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bioRxiv2024

Statistical signature of subtle behavioural changes inlarge-scale behavioural assays

The central nervous system can generate various behaviours, including motor responses, which we can observe through video recordings. Recent advancements in genetics, automated behavioural acquisition at scale, and machine learning enable us to link behaviours to their underlying neural mechanisms causally. Moreover, in some animals, such as the Drosophila larva, this mapping is possible at unprecedented scales of millions of animals and single neurons, allowing us to identify the neural circuits generating particular behaviours. These high-throughput screening efforts are invaluable, linking the activation or suppression of specific neurons to behavioural patterns in millions of animals. This provides a rich dataset to explore how diverse nervous system responses can be to the same stimuli. However, challenges remain in identifying subtle behaviours from these large datasets, including immediate and delayed responses to neural activation or suppression, and understanding these behaviours on a large scale. We introduce several statistically robust methods for analyzing behavioural data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioural spaces aimed at detecting subtle deviations in behaviour. 3) A generative model for larval behavioural sequences, providing a benchmark for identifying complex behavioural changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioural screen focused on responses to an air puff, analyzing data from 280,716 larvae across 568 genetic lines. Author SummaryThere is a significant gap in understanding between the architecture of neural circuits and the mechanisms of action selection and behaviour generation.Drosophila larvae have emerged as an ideal platform for simultaneously probing behaviour and the underlying neuronal computation [1]. Modern genetic tools allow efficient activation or silencing of individual and small groups of neurons. Combining these techniques with standardized stimuli over thousands of individuals makes it possible to relate neurons to behaviour causally. However, extracting these relationships from massive and noisy recordings requires the development of new statistically robust approaches. We introduce a suite of statistical methods that utilize individual behavioural data and the overarching structure of the behavioural screen to deduce subtle behavioural changes from raw data. Given our studys extensive number of larvae, addressing and preempting potential challenges in body shape recognition is critical for enhancing behaviour detection. To this end, we have adopted a physics-informed inference model. Our first group of techniques enables robust statistical analysis within a learned continuous behaviour latent space, facilitating the detection of subtle behavioural shifts relative to reference genetic lines. A second array of methods probes for subtle variations in action sequences by comparing them to a bespoke generative model. Together, these strategies have enabled us to construct representations of behavioural patterns specific to a lineage and identify a roster of "hit" neurons with the potential to influence behaviour subtly.

neuroscience↗

bioRxiv2025

GM1-oligosaccharide rescues rotenone-impaired neuronal polarization through RhoA/ROCK modulation and mitochondrial protection

Neuronal polarization is a fundamental process in the formation of functional neural circuits, relying on the precise coordination between cytoskeletal regulatory signals and mechanisms that sustain cellular integrity. Disruption of these processes compromises neuronal differentiation and survival, and various neurotoxic compounds, including certain pesticides, have been associated with such dysfunctions. In this context, identifying molecules that counteract these detrimental effects is of significant therapeutic interest. Neuronal polarization is essential for the establishment of functional neural circuits and relies on coordinated regulation of actin cytoskeleton dynamics, RhoA/ROCK signaling, and mitochondrial function. Here, we investigated the neuroprotective and neurorestorative potential of the ganglioside GM1 and its oligosaccharide derivative, osGM1, in primary hippocampal pyramidal neurons exposed to the mitochondrial neurotoxin rotenone. Rotenone induced a marked arrest of neuronal development, impaired axonal elongation, and disrupted mitochondrial organization and membrane potential. Both GM1 and osGM1 promoted recovery of neuronal polarity and axonal growth, exerting protective and restorative effects even under continuous toxin exposure, with osGM1 showing superior efficacy. Notably, osGM1 also reversed axonal growth deficits caused by pathological actin stabilization. Mechanistically, osGM1 normalized rotenone-induced hyperactivation of the RhoA/ROCK pathway without altering basal signaling and partially restored mitochondrial network integrity and function. Collectively, these findings identify osGM1 as a multi-target modulator of cytoskeletal and mitochondrial dysfunction and support its translational potential as a therapeutic strategy to counteract neurotoxin-induced neuronal damage.

neuroscience↗

bioRxiv2021

Measuring optimality of a neural system by a logic gate model

The nematode worm Caenorhabditis elegans has a relatively simple neural system for analysis of information transmission from sensory organ to muscle fiber. Therefore, an example of a neural circuit is analyzed that originates in the nematode worm, and a method is applied for measuring its information flow efficiency by use of a model of logic gates. This model-based approach is useful where the assumptions of a logic gate design are applicable. It is also an useful approach where there are competing mathematical models for explaining the role of a neural circuit since the logic gate model can estimate the computational complexity of a network, and distinguish which of the mathematical models require fewer computations. In addition, for generalization of the concept of information optimality in biological systems, there is an extensive discussion of its role in the genetic-based pathways of organisms.

neuroscience↗

bioRxiv2023

3D Printed Ti3C2Tx MXene/PCL Scaffolds for Guided Neuronal Growth and Photothermal Stimulation

The exploration of neural circuitry is essential for understanding the computational mechanisms and physiology of the brain. Despite significant advances in materials and fabrication techniques, controlling neuronal connectivity and response in three dimensions continues to present a formidable challenge. Here, we present a method for engineering the growth of three-dimensional (3D) neural circuits with the capability for optical stimulation. We fabricated bioactive interfaces by melt electrospinning writing (MEW) of 3D printed polycaprolactone (PCL) scaffolds followed by coating with titanium carbide (Ti3C2Tx MXene). Beyond enhancing hydrophilicity, cell adhesion, and electrical conductivity, the Ti3C2Tx MXene coating enabled optocapacitance-based neuronal stimulation due to illumination-induced local temperature increases. This work presents a strategy for additive manufacturing of neural tissues with optical control for functional tissue engineering and neural circuit computation.

bioengineering↗

bioRxiv2023

Acid-sensing ion channel 1a contributes to the prefrontal cortex ischemia-enhanced neuronal activities in the amygdala

Following a stroke, the emergence of amygdala-related disorders poses a significant challenge, with severe implications for post-stroke mental health, including conditions such as anxiety and depression. These disorders not only hinder post-stroke recovery but also elevate mortality rates. Despite their profound impact, the precise origins of aberrant amygdala function after stroke remain elusive. As a target of reduced brain pH in ischemia, acid-sensing ion channels (ASICs) have been implicated in synaptic transmission after ischemia, hinting at their potential role in reshaping neural circuits following a stroke. This study delves into the intriguing relationship between post-stroke alterations and ASICs, specifically focusing on postsynaptic ASIC1a enhancement in the amygdala following prefrontal cortex (PFC) ischemia induced by endothelin-1 (ET-1) injection. Our findings intriguingly illustrate that mPFC ischemia not only accentuates the PFC to amygdala circuit but also implicates ASIC1a in fostering augmented synaptic plasticity after ischemia. In contrast, the absence of ASIC1a impairs the heightened induction of long-term potentiation (LTP) in the amygdala induced by ischemia. This pivotal research introduces a novel concept with the potential to inaugurate an entirely new avenue of inquiry, thereby significantly enhancing our comprehension of the intricate mechanisms underlying post-stroke neural circuit reconfiguration. Importantly, these revelations hold the promise of paving the way for groundbreaking therapeutic interventions.

neuroscience↗

bioRxiv2024

The Glutamatergic Projection from the Substantia Nigra Pars Reticulata to the Dorsal Raphe Nucleus Facilitates Social Hierarchy in Mice

Social hierarchy constitutes a fundamental organizational characteristic among various social species, significantly influencing individual survival, health, and reproductive success within these societies. Neurons in the substantia nigra pars reticulata (SNr) exhibit extensive connectivity with the dorsal raphe nucleus (DRN), a critical structure implicated in social interaction, reward processing, and the establishment of social rank. However, the specific neuronal types within the SNr, as well as the associated neural circuits that regulate social dominance, remain inadequately characterized. This study aims to elucidate the crucial role of SNr glutamatergic neurons in the establishment and maintenance of social hierarchy in mice. Employing fiber photometry, we observed that the activation of SNr glutamatergic neurons increased during the initiation of effortful behaviors in the tube test. Further investigations revealed that optogenetic activation or chemogenetic inhibition of the glutamatergic neural circuit connecting the SNr to the DRN induced upward or downward shifts in social ranks, respectively. Additionally, our findings indicate that the activation of SNr glutamatergic terminals in the DRN reduces anxiety levels in mice. Collectively, these results underscore the critical role of the glutamatergic pathway from the SNr to the DRN in regulating social hierarchy. This work enhances our understanding of the functions of SNr glutamatergic neurons in both physiological contexts and neurological disorders.

neuroscience↗

bioRxiv2018

Histone deacetylase inhibitor treatment promotes spontaneous caregiving behavior in C57BL/6J male mice

Whereas the majority of mammalian species are uni-parental with the mother solely provisioning care for young conspecifics, fathering behaviors can emerge under certain circumstances. For example, a great deal of individual variation in response to young pups has been reported in multiple inbred strains of laboratory male mice. Further, sexual experience and subsequent cohabitation with a female conspecific can induce caregiving responses in otherwise indifferent, fearful or aggressive males. Thus, a highly conserved parental neural circuit is likely present in both sexes, however the extent to which infants are capable of accessing this circuit may vary. In support of this idea, fearful or indifferent responses toward pups in female mice are linked to greater immediate early gene (IEG) expression in a fear/defensive circuit involving the anterior hypothalamus than in an approach/attraction circuit involving the ventral tegmental area. However, experience with infants, particularly in combination with histone deacetylase inhibitor (HDACi) treatment, can reverse this pattern of neuronal activation and behavior. Thus, HDACi treatment may increase the transcription of primed/poised genes that play a role in the activation and selection of a maternal approach circuit in response to pup stimuli. Here, we asked whether HDACi treatment would impact behavioral response selection and associated IEG expression changes in virgin male mice that are capable of ignoring, attacking or caring for pups. Our results indicate that systemic HDACi treatment induces spontaneous caregiving behavior in non-aggressive male mice and alters the pattern of pup-induced IEG expression across a fear/defensive neural circuit.

neuroscience↗

bioRxiv2017

Simulating Extracted Connectomes

Connectomes derived from volume EM imaging of the brain can generate detailed physical models of every neuron, and simulators such as NEURON or GENESIS are designed to work with such models. In principal, combining these technologies, plus transmitter and channel models, should allow detailed and accurate simulation of real neural circuits. Here we experiment with this combination, using a well-studied system (motion detection in Drosophila). Since simulation requires both the physical geometry (which we have) and the models of the synapses (which are not currently available), we built approximate synapses corresponding to their known and estimated function. Once we did so, we reproduced direction selectivity in T4 cells, one of the main functions of this neural circuit. This verified the basic functionality of both extraction and simulations, and provided a biologically relevant computation we could use in further experiments. We then compared models with different degrees of physical realism, from full detailed models down to models consisting of a single node, to examine the tradeoff of simulation resources required versus accuracy achieved.\n\nOur results show that much simpler models may be adequate, at least in the case of medulla neurons in Drosophila. Such models can be easily derived from fully detailed models, and result in simulations that are much smaller, much faster, and accurate enough for many purposes. Biologically, we show that a lumped neuron model reproduces the main motion detector operation, confirming the result of Gruntman[1], that dendritic compution is not required for this function.

neuroscience↗

bioRxiv2020

Rigorous anterograde trans-monosynaptic tracing of genetic defined neurons with retargeted HSV1 H129

Neuroanatomical tracing technology is fundamental for unraveling the complex network of brain connectome. Tracing tools that could spread between neurons are urgently needed, especially the rigorous trans-monosynaptic anterograde tracer is still lacking. HSV1 strain H129 was proved to be an anterograde tracer and has been used to trace neuronal networks in several reports. However, H129 has a serious defect that it was demonstrated to infect neurons via axon terminals. Thus, when using H129 to dissect output neural circuit, its terminal take up capacity should be carefully considered. Here, we report a recombinant H129 that carrying the anti-Her2 scFv in glycoprotein D to target genetically defined neurons. With the usage of helper virus complementarily expressing Her2 and gD, we can realize the elucidation of direct projection regions of either a given brain nucleus or a specific neuron type. The retargeted H129 system complements the current neural circuit tracer arsenal, which provides a rigorous and practical anterograde trans-monosynaptic tool.

neuroscience↗

bioRxiv2021

Stable thalamocortical learning between medial-dorsal thalamus and cortical attractor networks captures cognitive flexibility

Primates and rodents are able to continually acquire, adapt, and transfer knowledge and skill, and lead to goal-directed behavior during their lifespan. For the case when context switches slowly, animals learn via slow processes. For the case when context switches rapidly, animals learn via fast processes. We build a biologically realistic model with modules similar to a distributed computing system. Specifically, we are emphasizing the role of thalamocortical learning on a slow time scale between the prefrontal cortex (PFC) and medial dorsal thalamus (MD). Previous work [1] has already shown experimental evidence supporting classification of cell ensembles in the medial dorsal thalamus, where each class encodes a different context. However, the mechanism by which such classification is learned is not clear. In this work, we show that such learning can be self-organizing in the manner of an automaton (a distributed computing system), via a combination of Hebbian learning and homeostatic synaptic scaling. We show that in the simple case of two contexts, the network with hierarchical structure can do context-based decision making and smooth switching between different contexts. Our learning rule creates synaptic competition [2] between the thalamic cells to create winner-take-all activity. Our theory shows that the capacity of such a learning process depends on the total number of task-related hidden variables, and such a capacity is limited by system size N. We also theoretically derived the effective functional connectivity as a function of an order parameter dependent on the thalamo-cortical coupling structure. Significance StatementAnimals need to adapt to dynamically changing environments and make decisions based on changing contexts. Here we propose a combination of neural circuit structure with learning mechanisms to account for such behaviors. Specifically, we built a reservoir computing network improved by a Hebbian learning rule together with a synaptic scaling learning mechanism between the prefrontal cortex and the medial-dorsal (MD) thalamus. This model shows that MD thalamus is crucial in such context-based decision making. I also make use of dynamical mean field theory to predict the effective neural circuit. Furthermore, theoretical analysis provides a prediction that the capacity of such a network increases with the network size and the total number of tasks-related latent variables.

neuroscience↗

bioRxiv2022

Early life sleep disruption has long lasting, sex specific effects on later development of sleep in prairie voles

In mammals, sleep duration is highest in the early postnatal period of life and is critical for shaping neural circuits that control the development of complex behaviors. The prairie vole is a wild, highly social rodent that serves as a unique model for the study of complex, species-typical social behaviors. Previous work in our laboratory has found that early life sleep disruption (ELSD) in prairie voles during a sensitive window of postnatal development leads to long lasting changes in social and cognitive behaviors as well as structural changes in excitatory and inhibitory neural circuits in the brain. However, it is currently unknown how later sleep is impacted by ELSD, both shortly after ELSD and over the long term. Therefore, the aim of this study was to describe the effects of ELSD on later life sleep, compared to sleep in normally developing prairie voles. First, we conducted tethered electroencephalogram/electromyogram (EEG/EMG) recordings in juvenile prairie voles undergoing ELSD, compared to Control conditions. Second, we conducted 24 hours of home cage tethered EEG/EMG recordings in either adolescent or adult male and female prairie voles that had previously undergone ELSD or Control conditions as juveniles. We found that, as adults, male ELSD prairie voles showed persistently lower REM sleep duration and female ELSD prairie voles showed persistently higher NREM sleep duration compared to Controls, but no other sleep parameters differed. We concluded that 1) persistent effects of ELSD on sleep into adulthood may contribute to the social and cognitive deficits observed in adult voles, and 2) sleep disruption early in life can influence later sleep patterns in adulthood.

neuroscience↗

bioRxiv2025

Neuronal oversight of germline small RNAs prevents heat-induced sterility in lab-domesticated Caenorhabditis elegans

Thermal pollution, whether local or driven by global warming, threatens biodiversity in part through its detrimental effects on reproduction. Non-coding small RNAs (sRNAs) are crucial for maintaining germline developmental robustness under heat stress. Remarkably, we uncovered that neuronal sRNAs regulate germ cells thermotolerance, affecting both spermatogenic and oogenic germline in a cell non-autonomous manner. Furthermore, we demonstrate that an oxygen-sensing neural circuit antagonizes germline maintenance, likely reflecting the nematodes innate association of reduced oxygen levels with food availability and reproductive permissive environments. Finally, we provide evidence that sRNAs buffer the negative consequences of laboratory domestication, which otherwise cause the laboratory strain to lose germline integrity and become sterile at elevated temperatures. Hence, our findings reveal that mere sensory perception, independent of direct environmental change, modulates germline integrity through sRNA pathways, highlighting a novel mechanism by which neural circuits integrate environmental information to safeguard reproductive fitness in fluctuating environments.

genetics↗

bioRxiv2023

Homeostatic synaptic scaling optimizes learning in network models of neural population codes

Studying and understanding the code of large neural populations hinge on accurate statistical models of population activity. A novel class of models, based on learning to weigh sparse non-linear Random Projections (RP) of the population, has demonstrated high accuracy, efficiency, and scalability. Importantly, these RP models have a clear and biologically-plausible implementation as shallow neural networks. We present a new class of RP models that are learned by optimizing the randomly selected sparse projections themselves. This "reshaping" of projections is akin to changing synaptic connections in just one layer of the corresponding neural circuit model. We show that Reshaped RP models are more accurate and efficient than the standard RP models in recapitulating the code of tens of cortical neurons from behaving monkeys. Incorporating more biological features and utilizing synaptic normalization in the learning process, results in accurate models that are more efficient. Remarkably, these models exhibit homeostasis in firing rates and total synaptic weights of projection neurons. We further show that these sparse homeostatic reshaped RP models outperform fully connected neural network models. Thus, our new scalable, efficient, and highly accurate population code models are not only biologically-plausible but are actually optimized due to their biological features. These findings suggest a dual functional role of synaptic normalization in neural circuits: maintaining spiking and synaptic homeostasis while concurrently optimizing network performance and efficiency in encoding information and learning.

neuroscience↗

bioRxiv2024

Structural diversity of mitochondria in the neuromuscular system across development

As an animal matures, its neural circuit undergoes alterations, yet the developmental changes in intracellular organelles to facilitate these changes is less understood. Using 3D electron microscopy and deep learning, we developed semi-automated methods for reconstructing mitochondria in C. elegans and collected mitochondria reconstructions from normal reproductive stages and dauer, enabling comparative study on mitochondria structure within the neuromuscular system. We found that various mitochondria structural properties in neurons correlate with synaptic connections and these properties are preserved across development in different neural circuits. To test the necessity of these universal mitochondria properties, we examined the behavior in drp-1 mutants with impaired mitochondria fission and discovered that it caused behavioral deficits. Moreover, we observed that dauer neurons display distinctive mitochondrial features, and mitochondria in dauer muscles exhibit unique reticulum-like structure. We propose that this specialized mitochondria structure may serve as an adaptive mechanism to support stage-specific behavioral and physiological needs.

neuroscience↗

bioRxiv2024

Endogenous opioids facilitate stress-induced binge eating via an insular cortex-claustrum pathway

Stress has been shown to promote the development and persistence of binge eating behaviors. However, the neural circuit mechanisms for stress-induced binge-eating behaviors are largely unreported. The endogenous dynorphin (dyn)/kappa opioid receptor (KOR) opioid neuropeptide system has been well established to be a crucial mediator of the anhedonic component of stress. Here, we aimed to dissect the basis of dynorphinergic control of stress-induced binge-like eating behavior. We first established a mouse behavioral model for stress-induced binge-like eating behaviors. We found that mice exposed to stress increased their food intake of familiar palatable food (high fat, high sugar, HPD) compared to non-stressed mice. Following a brain-wide analysis, we isolated robust cFos-positive cells in the Claustrum (CLA), a subcortical structure with highly abundant KOR expression, following stress-induced binge-eating behavior. We report that KOR signaling in CLA is necessary for this elevated stress-induced binge eating behavior using local pharmacology and local deletion of KOR. In vivo calcium recordings using fiber photometry revealed a disinhibition circuit structure in the CLA during the initiation of HPD feeding bouts. We further established the dynamics of endogenous dynorphinergic control of this behavior using a genetically encoded dynorphin biosensor, Klight. Combined with 1-photon single-cell calcium imaging, we report significant heterogeneity with the CLA population during stress-induced binge eating and such behavior attenuates local dynorphin tone. Furthermore, we isolate the anterior Insular cortex (aIC) as the potential source of endogenous dynorphin afferents in the CLA. By characterizing neural circuits and peptidergic mechanisms within the CLA, we uncover a pathway that implicates endogenous opioid regulation stress-induced binge eating.

neuroscience↗

bioRxiv2020

Chromatin remodeler Arid1a regulates subplate neuron identity and wiring of cortical connectivity

Loss-of-function mutations in chromatin remodeler gene ARID1A are a cause of Coffin-Siris syndrome, a developmental disorder characterized by dysgenesis of corpus callosum. Here, we characterize Arid1a function during cortical development and find unexpectedly selective roles for Arid1a in subplate neurons. Subplate neurons (SPNs), strategically positioned at the interface of cortical grey and white matter, orchestrate multiple developmental processes indispensable for neural circuit wiring. We find that pan-cortical deletion of Arid1a leads to extensive mistargeting of intracortical axons and agenesis of corpus callosum. Sparse Arid1a deletion, however, does not autonomously misroute callosal axons, implicating non-cell autonomous Arid1a functions in axon guidance. Supporting this possibility, the ascending axons of thalamocortical neurons, which are not autonomously affected by cortical Arid1a deletion, are also disrupted in their pathfinding into cortex and innervation of whisker barrels. Coincident with these miswiring phenotypes, which are reminiscent of subplate ablation, we unbiasedly find a selective loss of SPN gene expression following Arid1a deletion. In addition, multiple characteristics of SPNs crucial to their wiring functions, including subplate organization, subplate-thalamocortical axon co-fasciculation ("handshake"), and extracellular matrix, are severely disrupted. To empirically test Arid1a sufficiency in subplate, we generate a cortical plate deletion of Arid1a that spares SPNs. In this model, subplate Arid1a expression is sufficient for subplate-thalamocortical axon co-fasciculation and extracellular matrix assembly. Consistent with these wiring functions, subplate Arid1a sufficiently enables normal callosum formation, thalamocortical axon targeting, and whisker barrel development. Thus, Arid1a is a multifunctional regulator of subplate-dependent guidance mechanisms essential to cortical circuit wiring. SignificanceThe cognitive, perceptive, and motor capabilities of the mammalian cerebral cortex depend on assembly of circuit connectivity during development. Subplate neurons, strategically located at the junction of grey and white matter, orchestrate the wiring of cortical circuits. Using a new approach to study gene necessity and sufficiency in subplate neurons, we uncover an essential role for chromatin remodeler Arid1a in subplate neuron gene expression and axon guidance functions. Cortical deletion of Arid1a disrupts subplate-dependent formation of corpus callosum, targeting of thalamocortical axons, and development of sensory maps. Together, our study identifies Arid1a as a central regulator of subplate-dependent axon pathfinding, establishes subplate function as essential to callosum development, and highlights non-cell autonomous mechanisms in neural circuit formation and disorders thereof.

neuroscience↗

bioRxiv2024

Compressed sensing based approach identifies modular neural circuitry driving learned pathogen avoidance

An animals survival hinges on its ability to integrate past information to modify future behavior. The nematode C. elegans adapts its behavior based on prior experiences with pathogen exposure, transitioning from attraction to avoidance of the pathogen. A systematic screen for the neural circuits that integrate the information of previous pathogen exposure to modify behavior has not been feasible because of the lack of tools for neuron type specific perturbations. We overcame this challenge using methods based on compressed sensing to efficiently determine the roles of individual neuron types in learned avoidance behavior. Our screen revealed that distinct sets of neurons drive exit from lawns of pathogenic bacteria and prevent lawn re-entry. Using calcium imaging of freely behaving animals and optogenetic perturbations, we determined the neural dynamics that regulate one key behavioral transition after infection: stalled re-entry into bacterial lawns. We find that key neuron types govern pathogen lawn specific stalling but allow the animal to enter nonpathogenic E. coli lawns. Our study shows that learned pathogen avoidance requires coordinated transitions in discrete neural circuits and reveals the modular structure of this complex adaptive behavioral response to infection.

neuroscience↗

bioRxiv2026

Tracking multi-site somatic voltage dynamics via high-speed fiber photometry

Investigating neural circuit dynamics across distributed brain regions in awake, behaving animals is crucial for understanding complex behavior. Genetically encoded voltage indicators (GEVIs) offer a powerful approach to tracking transmembrane voltage with high temporal and cellular specificity. However, scaling high-sensitivity GEVI recordings across multiple brain regions and multiple animals simultaneously remains a major technical challenge. Furthermore, it is unclear whether soma-targeted GEVIs - typically used for single-cell resolution imaging - can be effectively adapted for fiber photometry. Here, we show that a sCMOS-based widefield imaging system achieves sensitive dual-color multi-site fiber photometry using soma-targeted GEVI indicators with high temporal resolution. We validated this approach in the mouse hippocampal CA1, capturing theta (3-10Hz) and gamma (30-80Hz) rhythms and theta-gamma cross-frequency coupling. Additionally, we recorded high-frequency neural entrainment (>100 Hz) and somatic depolarization induced by electrical stimulation in CA1. Lastly, we tracked synchronized neural activity between the bilateral CA1s as well as multi-site dual-color imaging across CA1 and cortex simultaneously in three freely running mice. This work provides a scalable, accessible platform for high-speed optical electrophysiology in distributed neural circuits. Key points- Implementation of a sCMOS-based widefield imaging setup for sensitive and scalable fiber photometry and cellular imaging. - Demonstration of population multi-site and inter-animal voltage imaging with soma-targeted genetically encoded voltage indicators. - Tracking of high-frequency gamma and high-gamma (>100Hz) neural entrainment

neuroscience↗