Moreover, this protocol does not require preparation of Flp-expressing transgenic mice.

For complete details on the use and execution of this protocol, please refer to Sakamoto et al. (2022).

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Preparation of G-CaMP9a knock-in mice for in vivo calcium imaging

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In utero electroporation to introduce Flp recombinase in G-CaMP9a mice

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Implantation of cranial window for in vivo two-photon calcium imaging

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Breaking trade-offs: development of fast, high-resolution, wide-field two-photon microscopes to reveal the computational principles of the brain

Information in the brain is represented by the collective and coordinated activity of single neurons. Activity is determined by a large amount of dynamic synaptic inputs from neurons in the same and/or distant brain regions. Therefore, the simultaneous recording of single neurons across several brain regions is critical for revealing the interactions among neurons that reflect the computational principles of the brain. Recently, several wide-field two-photon (2P) microscopes equipped with sizeable objective lenses have been reported. These microscopes enable large-scale in vivo calcium imaging and have the potential to make a significant contribution to the elucidation of information-processing mechanisms in the cerebral cortex. This review discusses recent reports on wide-field 2P microscopes and describes the trade-offs encountered in developing wide-field 2P microscopes. Large-scale imaging of neural activity allows us to test hypotheses proposed in theoretical neuroscience, and to identify rare but influential neurons that have potentially significant impacts on the whole-brain system.

Assessing transfer entropy from biochemical data

We address the problem of evaluating the transfer entropy (TE) produced by biochemical reactions from experimentally measured data. Although these reactions are generally nonlinear and nonstationary processes making it challenging to achieve accurate modeling, Gaussian approximation can facilitate the TE assessment only by estimating covariance matrices using multiple data obtained from simultaneously measured time series representing the activation levels of biomolecules such as proteins. Nevertheless, the nonstationary nature of biochemical signals makes it difficult to theoretically assess the sampling distributions of TE, which are necessary for evaluating the statistical confidence and significance of the data-driven estimates. We resolve this difficulty by computationally assessing the sampling distributions using techniques from computational statistics. The computational methods are tested by using them in analyzing data generated from a theoretically tractable time-varying signal model, which leads to the development of a method to screen only statistically significant estimates. The usefulness of the developed method is examined by applying it to real biological data experimentally measured from the ERBB-RAS-MAPK system that superintends diverse cell fate decisions. A comparison between cells containing wild-type and mutant proteins exhibits a distinct difference in the time evolution of TE while any apparent difference is hardly found in average profiles of the raw signals. Such a comparison may help in unveiling important pathways of biochemical reactions.

A Flp-dependent G-CaMP9a transgenic mouse for neuronal imaging in vivo

Genetically encoded calcium indicators (GECIs) are widely used to measure calcium transients in neuronal somata and processes, and their use enables the determination of action potential temporal series in a large population of neurons. Here, we generate a transgenic mouse line expressing a highly sensitive green GECI, G-CaMP9a, in a Flp-dependent manner in excitatory and inhibitory neuronal subpopulations downstream of a strong CAG promoter. Combining this reporter mouse with viral or mouse genetic Flp delivery methods produces a robust and stable G-CaMP9a expression in defined neuronal populations without detectable detrimental effects. In vivo two-photon imaging reveals spontaneous and sensory-evoked calcium transients in excitatory and inhibitory ensembles with cellular resolution. Our results show that this reporter line allows long-term, cell-type-specific investigation of neuronal activity with enhanced resolution in defined populations and facilitates dissecting complex dynamics of neural networks in vivo.

Orchestration of Ion Channels and Transporters in Neocortical Development and Neurological Disorders

Electrical activity plays crucial roles in neural circuit formation and remodeling. During neocortical development, neurons are generated in the ventricular zone, migrate to their correct position, elongate dendrites and axons, and form synapses. In this review, we summarize the functions of ion channels and transporters in neocortical development. Next, we discuss links between neurological disorders caused by dysfunction of ion channels (channelopathies) and neocortical development. Finally, we introduce emerging optical techniques with potential applications in physiological studies of neocortical development and the pathophysiology of channelopathies.

Live imaging approach of dynamic multicellular responses in ERK signaling during vertebrate tissue development

The chemical and mechanical responses of cells via the exchange of information during growth and development result in the formation of biological tissues. Information processing within the cells through the signaling pathways and networks inherent to the constituent cells has been well-studied. However, the cell signaling mechanisms responsible for generating dynamic multicellular responses in developing tissues remain unclear. Here, I review the dynamic multicellular response systems during the development and growth of vertebrate tissues based on the extracellular signal-regulated kinase (ERK) pathway. First, an overview of the function of the ERK signaling network in cells is provided, followed by descriptions of biosensors essential for live imaging of the quantification of ERK activity in tissues. Then adducing four examples, I highlight the contribution of live imaging techniques for studying the involvement of spatio-temporal patterns of ERK activity change in tissue development and growth. In addition, theoretical implications of ERK signaling are also discussed from the viewpoint of dynamic systems. This review might help in understanding ERK-mediated dynamic multicellular responses and tissue morphogenesis.

Non-telecentric two-photon microscopy for 3D random access mesoscale imaging

Diffraction-limited two-photon microscopy permits minimally invasive optical monitoring of neuronal activity. However, most conventional two-photon microscopes impose significant constraints on the size of the imaging field-of-view and the specific shape of the effective excitation volume, thus limiting the scope of biological questions that can be addressed and the information obtainable. Here, employing a non-telecentric optical design, we present a low-cost, easily implemented and flexible solution to address these limitations, offering a several-fold expanded three-dimensional field of view. Moreover, rapid laser-focus control via an electrically tunable lens allows near-simultaneous imaging of remote regions separated in three dimensions and permits the bending of imaging planes to follow natural curvatures in biological structures. Crucially, our core design is readily implemented (and reversed) within a matter of hours, making it highly suitable as a base platform for further development. We demonstrate the application of our system for imaging neuronal activity in a variety of examples in zebrafish, mice and fruit flies.

Fluorescence imaging of large-scale neural ensemble dynamics

Recent progress in fluorescence imaging allows neuroscientists to observe the dynamics of thousands of individual neurons, identified genetically or by their connectivity, across multiple brain areas and for extended durations in awake behaving mammals. We discuss advances in fluorescent indicators of neural activity, viral and genetic methods to express these indicators, chronic animal preparations for long-term imaging studies, and microscopes to monitor and manipulate the activity of large neural ensembles. Ca2+ imaging studies of neural activity can track brain area interactions and distributed information processing at cellular resolution. Across smaller spatial scales, high-speed voltage imaging reveals the distinctive spiking patterns and coding properties of targeted neuron types. Collectively, these innovations will propel studies of brain function and dovetail with ongoing neuroscience initiatives to identify new neuron types and develop widely applicable, non-human primate models. The optical toolkit's growing sophistication also suggests that "brain observatory" facilities would be useful open resources for future brain-imaging studies.

Neurophotonic tools for microscopic measurements and manipulation: status report

Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.

Imaging data analysis using non-negative matrix factorization

The rapid progress of imaging devices such as two-photon microscopes has made it possible to measure the activity of thousands to tens of thousands of cells at single-cell resolution in a wide field of view (FOV) data. However, it is not possible to manually identify thousands of cells in such wide FOV data. Several research groups have developed machine learning methods for automatically detecting cells from wide FOV data. Many of the recently proposed methods using dynamic activity information rather than static morphological information are based on non-negative matrix factorization (NMF). In this review, we outline cell-detection methods related to NMF. For the purpose of raising issues on NMF cell detection, we introduce our current development of a non-NMF method that is capable of detecting about 17,000 cells in ultra-wide FOV data.

Protocol for cortical-wide field-of-view two-photon imaging with quick neonatal adeno-associated virus injection

We recently established a simple and versatile adeno-associated virus (AAV) induction approach that enables dense (>90% labeled neurons) and cortical-wide Ca²⁺ sensor expression. Here, we describe the stepwise protocol for neonatal AAV injection of a Ca²⁺ sensor. We also detail the steps for subsequent craniotomy to generate a chronic cranial window, followed by wide-field two-photon Ca²⁺ imaging in an awake mouse. This protocol serves as an alternative to the use of transgenic animals and offers translatable options for cortical-wide experiments. For complete details on the use and execution of this protocol, please refer to Ota et al. (2021).

Diesel2p mesoscope with dual independent scan engines for flexible capture of dynamics in distributed neural circuitry

Imaging the activity of neurons that are widely distributed across brain regions deep in scattering tissue at high speed remains challenging. Here, we introduce an open-source system with Dual Independent Enhanced Scan Engines for Large field-of-view Two-Photon imaging (Diesel2p). Combining optical design, adaptive optics, and temporal multiplexing, the system offers subcellular resolution over a large field-of-view of ~25 mm2, encompassing distances up to 7 mm, with independent scan engines. We demonstrate the flexibility and various use cases of this system for calcium imaging of neurons in the living brain.