MIN1PIPE: A Miniscope 1-Photon-Based Calcium Imaging Signal Extraction Pipeline

Distinct functional classes of CA1 hippocampal interneurons are modulated by cerebellar stimulation in a coordinated manner

There is mounting evidence that the cerebellum impacts hippocampal functioning, but the impact of the cerebellum on hippocampal interneurons remains obscure. Using miniscopes in freely behaving animals, we find optogenetic stimulation of Purkinje cells alters the calcium activity of a large percentage of CA1 interneurons. This includes both increases and decreases in activity. Remarkably, this bidirectional impact occurs in a coordinated fashion, in line with interneurons' functional properties. Specifically, CA1 interneurons activated by cerebellar stimulation are commonly locomotion-active, while those inhibited by cerebellar stimulation are commonly rest-active interneurons. We additionally find that subsets of CA1 interneurons show altered activity during object investigations, suggesting a role in the processing of objects in space. Importantly, these neurons also show coordinated modulation by cerebellar stimulation: CA1 interneurons that are activated by cerebellar stimulation are more likely to be activated, rather than inhibited, during object investigations, while interneurons that show decreased activity during cerebellar stimulation show the opposite profile. Therefore, CA1 interneurons play a role in object processing and in cerebellar impacts on the hippocampus, providing insight into previously noted altered CA1 processing of objects in space with cerebellar stimulation. We examined two different stimulation locations (IV/V Vermis; Simplex) and two different stimulation approaches (7Hz or a single 1s light pulse) - in all cases, the cerebellum induces similar coordinated CA1 interneuron changes congruent with an explorative state. Overall, our data show that the cerebellum impacts CA1 interneurons in a bidirectional and coordinated fashion, positioning them to play an important role in cerebello-hippocampal communication.

Significance Statement

Acute manipulation of the cerebellum can affect the activity of cells in CA1, and perturbing normal cerebellar functioning can affect hippocampal-dependent spatial processing, including the processing of objects in space. Despite the importance of interneurons on the local hippocampal circuit, it was unknown how cerebellar activation impacts CA1 inhibitory neurons. We find that stimulating the cerebellum robustly affects multiple populations of CA1 interneurons in a bidirectional, coordinated manner, according to their functional profiles during behavior, including locomotion and object investigations. Our work also provides support for a role of CA1 interneurons in spatial processing of objects, with populations of interneurons showing altered activity during object investigations.

Threat-dependent scaling of prelimbic dynamics to enhance fear representation

Promptly identifying threatening stimuli is crucial for survival. Freezing is a natural behavior displayed by rodents toward potential or actual threats. Although it is known that the prelimbic cortex (PL) is involved in both risk evaluation and in fear and anxiety-like behavior expression, here we explored whether PL neuronal activity can dynamically represent different internal states of the same behavioral output (i.e., freezing). We found that freezing can always be decoded from PL activity at a population level. However, the sudden presentation of a fearful stimulus quickly reshaped the PL to a new neuronal activity state, an effect not observed in other cortical or subcortical regions examined. This shift changed PL freezing representation and is necessary for fear memory expression. Our data reveal the unique role of the PL in detecting threats and internally adjusting to distinguish between different freezing-related states in both unconditioned and conditioned fear representations.

CHRFAM7A diversifies human immune adaption through Ca2+ signalling and actin cytoskeleton reorganization

BACKGROUND

Human restricted genes contribute to human specific traits in the immune system. CHRFAM7A, a uniquely human fusion gene, is a negative regulator of the α7 nicotinic acetylcholine receptor (α7 nAChR), the highest Ca2+ conductor of the ACh receptors implicated in innate immunity. Understanding the mechanism of how CHRFAM7A affects the immune system remains unexplored.

METHODS

Two model systems are used, human induced pluripotent stem cells (iPSC) and human primary monocytes, to characterize α7 nAChR function, Ca2+ dynamics and decoders to elucidate the pathway from receptor to phenotype.

FINDINGS

CHRFAM7A/α7 nAChR is identified as a hypomorphic receptor with mitigated Ca2+ influx and prolonged channel closed state. This shifts the Ca2+ reservoir from the extracellular space to the endoplasmic reticulum (ER) leading to Ca2+ dynamic changes. Ca2+ decoder small GTPase Rac1 is then activated, reorganizing the actin cytoskeleton. Observed actin mediated phenotypes include cellular adhesion, motility, phagocytosis and tissue mechanosensation.

INTERPRETATION

CHRFAM7A introduces an additional, human specific, layer to Ca2+ regulation leading to an innate immune gain of function. Through the actin cytoskeleton it drives adaptation to the mechanical properties of the tissue environment leading to an ability to invade previously immune restricted niches. Human genetic diversity predicts profound translational significance as its understanding builds the foundation for successful treatments for infectious diseases, sepsis, and cancer metastasis.

FUNDING

This work is supported in part by the Community Foundation for Greater Buffalo (Kinga Szigeti) and in part by NIH grant R01HL163168 (Yongho Bae).

Hypothalamic CRH neurons represent physiological memory of positive and negative experience

Recalling a salient experience provokes specific behaviors and changes in the physiology or internal state. Relatively little is known about how physiological memories are encoded. We examined the neural substrates of physiological memory by probing CRHPVN neurons of mice, which control the endocrine response to stress. Here we show these cells exhibit contextual memory following exposure to a stimulus with negative or positive valence. Specifically, a negative stimulus invokes a two-factor learning rule that favors an increase in the activity of weak cells during recall. In contrast, the contextual memory of positive valence relies on a one-factor rule to decrease activity of CRHPVN neurons. Finally, the aversive memory in CRHPVN neurons outlasts the behavioral response. These observations provide information about how specific physiological memories of aversive and appetitive experience are represented and demonstrate that behavioral readouts may not accurately reflect physiological changes invoked by the memory of salient experiences.

Deep-brain optical recording of neural dynamics during behavior

In vivo fluorescence recording techniques have produced landmark discoveries in neuroscience, providing insight into how single cell and circuit-level computations mediate sensory processing and generate complex behaviors. While much attention has been given to recording from cortical brain regions, deep-brain fluorescence recording is more complex because it requires additional measures to gain optical access to harder to reach brain nuclei. Here we discuss detailed considerations and tradeoffs regarding deep-brain fluorescence recording techniques and provide a comprehensive guide for all major steps involved, from project planning to data analysis. The goal is to impart guidance for new and experienced investigators seeking to use in vivo deep fluorescence optical recordings in awake, behaving rodent models.

NeuroActivityToolkit-Toolbox for Quantitative Analysis of Miniature Fluorescent Microscopy Data

The visualization of neuronal activity in vivo is an urgent task in modern neuroscience. It allows neurobiologists to obtain a large amount of information about neuronal network architecture and connections between neurons. The miniscope technique might help to determine changes that occurred in the network due to external stimuli and various conditions: processes of learning, stress, epileptic seizures and neurodegenerative diseases. Furthermore, using the miniscope method, functional changes in the early stages of such disorders could be detected. The miniscope has become a modern approach for recording hundreds to thousands of neurons simultaneously in a certain brain area of a freely behaving animal. Nevertheless, the analysis and interpretation of the large recorded data is still a nontrivial task. There are a few well-working algorithms for miniscope data preprocessing and calcium trace extraction. However, software for further high-level quantitative analysis of neuronal calcium signals is not publicly available. NeuroActivityToolkit is a toolbox that provides diverse statistical metrics calculation, reflecting the neuronal network properties such as the number of neuronal activations per minute, amount of simultaneously co-active neurons, etc. In addition, the module for analyzing neuronal pairwise correlations is implemented. Moreover, one can visualize and characterize neuronal network states and detect changes in 2D coordinates using PCA analysis. This toolbox, which is deposited in a public software repository, is accompanied by a detailed tutorial and is highly valuable for the statistical interpretation of miniscope data in a wide range of experimental tasks.

Acetylcholine waves and dopamine release in the striatum

Striatal dopamine encodes reward, with recent work showing that dopamine release occurs in spatiotemporal waves. However, the mechanism of dopamine waves is unknown. Here we report that acetylcholine release in mouse striatum also exhibits wave activity, and that the spatial scale of striatal dopamine release is extended by nicotinic acetylcholine receptors. Based on these findings, and on our demonstration that single cholinergic interneurons can induce dopamine release, we hypothesized that the local reciprocal interaction between cholinergic interneurons and dopamine axons suffices to drive endogenous traveling waves. We show that the morphological and physiological properties of cholinergic interneuron - dopamine axon interactions can be modeled as a reaction-diffusion system that gives rise to traveling waves. Analytically-tractable versions of the model show that the structure and the nature of propagation of acetylcholine and dopamine traveling waves depend on their coupling, and that traveling waves can give rise to empirically observed correlations between these signals. Thus, our study provides evidence for striatal acetylcholine waves in vivo, and proposes a testable theoretical framework that predicts that the observed dopamine and acetylcholine waves are strongly coupled phenomena.

Neuronal actin cytoskeleton gain of function in the human brain

BACKGROUND

While advancements in imaging techniques have led to major strides in deciphering the human brain, successful interventions are elusive and represent some of the most persistent translational gaps in medicine. Human restricted CHRFAM7A has been associated with neuropsychiatric disorders.

METHODS

The physiological role of CHRFAM7A in human brain is explored using multiomics approach on 600 post mortem human brain tissue samples. The emerging pathways and mechanistic hypotheses are tested and validated in an isogenic hiPSC model of CHRFAM7A knock-in medial ganglionic eminence progenitors and neurons.

FINDINGS

CHRFAM7A is identified as a modulator of intracellular calcium dynamics and an upstream regulator of Rac1. Rac1 activation re-designs the actin cytoskeleton leading to dynamic actin driven remodeling of membrane protrusion and a switch from filopodia to lamellipodia. The reinforced cytoskeleton leads to an advantage to tolerate stiffer mechanical properties of the extracellular environment.

INTERPRETATION

CHRFAM7A modifies the actin cytoskeleton to a more dynamic and stiffness resistant state in an α7nAChR dependent manner. CHRFAM7A may facilitate neuronal adaptation to changes in the brain environment in physiological and pathological conditions contributing to risk or recovery. Understanding how CHRFAM7A affects human brain requires human studies in the areas of memory formation and erasure, cognitive reserve, and neuronal plasticity.

FUNDING

This work is supported in part by the Community Foundation for Greater Buffalo (Kinga Szigeti). Also, in part by the International Society for Neurochemistry (ISN) and The Company of Biologists (Nicolas Rosas). ROSMAP is supported by NIA grants P30AG10161, P30AG72975, R01AG15819, R01AG17917. U01AG46152, and U01AG61356.

Distributed processing for value-based choice by prelimbic circuits targeting anterior-posterior dorsal striatal subregions in male mice

Fronto-striatal circuits have been implicated in cognitive control of behavioral output for social and appetitive rewards. The functional diversity of prefrontal cortical populations is strongly dependent on their synaptic targets, with control of motor output mediated by connectivity to dorsal striatum. Despite evidence for functional diversity along the anterior-posterior striatal axis, it is unclear how distinct fronto-striatal sub-circuits support value-based choice. Here we found segregated prefrontal populations defined by anterior/posterior dorsomedial striatal target. During a feedback-based 2-alternative choice task, single-photon imaging revealed circuit-specific representations of task-relevant information with prelimbic neurons targeting anterior DMS (PL::A-DMS) robustly modulated during choices and negative outcomes, while prelimbic neurons targeting posterior DMS (PL::P-DMS) encoded internal representations of value and positive outcomes contingent on prior choice. Consistent with this distributed coding, optogenetic inhibition of PL::A-DMS circuits strongly impacted choice monitoring and responses to negative outcomes while inhibition of PL::P-DMS impaired task engagement and strategies following positive outcomes. Together our data uncover PL populations engaged in distributed processing for value-based choice.

Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data

Widefield microscopy can provide optical access to multi-millimeter fields of view and thousands of neurons in mammalian brains at video rate. However, tissue scattering and background contamination results in signal deterioration, making the extraction of neuronal activity challenging, laborious and time consuming. Here we present our deep-learning-based widefield neuron finder (DeepWonder), which is trained by simulated functional recordings and effectively works on experimental data to achieve high-fidelity neuronal extraction. Equipped with systematic background contribution priors, DeepWonder conducts neuronal inference with an order-of-magnitude-faster speed and improved accuracy compared with alternative approaches. DeepWonder removes background contaminations and is computationally efficient. Specifically, DeepWonder accomplishes 50-fold signal-to-background ratio enhancement when processing terabytes-scale cortex-wide functional recordings, with over 14,000 neurons extracted in 17 h.

FPGA-Based In-Vivo Calcium Image Decoding for Closed-Loop Feedback Applications

Miniaturized calcium imaging is an emerging neural recording technique that has been widely used for monitoring neural activity on a large scale at a specific brain region of rats or mice. Most existing calcium-image analysis pipelines operate offline. This results in long processing latency, making it difficult to realize closed-loop feedback stimulation for brain research. In recent work, we have proposed an FPGA-based real-time calcium image processing pipeline for closed-loop feedback applications. It can perform real-time calcium image motion correction, enhancement, fast trace extraction, and real-time decoding from extracted traces. Here, we extend this work by proposing a variety of neural network based methods for real-time decoding and evaluate the tradeoff among these decoding methods and accelerator designs. We introduce the implementation of the neural network based decoders on the FPGA, and show their speedup against the implementation on the ARM processor. Our FPGA implementation enables the real-time calcium image decoding with sub-ms processing latency for closed-loop feedback applications.

New imaging instrument in animal models: Two-photon miniature microscope and large field of view miniature microscope for freely behaving animals

Over the past decade, novel optical imaging tools have been developed for imaging neuronal activities along with the evolution of fluorescence indicators with brighter expression and higher sensitivity. Miniature microscopes, as revolutionary approaches, enable the imaging of large populations of neuron ensembles in freely behaving rodents and mammals, which allows exploring the neural basis of behaviors. Recent progress in two-photon miniature microscopes and mesoscale single-photon miniature microscopes further expand those affordable methods to navigate neural activities during naturalistic behaviors. In this review article, two-photon miniature microscopy techniques are summarized historically from the first documented attempt to the latest ones, and comparisons are made. The driving force behind and their potential for neuroscientific inquiries are also discussed. Current progress in terms of the mesoscale, i.e., the large field-of-view miniature microscopy technique, is addressed as well. Then, pipelines for registering single cells from the data of two-photon and large field-of-view miniature microscopes are discussed. Finally, we present the potential evolution of the techniques.

V-NeuroStack: Open-source 3D time stack software for identifying patterns in neuronal data

Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of neurons in a mouse brain slice as well as in vivo using two-photon imaging. V-NeuroStack creates 3D time stacks by stacking 2D time frames for a time-series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack's 3D view is used to explore patterns in dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of activity from an individual or a functional cluster of neurons. V-NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data.

Encoding of inflammatory hyperalgesia in mouse spinal cord

ABSTRACT

Inflammation modifies the input-output properties of peripheral nociceptive neurons such that the same stimulus produces enhanced nociceptive firing. This increased nociceptive output enters the superficial dorsal spinal cord (SDH), an intricate neuronal network composed largely of excitatory and inhibitory interneurons and a small percentage of projection neurons. The SDH network comprises the first central nervous system network integrating noxious information. Using in vivo calcium imaging and a computational approach, we characterized the responsiveness of the SDH network in mice to noxious stimuli in normal conditions and investigated the changes in SDH response patterns after acute burn injury-induced inflammation. We show that the application of noxious heat stimuli to the hind paw of naïve mice results in an overall increase in SDH network activity. Single-cell response analysis reveals that 70% of recorded neurons increase or suppress their activity, while ∼30% of neurons remain nonresponsive. After acute burn injury and the development of inflammatory hyperalgesia, application of the same noxious heat stimuli leads to the activation of previously nonresponding neurons and desuppression of suppressed neurons. We further demonstrate that an increase in afferent activity mimics the response of the SDH network to noxious heat stimuli under inflammatory conditions. Using a computational model of the SDH network, we predict that the changes in SDH network activity result in overall increased activity of excitatory neurons, amplifying the output from SDH to higher brain centers. We suggest that during acute local peripheral inflammation, the SDH network undergoes dynamic changes promoting hyperalgesia.

The deep cerebellar nuclei to striatum disynaptic connection contributes to skilled forelimb movement

Cerebellar-thalamo-striatal synaptic communication has been implicated in a wide range of behaviors, including goal-directed actions, and is altered in cerebellar dystonia. However, its detailed connectivity through the thalamus and its contribution to the execution of forelimb movements is unclear. Here, we use trans-synaptic and retrograde tracing, ex vivo slice recordings, and optogenetic inhibitions during the execution of unidirectional or sequential joystick displacements to demonstrate that the deep cerebellar nuclei (DCN) influence the dorsal striatum with a very high probability. We show that this mainly occurs through the centrolateral (CL), parafascicular (PF), and ventrolateral (VL) nuclei of the thalamus, observing that the DCN→VL and DCN→CL pathways contribute to the execution of unidirectional forelimb displacements while the DCN→PF and DCN→thalamo→striatal pathways contribute to the appropriate execution of forelimb reaching and sequential displacements. These findings highlight specific contributions of the different cerebellar-thalamo-striatal paths to the control of skilled forelimb movement.

A hardware system for real-time decoding of in vivo calcium imaging data

Epifluorescence miniature microscopes ('miniscopes') are widely used for in vivo calcium imaging of neural population activity. Imaging data are typically collected during a behavioral task and stored for later offline analysis, but emerging techniques for online imaging can support novel closed-loop experiments in which neural population activity is decoded in real time to trigger neurostimulation or sensory feedback. To achieve short feedback latencies, online imaging systems must be optimally designed to maximize computational speed and efficiency while minimizing errors in population decoding. Here we introduce DeCalciOn, an open-source device for real-time imaging and population decoding of in vivo calcium signals that is hardware compatible with all miniscopes that use the UCLA Data Acquisition (DAQ) interface. DeCalciOn performs online motion stabilization, neural enhancement, calcium trace extraction, and decoding of up to 1024 traces per frame at latencies of <50 ms after fluorescence photons arrive at the miniscope image sensor. We show that DeCalciOn can accurately decode the position of rats (n = 12) running on a linear track from calcium fluorescence in the hippocampal CA1 layer, and can categorically classify behaviors performed by rats (n = 2) during an instrumental task from calcium fluorescence in orbitofrontal cortex. DeCalciOn achieves high decoding accuracy at short latencies using innovations such as field-programmable gate array hardware for real-time image processing and contour-free methods to efficiently extract calcium traces from sensor images. In summary, our system offers an affordable plug-and-play solution for real-time calcium imaging experiments in behaving animals.

Topographic representation of current and future threats in the mouse nociceptive amygdala

Adaptive behaviors arise from an integration of current sensory context and internal representations of past experiences. The central amygdala (CeA) is positioned as a key integrator of cognitive and affective signals, yet it remains unknown whether individual populations simultaneously carry current- and future-state representations. We find that a primary nociceptive population within the CeA of mice, defined by CGRP-receptor (Calcrl) expression, receives topographic sensory information, with spatially defined representations of internal and external stimuli. While Calcrl+ neurons in both the rostral and caudal CeA respond to noxious stimuli, rostral neurons promote locomotor responses to externally sourced threats, while caudal CeA Calcrl+ neurons are activated by internal threats and promote passive coping behaviors and associative valence coding. During associative fear learning, rostral CeA Calcrl+ neurons stably encode noxious stimulus occurrence, while caudal CeA Calcrl+ neurons acquire predictive responses. This arrangement supports valence-aligned representations of current and future threats for the generation of adaptive behaviors.

Somatosensory cortical signature of facial nociception and vibrotactile touch-induced analgesia

Pain relief by vibrotactile touch is a common human experience. Previous neurophysiological investigations of its underlying mechanism in animals focused on spinal circuits, while human studies suggested the involvement of supraspinal pathways. Here, we examine the role of primary somatosensory cortex (S1) in touch-induced mechanical and heat analgesia. We found that, in mice, vibrotactile reafferent signals from self-generated whisking significantly reduce facial nociception, which is abolished by specifically blocking touch transmission from thalamus to the barrel cortex (S1B). Using a signal separation algorithm that can decompose calcium signals into sensory-evoked, whisking, or face-wiping responses, we found that the presence of whisking altered nociceptive signal processing in S1B neurons. Analysis of S1B population dynamics revealed that whisking pushes the transition of the neural state induced by noxious stimuli toward the outcome of non-nocifensive actions. Thus, S1B integrates facial tactile and noxious signals to enable touch-mediated analgesia.

Multiscale imaging informs translational mouse modeling of neurological disease

Multiscale neurophysiology reveals that simple motor actions are associated with changes in neuronal firing in virtually every brain region studied. Accordingly, the assessment of focal pathology such as stroke or progressive neurodegenerative diseases must also extend widely across brain areas. To derive mechanistic information through imaging, multiple resolution scales and multimodal factors must be included, such as the structure and function of specific neurons and glial cells and the dynamics of specific neurotransmitters. Emerging multiscale methods in preclinical animal studies that span micro- to macroscale examinations fill this gap, allowing a circuit-based understanding of pathophysiological mechanisms. Combined with high-performance computation and open-source data repositories, these emerging multiscale and large field-of-view techniques include live functional ultrasound, multi- and single-photon wide-scale light microscopy, video-based miniscopes, and tissue-penetrating fiber photometry, as well as variants of post-mortem expansion microscopy. We present these technologies and outline use cases and data pipelines to uncover new knowledge within animal models of stroke, Alzheimer's disease, and movement disorders.

Calcium imaging: A versatile tool to examine Huntington's disease mechanisms and progression

Huntington's disease (HD) is a fatal, hereditary neurodegenerative disorder that causes chorea, cognitive deficits, and psychiatric symptoms. It is characterized by accumulation of mutant Htt protein, which primarily impacts striatal medium-sized spiny neurons (MSNs), as well as cortical pyramidal neurons (CPNs), causing synapse loss and eventually cell death. Perturbed Ca²⁺ homeostasis is believed to play a major role in HD, as altered Ca²⁺ homeostasis often precedes striatal dysfunction and manifestation of HD symptoms. In addition, dysregulation of Ca²⁺ can cause morphological and functional changes in MSNs and CPNs. Therefore, Ca²⁺ imaging techniques have the potential of visualizing changes in Ca²⁺ dynamics and neuronal activity in HD animal models. This minireview focuses on studies using diverse Ca²⁺ imaging techniques, including two-photon microscopy, fiber photometry, and miniscopes, in combination of Ca²⁺ indicators to monitor activity of neurons in HD models as the disease progresses. We then discuss the future applications of Ca²⁺ imaging to visualize disease mechanisms and alterations associated with HD, as well as studies showing how, as a proof-of-concept, Ca²⁺imaging using miniscopes in freely-behaving animals can help elucidate the differential role of direct and indirect pathway MSNs in HD symptoms.

Multiregion neuronal activity: the forest and the trees

The past decade has witnessed remarkable advances in the simultaneous measurement of neuronal activity across many brain regions, enabling fundamentally new explorations of the brain-spanning cellular dynamics that underlie sensation, cognition and action. These recently developed multiregion recording techniques have provided many experimental opportunities, but thoughtful consideration of methodological trade-offs is necessary, especially regarding field of view, temporal acquisition rate and ability to guarantee cellular resolution. When applied in concert with modern optogenetic and computational tools, multiregion recording has already made possible fundamental biological discoveries - in part via the unprecedented ability to perform unbiased neural activity screens for principles of brain function, spanning dozens of brain areas and from local to global scales.

Review on data analysis methods for mesoscale neural imaging in vivo

Significance: Mesoscale neural imaging in vivo has gained extreme popularity in neuroscience for its capacity of recording large-scale neurons in action. Optical imaging with single-cell resolution and millimeter-level field of view in vivo has been providing an accumulated database of neuron-behavior correspondence. Meanwhile, optical detection of neuron signals is easily contaminated by noises, background, crosstalk, and motion artifacts, while neural-level signal processing and network-level coordinate are extremely complicated, leading to laborious and challenging signal processing demands. The existing data analysis procedure remains unstandardized, which could be daunting to neophytes or neuroscientists without computational background. Aim: We hope to provide a general data analysis pipeline of mesoscale neural imaging shared between imaging modalities and systems. Approach: We divide the pipeline into two main stages. The first stage focuses on extracting high-fidelity neural responses at single-cell level from raw images, including motion registration, image denoising, neuron segmentation, and signal extraction. The second stage focuses on data mining, including neural functional mapping, clustering, and brain-wide network deduction. Results: Here, we introduce the general pipeline of processing the mesoscale neural images. We explain the principles of these procedures and compare different approaches and their application scopes with detailed discussions about the shortcomings and remaining challenges. Conclusions: There are great challenges and opportunities brought by the large-scale mesoscale data, such as the balance between fidelity and efficiency, increasing computational load, and neural network interpretability. We believe that global circuits on single-neuron level will be more extensively explored in the future.

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).

An Open-Source Real-Time Motion Correction Plug-In for Single-Photon Calcium Imaging of Head-Mounted Microscopy

Single-photon-based head-mounted microscopy is widely used to record the brain activities of freely-moving animals. However, during data acquisition, the free movement of animals will cause shaking in the field of view, which deteriorates subsequent neural signal analyses. Existing motion correction methods applied to calcium imaging data either focus on offline analyses or lack sufficient accuracy in real-time processing for single-photon data. In this study, we proposed an open-source real-time motion correction (RTMC) plug-in for single-photon calcium imaging data acquisition. The RTMC plug-in is a real-time subpixel registration algorithm that can run GPUs in UCLA Miniscope data acquisition software. When used with the UCLA Miniscope, the RTMC algorithm satisfies real-time processing requirements in terms of speed, memory, and accuracy. We tested the RTMC algorithm by extending a manual neuron labeling function to extract calcium signals in a real experimental setting. The results demonstrated that the neural calcium dynamics and calcium events can be restored with high accuracy from the calcium data that were collected by the UCLA Miniscope system embedded with our RTMC plug-in. Our method could become an essential component in brain science research, where real-time brain activity is needed for closed-loop experiments.

cytoNet: Spatiotemporal network analysis of cell communities

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet’s capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.

Experimental and computational analyses of calcium dynamics in 22q11.2 deletion model astrocytes

Methods for deriving mechanistic information from intracellular calcium dynamics have largely been applied to neuronal data despite the knowledge of roles of glial cells in behavior, cognition, and psychiatric disorders. Using calcium imaging, computer vision, and Bayesian kinetic inference (BKI), we analyzed calcium dynamics in primary astrocytes derived from control or Df1/⁺ mice, a model of 22q11.2 deletion (DiGeorge syndrome). Inference of the highest-likelihood molecular kinetic characteristics of intracellular calcium dynamics identified changes in the activity of the sarcoendoplasmic reticulum calcium ATPase (SERCA). Application of a SERCA inhibitor to wild-type astrocytes reproduced the differences detected in Df1/⁺ astrocytes. Our work reveals the molecular changes driving the calcium kinetics in astrocytes from a 22q11.2 deletion model. BKI can be useful for mechanistically dissecting calcium dynamics in glial cells and formulating and testing hypotheses about underlying molecular mechanisms.

Minian an open-source miniscope analysis pipeline

Miniature microscopes have gained considerable traction for in vivo calcium imaging in freely behaving animals. However, extracting calcium signals from raw videos is a computationally complex problem and remains a bottleneck for many researchers utilizing single-photon in vivo calcium imaging. Despite the existence of many powerful analysis packages designed to detect and extract calcium dynamics, most have either key parameters that are hard-coded or insufficient step-by-step guidance and validations to help the users choose the best parameters. This makes it difficult to know whether the output is reliable and meets the assumptions necessary for proper analysis. Moreover, large memory demand is often a constraint for setting up these pipelines since it limits the choice of hardware to specialized computers. Given these difficulties, there is a need for a low memory demand, user-friendly tool offering interactive visualizations of how altering parameters at each step of the analysis affects data output. Our open-source analysis pipeline, Minian (Miniscope Analysis), facilitates the transparency and accessibility of single-photon calcium imaging analysis, permitting users with little computational experience to extract the location of cells and their corresponding calcium traces and deconvolved neural activities. Minian contains interactive visualization tools for every step of the analysis, as well as detailed documentation and tips on parameter exploration. Furthermore, Minian has relatively small memory demands and can be run on a laptop, making it available to labs that do not have access to specialized computational hardware. Minian has been validated to reliably and robustly extract calcium events across different brain regions and from different cell types. In practice, Minian provides an open-source calcium imaging analysis pipeline with user-friendly interactive visualizations to explore parameters and validate results.

PatchWarp: Corrections of non-uniform image distortions in two-photon calcium imaging data by patchwork affine transformations

Complex distortions on calcium imaging often impair image registration accuracy. Here, we developed a registration algorithm, PatchWarp, to robustly correct slow image distortion for calcium imaging data. PatchWarp is a two-step algorithm with rigid and non-rigid image registrations. To correct non-uniform image distortions, it splits the imaging field and estimates the best affine transformation matrix for each of the subfields. The distortion-corrected subfields are stitched together like a patchwork to reconstruct the distortion-corrected imaging field. We show that PatchWarp robustly corrects image distortions of calcium imaging data collected from various cortical areas through glass window or gradient-index (GRIN) lens with a higher accuracy than existing non-rigid algorithms. Furthermore, it provides a fully automated method of registering images from different imaging sessions for longitudinal neural activity analyses. PatchWarp improves the quality of neural activity analyses and is useful as a general approach to correct image distortions in a wide range of disciplines.

A perspective on astrocyte regulation of neural circuit function and animal behavior

Studies over the past two decades have demonstrated that astrocytes are tightly associated with neurons and play pivotal roles in neural circuit development, operation, and adaptation in health and disease. Nevertheless, precisely how astrocytes integrate diverse neuronal signals, modulate neural circuit structure and function at multiple temporal and spatial scales, and influence animal behavior or disease through aberrant excitation and molecular output remains unclear. This Perspective discusses how new and state-of-the-art approaches, including fluorescence indicators, opto- and chemogenetic actuators, genetic targeting tools, quantitative behavioral assays, and computational methods, might help resolve these longstanding questions. It also addresses complicating factors in interpreting astrocytes’ role in neural circuit regulation and animal behavior, such as their heterogeneity, metabolism, and inter-glial communication. Research on these questions should provide a deeper mechanistic understanding of astrocyte-neuron assemblies’ role in neural circuit function, complex behaviors, and disease.

Development of an Ergonomic User Interface Design of Calcium Imaging Processing System

An optical brain-machine interface (O-BMI) system using calcium imaging has various advantages such as high resolution, a comprehensive view of large neural populations, abilities such as long-term stable recording, and applicability to freely behaving animals in neuroscience research. The present study developed an ergonomic user interface (UI) design, based on a use scenario for an O-BMI system that can be used for the acquisition and processing of calcium imaging in freely behaving rodents. The UI design was developed in three steps: (1) identification of design and function requirements of users, (2) establishment of a use scenario, and (3) development of a UI prototype. The UI design requirements were identified by a literature review, a benchmark of existing systems, and a focus group interview with five neuroscience researchers. Then, the use scenario was developed for tasks of data acquisition, feature extraction, and neural decoding for offline and online processing by considering the sequences of operations and needs of users. Lastly, a digital prototype incorporating an information architecture, graphic user interfaces, and simulated functions was fabricated. A usability test was conducted with five neuroscientists (work experience = 3.4 ± 1.1 years) and five ergonomic experts (work experience = 3.6 ± 2.7 years) to compare the digital prototypes with four existing systems (Miniscope, nVista, Mosaic, and Suite2p). The usability testing results showed that the ergonomic UI design was significantly preferred to the UI designs of the existing systems by reducing the task completion time by 10.1% to 70.2% on average, the scan path length by 14.4% to 88.7%, and perceived workload by 12.2% to 37.9%, increasing satisfaction by 11.3% to 74.3% in data acquisition and signal-extraction tasks. The present study demonstrates the significance of the user-centered design approach in the development of a system for neuroscience research. Further research is needed to validate the usability test results of the UI prototype as a corresponding real system is implemented.

Calcium Imaging Reveals Fast Tuning Dynamics of Hippocampal Place Cells and CA1 Population Activity during Free Exploration Task in Mice

Hippocampal place cells are a well-known object in neuroscience, but their place field formation in the first moments of navigating in a novel environment remains an ill-defined process. To address these dynamics, we performed in vivo imaging of neuronal activity in the CA1 field of the mouse hippocampus using genetically encoded green calcium indicators, including the novel NCaMP7 and FGCaMP7, designed specifically for in vivo calcium imaging. Mice were injected with a viral vector encoding calcium sensor, head-mounted with an NVista HD miniscope, and allowed to explore a completely novel environment (circular track surrounded by visual cues) without any reinforcement stimuli, in order to avoid potential interference from reward-related behavior. First, we calculated the average time required for each CA1 cell to acquire its place field. We found that 25% of CA1 place fields were formed at the first arrival in the corresponding place, while the average tuning latency for all place fields in a novel environment equaled 247 s. After 24 h, when the environment was familiar to the animals, place fields formed faster, independent of retention of cognitive maps during this session. No cumulation of selectivity score was observed between these two sessions. Using dimensionality reduction, we demonstrated that the population activity of rapidly tuned CA1 place cells allowed the reconstruction of the geometry of the navigated circular maze; the distribution of reconstruction error between the mice was consistent with the distribution of the average place field selectivity score in them. Our data thus show that neuronal activity recorded with genetically encoded calcium sensors revealed fast behavior-dependent plasticity in the mouse hippocampus, resulting in the rapid formation of place fields and population activity that allowed the reconstruction of the geometry of the navigated maze.

BehaviorDEPOT is a simple, flexible tool for automated behavioral detection based on markerless pose tracking

Quantitative descriptions of animal behavior are essential to study the neural substrates of cognitive and emotional processes. Analyses of naturalistic behaviors are often performed by hand or with expensive, inflexible commercial software. Recently, machine learning methods for markerless pose estimation enabled automated tracking of freely moving animals, including in labs with limited coding expertise. However, classifying specific behaviors based on pose data requires additional computational analyses and remains a significant challenge for many groups. We developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a simple, flexible software program that can detect behavior from video timeseries and can analyze the results of experimental assays. BehaviorDEPOT calculates kinematic and postural statistics from keypoint tracking data and creates heuristics that reliably detect behaviors. It requires no programming experience and is applicable to a wide range of behaviors and experimental designs. We provide several hard-coded heuristics. Our freezing detection heuristic achieves above 90% accuracy in videos of mice and rats, including those wearing tethered head-mounts. BehaviorDEPOT also helps researchers develop their own heuristics and incorporate them into the software's graphical interface. Behavioral data is stored framewise for easy alignment with neural data. We demonstrate the immediate utility and flexibility of BehaviorDEPOT using popular assays including fear conditioning, decision-making in a T-maze, open field, elevated plus maze, and novel object exploration.

Bayesian inference of molecular kinetic parameters from astrocyte calcium imaging data

Model-based Bayesian inference from high-content data obtained on live specimens is a burgeoning field with demonstrated applications to neuroscience. In parallel, computer vision methods for extracting the calcium signaling information from imaging data have advanced in application to neuronal physiology. Here, we are describing in detail a method we have recently developed to study calcium dynamics in astrocytes, which combines computer vision with model-based Bayesian learning to deduce the most likely molecular kinetic parameters underlying the observed calcium activity. As reported in the companion experimental study, this method allowed us to identify the key molecular changes downstream of a multi-gene deletion modeling the human 22q11.2 deletion syndrome, the most common human microdeletion and the genetic factor with the highest penetrance for schizophrenia.•

Methodological details are laid out, from our imaging approach to our adaptation of the VBA-CaBBI algorithm previously developed primarily for brain functional imaging data.

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The analytical pipeline is suited for further applications to glial cells and adaptable to other cell types exhibiting complexcalcium dynamics.

Optogenetic inhibition of indirect pathway neurons in the dorsomedial striatum reduces excessive grooming in Sapap3-knockout mice

Excessive grooming of Sapap3-KO mice has been used as a model of obsessive-compulsive disorder (OCD). Previous studies suggest that dysregulation of cortico-striatal circuits is critically important in the generation of compulsive behaviors, and it has been proposed that the alteration in the activity patterns of striatal circuitry underlies the excessive grooming observed in Sapap3-KO mice. To test this hypothesis, we used in-vivo calcium imaging of individual cells to record striatal activity in these animals and optogenetic inhibition to manipulate this activity. We identified striatal neurons that are modulated during grooming behavior and found that their proportion is significantly larger in Sapap3-KO mice compared to wild-type littermates. Inhibition of striatal cells in Sapap3-KO mice increased the number of grooming episodes observed. Remarkably, the specific inhibition of indirect pathway neurons decreased the occurrence of grooming events. Our results indicate that there is striatal neural activity related to excessive grooming engagement in Sapap3-KO mice. We also demonstrate, for the first time, that specific inhibition of striatal indirect pathway neurons reduces this compulsive phenotype, suggesting that treatments that alleviate compulsive symptoms in OCD patients may exert their effects through this specific striatal population.

Distinct neuron populations for simple and compound calls in the primary auditory cortex of awake marmosets

Marmosets are highly social non-human primates that live in families. They exhibit rich vocalization, but the neural basis underlying this complex vocal communication is largely unknown. Here we report the existence of specific neuron populations in marmoset A1 that respond selectively to distinct simple or compound calls made by conspecific marmosets. These neurons were spatially dispersed within A1 but distinct from those responsive to pure tones. Call-selective responses were markedly diminished when individual domains of the call were deleted or the domain sequence was altered, indicating the importance of the global rather than local spectral-temporal properties of the sound. Compound call-selective responses also disappeared when the sequence of the two simple-call components was reversed or their interval was extended beyond 1 s. Light anesthesia largely abolished call-selective responses. Our findings demonstrate extensive inhibitory and facilitatory interactions among call-evoked responses, and provide the basis for further study of circuit mechanisms underlying vocal communication in awake non-human primates.

Advances in nonlinear optical microscopy techniques for in vivo and in vitro neuroimaging

Understanding the mechanism of the brain via optical microscopy is one of the challenges in neuroimaging, considering the complex structures. Advanced neuroimaging techniques provide a more comprehensive insight into patho-mechanisms of brain disorders, which is useful in the early diagnosis of the pathological and physiological changes associated with various neurodegenerative diseases. Recent advances in optical microscopy techniques have evolved powerful tools to overcome scattering of light and provide improved in vivo neuroimaging with sub-cellular resolution, endogenous contrast specificity, pinhole less optical sectioning capability, high penetration depth, and so on. The following article reviews the developments in various optical imaging techniques including two-photon and three-photon fluorescence, second-harmonic generation, third-harmonic generation, coherent anti-Stokes Raman scattering, and stimulated Raman scattering in neuroimaging. We have outlined the potentials and drawbacks of these techniques and their possible applications in the investigation of neurodegenerative diseases.

A versatile computational algorithm for time-series data analysis and machine-learning models

Here we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson’s disease from a single digital handwriting test.

Online Decoding System with Calcium Image From Mice Primary Motor Cortex

With the development of calcium imaging, neuroscientists have been able to study neural activity with a higher spatial resolution. However, the real-time processing of calcium imaging is still a big challenge for future experiments and applications. Most neuroscientists have to process their imaging data offline due to the time-consuming of most existing calcium imaging analysis methods. We proposed a novel online neural signal processing framework for calcium imaging and established an Optical Brain-Computer Interface System (OBCIs) for decoding neural signals in real-time. We tested and evaluated this system by classifying the calcium signals obtained from the primary motor cortex of mice when the mice were performing a lever-pressing task. The performance of our online system could achieve above 80% in the average decoding accuracy. Our preliminary results show that the online neural processing framework could be applied to future closed-loop OBCIs studies.

Open-Source Software for Real-time Calcium Imaging and Synchronized Neuron Firing Detection

We developed Carignan, a real-time calcium imaging software that can automatically detect activity patterns of neurons. Carignan can activate an external device when synchronized neural activity is detected in calcium imaging obtained by a one-photon (1p) miniscope. Combined with optogenetics, our software enables closed-loop experiments for investigating functions of specific types of neurons in the brain. In addition to making existing pattern detection algorithms run in real-time seamlessly, we developed a new classification module that distinguishes neurons from false-positives using deep learning. We used a combination of convolutional and recurrent neural networks to incorporate both spatial and temporal features in activity patterns. Our method performed better than existing neuron detection methods for false-positive neuron detection in terms of the F1 score. Using Carignan, experimenters can activate or suppress a group of neurons when specific neural activity is observed. Because the system uses a 1p miniscope, it can be used on the brain of a freely-moving animal, making it applicable to a wide range of experimental paradigms.

Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research

Neuroscience and artificial intelligence (AI) share a long history of collaboration. Advances in neuroscience, alongside huge leaps in computer processing power over the last few decades, have given rise to a new generation of in silico neural networks inspired by the architecture of the brain. These AI systems are now capable of many of the advanced perceptual and cognitive abilities of biological systems, including object recognition and decision making. Moreover, AI is now increasingly being employed as a tool for neuroscience research and is transforming our understanding of brain functions. In particular, deep learning has been used to model how convolutional layers and recurrent connections in the brain's cerebral cortex control important functions, including visual processing, memory, and motor control. Excitingly, the use of neuroscience-inspired AI also holds great promise for understanding how changes in brain networks result in psychopathologies, and could even be utilized in treatment regimes. Here we discuss recent advancements in four areas in which the relationship between neuroscience and AI has led to major advancements in the field; (1) AI models of working memory, (2) AI visual processing, (3) AI analysis of big neuroscience datasets, and (4) computational psychiatry.

A Modified Miniscope System for Simultaneous Electrophysiology and Calcium Imaging in vivo

The miniscope system is one of the calcium (Ca²⁺) imaging tools with small size and lightweight and can realize the deep-brain Ca²⁺ imaging not confined to the cerebral cortex. Combining Ca²⁺ imaging and electrophysiology recording has been an efficient method for extracting high temporal-spatial resolution signals in the brain. In this study, a particular electrode probe was developed and assembled on the imaging lens to modify the miniscope system. The electrode probe can be tightly integrated into the lens of the miniscope without increasing the volume, weight, and implantation complexity. In vivo tests verified that the proposed modified system has realized the simultaneous recording of Ca²⁺ signals and local field potential (LFP) signal in the hippocampus CA1 region of an adult mouse.

Lateral hypothalamic LEPR neurons drive appetitive but not consummatory behaviors

Assigning behavioral roles to genetically defined neurons within the lateral hypothalamus (LH) is an ongoing challenge. We demonstrate that a subpopulation of LH GABAergic neurons expressing leptin receptors (LH^LEPR) specifically drives appetitive behaviors in mice. Ablation of LH GABAergic neurons (LH^VGAT) decreases weight gain and food intake, whereas LH^LEPR ablation does not. Appetitive learning in a Pavlovian conditioning paradigm is delayed in LH^VGAT-ablated mice but prevented entirely in LH^LEPR-ablated mice. Both LH^VGAT and LH^LEPR neurons bidirectionally modulate reward-related behaviors, but only LH^VGAT neurons affect feeding. In the Pavlovian paradigm, only LH^LEPR activity discriminates between conditioned cues. Optogenetic activation or inhibition of either population in this task disrupts discrimination. However, manipulations of LH^LEPR→VTA projections evoke divergent effects on responding. Unlike food-oriented learning, chemogenetic inhibition of LH^LEPR neurons does not alter cocaine-conditioned place preference but attenuates cocaine sensitization. Thus, LH^LEPR neurons may specifically regulate appetitive behaviors toward non-drug reinforcers.

Constructing an adult orofacial premotor atlas in Allen mouse CCF

Premotor circuits in the brainstem project to pools of orofacial motoneurons to execute essential motor action such as licking, chewing, breathing, and in rodent, whisking. Previous transsynaptic tracing studies only mapped orofacial premotor circuits in neonatal mice, but the adult circuits remain unknown as a consequence of technical difficulties. Here, we developed a three-step monosynaptic transsynaptic tracing strategy to identify premotor neurons controlling vibrissa, tongue protrusion, and jaw-closing muscles in the adult mouse. We registered these different groups of premotor neurons onto the Allen mouse brain common coordinate framework (CCF) and consequently generated a combined 3D orofacial premotor atlas, revealing unique spatial organizations of distinct premotor circuits. We further uncovered premotor neurons that simultaneously innervate multiple motor nuclei and, consequently, are likely to coordinate different muscles involved in the same orofacial motor actions. Our method for tracing adult premotor circuits and registering to Allen CCF is generally applicable and should facilitate the investigations of motor controls of diverse behaviors.

Calcium imaging analysis - how far have we come?

Techniques for calcium imaging were first demonstrated in the mid-1970s, whilst tools to analyse these markers of cellular activity are still being developed and improved today. For image analysis, custom tools were developed within labs and until relatively recently, software packages were not widely available between researchers. We will discuss some of the most popular methods for calcium imaging analysis that are now widely available and describe why these protocols are so effective. We will also describe some of the newest innovations in the field that are likely to benefit researchers, particularly as calcium imaging is often an inherently low signal-to-noise method. Although calcium imaging analysis has seen recent advances, particularly following the rise of machine learning, we will end by highlighting the outstanding requirements and questions that hinder further progress and pose the question of how far we have come in the past sixty years and what can be expected for future development in the field.

Calcium imaging analysis - how far have we come?

Techniques for calcium imaging were first achieved in the mid-1970s, whilst tools to analyse these markers of cellular activity are still being developed and improved. For image analysis, custom tools were developed within labs and until relatively recently, software packages were not widely available between researchers. We will discuss some of the most popular, alongside our preferred, methods for calcium imaging analysis that are now widely available and describe why these protocols are so effective. We will also describe some of the newest innovations in the field that are likely to benefit researchers, particularly as calcium imaging is often an inherently low signal-to-noise method. Although calcium imaging analysis has seen recent advances, particularly following the rise of machine learning, we will end by highlighting the outstanding requirements and questions that hinder further progress, and pose the question of how far we have come in the past sixty years and what can be expected for future development in the field.

Online analysis of microendoscopic 1-photon calcium imaging data streams

In vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements. These drawbacks prevent its applicability to the analysis of large datasets and closed-loop experimental settings. Here we address both issues by introducing two different online algorithms for extracting neuronal activity from streaming microendoscopic data. Our first algorithm, OnACID-E, presents an online adaptation of the CNMF-E algorithm, which dramatically reduces its memory and computation requirements. Our second algorithm proposes a convolution-based background model for microendoscopic data that enables even faster (real time) processing. Our approach is modular and can be combined with existing online motion artifact correction and activity deconvolution methods to provide a highly scalable pipeline for microendoscopic data analysis. We apply our algorithms on four previously published typical experimental datasets and show that they yield similar high-quality results as the popular offline approach, but outperform it with regard to computing time and memory requirements. They can be used instead of CNMF-E to process pre-recorded data with boosted speeds and dramatically reduced memory requirements. Further, they newly enable online analysis of live-streaming data even on a laptop.

Calcium imaging methods enable researchers to measure the activity of genetically-targeted large-scale neuronal subpopulations. Whereas previous methods required the specimen to be stable, e.g. anesthetized or head-fixed, new brain imaging techniques using microendoscopic lenses and miniaturized microscopes have enabled deep brain imaging in freely moving mice. However, the very large background fluctuations, the inevitable movements and distortions of imaging field, and the extensive spatial overlaps of fluorescent signals complicate the goal of efficiently extracting accurate estimates of neural activity from the observed video data. Further, current activity extraction methods are computationally expensive due to the complex background model and are typically applied to imaging data long after the experiment is complete. Moreover, in some scenarios it is necessary to perform experiments in real-time and closed-loop—analyzing data on-the-fly to guide the next experimental steps or to control feedback –, and this calls for new methods for accurate real-time processing. Here we address both issues by adapting a popular extraction method to operate online and extend it to utilize GPU hardware that enables real time processing. Our algorithms yield similar high-quality results as the original offline approach, but outperform it with regard to computing time and memory requirements. Our results enable faster and scalable analysis, and open the door to new closed-loop experiments in deep brain areas and on freely-moving preparations. Our algorithms can be used for newly enabled real-time analysis of streaming data, as well as swapped in directly to replace the computationally costly offline approach.

Fluorescence microendoscopy for in vivo deep-brain imaging of neuronal circuits

Imaging neuronal activity in awake, behaving animals has become a groundbreaking method in neuroscience that has rapidly enhanced our understanding of how the brain works. In vivo microendoscopic imaging has enabled researchers to see inside the brains of experimental animals and thus has emerged as a technology fit to answer many experimental questions. By combining microendoscopy with cutting edge targeting strategies and sophisticated analysis tools, neuronal activity patterns that underlie changes in behavior and physiology can be identified. However, new users may find it challenging to understand the techniques and to leverage this technology to best suit their needs. Here we present a background and overview of the necessary components for performing in vivo optical calcium imaging and offer some detailed guidance for current recommended approaches.

Direct Neuronal Reprogramming of Common Marmoset Fibroblasts by ASCL1, microRNA-9/9*, and microRNA-124 Overexpression

The common marmoset (Callithrix jacchus) has attracted considerable attention, especially in the biomedical science and neuroscience research fields, because of its potential to recapitulate the complex and multidimensional phenotypes of human diseases, and several neurodegenerative transgenic models have been reported. However, there remain several issues as (i) it takes years to generate late-onset disease models, and (ii) the onset age and severity of phenotypes can vary among individuals due to differences in genetic background. In the present study, we established an efficient and rapid direct neuronal induction method (induced neurons; iNs) from embryonic and adult marmoset fibroblasts to investigate cellular-level phenotypes in the marmoset brain in vitro. We overexpressed reprogramming effectors, i.e., microRNA-9/9*, microRNA-124, and Achaete-Scute family bHLH transcription factor 1, in fibroblasts with a small molecule cocktail that facilitates neuronal induction. The resultant iNs from embryonic and adult marmoset fibroblasts showed neuronal characteristics within two weeks, including neuron-specific gene expression and spontaneous neuronal activity. As directly reprogrammed neurons have been shown to model neurodegenerative disorders, the neuronal reprogramming of marmoset fibroblasts may offer new tools for investigating neurological phenotypes associated with disease progression in non-human primate neurological disease models.

Human Astrocytes Model Derived from Induced Pluripotent Stem Cells

Induced pluripotent stem cell (iPSC)-based disease modeling has a great potential for uncovering the mechanisms of pathogenesis, especially in the case of neurodegenerative diseases where disease-susceptible cells can usually not be obtained from patients. So far, the iPSC-based modeling of neurodegenerative diseases has mainly focused on neurons because the protocols for generating astrocytes from iPSCs have not been fully established. The growing evidence of astrocytes' contribution to neurodegenerative diseases has underscored the lack of iPSC-derived astrocyte models. In the present study, we established a protocol to efficiently generate iPSC-derived astrocytes (iPasts), which were further characterized by RNA and protein expression profiles as well as functional assays. iPasts exhibited calcium dynamics and glutamate uptake activity comparable to human primary astrocytes. Moreover, when co-cultured with neurons, iPasts enhanced neuronal synaptic maturation. Our protocol can be used for modeling astrocyte-related disease phenotypes in vitro and further exploring the contribution of astrocytes to neurodegenerative diseases.