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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 379 records · Page 21Linked to original sources
bioRxiv2026

Reduced cortico-accumbal excitatory input due to Nav1.2 haploinsufficiency impairs sociability independently of dopamine

Mutations in SCN2A, which encodes the voltage-gated sodium channel Nav1.2, are associated with a wide spectrum of neurodevelopmental and neuropsychiatric disorders, including epilepsy, autism spectrum disorder (ASD), and schizophrenia. Although dysfunction of SCN2A-dependent neural circuits has been implicated in these disorders, the circuit mechanisms underlying social behavioral abnormalities remain poorly understood. Here, we investigated the neural circuit basis of social behavioral deficits associated with Scn2a dysfunction, focusing on the nucleus accumbens (NAc), a key hub in cortico-limbic circuits that regulates emotional and motivational behaviors. Using conditional genetic and chemogenetic approaches in mice, we examined the roles of dorsal telencephalic excitatory neurons, including those in the cerebral cortex, hippocampus, and amygdala, as well as parvalbumin-positive fast-spiking interneurons (PV FSIs) in the NAc. Mice with Scn2a haploinsufficiency in dorsal telencephalic excitatory neurons (Scn2afl/+/Emx1-Cre) exhibited reduced sociability in the three-chamber social interaction test. Similarly, chemogenetic inhibition of NAc PV FSIs decreased sociability without affecting locomotor activity or anxiety-like behavior. Scn2afl/+/Emx1-Cre mice also showed a trend toward reduced prepulse inhibition of the acoustic startle response. Notably, dopamine release into the NAc in the Scn2afl/+/Emx1-Cre and systemic Scn2a heterozygous knockout (Scn2a+/-) mice was largely comparable to that in control mice. Together, these findings indicate that reduced activity of dorsal telencephalic excitatory neurons or NAc PV FSIs is sufficient to impair sociability independently of mesolimbic dopamine hypofunction. Our results highlight a potential role of cortico-accumbal circuits in social behavioral deficits associated with SCN2A dysfunction.

neuroscience↗
bioRxiv2020

FlyBrainLab: Accelerating the Discovery of the Functional Logic of the Fruit Fly Brain in the Connectomic/Synaptomic Era

In recent years, a wealth of Drosophila neuroscience data have become available. These include cell type, connectome and synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we developed an interactive computing environment called FlyBrainLab. FlyBrainLab is uniquely positioned to accelerate the discovery of the functional logic of the Drosophila brain. Its interactive open source architecture seamlessly integrates and brings together computational models with neuroanatomical, neurogenetic and electrophysiological data, changing the organization of neuroscientific fly brain data from a group of seemingly disparate databases, arrays and tables, to a well structured data and executable circuit repository. The FlyBrainLab User Interface supports a highly intuitive and automated work-flow that streamlines the 3D exploration and visualization of fly brain circuits, and the interactive exploration of the functional logic of executable circuits created directly from the explored and visualized fly brain data. Furthermore, efficient comparisons of circuit models are supported, across models developed by different researchers, across different developmental stages of the fruit fly and across different datasets. The FlyBrainLab Utility Libraries help untangle the graph structure of neural circuits from raw connectome and synaptome data. The Circuit Libraries facilitate the exploration of neural circuits of the neuropils of the central complex and, the development and implementation of models of the adult and larva fruit fly early olfactory systems. Seeking to transcend the limitations of the connectome, FlyBrainLab provides additional libraries for molecular transduction arising in sensory coding in vision and olfaction. Together with sensory neuron activity data, these libraries serve as entry points for discovering circuit function in the sensory systems of the fruit fly brain. They also enable the biological validation of developed executable circuits within the same platform.

neuroscience↗
bioRxiv2022

Glutamatergic Supramammillary Nucleus Neurons Promote Active Coping to Stress

Threat-response neural circuits are conserved across species and play roles in normal behavior and psychiatric diseases. Maladaptive changes in these neural circuits contribute to stress, mood, and anxiety disorders. Active coping in response to stressors is a psychosocial factor associated with resilience against stress-induced mood and anxiety disorders. The neural circuitry underlying active coping is poorly understood, but the functioning of these circuits could be key for overcoming anxiety and related disorders. The supramammillary nucleus (SuM) has been suggested to be engaged by threat. SuM has many projections and contains a poorly understood diversity of populations. We identified a unique population of glutamatergic SuM neurons (SuMVGLUT2+::POA) based on projection to the preoptic area of the hypothalamus (POA) and found SuMVGLUT2+::POA neurons have extensive arborizations. SuMVGLUT2+::POA neurons project to brain areas that mediate various features of the stress and threat responses including the paraventricular nucleus thalamus (PVT), periaqueductal gray (PAG), and the habenula (Hb). Thus, SuMVGLUT2+::POA neurons are positioned as a hub, connecting to areas implicated in regulating stress responses. Here we report SuMVGLUT2+::POA neurons are recruited by diverse threatening stressors, and recruitment of SuMVGLUT2+::POA neurons correlated with active coping behaviors. We found that selective photoactivation of the SuMVGLUT2+::POA population drove aversion but not anxiety like behaviors. Activation of SuMVGLUT2+::POA neurons in the absence of acute stressors evoked active coping like behaviors and drove instrumental behavior (selective port activations) (Figure 6). Also, activation of SuMVGLUT2+::POA neurons was sufficient to convert passive coping strategies to active behaviors during acute stress. In contrast, we found activation of GABAergic (VGAT+) SuM neurons (SuMVGAT+) neurons did not alter drive aversion or active coping, but termination of photostimulation was followed by increased mobility in the forced swim test. These findings establish a new node in stress response circuitry that has projections to many brain areas, evokes flexible active coping behaviors, and offers new opportunities for furthering our neurobiological understanding of stress. O_FIG O_LINKSMALLFIG WIDTH=134 HEIGHT=200 SRC="FIGDIR/small/507167v4_fig6.gif" ALT="Figure 6"> View larger version (30K): org.highwire.dtl.DTLVardef@717f84org.highwire.dtl.DTLVardef@16ad323org.highwire.dtl.DTLVardef@4ba5b6org.highwire.dtl.DTLVardef@6c811e_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 6.C_FLOATNO SuMVGLUT2+::POA neurons can drive instrumental action-outcome operant behavior (A) Schematic of injection and implant in VGLUT2+ Cre mice and paradigm of testing in 10-minute trials with four days before a progressive ratio (PR) trial on day five. (B) Illustration of the testing paradigm and set up. 10 Hz photostimulation was applied during the trials. Activation of the active port triggered the house light and paused stimulation for 10 seconds. Also shown is the progressive ratio used with number of required port activations per reward on vertical axis and reward number on the horizontal. (C) Cre+ mice (n=11) activated the active port triggering significantly (****p<0.0001) more pauses in stimulation than Cre- mice (n=11) mice on all four days of testing. (D) Cre+ mice activated the port significantly (****p<0.0001, ***p<0.001) more than the Cre- mice on all four trials. (E) The time to first activation of the active port was significantly (*p=0.046) different during the first trial, and Cre+ mice activated the active port significantly (**p<0.01, **p<0.01, **p=0.034) faster on subsequent trials. (F) On the fifth day after four 10-minute trials, mice were tested for 30 minutes using a progressive ratio. Cre+ performed significantly (****p<0.001) more active port activations than Cre- mice and activated the inactive port significantly more (**p=0.001) times. Cre+ mice also triggered significantly (****p<0.001) more pauses in photostimulation than Cre- mice. (G) Individual data for a representative (n=7) cohort of Cre+ mice showing cumulative active port activations as a function of time during the progressive ratio test show on-going engagement of the active port throughout the 30-minute trial. (H) The cumulative pauses in photostimulation (rewards) earned as a function of time during the progressive ratio trial are shown for individual Cre+ mice. Mice earned between 7 and 11 pause rewards during the trial. All data plotted as means {+/-}SEM. C_FIG

neuroscience↗
bioRxiv2021

Functional alterations in cortical processing of speech in glioma-infiltrated cortex

Recent developments in the biology of malignant gliomas have demonstrated that glioma cells interact with neurons through both paracrine signaling and electrochemical synapses. Glioma-neuron interactions consequently modulate the excitability of local neuronal circuits, and it is unclear the extent to which glioma-infiltrated cortex can meaningfully participate in neural computations. For example, gliomas may result in a local disorganization of activity that impedes the transient synchronization of neural oscillations. Alternatively, glioma-infiltrated cortex may retain the ability to engage in synchronized activity, in a manner similar to normal-appearing cortex, but exhibit other altered spatiotemporal patterns of activity with subsequent impact on cognitive processing. Here, we use subdural electrocorticography to sample both normal-appearing and glioma-infiltrated cortex during speech. We find that glioma-infiltrated cortex engages in synchronous activity during task performance in a manner similar to normal-appearing cortex, but recruits a diffuse spatial network. On a temporal scale, we show that glioma-infiltrated cortex has lower capacity for information encoding when performing nuanced tasks such as speech production of monosyllabic versus polysyllabic words. As a result, temporal decoding strategies for distinguishing monosyllabic from polysyllabic words were feasible for signals arising from normal-appearing cortex, but not from glioma-infiltrated cortex. These findings inform our understanding of cognitive processing in chronic disease states and have implications for neuromodulation and prosthetics in patients with malignant gliomas. Significance StatementAs gliomas proliferate, they infiltrate healthy brain tissue. Often, patients with such tumors in the language areas of the brain develop aphasia. Understanding how gliomas interact with normal neural circuits is critical for developing neuroprostheses that restore speech. Recent evidence demonstrates that glioma cells interact synaptically with neurons, and thus can modulate neural circuits. However, it is unclear the extent to which glioma-infiltrated cortex participates in cognitive processing. Using electrocorticography to record both glioma-infiltrated and normal-appearing cortex during speech, we found that glioma-infiltrated cortex is capable of coordinated neural responses, but has reduced capacity for information encoding. Instead, glioma-infiltrated cortex recruits a broader network of cortical regions during speech, which may represent a compensatory mechanism with implications for future neuroprostheses.

neuroscience↗
bioRxiv2023

Efficient Search for Circuit Structure by 'Smooth-Index' Matrix-Reordering

Volume electron microscopy together with computer-based image analysis are yielding neural circuit diagrams of ever larger regions of the brain [1-10]. These datasets are usually represented in a cell-to-cell connectivity matrix and contain important information about prevalent circuit motifs allowing to directly test various theories on the computation in that brain structure [11,12]. Of particular interest are the detection of cell assemblies and the quantification of feedback, which can profoundly change circuit properties. While the ordering of cells along the rows and columns doesnt change the connectivity, it can make special connectivity patterns recognizable. For example, ordering the cells along the flow of information, feedback and feedforward connections are segregated above and below the main matrix diagonal, respectively. Different algorithms are used to renumber matrices such as to minimize a given cost function, but either their performance becomes unsatisfying at a given size of the circuit or the CPU time needed to compute them scales in an unfavorable way with increasing number of neurons [13-15]. Based on previous ideas [16-18], we describe an algorithm which is effective in matrix reordering with respect to both its performance as well as to its scaling in computing time. Rather than trying to reorder the matrix in discrete steps, the algorithm transiently assigns a real-valued parameter to each cell describing its location on a continuous axis ( smooth-index) and finds the parameter set that minimizes the cost. We find that the smooth-index algorithm outperforms all algorithms we compared it to, including those based on topological sorting. Author SummaryConnectomic data provide researchers with neural circuit diagrams of ever larger regions of the brain. These datasets are usually represented in a cell-to-cell connectivity matrix and contain important information about prevalent circuit motifs. Such motifs, however, only become visible if the connectivity matrix is reordered appropriately. For example, ordering the cells along the flow of information, feedback and feedforward connections are segregated above and below the main matrix diagonal, respectively. While most previous approaches rely on topological sorting, our method treats the discrete vertex indices as real numbers ( smooth-index) along independent parameter axes and defines a differentiable cost function, thus, allowing gradient-based algorithms to find a minimum. The parameter set at this minimum is then re-discretized to reorder the connectivity matrix accordingly. We find our method to scale favorably with the circuit size and to outperform all algorithms we compared it to.

neuroscience↗
bioRxiv2025

iSTTC: a robust method for accurate estimation of intrinsic neural timescales from single-unit recordings

Intrinsic neural timescales (ITs) are an emerging measure of how neural circuits integrate information over time. ITs are dynamically regulated by behavioral context and cognitive demands, making them suitable for mapping high-level cognitive phenomena onto the underlying neural computations. In particular, IT measurements derived from single-unit activity (SUA) offer fine-grained resolution, critical for mechanistically linking individual neuron dynamics to cognition. However, current methods for estimating ITs from SUA suffer significant biases and instabilities, particularly when applied to sparse, noisy, or epoched neural spike data. Here, we introduce the intrinsic Spike Time Tiling Coefficient (iSTTC), a novel metric specifically developed to address these limitations. Leveraging synthetic and experimental single-unit recordings, we systematically assessed the performance of iSTTC relative to traditional approaches. Our findings demonstrate that iSTTC provides more accurate estimates of neural timescales across a wide range of conditions, reducing estimation error especially under challenging yet biologically relevant conditions. Crucially, iSTTC can be applied to both continuous and epoched data, overcoming a critical limitation of existing methods. Furthermore, iSTTC substantially relaxes inclusion criteria, increasing the fraction of neurons suitable for analysis and thereby improving the representativeness and robustness of IT measurements. The methodological advances introduced by iSTTC represent a substantial step forward in accurately capturing neural circuit dynamics, ultimately enhancing our ability to link neural mechanisms to cognitive phenomena.

neuroscience↗
bioRxiv2023

Inhibitory subpopulations in preBötzinger Complex play distinct roles in modulating inspiratory rhythm and pattern

Inhibitory neurons embedded within mammalian neural circuits shape breathing, walking, chewing, and other rhythmic motor behaviors. At the core of the neural circuit controlling breathing is the preBotzinger Complex (preBotC), a nucleus in the ventrolateral medulla necessary for generation of inspiratory rhythm. In the preBotC, a recurrently connected network of glutamatergic Dbx1-derived (Dbx1+) neurons generates rhythmic inspiratory drive. Functionally and anatomically intercalated among preBotC Dbx1+ neurons are GABAergic (GAD1/2+) and glycinergic (GlyT2+) neurons, whose roles in breathing remain unclear. To elucidate the inhibitory microcircuits within preBotC, we first characterized the spatial distribution of molecularly-defined preBotC inhibitory subpopulations in double reporter mice expressing either the red fluorescent protein tdTomato or EGFP in GlyT2+, GAD1+, or GAD2+ neurons. We found that, in neonatal mice, the majority of preBotC inhibitory neurons expressed a combination of GlyT2 and GAD2 while a much smaller subpopulation also expressed GAD1. To determine the functional role of these subpopulations, we used holographic photostimulation, a patterned illumination technique with high spatiotemporal resolution, in rhythmically active medullary slices from neonatal Dbx1tdTomato;GlyT2EGFP and Dbx1tdTomato;GAD1EGFP double reporter mice. Stimulation of 4 or 8 preBotC GlyT2+ neurons during endogenous rhythm prolonged the interburst interval in a phase-dependent manner and increased the latency to burst initiation when bursts were evoked by stimulation of Dbx1+ neurons. In contrast, stimulation of 4 or 8 preBotC GAD1+ neurons did not affect interburst interval or latency to burst initiation. Instead, photoactivation of GAD1+ neurons during the inspiratory burst prolonged endogenous and evoked burst duration and decreased evoked burst amplitude. We conclude that the majority of preBotC inhibitory neurons express both GlyT2 and GAD2 and modulate breathing rhythm by delaying burst initiation while a smaller GAD1+ subpopulation shapes inspiratory patterning by altering burst duration and amplitude.

neuroscience↗
bioRxiv2026

Uncovering putative neural mechanisms of neurotherapeutic impacts on EEG using the Human Neocortical Neurosolver

SHORT ABSTRACTA key barrier to developing effective drugs for disorders of the central nervous system (CNS) is understanding their impact on neural circuits. This protocol demonstrates how physics-based neural simulations can be used to interpret electrophysiological biomarkers of neurotherapeutics, providing a mechanistically grounded approach to the development of neurotherapeutics. LONG ABSTRACTElectroencephalography (EEG) and electrophysiology methods provide millisecond resolution biomarkers for central nervous system disorders and are used to assess treatment-related effects. However, lack of understanding about the neural mechanisms generating such biomarkers impedes the development of diagnostics and therapeutics based on these signals. The Human Neocortical Neurosolver (HNN) is an open-source biophysical modeling software that connects localized EEG biomarkers to their multi-scale neural generators. This protocol demonstrates a hypothesis-driven workflow using HNN to test possible neural mechanisms of neurotherapy-induced EEG biomarkers by optimizing parameters to achieve a fit between simulated and empirical current source waveforms. Corresponding multi-scale cell- and circuit-level activity can then be visualized and quantified, providing validation targets for model predictions in follow up empirical studies. An example is provided which shows how to examine the generating mechanisms of the early event-related potential (ERP) components of an auditory evoked response (P1, N1 and P2) and to assess changes following neural circuit modification due to neurotherapeutic administration. This protocol demonstration enables scientists to design simulation experiments to develop testable predictions on how EEG biomarkers reflect neural circuit mechanisms of example therapeutics. A similar protocol can be applied to study disease mechanisms or other therapies.

neuroscience↗
bioRxiv2026

Low-latency neuromorphic closed-loop control of hippocampal ripples in vivo

Real-time closed-loop neuromodulation, in which stimulation is precisely timed to ongoing brain dynamics, holds transformative potential for treating neurological disorders and probing neural circuit function. However, it requires low-latency, energy-efficient processing of high-bandwidth neural signals that conventional computing architectures struggle to deliver. Neuromorphic computing, which emulates the event-driven and massively parallel operation of biological neural circuits, offers a compelling alternative. Yet, its integration into closed-loop frameworks validated in vivo for fast, transient oscillations has not been demonstrated. Here, we present a fully integrated neuromorphic framework for real-time detection and manipulation of hippocampal ripples: brief (30-100 ms), high-frequency (100-250 Hz) oscillations that are critical for memory consolidation and implicated in neurological disorders. We train compact spiking neural networks comprising 41 neurons and 530 parameters using surrogate-gradient backpropagation, achieving detection performance competitive with deep learning models across 23 recording sessions while consuming up to 200-fold less energy when deployed on SpiNNaker neuromorphic hardware. Integration with the open-source Open Ephys platform yields total closed-loop latencies of approximately 50 ms, enabling intra-event stimulation in up to 80% of ripples. Validating the complete sensing-processing-stimulation pipeline in awake, head-fixed mice, we demonstrate that neuromorphic-triggered optogenetic inhibition significantly alters ripple dynamics and reduces oscillatory energy. This work establishes a practical and accessible neuromorphic framework for low-latency closed-loop control of fast brain dynamics in vivo.

neuroscience↗
bioRxiv2026

Evidence Accumulation in Synaptic Efficacy: Activity-Silent Decision-Making with Short-Term Plasticity

Decision-making requires accumulation of evidence over time, yet exactly how neural circuits implement this computation remains debated. Classical models posit that evidence accumulation is realized by ramping neural activity, whereas recent experimental findings suggest an alternative mechanism in which neural activity remains largely silent before abruptly transitioning into an active decision state. This mechanism leaves a fundamental question unanswered: how is evidence accumulated during the neural activity-silent period? To address this question, we trained excitatory-inhibitory recurrent neural networks (RNNs) with short-term synaptic plasticity (STP) on a reaction-time decision task. We found that after training, the networks learned to leverage STP to make decisions in an activity-silent manner. Choice-tuning analysis revealed a clear functional organization of the network, and connection-pruning analysis uncovered the coupling between choice-selective excitatory and inhibitory neural groups. Guided by the architecture of trained RNNs, we constructed a minimal neural circuit model, in which evidence accumulates in facilitated synaptic efficacy that progressively reshapes the circuit's phase portrait, eventually driving the network state across the stability boundary and triggering a rapid transition to a choice state. We further compared activity before a choice between the theoretical STP and Static models to examine the potential energy benefit of activity-silent decision-making. Together, our results suggest STP as a mechanism for evidence accumulation in activity-silent decision-making and offer insights into the design of energy-efficient artificial neural networks.

neuroscience↗
bioRxiv2024

Circuit function is more robust to changes in synaptic than intrinsic conductances

Circuit function results from both intrinsic conductances of network neurons and the synaptic conductances that connect them. In models of neural circuits, different combinations of maximal conductances can give rise to similar activity. We compared the robustness of a neural circuit to changes in their intrinsic versus synaptic conductances. To address this, we performed a sensitivity analysis on a population of conductance-based models of the pyloric network from the crustacean stomatogastric ganglion (STG). The model network consists of three neurons with nine currents: a sodium current (Na), three potassium currents (Kd, KCa, A-type), two calcium currents (CaS and CaT), a hyperpolarization-activated current (H), a non-voltage-gated leak current (leak), and a neuromodulatory current (MI). The model cells are connected by seven synapses of two types, glutamatergic and cholinergic. We produced one hundred models of the pyloric network that displayed similar activities with values of maximal conductances distributed over wide ranges. We evaluated the robustness of each model to changes in their maximal conductances. We found that individual models have different sensitivities to changes in their maximal conductances, both in their intrinsic and synaptic conductances. As expected the models become less robust as the extent of the changes increase. Despite quantitative differences in their robustness, we found that in all cases, the model networks are more sensitive to the perturbation of their intrinsic conductances than their synaptic conductances.

neuroscience↗
bioRxiv2024

An applicable and efficient retrograde monosynaptic circuit mapping tool for larval zebrafish

The larval zebrafish is a vertebrate model for in vivo monitoring and manipulation of whole-brain neuronal activity. Tracing its neural circuits remains challenging. Here, we report an applicable methodology tailored for larval zebrafish to achieve efficient retrograde trans-monosynaptic tracing from genetically defined neurons via EnvA-pseudotyped glycoprotein-deleted rabies viruses. By combinatorially optimizing multiple factors involved, we identified the CVS strain trans-complemented with advanced expression of N2cG at 36 {degrees}C as the optimal combination. It yielded a tracing efficiency of up to 20 inputs per starter cell. Its low cytotoxicity enabled the viable labeling and calcium imaging of infected neurons 10 days post-infection, spanning larval ages commonly used for functional examination. Cre-dependent labeling was further developed to enable cell-type-specific input tracing and circuit reconstruction. We mapped cerebellar circuits and uncovered the ipsilateral preference and subtype specificity of granule cell-to-Purkinje cell connections. Our method offers an efficient way for tracing neural circuits in larval zebrafish.

neuroscience↗
bioRxiv2024

Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics

One of the central goals of neuroscience is to gain a mechanistic understanding of how the dynamics of neural circuits give rise to their observed function. A popular approach towards this end is to train recurrent neural networks (RNNs) to reproduce experimental recordings of neural activity. These trained RNNs are then treated as surrogate models of biological neural circuits, whose properties can be dissected via dynamical systems analysis. How reliable are the mechanistic insights derived from this procedure? While recent advances in population-level recording technologies have allowed simultaneous recording of up to tens of thousands of neurons, this represents only a tiny fraction of most cortical circuits. Here we show that observing only a subset of neurons in a circuit can create mechanistic mismatches between a simulated teacher network and a data-constrained student, even when the two networks have matching single-unit dynamics. In particular, partial observation of models of low-dimensional cortical dynamics based on functionally feedforward or low-rank connectivity can lead to surrogate models with spurious attractor structure. Our results illustrate the challenges inherent in accurately uncovering neural mechanisms from single-trial data, and suggest the need for new methods of validating data-constrained models for neural dynamics.

neuroscience↗
bioRxiv2026

Oxytocin regulates TN-GnRH3 circuit maturation and mate preference through C1q-dependent synaptic mechanisms

Oxytocin is a key regulator of social behavior, yet how it shapes the neural circuits underlying these behaviors remains unclear. Here, we show that oxytocin signaling is required for the proper maturation of terminal nerve gonadotropin-releasing hormone 3 (TN-GnRH3) circuits that regulate female mate preference in medaka. Disruption of oxytocin signaling reduced expression of the complement component C1q, altered microglial properties, and increased TN-GnRH3 innervation in the optic tectum and dorsolateral telencephalon, suggesting impaired synaptic refinement. In addition, TN-GnRH3 neurons in mutants exhibited disrupted clustering and failed to increase firing frequency in response to visual stimulation, indicating functional deficits. Furthermore, c1qb mutants phenocopied the behavioral and neuronal abnormalities observed in oxytocin signaling-deficient mutants. Together, our findings suggest that oxytocin signaling links microglia-associated synaptic refinement to neural circuit maturation, thereby revealing a previously unrecognized role for oxytocin in shaping social behavior.

neuroscience↗
bioRxiv2025

Homosensory and heterosensory dishabituation engage distinct circuits in Drosophila

Habituation adaptively filters repeated, inconsequential sensory input, while the response to such stimuli re-emerges upon appearance of novel or salient cues (dishabituation).However, the neural circuits underlying dishabituation remain poorly defined, particularly in the central nervous system. Using Drosophila olfactory habituation to the odorant 3-octanol (OCT), we dissect the circuit basis of intramodal (odor-odor) and cross-modal (footshock-odor) dishabituation. A brief yeast odor puff dishabituates intramodally, whereas footshock cross-modally and neither operate through sensitization. Genetic silencing and optogenetics demonstrate that Mushroom Body (MBs) output drives both dishabituation forms. {beta} and {gamma} Kenyon cells (KCs) mediate dishabituation, while '{beta}' Kenyon cells mediate habituation. Dopaminergic neurons encode and PAM neurons mediating appetitive and PPL1 neurons aversive dishabituation, including that triggered by footshock and OCT itself. GABAergic APL neurons and specific MB output neurons tune the balance between habituation and dishabituation, relaying signals via MBONs to the Lateral Horn, a proposed decision node. Connectomic analysis reveals inhibitory interactions supporting this balance. Collectively, we reveal a multi-node circuit that dynamically overrides, rather than erases, habituation findings offering insight into habituation deficits in intellectual disability, autism, and schizophrenia. SIGNIFICANCE STATEMENTThis study identifies a multi-node neural circuit spanning the Mushroom Bodies, dopaminergic and GABAergic modulatory neurons and the Lateral Horn, that governs intramodal and cross-modal dishabituation in Drosophila. Showing that dishabituation dynamically overrides rather than erases habituation and that distinct dopaminergic populations encode stimulus valence to bias this switch, the work reveals a general logic for how the brain flexibly regulates sensory filtering.

animal behavior and cognition↗
bioRxiv2021

biPACT: a method for three-dimensional visualization of mouse spinal cord circuits of long segments with high resolution

BackgroundThe spatial complexity of neuronal circuits in the central nervous system is a hurdle in understanding and treating brain and spinal cord injuries. Although several methods have recently been developed to render the spinal cord transparent and label specific neural circuits, three-dimensional visualization of long segments of spinal cord with high resolution remains challenging. New MethodWe present a method that combines tissue staining of neuronal tracts traced with biotinylated dextran amine (BDA) and a modified passive clarity clearing protocol to describe individual fibers in long segments of mouse spinal cord. ResultsCorticospinal tract was traced with BDA with a mouse model of thoracic spinal cord injury. The spinal cord was stained and cleared in two weeks with four solutions: staining solution, hydrogel solution, clearing solution, and observation solution. The samples were observed with a light-sheet microscope, and three-dimensional reconstruction was performed with ImageJ software. High resolution-images comparable with tissue sections were obtained continuously and circumferentially. By tiling, it was possible to obtain high-resolution images of long segments of the spinal cord. The tissue could be easily re-stained in case of fading, Comparison with Existing MethodsThe present method does not require special equipment, can label specific circuits without genetic technology, and re-staining rounds can be easily implemented. It enables to visualize individual neural fiber of specific neural circuit in long spinal cord segments. ConclusionsBy using simple neural staining, and clearing methods, it was possible to acquire a wide range of high-resolution three-dimensional images of the spinal cord. HighlightsO_LINo special devices or genetic tracers are required for a new clearing method C_LIO_LINeuronal fibers are individually depicted in long segments of mouse spinal cord. C_LIO_LIRe-staining of neuronal fiber is possible. C_LIO_LIStereotaxic observation is achieved by 3-D reconstruction with open-source software. C_LI

neuroscience↗
bioRxiv2026

Cortical oscillations reflect opponent ensemble dynamics through coordinated multifrequency activity

Neural oscillations are widely used as proxies for neuronal activity, where power in individual frequency bands is commonly interpreted as functionally indexing neural circuit engagement. However, power in individual frequency bands shows heterogeneous and sometimes opposing relationships with neuronal activity across regions and behavioral contexts, challenging the assumption of a stable frequency-to-circuit mapping. Here we show that glutamatergic population activity in rat medial prefrontal cortex is not stably linked with power in isolated frequency bands, but rather with dynamically recurring multi-frequency amplitude co-fluctuations. These multi-frequency patterns, termed spectral motifs, occurred in opponent pairs with nearly identical frequency composition but inverted relationships to population calcium activity. This opponent motif structure, observed across cortical regions and species, provides a key component for understanding how oscillations are linked to neuronal activity. We found that shifts in motif opponency balance explained changes in glutamatergic activity that occur during brain-computer interface learning better than models based on frequency band power alone. Furthermore, opponent motifs map selectively onto opponent cell ensembles and enable bidirectional mapping between local field potentials and ensemble activity. These findings identify multi-frequency opponent motifs as a conserved organizational principle linking oscillatory dynamics to population-level circuit states and challenge the notion that individual frequency bands can serve as interpretable functional units mapping onto neural circuit activity.

neuroscience↗
bioRxiv2026

Optogenetic Analysis of Behavior in the Mosquito Aedes aegypti

The mosquito Aedes aegypti is an important vector of viral pathogens and serves as a model for other vector species. Pathogens are transmitted when a mosquito bites a host animal, but the neural circuits that control seeking and biting behavior are not known. Here, we detail methods for the manipulation of neural activity in the mosquito using optogenetics, a key technique to determine the causal relationship between neural circuits and behavior. These methods include rearing mosquitoes for optogenetics and three assays that are designed to measure different steps in the sequence of arousal, attraction, proboscis probing, and engorgement on the blood of host animals. These behaviors occur at different spatial scales and in response to different sensory stimuli. Each behavioral assay is outfitted with red (~625 nm) LEDs for optogenetic activation. To detect arousal in response to olfactory stimuli, flight and walking are quantified. To assay attraction or landing, mosquitoes are presented with a heated blood meal in a large arena. Proboscis probing and engorgement are assayed with video resolution that enables measurement of appendages and abdomen size. The protocol describes machine vision models to enable high-resolution temporal quantification of behavior as well as endpoint measurements of feeding. These methods can be used to test the role of any genetically accessible population of neurons in mosquito biting behavior and can be extended to additional behaviors.

neuroscience↗