Neural Circuits Research Articles

Establishing connectivity through microdissections of midbrain stimulation-related neural circuits.

Comprehensive understanding of the neural circuits involving the ventral tegmental area is essential for elucidating the anatomo-functional mechanisms governing human behaviour as well as the therapeutic and adverse effects of deep brain stimulation for neuropsychiatric diseases. While the ventral tegmental area has been successfully targeted with deep brain stimulation for different neuropsychiatric diseases, the axonal connectivity of the region has not been fully understood. Here using fiber micro-dissections in human cadaveric hemispheres, population-based high-definition fiber tractography, and previously reported deep brain stimulation hotspots, we find that the ventral tegmental area participates in an intricate network involving the serotonergic pontine nuclei, basal ganglia, limbic system, basal forebrain, and prefrontal cortex, which is implicated in the treatment of obsessive-compulsive disorder, major depressive disorder, Alzheimer's disease, cluster headaches, and aggressive behaviors.

A targeted CRISPR-Cas9 mediated F0 screen identifies genes involved in establishment of the enteric nervous system.

The vertebrate enteric nervous system (ENS) is a crucial network of enteric neurons and glia resident within the entire gastrointestinal tract (GI). Overseeing essential GI functions such as gut motility and water balance, the ENS serves as a pivotal bidirectional link in the gut-brain axis. During early development, the ENS is primarily derived from enteric neural crest cells (ENCCs). Disruptions to ENCC development, as seen in conditions like Hirschsprung disease (HSCR), lead to the absence of ENS in the GI, particularly in the colon. In this study, using zebrafish, we devised an in vivo F0 CRISPR-based screen employing a robust, rapid pipeline integrating single-cell RNA sequencing, CRISPR reverse genetics, and high-content imaging. Our findings unveil various genes, including those encoding opioid receptors, as possible regulators of ENS establishment. In addition, we present evidence that suggests opioid receptor involvement in the neurochemical coding of the larval ENS. In summary, our work presents a novel, efficient CRISPR screen targeting ENS development, facilitating the discovery of previously unknown genes, and increasing knowledge of nervous system construction.

A bistable inhibitory optoGPCR for multiplexed optogenetic control of neural circuits

Information is transmitted between brain regions through the release of neurotransmitters from long-range projecting axons. Understanding how the activity of such long-range connections contributes to behavior requires efficient methods for reversibly manipulating their function. Chemogenetic and optogenetic tools, acting through endogenous G-protein-coupled receptor pathways, can be used to modulate synaptic transmission, but existing tools are limited in sensitivity, spatiotemporal precision or spectral multiplexing capabilities. Here we systematically evaluated multiple bistable opsins for optogenetic applications and found that the Platynereis dumerilii ciliary opsin (PdCO) is an efficient, versatile, light-activated bistable G-protein-coupled receptor that can suppress synaptic transmission in mammalian neurons with high temporal precision in vivo. PdCO has useful biophysical properties that enable spectral multiplexing with other optogenetic actuators and reporters. We demonstrate that PdCO can be used to conduct reversible loss-of-function experiments in long-range projections of behaving animals, thereby enabling detailed synapse-specific functional circuit mapping.

A large-scale neuronal network modelling study: Stimulus size modulates gamma oscillations in the primary visual cortex by long-range connections.

Stimulus size modulation of neuronal firing activity is a fundamental property of the primary visual cortex. Numerous biological experiments have shown that stimulus size modulation is affected by multiple factors at different spatiotemporal scales, but the exact pathways and mechanisms remain incompletely understood. In this paper, we establish a large-scale neuronal network model of primary visual cortex withlayer 2/3 to study how gamma oscillation properties are modulated by stimulus size and especially how long-range connections affect the modulation as realistic neuronal properties and spatial distributions of synaptic connections are considered. It is shown that long-range horizontal synaptic connections are sufficient to produce dimensional modulation of firing rates and gamma oscillations. In particular, with increasing grating stimulus size, the firing rate increases and then decreases, the peak frequency of gamma oscillations decreases and the spectral power increases. These are consistent with biological experimental observations. Furthermore, we explain in detail how the number and spatial distribution of long-range connections affect the size modulation of gamma oscillations by using the analysis of neuronal firing activity and synaptic current fluctuations. Our results provide a mechanism explanation for size modulation of gamma oscillations in the primary visual cortex and reveal the important and unique role played by long-range connections, which contributes to a deeper understanding of the cognitive function of gamma oscillations in visual cortex.

Preictal dysfunctions of inhibitory interneurons paradoxically lead to their rebound hyperactivity and to low-voltage-fast onset seizures in Dravet syndrome

Epilepsies have numerous specific mechanisms. The understanding of neural dynamics leading to seizures is important for disclosing pathological mechanisms and developing therapeutic approaches. We investigated electrographic activities and neural dynamics leading to convulsive seizures in patients and mouse models of Dravet syndrome (DS), a developmental and epileptic encephalopathy in which hypoexcitability of GABAergic neurons is considered to be the main dysfunction. We analyzed EEGs from DS patients carrying a SCN1A pathogenic variant, as well as epidural electrocorticograms, hippocampal local field potentials, and hippocampal single-unit neuronal activities in Scn1a+/- and Scn1aRH/+ DS mice. Strikingly, most seizures had low-voltage-fast onset in both patients and mice, which is thought to be generated by hyperactivity of GABAergic interneurons, the opposite of the main pathological mechanism of DS. Analyzing single-unit recordings, we observed that temporal disorganization of the firing of putative interneurons in the period immediately before the seizure (preictal) precedes the increase of their activity at seizure onset, together with the entire neuronal network. Moreover, we found early signatures of the preictal period in the spectral features of hippocampal and cortical field potential of Scn1a mice and of patients' EEG, which are consistent with the dysfunctions that we observed in single neurons and that allowed seizure prediction. Therefore, the perturbed preictal activity of interneurons leads to their hyperactivity at the onset of generalized seizures, which have low-voltage-fast features that are similar to those observed in other epilepsies and are triggered by hyperactivity of GABAergic neurons. Preictal spectral features may be used as predictive seizure biomarkers.

Ca2+ channel and active zone protein abundance intersects with input-specific synapse organization to shape functional synaptic diversity.

Synaptic heterogeneity is a hallmark of nervous systems that enables complex and adaptable communication in neural circuits. To understand circuit function, it is thus critical to determine the factors that contribute to the functional diversity of synapses. We investigated the contributions of voltage-gated calcium channel (VGCC) abundance, spatial organization, and subunit composition to synapse diversity among and between synapses formed by two closely related Drosophila glutamatergic motor neurons with distinct neurotransmitter release probabilities (Pr). Surprisingly, VGCC levels are highly predictive of heterogeneous Pr among individual synapses of either low- or high-Pr inputs, but not between inputs. We find that the same number of VGCCs are more densely organized at high-Pr synapses, consistent with tighter VGCC-synaptic vesicle coupling. We generated endogenously tagged lines to investigate VGCC subunits in vivo and found that the α2δ-3 subunit Straightjacket along with the CAST/ELKS active zone (AZ) protein Bruchpilot, both key regulators of VGCCs, are less abundant at high-Pr inputs, yet positively correlate with Pr among synapses formed by either input. Consistently, both Straightjacket and Bruchpilot levels are dynamically increased across AZs of both inputs when neurotransmitter release is potentiated to maintain stable communication following glutamate receptor inhibition. Together, these findings suggest a model in which VGCC and AZ protein abundance intersects with input-specific spatial and molecular organization to shape the functional diversity of synapses.

Synchronization patterns in a network of diffusively delay-coupled memristive Chialvo neuron map

The inescapable presence of time delay in numerous real-world systems, particularly in neuronal networks, prompts an exploration of its impact on synchronization dynamics. This study employs memristive Chialvo maps to capture the local dynamics of network nodes, while connections are homogeneously described by a linear diffusive function—referred to as electrical couplings in mathematical neuroscience—incorporating a specific time delay. Using Master stability functions (MSFs), analytical assessments are conducted to examine the stability of synchronous solutions, a validation supported by time-averaged synchronization error calculations. This research reveals the time delay's influence on the synchrony, making the synchronous state dependent on the coupling parameter's strength. The behavior of the synchronization manifold is systematically probed across synchronous and asynchronous regions analyzed by the MSF analysis. Lastly, an investigation involving a neuronal network with a global coupling configuration reveals that neurons have a tendency to organize into clusters with distinct time lags.

Neural Circuit Policies for Virtual Character Control

The development of high-stakes decision-making neural agents that interact with complex environments, such as video games, is an important aspect of AI research with numerous potential applications. Reinforcement learning combined with deep learning architectures (DRL) has shown remarkable success in various genres of games. The performance of DRL is heavily dependent upon the neural networks resides within them. Although these algorithms perform well in offline testing but the performance deteriorates in noisy and sub-optimal conditions, creating safety and security issues. To address these, we propose a hybrid deep learning architecture that combines a traditional convolutional neural network with worm brain-inspired neural circuit policies. This allows the agent to learn key coherent features from the environment and interpret its dynamics. The obtained DRL agent was not only able to achieve an optimal policy quickly, but it was also the most noise-resilient with the highest success rate. Our research indicates that only 20 control neurons (12 inter-neurons and 8 command neurons) are sufficient to achieve competitive results. We implemented and analyzed the agent in the popular video game Doom, demonstrating its effectiveness in practical applications.

Common principles for odour coding across vertebrates and invertebrates.

The olfactory system is an ideal and tractable system for exploring how the brain transforms sensory inputs into behaviour. The basic tasks of any olfactory system include odour detection, discrimination and categorization. The challenge for the olfactory system is to transform the high-dimensional space of olfactory stimuli into the much smaller space of perceived objects and valence that endows odours with meaning. Our current understanding of how neural circuits address this challenge has come primarily from observations of the mechanisms of the brain for processing other sensory modalities, such as vision and hearing, in which optimized deep hierarchical circuits are used to extract sensory features that vary along continuous physical dimensions. The olfactory system, by contrast, contends with an ill-defined, high-dimensional stimulus space and discrete stimuli using a circuit architecture that is shallow and parallelized. Here, we present recent observations in vertebrate and invertebrate systems that relate the statistical structure and state-dependent modulation of olfactory codes to mechanisms of perception and odour-guided behaviour.

Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware

We report a breakthrough in the hardware implementation of energy-efficient all-spin synapse and neuron devices for highly scalable integrated neuromorphic circuits. Our work demonstrates the successful execution of all-spin synapse and activation function generator using domain wall-magnetic tunnel junctions. By harnessing the synergistic effects of spin-orbit torque and interfacial Dzyaloshinskii-Moriya interaction in selectively etched spin-orbit coupling layers, we achieve a programmable multi-state synaptic device with high reliability. Our first-principles calculations confirm that the reduced atomic distance between 5d and 3d atoms enhances Dzyaloshinskii-Moriya interaction, leading to stable domain wall pinning. Our experimental results, supported by visualizing energy landscapes and theoretical simulations, validate the proposed mechanism. Furthermore, we demonstrate a spin-neuron with a sigmoidal activation function, enabling high operation frequency up to 20 MHz and low energy consumption of 508 fJ/operation. A neuron circuit design with a compact sigmoidal cell area and low power consumption is also presented, along with corroborated experimental implementation. Our findings highlight the great potential of domain wall-magnetic tunnel junctions in the development of all-spin neuromorphic computing hardware, offering exciting possibilities for energy-efficient and scalable neural network architectures.

Advances in pharmacogenomics: optimizing antiepileptic drug therapy for drug-resistant epilepsy

Epilepsy, a complex neurological disorder, is influenced by intricate interactions within cortical, hippocampal, or thalamocortical neuronal networks, presenting a genetically complex condition with non-Mendelian inheritance patterns. This complexity is underscored by the involvement of numerous “susceptibilities” or “modifier” genes, complicating the assessment of risk and therapy outcomes. A critical inquiry in epilepsy treatment involves understanding how genetic diversity impacts treatment strategies and efficacy. Pharmacogenomic advancements have elaborated the connection between genetic variants and antiseizure medication (ASM) safety and response, marking a shift towards precision medicine in epilepsy care. Notably, genetic screening for variants such as HLA-B*1502 and HLA-A*3101 has demonstrated significant efficacy in preventing severe hypersensitivity reactions, including toxic epidermal necrolysis (TEN) and Stevens-Johnson syndrome (SJS), particularly among specific ethnic populations. However, putting pharmacogenomic discoveries into clinical practice faces numerous challenges, including educational, legal, and economic barriers, emphasizing the need for broader acceptance and integration of pharmacogenomic data. This review synthesizes recent studies on pharmacogenomics in epilepsy, highlighting the current advances and prospects for personalizing epilepsy treatment through genetic insights, aiming to enhance ASM safety, reduce adverse effects, and improve treatment outcomes. Through a comprehensive examination of the genetic basis of epilepsy and its influence on pharmacotherapy, this review endeavors to contribute to the evolving landscape of precision medicine in epilepsy care, advocating for a more individualized and effective treatment approach.

Artificial neural network modelling of natural gas dehydration process

A multi-input–multi-output artificial neuron network (MIMO-ANN) model has been developed for process monitoring and improvement on a natural gas glycol dehydration process. The MIMO-ANN model was based on a steady-state process simulation model constructed in commercial software Aspen HYSYS. A set of training data was generated with the converged simulation model for the training of the MIMO-ANN model in Python. The process input of the model includes lean glycol recirculation rate and purity along with wet gas inlet pressure. On the other hand, the process output considered includes dehydrated gas water and aromatics content, dehydrated gas hydrate formation temperature and water dew point, stripping gas flow rate as well as reboiler duty. The overall mean squared error (MSE) of the MIMO-ANN model was calculated as 1.79. The best-fit line that highly overlaps with a 45° diagonal line is constructed with a correlation coefficient (R2) score of more than 0.999 for all studied process output testing datasets. The mean absolute percentage error (MAPE) of the predicted outputs is generally less than 1%, except for dehydrated gas water dew point (16.63%) and stripping gas flow rate (11.55%), due to error predictions attempted on pseudo-zero values. This successfully displays an exceptional predictive performance of the MIMO-ANN model developed in this work which can be further deployed as an online dashboard for real-time monitoring tool.

Altered projection-specific synaptic remodeling and its modification by oxytocin in an idiopathic autism marmoset model.

Alterations in the experience-dependent and autonomous elaboration of neural circuits are assumed to underlie autism spectrum disorder (ASD), though it is unclear what synaptic traits are responsible. Here, utilizing a valproic acid-induced ASD marmoset model, which shares common molecular features with idiopathic ASD, we investigate changes in the structural dynamics of tuft dendrites of upper-layer pyramidal neurons and adjacent axons in the dorsomedial prefrontal cortex through two-photon microscopy. In model marmosets, dendritic spine turnover is upregulated, and spines are generated in clusters and survived more often than in control marmosets. Presynaptic boutons in local axons, but not in commissural long-range axons, demonstrate hyperdynamic turnover in model marmosets, suggesting alterations in projection-specific plasticity. Intriguingly, nasal oxytocin administration attenuates clustered spine emergence in model marmosets. Enhanced clustered spine generation, possibly unique to certain presynaptic partners, may be associated with ASD and be a potential therapeutic target.

Glioblastoma disrupts cortical network activity at multiple spatial and temporal scales.

The emergence of glioblastoma in cortical tissue initiates early and persistent neural hyperexcitability with signs ranging from mild cognitive impairment to convulsive seizures. The influence of peritumoral synaptic density, expansion dynamics, and spatial contours of excess glutamate upon higher order neuronal network modularity is unknown. We combined cellular and widefield imaging of calcium and glutamate fluorescent reporters in two glioblastoma mouse models with distinct synaptic microenvironments and infiltration profiles. Functional metrics of neural ensembles are dysregulated during tumor invasion depending on the stage of malignant progression and tumor cell proximity. Neural activity is differentially modulated during periods of accelerated and inhibited tumor expansion. Abnormal glutamate accumulation precedes and outpaces the spatial extent of baseline neuronal calcium signaling, indicating these processes are uncoupled in tumor cortex. Distinctive excitability homeostasis patterns and functional connectivity of local and remote neuronal populations support the promise of precision genetic diagnosis and management of this devastating brain disease.

Ventral hippocampus mediates inter-trial responding in signaled active avoidance

The hippocampus has a central role in regulating contextual processes in memory. We have shown that pharmacological inactivation of ventral hippocampus (VH) attenuates the context-dependence of signaled active avoidance (SAA) in rats. Here, we explore whether the VH mediates intertrial responses (ITRs), which are putative unreinforced avoidance responses that occur between trials. First, we examined whether VH inactivation would affect ITRs. Male rats underwent SAA training and subsequently received intra-VH infusions of saline or muscimol before retrieval tests in the training context. Rats that received muscimol performed significantly fewer ITRs, but equivalent avoidance responses, compared to controls. Next, we asked whether chemogenetic VH activation would increase ITR vigor. In male and female rats expressing excitatory (hM3Dq) DREADDs, systemic CNO administration produced a robust ITR increase that was not due to nonspecific locomotor effects. Then, we examined whether chemogenetic VH activation potentiated ITRs in an alternate (non-training) test context and found it did. Finally, to determine if context-US associations mediate ITRs, we exposed rats to the training context for three days after SAA training to extinguish the context. Rats submitted to context extinction did not show a reliable decrease in ITRs during a retrieval test, suggesting that context-US associations are not responsible for ITRs. Collectively, these results reveal an important role for the VH in context-dependent ITRs during SAA. Further work is required to explore the neural circuits and associative basis for these responses, which may be underlie pathological avoidance that occurs in humans after threat has passed.

Informational Analysis and Prediction of Obsessive-Compulsive Disorder Pathogenesis.

We aimed to predict the possible mechanism of obsessive-compulsive disorder (OCD) by integrating and analyzing mRNA sequencing results from two datasets and to provide direction for future studies into the pathogenesis of OCD. Two OCD datasets, GSE78104 and GSE60190, were obtained, and the intersection of the two gene sets with differential expression in OCD samples was selected. Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment and Gene Ontology (GO) analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) online analysis website for the genes at the intersection, and the data were mapped using http://www.bioinformatics.com.cn. After genes with p≤0.05 had been screened out, protein-protein interaction (PPI) interaction analysis was conducted using Metascape to screen the key Molecular Complex Detection (MCODE) genes. MCODE genes were then enriched using the KEGG signaling pathway and GO classification. A total of 3,449 differentially expressed genes (DEGs) were obtained from the GSE78104 and GSE60190 datasets. KEGG, GO, and Gene Set Enrichment Analysis analyses of DEGs showed that the onset of OCD was related to oxidative phosphorylation and other metabolic processes, which may have a similar pathogenesis to other neurodegenerative diseases. Single-gene PPI analysis of SAPAP3 revealed that the mechanism by which SAPAP3 knockout induces OCD may also be caused by affecting oxidative phosphorylation. The mechanism of SAPAP3 knockout-induced OCD in mice may be due to the oxidative phosphorylation process in the body. Future studies on the neural circuit mechanism of OCD should be conducted.

Non-Invasive Spinal Cord Stimulation for Motor Rehabilitation of Patients with Spinal Muscular Atrophy Treated with Orphan Drugs.

Spinal muscular atrophy (SMA) is an orphan disease characterized by the progressive degeneration of spinal alpha motor neurons. In recent years, nusinersen and several other drugs have been approved for the treatment of this disease. Transcutaneous spinal cord stimulation (tSCS) modulates spinal neuronal networks, resulting in changes in locomotion and posture in patients with severe spinal cord injury and stroke. We hypothesize that tSCS can activate motor neurons that are intact and restored by medication, slow the decline in motor activity, and contribute to the development of motor skills in SMA patients. Thirty-seven children and adults with SMA types 2 and 3 participated in this study. The median duration of drug treatment was over 20 months. The application of tSCS was performed during physical therapy for 20-40 min per day for ~12 days. Outcome measures were specific SMA motor scales, goniometry of contractured joints, and forced vital capacity. Significant increases in motor function, improved respiratory function, and decreased contracture were observed in both type 2 and 3 SMA participants. The magnitude of functional changes was not associated with participant age. Further studies are needed to elucidate the reasons for the beneficial effects of spinal cord electrical stimulation on SMA.

Innate Orientating Behavior of a Multi-Legged Robot Driven by the Neural Circuits of C. elegans.

The objective of this research is to achieve biologically autonomous control by utilizing a whole-brain network model, drawing inspiration from biological neural networks to enhance the development of bionic intelligence. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of the cellular synapses. The neural network simulation integrates computational programming and the visualization of the neurons and synapse connections of C. elegans, containing the specific controllable circuits and their dynamic characteristics. To illustrate the biological neural network (BNN)'s particular intelligent control capability, we introduced an innovative methodology for applying the BNN model to a 12-legged robot's movement control. Two methods were designed, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of the BNN. Both the simulation and experimental results indicate that the robot exhibits more autonomy and a more intelligent movement performance under BNN control. The systematic approach of employing the whole-brain BNN for robot control provides biomimetic research with a framework that has been substantiated by innovative methodologies and validated through the observed positive outcomes. This method is established as follows: (1) two integrated dynamic models of the C. elegans' whole-brain network and the robot moving dynamics are built, and all of the controllable circuits are discovered and verified; (2) real-time communication is achieved between the BNN model and the robot's dynamical model, both in the simulation and the experiments, including applicable encoding and decoding algorithms, facilitating their collaborative operation; (3) the designed mechanisms using the BNN model to control the robot are shown to be effective through numerical and experimental tests, focusing on 'foraging' behavior control and locomotion control.

Learning with sparse reward in a gap junction network inspired by the insect mushroom body.

Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This 'distal' or 'sparse' reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body.

Construction of a Multicellular Communication Network Model for Cell Co-Culture Technology and Evaluation of Its Simulation Capability

Abstract Objective: Cell co-culture technology has been widely used to analyze the effects of drugs on cell proliferation and the expression of some proteins in cells, especially in the field of traditional Chinese medicine (TCM); however, the interactions between cells and the transmission of TCM effects between cells have not been adequately studied. Materials and Methods: Using data on gene transcription regulation, biological response, signal channel, and cell-specific expression protein, we built a network for cell types based on entity grammar. Through the correspondence and location information of signal molecules and receptors, type-specific networks of single cells were connected and a multicellular network of smooth muscle cells, neurons, and vascular endothelial cells was constructed. The mechanism of action of nimodipine was analyzed based on the multicellular communication network and its simulation capability was evaluated. Results: The outputs generated by the model developed in this study showed that nimodipine inhibited smooth muscle contraction, due to the overload of Ca2+ and the toxicity of excitatory amino acids, and protected neurons and vascular endothelial cells by supporting cell proliferation and inhibiting cell apoptosis. These results were consistent with the known mechanism of nimodipine action, thus confirming that the multicellular network can be used to study the transmission of drug effects among cells. Conclusions: This study lays a foundation for the analysis of the transmission of drug effects in multi-cells, tissues, organs, and other spatial scales through multicellular co-culture experiments, based on a multicellular communication network. In addition, it provides a biological network model for the analysis of TCM action mechanisms.