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bioRxiv2024

Astrocyte CCN1 stabilizes neural circuits in the adult brain

Neural circuits in many brain regions are refined by experience. Sensory circuits support higher plasticity at younger ages during critical periods - times of circuit refinement and maturation - and limit plasticity in adulthood for circuit stability. The mechanisms underlying these differing plasticity levels and how they serve to maintain and stabilize the properties of sensory circuits remain largely unclear. By combining a transcriptomic approach with ex vivo electrophysiology and in vivo imaging techniques, we identify that astrocytes release cellular communication network factor 1 (CCN1) to maintain synapse and circuit stability in the visual cortex. By overexpressing CCN1 in critical period astrocytes, we find that it promotes the maturation of inhibitory circuits and limits ocular dominance plasticity. Conversely, by knocking out astrocyte CCN1 in adults, binocular circuits are destabilized. These studies establish CCN1 as a novel astrocyte-secreted factor that stabilizes neuronal circuits. Moreover, they demonstrate that the composition and properties of sensory circuits require ongoing maintenance in adulthood, and that these maintenance cues are provided by astrocytes.

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bioRxiv2025

Exploring Stress-Induced Neural Circuit Remodeling through Data-Driven Analysis and Artificial Neural Network Simulation

Chronic stress is associated with alterations in neural circuit dynamics, yet the computational principles underlying these changes remain incompletely understood. Here, we reanalyzed in vivo GCaMP8s recordings from BLA-DMS and CeA-DMS projection pathways using three complementary descriptive approaches. First, Kullback-Leibler (KL) divergence was used to quantify stress-related changes in activity distributions, revealing context-dependent alterations in population-level neural responses. Second, a phenomenological second-order regression framework was employed to characterize local recovery-related dynamical features, providing a compact descriptive representation of pathway activity without implying mechanistic identification. Third, a simplified artificial neural network (ANN) was implemented as a hypothesis-generating sufficiency test. Under asymmetric optimization constraints, the model generated response profiles qualitatively resembling selected features observed experimentally. Robustness analyses indicated that similar qualitative behaviors emerged across multiple parameter initializations. Together, these results provide a multi-level descriptive characterization of stress-associated neural activity alterations across statistical, dynamical, and computational representations, while explicitly avoiding assumptions of a unified mechanistic mapping between analytical levels.

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bioRxiv2020

A spiking neural circuit model for learning multi-sensory integration

Neurons in visual and vestibular information integration areas of macaque brain such as medial superior temporal (MSTd) and ventral intraparietal (VIP) have been classified into congruent neurons and opposite neurons, which prefer congruent inputs and opposite inputs from the two sensory modalities, respectively. In this work, we propose a mechanistic spiking neural model that can account for the emergence of congruent and opposite neurons and their interactions in a neural circuit for multi-sensory integration. The spiking neural circuit model is adopted from an established model for the circuits of the primary visual cortex with little changes in parameters. The network can learn, based on the basic Hebbian learning principle, the correct topological organization and behaviors of the congruent and opposite neurons that have been proposed to play a role in multi-sensory integration. This work explore the constraints and the conditions that lead to the development of a proposed neural circuit for cue integration. It also demonstrates that such neural circuit might indeed be a canonical circuit shared by computations in many cortical areas.

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bioRxiv2024

Central nucleus of the amygdala to medial prefrontal cortex 5-HTergic neural circuit modulates the recovery of consciousness during sevoflurane anesthesia

Preoperative anxiety and depression are significant risk factors for prolonged general anesthesia emergence, raising postoperative complications and mortality. However, the neural mechanisms underlying this association remain unclear, limiting effective interventions and development of effective preventive and therapeutic strategies. Previous studies have shown that sleep-wakefulness neural circuit is closely related to neural circuitry of anesthesia recovery, with 5-Hydroxytryptamine (5-HT) playing a pivotal role of anesthesia arousal. Herein, we utilized pharmacology, optogenetics, chemogenetics, fiber photometry and retrograde tracing to demonstrate that activation of the 5-HTergic neural circuit between the central amygdala (CeA) and medial prefrontal cortex (mPFC) accelerates recovery from sevoflurane anesthesia. Particularly for individuals with anxiety- and depression-like symptoms, who exhibit more severe delayed emergence, it is necessary to enhance 5-HT neuronal activity by further optimizing parameters. To further validate the reliability of our findings and the specificity of viral tools, we successfully bred TPH2-Cre transgenic mice and repeated key experiments, which robustly confirmed all original conclusions. Pharmacological experiments further demonstrated that both 5-HTA and 5-HT2A receptors mediate the pro-arousal function of this circuit. Collectively, our study identifies a key CeA-mPFC serotonergic pathway linking emotional disorders to abnormal anesthetic emergence, providing potential targets for optimizing anesthesia management in vulnerable patients. HighlightsO_LIBoosting the 5-HTergic neural circuit between the CeA and mPFC promotes emergence from sevoflurane anesthesia, with 5-HT1A and 5-HT2A receptors playing a critical role in modulating consciousness transitions. C_LIO_LIPreoperative anxiety and depression exacerbate delayed emergence from anesthesia, highlighting the need for targeted interventions. C_LIO_LIDeciphering the aberrant neurocircuitry mechanisms underlying delayed emergence to inversely deduce the neurocircuitry principles of normal physiology represents a pathological-to-physiological research paradigm. C_LIO_LIStepwise enhancement of intervention parameters reverses delayed emergence in pathological states. C_LIO_LITailored activation of the 5-HTergic circuit offers a novel approach to mitigate complications from delayed emergence in vulnerable patient populations. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/599720v3_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@589b26org.highwire.dtl.DTLVardef@13204fforg.highwire.dtl.DTLVardef@e6374dorg.highwire.dtl.DTLVardef@38cac0_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2023

Magnetogenetic stimulation inside MRI induces spontaneous and evoked changes in neural circuits activity in rats

The ability to modulate specific neural circuits and simultaneously visualize and measure brain activity with MRI would greatly impact understanding brain function in health and disease. The combination of neurostimulation methods and MRI in animal models have already shown promise in elucidating fundamental mechanisms associated with brain activity. We developed an innovative magnetogenetics neurostimulation technology that can trigger neural activity through magnetic fields. Similar to other genetic-based neuromodulation methods, magnetogenetics offers cell-, area- and temporal- specific control of neural activity. However, the magnetogenetics protein (Electromagnetic Preceptive Gene (EPG)) are activated by non-invasive magnetic fields, providing a unique way to target neural circuits by the MRI gradients while simultaneously measure their effect on brain activity. EPG was expressed in rats visual cortex and the amplitude of low-frequency fluctuation (fALFF), resting-state functional connectivity (FC), and sensory activation was measured using a 7T MRI. The results demonstrate that EPG-expressing rats had significantly higher signal fluctuations in the visual areas and stronger FC in sensory areas consistent with known anatomical visuosensory and visuomotor connections. This new technology complements the existing neurostimulation toolbox and provides a mean to study brain function in a minimally-invasive way which was not possible previously.

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bioRxiv2022

Transcriptional control of visual neural circuit development by GS homeobox 1

As essential components of gene expression networks, transcription factors regulate neural circuit assembly. GS homeobox 1 (gsx1) is expressed in the developing visual system; however, no studies have examined its role in visual system formation. In zebrafish, retinal ganglion cell (RGC) axons terminate in ten arborization fields (AFs) in the optic tectum (TeO) and pretectum (Pr). Pretectal AFs (AF1-AF9) mediate distinct and essential visual behaviors, yet we understand less about their development compared to AF10 in the TeO. Using gsx1 zebrafish mutants, immunohistochemistry, and transgenic lines, we observed that gsx1 is required for vesicular glutamate transporter, slc17a6b, expression in the Pr, but not overall neuron number. gsx1 mutants have normal eye morphology, yet exhibit impaired vision and a significantly reduced volume of RGC axons innervating the Pr and TeO, including loss of AF7. Consistent with this, prey capture is reduced in gsx1 mutants. Timed laser ablation of slc17a6b-positive neurons reveals that they aide directly in AF7 formation. This work is the first to implicate gsx1 in establishing cell identity and functional neural circuits in the visual system. SUMMARY STATEMENTThis is the first study in any vertebrate model to establish a requirement for the homeobox transcription factor encoding gene, gsx1, in visual neural circuit formation and function.

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bioRxiv2018

Acoustically Targeted Chemogenetics for Noninvasive Control of Neural Circuits

Neurological and psychiatric diseases often involve the dysfunction of specific neural circuits in particular regions of the brain. Existing treatments, including drugs and implantable brain stimulators, aim to modulate the activity of these circuits, but are typically not cell type-specific, lack spatial targeting or require invasive procedures. Here, we introduce an approach to modulating neural circuits noninvasively with spatial, cell-type and temporal specificity. This approach, called acoustically targeted chemogenetics, or ATAC, uses transient ultrasonic opening of the blood brain barrier to transduce neurons at specific locations in the brain with virally-encoded engineered G-protein-coupled receptors, which subsequently respond to systemically administered bio-inert compounds to activate or inhibit the activity of these neurons. We demonstrate this concept in mice by using ATAC to noninvasively modify and subsequently activate or inhibit excitatory neurons within the hippocampus, showing that this enables pharmacological control of memory formation. This technology allows a brief, noninvasive procedure to make one or more specific brain regions capable of being selectively modulated using orally bioavailable compounds, thereby overcoming some of the key limitations of conventional brain therapies.

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bioRxiv2018

A genetic, genomic, and computational resource for exploring neural circuit function

The anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. Here, we characterize gene expression patterns in distinct neural cell types of the Drosophila visual system using genetic lines to access individual cell types, the TAPIN-seq method to measure their transcriptomes, and a probabilistic method to interpret these measurements. We used these tools to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types, available at http://www.opticlobe.com. Combining these transcriptomes with recently reported connectomes helps characterize how information is transmitted and processed across a range of scales, from individual synapses to circuit pathways. We describe examples that include identifying neurotransmitters, including cases of co-release, generating functional hypotheses based on receptor expression, as well as identifying strong commonalities between different cell types.\n\nHighlightsO_LITranscriptomes reveal transmitters and receptors expressed in Drosophila visual neurons\nC_LIO_LITandem affinity purification of intact nuclei (TAPIN) enables neuronal genomics\nC_LIO_LITAPIN-seq and genetic drivers establish transcriptomes of 67 Drosophila cell types\nC_LIO_LIProbabilistic modeling simplifies interpretation of large transcriptome catalogs\nC_LI

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bioRxiv2021

All-optical interrogation of neural circuits in behaving mice

Recent advances combining two-photon calcium imaging and two-photon optogenetics with digital holography now allow us to read and write neural activity in vivo at cellular resolution with millisecond temporal precision. Such "all-optical" techniques enable experimenters to probe the impact of functionally defined neurons on neural circuit function and behavioural output with new levels of precision. This protocol describes the experimental strategy and workflow for successful completion of typical all-optical interrogation experiments in awake, behaving head-fixed mice. We describe modular procedures for the setup and calibration of an all-optical system, the preparation of an indicator and opsin-expressing and task-performing animal, the characterization of functional and photostimulation responses and the design and implementation of an all-optical experiment. We discuss optimizations for efficiently selecting and targeting neuronal ensembles for photostimulation sequences, as well as generating photostimulation response maps from the imaging data that can be used to examine the impact of photostimulation on the local circuit. We demonstrate the utility of this strategy using all-optical experiments in three different brain areas - barrel cortex, visual cortex and hippocampus - using different experimental setups. This approach can in principle be adapted to any brain area for all-optical interrogation experiments to probe functional connectivity in neural circuits and for investigating the relationship between neural circuit activity and behaviour.

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bioRxiv2024

Direct imaging of neural activity reveals neural circuits via spatiotemporal activation mapping

Two years ago, our group reported direct imaging of neuronal activity (DIANA), a functional magnetic resonance imaging (fMRI) technique that directly detects neuronal activity at high spatiotemporal resolution. In this study, we successfully reproduced the DIANA response in medetomidine-anesthetized mice using forelimb electrical stimulation at 11.7 T. More importantly, we showed that multiple neural circuits can be effectively revealed by DIANA fMRI through spatiotemporal activation mapping. The spatiotemporal activation mapping proposed here utilizes the temporal information of the DIANA response, that is, the time when the DIANA response reaches its peak, which is a unique feature that distinguishes it from the activation mapping method used in existing fMRI. Based on DIANA activation areas, we identified several neural circuits involved in forelimb sensory processing in the somatosensory network, which includes multiple brain regions: ventral posterolateral nucleus of the thalamus (VPL), posteromedial thalamic nucleus (POm), forelimb primary somatosensory cortex (S1FL), secondary somatosensory cortex (S2), primary motor cortex (M1), and secondary motor cortex (M2). Additionally, we also identified a pain-related neural circuit involving brain regions of the anterior cingulate cortex (ACC) and mediodorsal nucleus (MD). Interestingly, the spatiotemporal activation mapping also allowed us to identify subregions with different DIANA response times within the same functional region (e.g., VPL, POm, S1FL, and S2). Our study highlights the potential of DIANA fMRI to advance our understanding of sensory information processing throughout the brain and to provide insight into the spatiotemporal dynamics of brain networks at the level of neural circuits.

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bioRxiv2022

High performance, large-scale multi-compartment Hodgkin-Huxley simulation of Drosophila's whole-brain neural circuit model

A major challenge in neurosciences is the elucidation of neural mechanisms in brains that are crucial for the processing of sensory information and the generation of adaptive behavior. In conjunction with the ever-growing body of experimental data, computational simulations have become crucial in integrating information and testing hypotheses, requiring fast large-scale simulators. We constructed a whole-brain neural circuit model of the fly Drosophila with biophysically detailed multi-compartment Hodgkin-Huxley models based on the morphologies of individual neurons published in open databases. Performance tuning of the simulator enabled near real-time simulation of the resting state of the Drosophila whole-brain model in the large-scale computational environment of the supercomputer Fugaku, for which we achieved in excess of 630 TFLOPS using 480k cores. In our whole-brain model, neural circuit dynamics related to a standard insect learning paradigm, the association of taste rewards with odors could be simulated.

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bioRxiv2022

Reversal of obesogenic feeding and hypometabolism by a bifurcating GABAergic neural circuit

Diet-induced obesity is characterized by unsatiated consumption of energy-dense diets and impaired metabolism, whereby anti-obesity effect of the high-level of circulating leptin is unknowingly blunted. Emerging evidence suggests that the leptin receptor (LepR) signaling system, residing within the agouti-related protein (AgRP) neurons of the hypothalamus, critically contributes to obesogenic feeding, nutrient partitioning, and energy metabolism. However, the neural circuit mechanism underlying the leptin-dependent control of obesogenic feeding and energy balance remains largely elusive. Here, we show that two distinct subgroups of LepR-expressing AgRP neurons send non-collateral, GABAergic projections to the dorsomedial hypothalamic nucleus (DMH) and to the medial part of the medial preoptic nucleus (MPO) for the differential control of metabolic homeostasis and obesogenic feeding, respectively. We found that the AgRPLepR-DMH neural circuit plays a significant role in leptin-dependent control of metabolic homeostasis through the 3-containing GABAA receptor signaling on the melanocortin 4 receptor neurons within the DMH (MC4RDMH). In contrast, the AgRPLepR-MPO neural circuit elicits dominant effects on the appetitive response to high-fat diet through the 2-containing GABAA receptors on the MC4RMPO neurons. Consistent with these behavioral results, the post-synaptic GABAA neurons located within the DMH and MPO displayed differentiated firing responses under various feeding and nutrient conditions. Our results demonstrate that these novel GABAergic neural circuits exert differentiated control of metabolic hemostasis and obesogenic feeding via distinct post-synaptic targets of leptin-responsive AgRP neurons. The findings of two genetically and anatomically distinct GABAA receptor signaling pathways within the DMH and MPO would undoubtedly accelerate the development of targeted, individualized, anti-obesity therapy.

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bioRxiv2020

Addition of new neurons and the emergence of a local neural circuit for precise timing

During development, neurons arrive at local brain areas in extended period of time, but how they form local neural circuits is unknown. Here we computationally model the emergence of a network for precise timing in the premotor nucleus HVC in songbird. We show that new motor projection neurons, mostly added to HVC before and during song learning, are recruited to the end of a growing feedforward network. High spontaneous activity of new neurons makes them the prime targets for recruitment in a self-organized process via synaptic plasticity. Once recruited, the new neurons fire readily at precise times, and they become mature. Neurons that are not recruited become silent and replaced by new immature neurons. Our model incorporates realistic HVC features such as interneurons, spatial distributions of neurons, and distributed axonal delays. The model predicts that the birth order of the projection neurons correlates with their burst timing during the song. Significance StatementFunctions of local neural circuits depend on their specific network structures, but how the networks are wired is unknown. We show that such structures can emerge during development through a self-organized process, during which the network is wired by neuron-by-neuron recruitment. This growth is facilitated by steady supply of immature neurons, which are highly excitable and plastic. We suggest that neuron maturation dynamics is an integral part of constructing local neural circuits.

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bioRxiv2019

An Algorithmic Barrier to Neural Circuit Understanding

Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand how neural circuit computations mechanistically cause behavior. Here, using techniques from Theoretical Computer Science, we examine how many experiments are needed to obtain such an empirical understanding. It is proved, mathematically, that establishing the most extensive notions of understanding need exponentially-many experiments in the number of neurons, in general, unless a widely-posited hypothesis about computation is false. Worse still, the feasible experimental regime is one where the number of experiments scales sub-linearly in the number of neurons, suggesting a fundamental impediment to such an understanding. Determining which notions of understanding are algorithmically tractable, thus, becomes an important new endeavor in Neuroscience.

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bioRxiv2019

Neural circuits in the mouse retina support color vision in the upper visual field

Color vision is essential to the survival of most animals. Its neural basis lies in the retina, where chromatic signals from different photoreceptor types sensitive to distinct wavelengths are locally compared by neural circuits. Mice, like most mammals, are generally dichromatic and have two cone photoreceptor types. However, in the ventral retina most cones display the same spectral preference, impairing spectral comparisons necessary for color vision. This conflicts with behavioral evidence showing that mice can discriminate colors only in the corresponding upper visual field. Here, we systematically investigated the neural circuits underlying mouse color vision across three processing stages of the retina by recording the output of cones, bipolar and ganglion cells using two-photon imaging. Surprisingly, we found that across all retinal layers most color-opponent cells were located in the ventral retina. This started at the level of the cone output, where color-opponency was mediated by horizontal cells and likely involving rod photoreceptors. Next, bipolar cells relayed the chromatic information to ganglion cells in the inner retina, where type-specific, non-linear center-surround interactions resulted in specific color-opponent output channels to the brain. This suggests that neural circuits in the mouse retina are specifically tuned to extract color information from the upper visual field, aiding robust detection of aerial predators and ensuring the animals survival.

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bioRxiv2026

Neural circuit mechanisms for multi-tasking

Neural activity during performance of a cognitive or motor task is often described as lying on a low-dimensional manifold in the space of all possible activity patterns. When multiple tasks are performed, an individual brain region's activity may generate multiple such manifolds. Several models have been introduced to explain this capability, each with different assumptions concerning architecture and inputs. Here, we develop a theory of multi-tasking according to which models and experimental paradigms can be organized. We introduce a distinction between sequential and simultaneous multi-tasking, the performance of one task at a time or multiple in parallel. We show that interference between tasks often prevents multi-tasking and introduce a model of ``task barcodes" that circumvents this problem and parameterizes a neuronal population's task selectivity. Our theory reveals categorical distinctions between neural circuit mechanisms supporting multi-tasking that can be probed using physiological recordings.

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bioRxiv2024

A simple neural circuit model explains diverse types of integration kernels in perceptual decision-making

The ability to accumulate evidence over time for deliberate decision is essential for both humans and animals. Decades of decision-making research have documented various types of integration kernels that characterize how evidence is temporally weighted. While numerous normative models have been proposed to explain these kernels, there remains a gap in circuit models that account for the complexity and heterogeneity of single neuron activities. In this study, we sought to address this gap by using low-rank neural network modeling in the context of a perceptual decision-making task. Firstly, we demonstrated that even a simple rank-one neural network model yields diverse types of integration kernels observed in human data--including primacy, recency, and non-monotonic kernels--with a performance comparable to state-of-the-art normative models such as the drift diffusion model and the divisive normalization model. Moreover, going beyond the previous normative models, this model enabled us to gain insights at two levels. At the collective level, we derived a novel explicit mechanistic expression that explains how these kernels emerge from a neural circuit. At the single neuron level, this model exhibited heterogenous single neuron response kernels, resembling the diversity observed in neurophysiological recordings. In sum, we present a simple rank-one neural circuit that reproduces diverse types of integration kernels at the collective level while simultaneously capturing complexity of single neuron responses observed experimentally. Author SummaryThis study introduces a simple rank-one neural network model that replicates diverse integration kernels--such as primacy and recency--observed in human decision-making tasks. The model performs comparably to normative models like the drift diffusion model but offers novel insights by linking neural circuit dynamics to these kernels. Additionally, it captures the heterogeneity of single neuron responses, resembling diversity observed in experimental data. This work bridges the gap between decision-making models and the complexity of neural activity, offering a new perspective on how evidence is integrated in the brain.

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bioRxiv2019

A neural circuit model for human sensorimotor timing

Humans can rapidly and flexibly coordinate their movements with external stimuli. Theoretical considerations suggest that this flexibility can be understood in terms of how sensory responses reconfigure the neural circuits that control movements. However, because external stimuli can occur at unexpected times, it is unclear how the corresponding sensory inputs can be used to exert flexible control over the ongoing activity of recurrent neural circuits. Here, we tackle this problem in the domain of sensorimotor timing and develop a circuit-level model that provides insight into how the brain coordinates movement times with expected and unexpected temporal events. The model consists of two interacting modules, a motor planning module that controls movement times and a sensory anticipation module that anticipates external events. Both modules harbor a reservoir of latent dynamics and their interaction forms a control system whose output is adjusted adaptively to minimize timing errors. We show that the models output matches human behavior in a range of tasks including time interval production, periodic production, synchronization/continuation, and Bayesian time interval reproduction. These results demonstrate how recurrent interactions in a simple and modular neural circuit could create the dynamics needed to control temporal aspects of behavior.

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