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bioRxiv2025

PACAP Signaling Network in the Nucleus Accumbens Core Regulates Reinstatement Behavior in Rat

Cocaine use disorder (CUD) lacks FDA-approved treatments, partly due to the difficulty of creating therapeutics that target behavior-related neural circuits without disrupting signaling throughout the brain. Recent evidence highlights the therapeutic potential of targeting gut-brain axis components, such as GLP-1 receptors, to modulate neural circuits with minimal central nervous system disruption. Like GLP-1, pituitary adenylate cyclase polypeptide (PACAP) is a component of the gut-brain axis that regulates behavior through a network spanning the gut and brain. Here, we investigated the potential existence and function of an endogenous PACAP signaling network within the nucleus accumbens core (NAcc), which is a structure that integrates emotional, cognitive, and reward processes underlying behavior. We found that PACAP and its receptor, PAC1R, are endogenously expressed in the rat NAcc and that PACAP mRNA is present in medial prefrontal cortical projections to the NAcc. Behaviorally, intra-NAcc infusions of PACAP (100 pm) did not induce seeking behavior but blocked cocaine-primed reinstatement (10 mg/kg, IP). Intra-NAcc PACAP also inhibited reinstatement driven by co-infusion of the D1 receptor agonist (SKF 81297, 3 {micro}g) but not the D2 receptor agonist (sumanirole, 10 ng). These findings are significant since D1 and D2 receptor activities in the NAcc govern distinct behavioral mechanisms indicating precise actions of PACAP even within the NAcc. Future research should examine whether NAcc PACAP signaling can be selectively engaged by peripheral gut-brain axis mechanisms, potentially unveiling novel therapeutic approaches for CUD and related disorders.

neuroscience↗

bioRxiv2025

Rho signaling promotes cell excitability and synaptic transmission

Gq signaling through Trio RhoGEF and Phospholipase C effectors promotes neurotransmitter release at synapses. Whether activated Rho and Phospholipase C signaling acts through shared or independent pathways remains unclear. We use the egg-laying circuit of C. elegans, which is regulated positively by Gq signaling and negatively by Go signaling, to determine the role of Rho GTPase in G-protein regulation of neural circuit excitability. We previously showed that Trio signals in both the presynaptic Hermaphrodite-Specific command Neurons (HSNs) and the postsynaptic egg-laying vulval muscles they innervate to promote egg laying. Here we show that its effector Rho similarly signals both pre- and post-synaptically. Expression of constitutively active Rho-1(G14V) in either the HSNs or vulval muscles increased their Ca2+ activity and stimulated egg laying. Conversely, reducing Rho signaling in the HSNs via expression of Rho-1(T19N) or C3 transferase expression inhibited HSN Ca2+ activity and egg laying. Animals lacking Rho function in HSN still had vulval muscle Ca2+ activity that supported contraction and egg release, although this Ca2+ activity lacked the enhancement associated with HSN neurotransmitter release, suggesting it was largely driven by stretch-dependent feedback. Together, these results show that Rho signals in both the HSNs and vulval muscles to promote excitability and egg laying. Article SummaryRho-type G-proteins have been extensively studied for their role in neural development, but accumulating evidence points to a role in regulating synaptic transmission and neural circuit activity. Using powerful in vivo imaging and genetic techniques in the nematode Caenorhabditis elegans, the authors show that Rho signals in both presynaptic neurons and postsynaptic muscles to promote circuit activity and behavior. These findings solidify Rho as a key regulator of neurotransmitter signaling.

neuroscience↗

bioRxiv2022

Rewarding capacity of optogenetically activating a giant GABAergic central-brain interneuron in larval Drosophila

Larvae of the fruit fly Drosophila melanogaster are a powerful study case for understanding the neural circuits underlying behavior. Indeed, the numerical simplicity of the larval brain has permitted the reconstruction of its synaptic connectome, and genetic tools for manipulating single, identified neurons allow neural circuit function to be investigated with relative ease and precision. We focus on one of the most complex neurons in the brain of the larva (of either sex), the GABAergic anterior paired lateral neuron (APL). Using behavioral and connectomic analyses, optogenetics, Ca2+ imaging and pharmacology, we study how APL affects associative olfactory memory. We first provide a detailed account of the structure, regional polarity, connectivity, and metamorphic development of APL, and further confirm that optogenetic activation of APL has an inhibiting effect on its main targets, the mushroom body Kenyon cells. All these findings are consistent with the previously identified function of APL in the sparsening of sensory representations. To our surprise, however, we found that optogenetically activating APL can also have a strong rewarding effect. Specifically, APL activation together with odor presentation establishes an odor-specific, appetitive, associative short-term memory, whereas naive olfactory behavior remains unaffected. An acute, systemic inhibition of dopamine synthesis as well as an ablation of the dopaminergic pPAM neurons impair reward learning through APL activation. Our findings provide a study case of complex circuit function in a numerically simple brain, and suggest a previously unrecognized capacity of central-brain GABAergic neurons to engage in dopaminergic reinforcement. Significance statementThe single, identified giant anterior paired lateral (APL) neuron is one of the most complex neurons in the insect brain. It is GABAergic and contributes to the sparsening of neuronal activity in the mushroom body, the memory center of insects. We provide the most detailed account yet of the structure of APL in larval Drosophila as a neurogenetically accessible study case. We further reveal that, contrary to expectations, the experimental activation of APL can exert a rewarding effect, likely via dopaminergic reward pathways. The present study both provides an example of unexpected circuit complexity in a numerically simple brain, and reports an unexpected effect of activity in central-brain GABAergic circuits.

neuroscience↗

bioRxiv2025

Inverse expression of Ten3 and Lphn2 across the developing mouse brain reveals a global strategy for circuit assembly

Precise wiring of neural circuits requires molecular strategies that ensure accurate target selection across diverse brain regions. Here, we identify inverse expression between a ligand-receptor pair, Teneurin-3 (Ten3) and Latrophilin-2 (Lphn2), across the developing mouse brain. Ten3 and Lphn2 exhibit inverse expression gradients along a retinotopic axis orthogonal to the ephrin-A and EphA gradients; along the tonotopic axis across multiple brainstem auditory nuclei; and along the dorsomedial-ventrolateral axis in striatum and pallidum. Their inverse expression also creates discrete domains of cerebellar Purkinje cells and cerebellar nuclei. Using conditional tag mice, we show that inverse Ten3 and Lphn2 expression patterns predict connectivity, following a Ten3[->]Ten3, Lphn2[->]Lphn2 rule in all above circuits, and that Lphn2 is required in executing this rule in Purkinje cells[->]cerebellar nuclei projection. Our findings suggest a global strategy of coordinating gene expression of key wiring molecules with circuit connectivity across the developing brain.

neuroscience↗

bioRxiv2026

One Circuit, Many Flow Fields: Mechanistic Models of Single-Trial Neural Dynamics

A single neural circuit can exhibit qualitatively different dynamics across trials: one circuit, many flow fields. Standard models treat this trial-to-trial variability as noise around a fixed dynamical system or as discrete switches between regimes, yet neither captures how continuous internal-state variables, such as arousal or engagement, can gradually deform the circuits flow field. We propose that singletrial fitting can be reframed as inferring the low-dimensional control parameters that reshape a shared circuits flow field. We realize this with a low-rank recurrent network in which trial-specific static input biases act as bifurcation parameters: constant within a trial, they deform the flow field without directly driving activity over time. In a teacher-student setting, the model recovers the underlying dynamical system and its bifurcation structure from activity alone. Applied to large-scale recordings of mouse motor cortex during a delayed movement task, the model identifies a disengagement axis that separates engaged from disengaged trials and, when perturbed in silico, causally shifts the flow field between engaged and disengaged regimes. A generative extension reproduces the distribution of single-trial activity, and the inferred latent structure partially transfers across sessions and animals, suggesting shared low-dimensional structure across motor-cortical circuits. Together, these results reframe a methodological problem of fitting single-trial activity as a scientific opportunity: reading off the control parameters of the underlying dynamics, and connecting data-driven inference of neural dynamics to mechanistic theories of how a single circuit reuses its dynamics for flexible behavior.

neuroscience↗

bioRxiv2025

Learning induces persistent chromatin loops underlying robust gene expression during memory recall

Long-term memories are stored in neuronal ensembles called engrams, but the existence of persistent molecular traces in nuclei of engram neurons remain unknown. Using activity-dependent nuclear tagging in vivo, we profiled higher-order chromatin folding and DNA methylation in thousands of single hippocampal neurons up to a month after contextual fear conditioning (CFC). We find CFC-induced chromatin loop plasticity genome-wide, including persistently gained and lost loops with enduring traces in vivo. DNA methylation showed minimal CFC-induced persistence at promoters and enhancers. Persistently gained and lost loops connect distinct enhancers and promoters in excitatory and inhibitory subtypes and correlate with robust CFC-upregulated and -downregulated gene expression, respectively, upon recall. Synaptic genes associated with post-traumatic stress disorder and autism anchor neuronal subtype-specific persistent loops, suggesting relevance to neuropsychiatric dysfunction. We harness the power and sensitivity of multi-modal single-cell measurements to find enduring chromatin traces linked to robust gene expression during fear memory recall. Structured AbstractO_ST_ABSIntroductionC_ST_ABSLong-term memory is thought to be stored in specific neural circuits called engrams (1). Mechanisms centered at the synapse have been proposed, but what is altered within each engram cell that persists for the duration of the memory is unknown. Neural circuits fire action potentials in response to experiences. Such electrochemical signals are converted to molecular signaling pathways which travel from the synapse to the nucleus to activate new gene expression programs. However, experience-dependent RNA and protein molecules as well as synaptic plasticity phenomena are short-lived, lasting for only a few hours to several days (2). Thus, the extent to which chromatin and gene expression changes induced by behavior persist and functionally contribute to memory encoding, consolidation, and retrieval remains an important answered question. DNA is folded in the mammalian nucleus into higher-order long-range chromatin looping interactions (3). Loops form mechanistically through extrusion in which cohesin subunits form a ring that shuttles along chromatin and extrudes out the intervening DNA until it stalls at boundaries occupied by architectural proteins such as CTCF (4, 5). CTCF-independent looping mechanisms have also been uncovered (6-10). A subset of loops regulates gene expression by bringing distant enhancers into spatial proximity with their target promoters. Loops connect activity-dependent enhancers to their distal target genes to govern gene expression during in vitro neural stimulation and in vivo behavior paradigms (11-14). However, the extent to which chromatin changes persist on time scales to support long-term memory storage in vivo remains unclear. RationaleMultiple studies support the idea that higher-order chromatin architecture is plastic and can undergo activity- and experience-dependent remodeling linked to gene expression. Electron microscopy measurements provide direct evidence of chromatin architecture plasticity within minutes, with some changes persisting for up to an hour after potassium chloride stimulation of primary hippocampal neurons (15). Perturbing loops by selectively eliminating either CTCF or cohesin subunits impairs memory encoding in multiple behavior models (11, 14, 16-18), disrupts long-term potentiation (17, 18), and alters dendritic morphology (13, 19, 20). Cohesin-mediated loops are necessary for the establishment of new gene expression programs in post-mitotic neurons, including the upregulation of genes encoding axon guidance, dendritic spine morphology, and synaptic plasticity during neuron maturation in vivo as well as activity-dependent regulation of secondary response genes during neural stimulation in vitro (12, 13). The state of histone modifications before learning influences which neurons are recruited into memory traces, suggesting chromatin carries long-lasting yet adaptable information during memory encoding and long-term storage (21). Moreover, a subset of enhancers retains chromatin accessibility after learning in vivo (11). Together, these observations suggest that chromatin might provide a durable regulatory scaffold that persists long after the initial experience to govern gene expression programs required for the aspects of long-term memory storage. ResultsHere, we employ activity-dependent nuclear labeling in TRAP2 (targeted recombination in active populations) mice to isolate nuclei from neurons stimulated during contextual fear conditioning (CFC) (22, 23). Using single-nucleus methyl 3C-sequencing (snm3C-seq3), we simultaneously profiled DNA methylation and 3D genome folding in the same single tagged neurons from the hippocampus in a time course up to 28 days after CFC and during fear memory recall. Our multi-modal inquiry offered ability to group neurons by their neuronal subtype-specific DNA methylation profile, and thereby computationally generate pseudobulk Chromosome Conformation Capture heatmaps per each sorted hippocampal neuron subtype. We find CFC-induced chromatin loop plasticity genome-wide, including persistently gained and persistently lost loops with enduring structural traces up to 28 days after training. Persistent loops connect distinct distal enhancers to target genes encoding synaptic plasticity and neurotransmitter signaling pathways unique to each excitatory and inhibitory neuron cell type. By contrast to loops, we observe negligible persistence of CFC-induced changes in DNA methylation at enhancers and promoters. Persistently gained and lost loops show upregulation and downregulation of gene expression upon recall, respectively, with the functional effect focused on genes with mRNA levels influenced during training. We uncovered a strong enrichment for genes associated with post-traumatic stress disorder and autism spectrum disorder anchoring the base of persistent loops, suggesting the relevance of enduring chromatin architecture traces to dysregulation of fear, anxiety, and stress in neuropsychiatric disorders. ConclusionOur findings reveal that learning induces neuronal subtype-specific changes in chromatin loops, a subset of which remain durable for at least a month in vivo. Persistent chromatin loops are linked to expression of disease-associated synaptic genes during fear memory recall, thus highlighting relevance to neuropsychiatric and neurodevelopmental disorders. Our work opens up new avenues for investigating enduring chromatin traces for their role in governing cell-wide mRNA levels of genes that impact synaptic plasticity during fear learning and long-term memory storage.

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bioRxiv2021

Multi-Scale LM/EM Neuronal Imaging from Brain to Synapse with a Tissue Clearing Method, ScaleSF

The mammalian brain is organized over sizes that span several orders of magnitude, from synapses to the entire brain. Thus, a technique to visualize neural circuits across multiple spatial scales (multi-scale neuronal imaging) is vital for deciphering brain-wide connectivity. Here, we developed this technique by coupling successive light microscope/electron microscope (LM/EM) imaging with an ultrastructurally-preserved tissue clearing method, ScaleSF. Our multi-scale neuronal imaging incorporates 1) brain-wide macroscopic observation, 2) mesoscopic circuit mapping, 3) microscopic subcellular imaging, and 4) EM imaging of nanoscopic structures, allowing seamless integration of structural information from the brain to synapses. We applied the technique to three neural circuits of two different species, mouse striatofugal, mouse callosal, and marmoset corticostriatal projection systems, and succeeded in the simultaneous interrogation of their circuit structure and synaptic connectivity in a targeted way. Our multi-scale neuronal imaging will significantly advance the understanding of brain-wide connectivity by expanding the scales of objects.

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bioRxiv2017

From the statistics of connectivity to the statistics of spike times in neuronal networks

An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles underlying collective spiking activity in neural circuits. The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network. The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity. We close by showing how these ideas, together with plasticity rules, can help to close the loop between network structure and activity statistics.\n\nHighlightsO_LIRemarkable new data on connectivity and activity raise the promise and raise the bar for linking structure and dynamics in neural networks.\nC_LIO_LIRecent theories aim at a statistical approach, in which the enormous complexity of wiring diagrams is reduced to key features of that connectivity that drive coherent, network-wide activity.\nC_LIO_LIWe provide a unified view of three branches of this work, tied to a broadly useful \"neural response\" formula that explicitly relates connectivity to spike train statistics.\nC_LIO_LIThis isolates a surprisingly systematic role for the local structure and spatial scale of connectivity in determining spike correlations, and shows how the coevolution of structured connectivity and spiking statistics through synaptic plasticity can be predicted self-consistently.\nC_LI

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bioRxiv2023

Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability

Correlated variability in the visual cortex is modulated by stimulus properties. The stimulus dependence of correlated variability impacts stimulus coding and is indicative of circuit structure. An affine model combining a factor proportional to mean stimulus response and an additive offset has been proposed to explain how correlated variability in primary visual cortex (V1) depends on stimulus orientations. However, whether the affine model could be extended to explain modulations by other stimulus variables or variability shared between two brain areas is unknown. Motivated by a simple neural circuit mechanism, we modified the affine model to better explain the contrast-dependence of neural variability shared within either primary or secondary visual cortex (V1 or V2) as well as the orientation-dependence of neural variability shared between V1 and V2. Our results bridge neural circuit mechanisms and statistical models, and provide a parsimonious explanation for the stimulus-dependence of correlated variability within and between visual areas.

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bioRxiv2020

A C. elegans neuron both promotes and suppresses motor behavior to fine tune motor output

How neural circuits drive behavior is a central question in neuroscience. Proper execution of motor behavior requires the precise coordination of many neurons. Within a motor circuit, individual neurons tend to play discrete roles by promoting or suppressing motor output. How exactly neurons function in specific roles to fine tune motor output is not well understood. In C. elegans, the interneuron RIM plays important yet complex roles in locomotion behavior. Here, we show that RIM both promotes and suppresses distinct features of locomotion behavior to fine tune motor output. This dual function is achieved via the excitation and inhibition of the same motor circuit by electrical and chemical neurotransmission, respectively. Additionally, this bi-directional regulation contributes to motor adaptation in animals placed in novel environments. Our findings reveal that individual neurons within a neural circuit may act in opposing ways to regulate circuit dynamics to fine tune behavioral output.

neuroscience↗

bioRxiv2021

Functional coupling between target selection and acquisition in the superior colliculus

To survive in unpredictable environments, animals must continuously evaluate their surroundings for behavioral targets, such as food and shelter, and direct their movements to acquire those targets. Although the ability to accurately select and acquire spatial targets depends on a shared network of brain regions, how these processes are linked by neural circuits remains unknown. The superior colliculus (SC) mediates the selection of spatial targets and remains active during orienting movements to acquire targets, which suggests the underexamined possibility that common SC circuits underlie both selection and acquisition processes. Here, we test the hypothesis that SC functional circuitry couples target selection and acquisition using a default motor plan generated by selection-related neuronal activity. Single-unit recordings from intermediate and deep layer SC neurons in male mice performing a spatial choice task demonstrated that choice-predictive neurons, including optogenetically identified GABAergic SC neurons whose activity was causally related to target selection, exhibit increased activity during movement to the target. By strategically recording from both rostral and caudal SC neurons, we also revealed an overall caudal-to-rostral shift in activity as targets were acquired. Finally, we used an attractor model to examine how target selection activity in the SC could generate a rostral shift in activity during target acquisition using only intrinsic SC circuitry. Overall, our results suggest a functional coupling between SC circuits that underlie target selection and acquisition, elucidating a key mechanism for goal-directed behavior. Significance StatementThe ability to quickly select and acquire spatial targets is essential to animal survival. Neural circuits underlying these processes are shared in an interconnected network of brain regions, however it is unclear how circuits link decision-making processes with motor commands to execute choices. Here, we examine single-unit activity in the superior colliculus (SC) as mice select and acquire spatial targets to test the hypothesis that choice-related activity promotes target acquisition by generating a default motor plan for orienting movements. By demonstrating that choice-predictive neurons increase their firing rates during movement and capturing the dynamics of SC activity with an attractor model of intrinsic SC circuitry, our results support a role for SC circuits in coupling target selection and acquisition.

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bioRxiv2019

Dynamic compression and expansion in a classifying recurrent network

Recordings of neural circuits in the brain reveal extraordinary dynamical richness and high variability. At the same time, dimensionality reduction techniques generally uncover low-dimensional structures underlying these dynamics when tasks are performed. In general, it is still an open question what determines the dimensionality of activity in neural circuits, and what the functional role of this dimensionality in task learning is. In this work we probe these issues using a recurrent artificial neural network (RNN) model trained by stochastic gradient descent to discriminate inputs. The RNN family of models has recently shown promise in revealing principles behind brain function. Through simulations and mathematical analysis, we show how the dimensionality of RNN activity depends on the task parameters and evolves over time and over stages of learning. We find that common solutions produced by the network naturally compress dimensionality, while variability-inducing chaos can expand it. We show how chaotic networks balance these two factors to solve the discrimination task with high accuracy and good generalization properties. These findings shed light on mechanisms by which artificial neural networks solve tasks while forming compact representations that may generalize well.

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bioRxiv2020

Receptors, circuits and neural dynamics for prediction

Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior; e.g., hearing a siren, we expect to see an ambulance and quickly make way. While there are theoretical and computational frameworks for prediction, the circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling and machine learning, we show that inferred "causal" relationships between stimuli and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audio-visual delayed match-to-sample task which elicited predictions. Predictive beta feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, a NMDA receptor blocker - but not the control drug dexmedetomidine - perturbed behavioral indices of predictions, their representation in higher-order cortex, feedback to posterior cortex and pre-activation of sensory templates in higher-order sensory cortex. This study suggests predictions depend on alpha activity in higher-order cortex, beta feedback and NMDA receptors, and ketamine blocks access to learned predictive information.

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bioRxiv2025

Emergence of clustered synapses during the development of a nervous system

Synaptic organization is central for proper transmission of neural information. Studies in invertebrates and mammalian cortices show that synapses are clustered along neurite extensions, an organization that promotes key functional roles. Here we studied how these synaptic clusters emerge during the development of a nervous system. Leveraging the available C. elegans connectomes that span all larval developmental stages, we show that clustered synapses are formed at the early stages of the neural network development and that their occurrence further increases throughout development. These synaptic clusters significantly constitute small neural circuits that endow the network with important functional roles, such as feedback between mutually synapsing neurons and information transfer in mutually regulated neurons. Moreover, clustered synapses emerge early on during development of the head motor system, where they facilitate the crucial 3D head swings. Finally, the synaptic clusters within these key neural circuits are maintained throughout all developmental stages and are robustly found across different individuals, further accentuating their central functional roles in neural networks.

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bioRxiv2017

Correlated Light-Serial Scanning Electron Microscopy (CoLSSEM) for ultrastructural visualization of single neurons in vivo

A challenging aspect of neuroscience revolves around mapping the synaptic connections within neural circuits (connectomics) over scales spanning several orders of magnitude (nanometers to meters). Despite significant improvements in serial section electron microscopy (SSEM) technologies, several major roadblocks have impaired its general applicability to mammalian neural circuits. In the present study, we introduce a new approach that circumvents these roadblocks by adapting a genetically-encoded ascorbate peroxidase (APEX2) as a fusion protein to a membrane-targeted fluorescent reporter (CAAX-Venus), and introduce it in single pyramidal neurons in vivo using extremely sparse in utero cortical electroporation (IUCE). This approach allows to perform Correlated Light-SSEM (CoLSSEM) on individual neurons, reconstructing their dendritic and axonal arborization in a targeted way via combination of high-resolution confocal microscopy, and subsequently imaging of its ultrastuctural features and synaptic connections with the ATUM-SEM (automated tape-collecting ultramicrotome - scanning electron microscopy) technology. Our method significantly improves the the feasibility of large-scale reconstructions of neurons within a circuit, and bridges the description of ultrastructural features of genetically-identified neurons with their functional and/or structural connectivity, one of the main goal of connectomics.

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bioRxiv2025

Multi-timescale Rhythmic Dynamics in Rostral Ventromedial Medulla Neurons

Effective pain therapies increasingly target neural circuits that regulate nociceptive processing; yet, how descending control systems regulate pain across time remains poorly understood. Because pain regulation must coordinate rapid defensive responses with slower fluctuations in physiological state, these neural circuits are likely to operate across multiple timescales. However, whether such dynamics exist in brainstem pain-control circuits remains largely unknown. Here, we investigated this question in populations of rostral ventromedial medullary (RVM) pain-modulating neurons. The RVM contains ON- and OFF-cells that exert descending control over spinal nociceptive transmission, regulating pain sensitivity and behaviors. By integrating neuronal recordings with probabilistic modeling, we show that unstimulated and stimulus-driven conditions give rise to distinct timescales of ON- and OFF-cell dynamics. During noxious stimulation, we find that population responses undergo rapid activation followed by superimposed slow and fast recovery dynamics over tens of seconds. In contrast, the same neurons exhibit quasi-periodic fluctuations in firing activity on the order of minutes in the absence of stimulation. Gaussian-process models show that these slow dynamics are statistically predictable from past activity, indicating structured temporal organization beyond stimulus-evoked responses. Taken together, these results indicate that descending pain-control circuits exhibit structured dynamics spanning rapid pain-related signaling and slower fluctuations associated with ongoing physiological state. SignificancePain regulation requires coordination between rapid defensive responses and slower changes in physiological state, yet how these processes are integrated in the brain remains unclear. We show that neurons in a key brainstem pain-control center, the rostral ventromedial medulla, operate across multiple timescales. Using neuronal recordings and computational modeling, we find that these neurons exhibit both fast responses to painful stimuli and slow, structured fluctuations in ongoing activity. These results demonstrate that descending pain control is temporally organized beyond immediate stimulus-evoked responses. This provides a framework for understanding how pain is regulated over time.

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bioRxiv2020

Sex-specific stress-related behavioral phenotypes and central amygdala dysfunction in a mouse model of 16p11.2 microdeletion

Substantial evidence indicates that a microdeletion on human chromosome 16p11.2 is linked to neurodevelopmental disorders including autism spectrum disorders (ASD). Carriers of this deletion show divergent symptoms besides the core features of ASD, such as anxiety and emotional symptoms. The neural mechanisms underlying these symptoms are poorly understood. Here we report mice heterozygous for a deletion allele of the genomic region corresponding to the human 16p11.2 microdeletion locus (i.e., the 16p11.2 del/+ mice) have sex-specific anxiety-related behavioral and neural circuit changes. We found that female, but not male 16p11.2 del/+ mice showed enhanced fear generalization - a hallmark of anxiety disorders - after auditory fear conditioning, and displayed increased anxiety-like behaviors after physical restraint stress. Notably, such sex-specific behavioral changes were paralleled by an increase in activity in central amygdala neurons projecting to the globus pallidus in female, but not male 16p11.2 del/+ mice. Together, these results reveal female-specific anxiety phenotypes related to 16p11.2 microdeletion syndrome and a potential underlying neural circuit mechanism. Our study therefore identifies previously underappreciated sex-specific behavioral and neural changes in a genetic model of 16p11.2 microdeletion syndrome, and highlights the importance of investigating female-specific aspects of this syndrome for targeted treatment strategies.

neuroscience↗

bioRxiv2025

Low-intensity transcranial focused ultrasound engages parvalbumin-positive GABAergic interneurons in a humanized mouse model of chronic pain: from electrophysiology to cellular investigation

BackgroundLow-intensity transcranial focused ultrasound (tFUS) offers high spatial specificity and deep brain penetration, showing great promise as a non-invasive stimulation technology for modulating brain activity and behavior. Recent studies show that specific tFUS parameters targeted to pain-processing brain circuits can significantly alter pain-related behaviors in rodent models and humans. However, a comprehensive understanding of how tFUS influences brain networks and cellular mechanisms is essential to optimize efficacy and facilitate safe translation to clinical pain therapies. ObjectiveWe aimed to evaluate the modulation of inhibitory neural circuits induced by tFUS of 40 Hz pulse repetition frequency (PRF) in a humanized mouse model of chronic pain, integrating local and network-level electrophysiological investigations, molecular analyses, and histological assessment to confirm safety. Methods and ResultsWe used a 128-element random array transducer for stimulation, along with a non-invasive and flexible 30-channel electroencephalography (EEG) to assess local evoked responses, topographical brain activity, and global brain dynamics including excitation and inhibition (E/I) balance. To further assess tFUS neuromodulation effects at the cellular level, we performed immunohistochemistry (IHC) analysis and found that tFUS significantly increased the activity of inhibitory neurons as indicated by elevated expression of Glutamate Decarboxylase 67 (GAD67) and Parvalbumin (PV). Finally, safety was evaluated in the same brain samples used for mechanistic analysis, with blinded histological assessment revealing no signs of tissue damage. ConclusionsThese findings provide new evidence that tFUS non-invasively engages PV GABAergic inhibitory circuits in a chronic pain mouse model, supporting its development as a robust neuromodulation strategy. TopicsChronic pain; Transcranial focused ultrasound; Non-invasive brain neuromodulation; GABAergic neural circuit modulation HighlightsO_LIMulti-modal assessment of low-intensity tFUS in a humanized chronic pain model. C_LIO_LI40 Hz tFUS enhances inhibition, mirroring optogenetic PV neuron activation. C_LIO_LIRepeated tFUS restored chronic pain-disrupted E/I balance. C_LIO_LIMulti-session tFUS upregulates GAD67 and PV interneuron expressions. C_LI

bioengineering↗