Architectures of neuronal circuits

Multiplexed neurochemical sensing with sub-nM sensitivity across 2.25 mm2 area

Multichannel arrays capable of real-time sensing of neuromodulators in the brain are crucial for gaining insights into new aspects of neural communication. However, measuring neurochemicals, such as dopamine, at low concentrations over large areas has proven challenging. In this research, we demonstrate a novel approach that leverages the scalability and processing power offered by microelectrode array devices integrated with a functionalized, high-density microwire bundle, enabling electrochemical sensing at an unprecedented scale and spatial resolution. The sensors demonstrate outstanding selective molecular recognition by incorporating a selective polymeric membrane. By combining cutting-edge commercial multiplexing, digitization, and data acquisition hardware with a bio-compatible and highly sensitive neurochemical interface array, we establish a powerful platform for neurochemical analysis. This multichannel array has been successfully utilized in vitro and ex vivo systems. Notably, our results show a sensing area of 2.25 mm2 with an impressive detection limit of 820 pM for dopamine. This new approach paves the way for investigating complex neurochemical processes and holds promise for advancing our understanding of brain function and neurological disorders.

Linking brain activity across scales with simultaneous opto- and electrophysiology

The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.

Thermodynamic model for memory

A thermodynamic model for memory formation is proposed. Key points include: 1) Any thought or consciousness corresponds to a thermodynamic system of nerve cells. 2) The system concept of nerve cells can only be described by thermodynamics of condensed matter. 3) The memory structure is logically associated with the system structure or the normal structure of biology. 4) The development of our thoughts is processed irreversibly, and numerous states or thoughts can be generated. 5) Memory formation results from the reorganization and change of cellular structures (or memory structures), which are related to nerve cell skeleton and membrane. Their alteration can change the excitability of nerve cells and the pathway of neural impulse conduction. 6) Amnesia results from the loss of thermodynamic stability of the memory structure, which can be achieved by different ways. Some related phenomena and facts are discussed. The analysis shows that thermodynamics can account for the basic properties of memory.

SynBot: An open-source image analysis software for automated quantification of synapses

The formation of precise numbers of neuronal connections, known as synapses, is crucial for brain function. Therefore, synaptogenesis mechanisms have been one of the main focuses of neuroscience. Immunohistochemistry is a common tool for visualizing synapses. Thus, quantifying the numbers of synapses from light microscopy images enables screening the impacts of experimental manipulations on synapse development. Despite its utility, this approach is paired with low throughput analysis methods that are challenging to learn and results are variable between experimenters, especially when analyzing noisy images of brain tissue. We developed an open-source ImageJ-based software, SynBot, to address these technical bottlenecks by automating the analysis. SynBot incorporates the advanced algorithms ilastik and SynQuant for accurate thresholding for synaptic puncta identification, and the code can easily be modified by users. The use of this software will allow for rapid and reproducible screening of synaptic phenotypes in healthy and diseased nervous systems.

Metabolic Crosstalk between Liver and Brain: From Diseases to Mechanisms

Multiple organs and tissues coordinate to respond to dietary and environmental challenges. It is interorgan crosstalk that contributes to systemic metabolic homeostasis. The liver and brain, as key metabolic organs, have their unique dialogue to transmit metabolic messages. The interconnected pathogenesis of liver and brain is implicated in numerous metabolic and neurodegenerative disorders. Recent insights have positioned the liver not only as a central metabolic hub but also as an endocrine organ, capable of secreting hepatokines that transmit metabolic signals throughout the body via the bloodstream. Metabolites from the liver or gut microbiota also facilitate a complex dialogue between liver and brain. In parallel to humoral factors, the neural pathways, particularly the hypothalamic nuclei and autonomic nervous system, are pivotal in modulating the bilateral metabolic interplay between the cerebral and hepatic compartments. The term "liver-brain axis" vividly portrays this interaction. At the end of this review, we summarize cutting-edge technical advancements that have enabled the observation and manipulation of these signals, including genetic engineering, molecular tracing, and delivery technologies. These innovations are paving the way for a deeper understanding of the liver-brain axis and its role in metabolic homeostasis.

Proposal for a Mechanistic Disease Conceptualization in Clinical Neurosciences: The Neural Network Components (NNC) Model

ABSTRACT

Clinical neurosciences, and psychiatry specifically, have been challenged by the lack of a comprehensive and practical framework that explains the core mechanistic processes of variable psychiatric presentations. Current conceptualization and classification of psychiatric presentations are primarily centered on a non-biologically based clinical descriptive approach. Despite various attempts, advances in neuroscience research have not led to an improved conceptualization or mechanistic classification of psychiatric disorders. This perspective article proposes a new-work-in-progress-framework for conceptualizing psychiatric presentations based on neural network components (NNC). This framework could guide the development of mechanistic disease classification, improve understanding of underpinning pathology, and provide specific intervention targets. This model also has the potential to dissolve artificial barriers between the fields of psychiatry and neurology.

Network motifs in cellular neurophysiology

Concepts from network science and graph theory, including the framework of network motifs, have been frequently applied in studying neuronal networks and other biological complex systems. Network-based approaches can also be used to study the functions of individual neurons, where cellular elements such as ion channels and membrane voltage are conceptualized as nodes within a network, and their interactions are denoted by edges. Network motifs in this context provide functional building blocks that help to illuminate the principles of cellular neurophysiology. In this review we build a case that network motifs operating within neurons provide tools for defining the functional architecture of single-neuron physiology and neuronal adaptations. We highlight the presence of such computational motifs in the cellular mechanisms underlying action potential generation, neuronal oscillations, dendritic integration, and neuronal plasticity. Future work applying the network motifs perspective may help to decipher the functional complexities of neurons and their adaptation during health and disease.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Embryonically active piriform cortex neurons promote intracortical recurrent connectivity during development

Neuronal activity plays a critical role in the maturation of circuits that propagate sensory information into the brain. How widely does early activity regulate circuit maturation across the developing brain? Here, we used targeted recombination in active populations (TRAP) to perform a brain-wide survey for prenatally active neurons in mice and identified the piriform cortex as an abundantly TRAPed region. Whole-cell recordings in neonatal slices revealed preferential interconnectivity within embryonically TRAPed piriform neurons and their enhanced synaptic connectivity with other piriform neurons. In vivo Neuropixels recordings in neonates demonstrated that embryonically TRAPed piriform neurons exhibit broad functional connectivity within piriform and lead spontaneous synchronized population activity during a transient neonatal period, when recurrent connectivity is strengthening. Selectively activating or silencing these neurons in neonates enhanced or suppressed recurrent synaptic strength, respectively. Thus, embryonically TRAPed piriform neurons represent an interconnected hub-like population whose activity promotes recurrent connectivity in early development.

Association cortical areas in the mouse contain a large population of fast-spiking GABAergic neurons that do not express parvalbumin

GABAergic neurons represent 10-15% of the neuronal population of the cortex but exert a powerful control over information flow in cortical circuits. The largest GABAergic class in the neocortex is represented by the parvalbumin-expressing fast-spiking neurons, which provide powerful somatic inhibition to their postsynaptic targets. Recently, the density of parvalbumin interneurons has been shown to be lower in associative areas of the mouse cortex as compared with sensory and motor areas. Modelling work based on these quantifications linked the low-density of parvalbumin interneurons with specific computations of associative cortices. However, it is still unknown whether the total GABAergic population of association cortices is smaller or whether another GABAergic type can compensate for the low density of parvalbumin interneurons. In the present study, we investigated these hypotheses using a combination of neuroanatomy, mouse genetics and neurophysiology. We found that the GABAergic population of association areas is comparable with that of primary sensory areas, and it is enriched of fast-spiking neurons that do not express parvalbumin and were not accounted for by previous quantifications. We developed an intersectional viral strategy to demonstrate that the population of fast-spiking neurons is comparable across cortical regions. Our results provide quantifications of the density of fast-spiking GABAergic neurons and offers new biological constrains to refine current models of cortical computations.

Molecular mechanisms underlying microglial sensing and phagocytosis in synaptic pruning

ABSTRACT

Microglia are the main non-neuronal cells in the central nervous system that have important roles in brain development and functional connectivity of neural circuits. In brain physiology, highly dynamic microglial processes are facilitated to sense the surrounding environment and stimuli. Once the brain switches its functional states, microglia are recruited to specific sites to exert their immune functions, including the release of cytokines and phagocytosis of cellular debris. The crosstalk of microglia between neurons, neural stem cells, endothelial cells, oligodendrocytes, and astrocytes contributes to their functions in synapse pruning, neurogenesis, vascularization, myelination, and blood-brain barrier permeability. In this review, we highlight the neuron-derived "find-me," "eat-me," and "don't eat-me" molecular signals that drive microglia in response to changes in neuronal activity for synapse refinement during brain development. This review reveals the molecular mechanism of neuron-microglia interaction in synaptic pruning and presents novel ideas for the synaptic pruning of microglia in disease, thereby providing important clues for discovery of target drugs and development of nervous system disease treatment methods targeting synaptic dysfunction.

The Profile of Network Spontaneous Activity and Functional Organization Interplay in Hierarchically Connected Modular Neural Networks In Vitro

Modern microtechnology methods are widely used to create neural networks on a chip with a connection architecture demonstrating properties of modularity and hierarchy similar to brain networks. Such in vitro networks serve as a valuable model for studying the interplay of functional architecture within modules, their activity, and the effectiveness of inter-module interaction. In this study, we use a two-chamber microfluidic platform to investigate functional connectivity and global activity in hierarchically connected modular neural networks. We found that the strength of functional connections within the module and the profile of network spontaneous activity determine the effectiveness of inter-modular interaction and integration activity in the network. The direction of intermodular activity propagation configures the different densities of inhibitory synapses in the network. The developed microfluidic platform holds the potential to explore function-structure relationships and efficient information processing in two- or multilayer neural networks, in both healthy and pathological states.

Intrinsic Dynamics and Neural Implementation of a Hypothalamic Line Attractor Encoding an Internal Behavioral State

Line attractors are emergent population dynamics hypothesized to encode continuous variables such as head direction and internal states. In mammals, direct evidence of neural implementation of a line attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles. Estrogen receptor type 1 (Esr1)-expressing neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) show line attractor dynamics in male mice during fighting. We hypothesized that these dynamics may encode continuous variation in the intensity of an internal aggressive state. Here, we report that these neurons also show line attractor dynamics in head-fixed mice observing aggression. We exploit this finding to identify and perturb line attractor-contributing neurons using 2-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations demonstrate that integration and persistent activity are intrinsic properties of these neurons which drive the system along the line attractor, while transient off-manifold perturbations reveal rapid relaxation back into the attractor. Furthermore, stimulation and imaging reveal selective functional connectivity among attractor-contributing neurons. Intriguingly, individual differences among mice in line attractor stability were correlated with the degree of functional connectivity among contributing neurons. Mechanistic modelling indicates that dense subnetwork connectivity and slow neurotransmission are required to explain our empirical findings. Our work bridges circuit and manifold paradigms, shedding light on the intrinsic and operational dynamics of a behaviorally relevant mammalian line attractor.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

A Decade of Dedication: Pioneering Perspectives on Neurological Diseases and Mental Illnesses

Welcome to Biomedicines' 10th Anniversary Special Issue, a journey through the human mind's labyrinth and complex neurological pathways [...].

Embryonically Active Piriform Cortex Neurons Promote Intracortical Recurrent Connectivity during Development

Neuronal activity plays a critical role in the maturation of circuits that propagate sensory information into the brain. How widely does early activity regulate circuit maturation across the developing brain? Here, we used Targeted Recombination in Active Populations (TRAP) to perform a brain-wide survey for prenatally active neurons in mice and identified the piriform cortex as an abundantly TRAPed region. Whole-cell recordings in neonatal slices revealed preferential interconnectivity within embryonically TRAPed piriform neurons and their enhanced synaptic connectivity with other piriform neurons. In vivo Neuropixels recordings in neonates demonstrated that embryonically TRAPed piriform neurons exhibit broad functional connectivity within piriform and lead spontaneous synchronized population activity during a transient neonatal period, when recurrent connectivity is strengthening. Selectively activating or silencing of these neurons in neonates enhanced or suppressed recurrent synaptic strength, respectively. Thus, embryonically TRAPed piriform neurons represent an interconnected hub-like population whose activity promotes recurrent connectivity in early development.

Effect of terahertz waves on the aggregation behavior of neurotransmitters

Understanding the dynamics of neurotransmitters is crucial for unraveling synaptic transmission mechanisms in neuroscience. In this study, we investigated the impact of terahertz (THz) waves on the aggregation of four common neurotransmitters through all-atom molecular dynamics (MD) simulations. The simulations revealed enhanced nicotine (NCT) aggregation under 11.05 and 21.44 THz, with a minimal effect at 42.55 THz. Structural analysis further indicated strengthened intermolecular interactions and weakened hydration effects under specific THz stimulation. In addition, enhanced aggregation was observed at stronger field strengths, particularly at 21.44 THz. Furthermore, similar investigations on epinephrine (EPI), 5-hydroxytryptamine (5-HT), and γ-aminobutyric acid (GABA) corroborated these findings. Notably, EPI showed increased aggregation at 19.05 THz, emphasizing the influence of vibrational modes on aggregation. However, 5-HT and GABA, with charged or hydrophilic functional groups, exhibited minimal aggregation under THz stimulation. The present study sheds some light on neurotransmitter responses to THz waves, offering implications for neuroscience and interdisciplinary applications.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Activation of feedforward wiring in adult hippocampal neurons by the basic-helix-loop-helix transcription factor Ascl4

Although evidence indicates that the adult brain retains a considerable capacity for circuit formation, adult wiring has not been broadly considered and remains poorly understood. In this study, we investigate wiring activation in adult neurons. We show that the basic-helix-loop-helix transcription factor Ascl4 can induce wiring in different types of hippocampal neurons of adult mice. The new axons are mainly feedforward and reconfigure synaptic weights in the circuit. Mice with the Ascl4-induced circuits do not display signs of pathology and solve spatial problems equally well as controls. Our results demonstrate reprogrammed connectivity by a single transcriptional factor and provide insights into the regulation of brain wiring in adults.

Evolution of neural circuitry and cognition

Neural circuits govern the interface between the external environment, internal cues and outwardly directed behaviours. To process multiple environmental stimuli and integrate these with internal state requires considerable neural computation. Expansion in neural network size, most readily represented by whole brain size, has historically been linked to behavioural complexity, or the predominance of cognitive behaviours. Yet, it is largely unclear which aspects of circuit variation impact variation in performance. A key question in the field of evolutionary neurobiology is therefore how neural circuits evolve to allow improved behavioural performance or innovation. We discuss this question by first exploring how volumetric changes in brain areas reflect actual neural circuit change. We explore three major axes of neural circuit evolution-replication, restructuring and reconditioning of cells and circuits-and discuss how these could relate to broader phenotypes and behavioural variation. This discussion touches on the relevant uses and limitations of volumetrics, while advocating a more circuit-based view of cognition. We then use this framework to showcase an example from the insect brain, the multi-sensory integration and internal processing that is shared between the mushroom bodies and central complex. We end by identifying future trends in this research area, which promise to advance the field of evolutionary neurobiology.

Synergism between two BLA-to-BNST pathways for appropriate expression of anxiety-like behaviors in male mice

Understanding how distinct functional circuits are coordinated to fine-tune mood and behavior is of fundamental importance. Here, we observe that within the dense projections from basolateral amygdala (BLA) to bed nucleus of stria terminalis (BNST), there are two functionally opposing pathways orchestrated to enable contextually appropriate expression of anxiety-like behaviors in male mice. Specifically, the anterior BLA neurons predominantly innervate the anterodorsal BNST (adBNST), while their posterior counterparts send massive fibers to oval BNST (ovBNST) with moderate to adBNST. Optogenetic activation of the anterior and posterior BLA inputs oppositely regulated the activity of adBNST neurons and anxiety-like behaviors, via disengaging and engaging the inhibitory ovBNST-to-adBNST microcircuit, respectively. Importantly, the two pathways exhibited synchronized but opposite responses to both anxiolytic and anxiogenic stimuli, partially due to their mutual inhibition within BLA and the different inputs they receive. These findings reveal synergistic interactions between two BLA-to-BNST pathways for appropriate anxiety expression with ongoing environmental demands.

Building Uniformly Structured Polymer Memristors via a 2D Conjugation Strategy for Neuromorphic Computing

Polymer memristors represent a highly promising avenue for the advancement of next-generation computing systems. However, the intrinsic structural heterogeneity characteristic of most polymers often results in organic polymer memristors displaying erratic resistive switching phenomena, which in turn lead to diminished production yields and compromised reliability. In this study, a 2D conjugated polymer, named PBDTT-BPQTPA, is synthesized by integrating the coplanar bis(thiophene)-4,8-dihydrobenzo[1,2-b:4,5-b]dithiophene (BDTT) as an electron-donating unit with a quinoxaline derivative serving as an electron-accepting unit. The incorporation of triphenylamine groups at the quinoxaline termini significantly enhances the polymer's conjugation and planarity, thereby facilitating more efficient charge transport. The fabricated polymer memristor with the structure of Al/PBDTT-BPQTPA/ITO exhibits typical non-volatile resistive switching behavior under high voltage conditions, along with history-dependent memristive properties at lower voltages. The unique memristive behavior of the device enables the simulation of synaptic enhancement/inhibition, learning algorithms, and memory operations. Additionally, the memristor demonstrates its capability for executing logical operations and handling decimal calculations. This study offers a promising and innovative approach for the development of artificial neuromorphic computing systems.

Brain topology improved spiking neural network for efficient reinforcement learning of continuous control

The brain topology highly reflects the complex cognitive functions of the biological brain after million-years of evolution. Learning from these biological topologies is a smarter and easier way to achieve brain-like intelligence with features of efficiency, robustness, and flexibility. Here we proposed a brain topology-improved spiking neural network (BT-SNN) for efficient reinforcement learning. First, hundreds of biological topologies are generated and selected as subsets of the Allen mouse brain topology with the help of the Tanimoto hierarchical clustering algorithm, which has been widely used in analyzing key features of the brain connectome. Second, a few biological constraints are used to filter out three key topology candidates, including but not limited to the proportion of node functions (e.g., sensation, memory, and motor types) and network sparsity. Third, the network topology is integrated with the hybrid numerical solver-improved leaky-integrated and fire neurons. Fourth, the algorithm is then tuned with an evolutionary algorithm named adaptive random search instead of backpropagation to guide synaptic modifications without affecting raw key features of the topology. Fifth, under the test of four animal-survival-like RL tasks (i.e., dynamic controlling in Mujoco), the BT-SNN can achieve higher scores than not only counterpart SNN using random topology but also some classical ANNs (i.e., long-short-term memory and multi-layer perception). This result indicates that the research effort of incorporating biological topology and evolutionary learning rules has much in store for the future.

Meta predictive learning model of languages in neural circuits

Large language models based on self-attention mechanisms have achieved astonishing performances, not only in natural language itself, but also in a variety of tasks of different nature. However, regarding processing language, our human brain may not operate using the same principle. Then, a debate is established on the connection between brain computation and artificial self-supervision adopted in large language models. One of most influential hypotheses in brain computation is the predictive coding framework, which proposes to minimize the prediction error by local learning. However, the role of predictive coding and the associated credit assignment in language processing remains unknown. Here, we propose a mean-field learning model within the predictive coding framework, assuming that the synaptic weight of each connection follows a spike and slab distribution, and only the distribution, rather than specific weights, is trained. This meta predictive learning is successfully validated on classifying handwritten digits where pixels are input to the network in sequence, and moreover, on the toy and real language corpus. Our model reveals that most of the connections become deterministic after learning, while the output connections have a higher level of variability. The performance of the resulting network ensemble changes continuously with data load, further improving with more training data, in analogy with the emergent behavior of large language models. Therefore, our model provides a starting point to investigate the connection among brain computation, next-token prediction, and general intelligence.

Diadenosine pentaphosphate regulates dendrite growth and number in cultured hippocampal neurons

During the establishment of neuronal circuits, axons and dendrites grow and branch to establish specific synaptic connections. This complex process is highly regulated by positive and negative extracellular cues guiding the axons and dendrites. Our group was pioneer in describing that one of these signals are the extracellular purines. We found that extracellular ATP, through its selective ionotropic P2X7 receptor (P2X7R), negatively regulates axonal growth and branching. Here, we evaluate if other purinergic compounds, such as the diadenosine pentaphosphate (Ap5A), may module the dynamics of dendritic or axonal growth and branching in cultured hippocampal neurons. Our results show that Ap5A negatively modulates the dendrite's growth and number by inducing transient intracellular calcium increases in the dendrites' growth cone. Interestingly, phenol red, commonly used as a pH indicator in culture media, also blocks the P2X1 receptors, avoided the negative modulation of Ap5A on dendrites. Subsequent pharmacological studies using a battery of selective P2X1R antagonists confirmed the involvement of this subunit. In agreement with pharmacological studies, P2X1R overexpression caused a similar reduction in dendritic length and number as that induced by Ap5A. This effect was reverted when neurons were co-transfected with the vector expressing the interference RNA for P2X1R. Despite small hairpin RNAs reverting the reduction in the number of dendrites caused by Ap5A, it did not avoid the dendritic length decrease induced by the polyphosphate, suggesting, therefore, the involvement of a heteromeric P2X receptor. Our results are indicating that Ap5A exerts a negative influence on dendritic growth.

Role and limitation of cell therapy in treating neurological diseases

The central role of the brain in governing systemic functions within human physiology underscores its paramount significance as the focal point of physiological regulation. The brain, a highly sophisticated organ, orchestrates a diverse array of physiological processes encompassing motor control, sensory perception, cognition, emotion, and the regulation of vital functions, such as heartbeat, respiration, and hormonal equilibrium. A notable attribute of neurological diseases manifests as the depletion of neurons and the occurrence of tissue necrosis subsequent to injury. The transplantation of neural stem cells (NSCs) into the brain exhibits the potential for the replacement of lost neurons and the reconstruction of neural circuits. Furthermore, the transplantation of other types of cells in alternative locations can secrete nutritional factors that indirectly contribute to the restoration of nervous system equilibrium and the mitigation of neural inflammation. This review summarized a comprehensive investigation into the role of NSCs, hematopoietic stem cells, mesenchymal stem cells, and support cells like astrocytes and microglia in alleviating neurological deficits after cell infusion. Moreover, a thorough assessment was undertaken to discuss extant constraints in cellular transplantation therapies, concurrently delineating indispensable model-based methodologies, specifically on organoids, which were essential for guiding prospective research initiatives in this specialized field.

Control of feeding by a bottom-up midbrain-subthalamic pathway

Investigative exploration and foraging leading to food consumption have vital importance, but are not well-understood. Since GABAergic inputs to the lateral and ventrolateral periaqueductal gray (l/vlPAG) control such behaviors, we dissected the role of vgat-expressing GABAergic l/vlPAG cells in exploration, foraging and hunting. Here, we show that in mice vgat l/vlPAG cells encode approach to food and consumption of both live prey and non-prey foods. The activity of these cells is necessary and sufficient for inducing food-seeking leading to subsequent consumption. Activation of vgat l/vlPAG cells produces exploratory foraging and compulsive eating without altering defensive behaviors. Moreover, l/vlPAG vgat cells are bidirectionally interconnected to several feeding, exploration and investigation nodes, including the zona incerta. Remarkably, the vgat l/vlPAG projection to the zona incerta bidirectionally controls approach towards food leading to consumption. These data indicate the PAG is not only a final downstream target of top-down exploration and foraging-related inputs, but that it also influences these behaviors through a bottom-up pathway.

The mechanistic effects of acupuncture in rodent neurodegenerative disease models: a literature review

Neurodegenerative diseases refer to a battery of medical conditions that affect the survival and function of neurons in the brain, which are mainly presented with progressive loss of cognitive and/or motor function. Acupuncture showed benign effects in improving neurological deficits, especially on movement and cognitive function impairment. Here, we reviewed the therapeutic mechanisms of acupuncture at the neural circuit level in movement and cognition disorders, summarizing the influence of acupuncture in the dopaminergic system, glutamatergic system, γ-amino butyric acid-ergic (GABAergic) system, serotonergic system, cholinergic system, and glial cells at the circuit and synaptic levels. These findings can provide targets for clinical treatment and perspectives for further studies.

Neuronal dynamics direct cerebrospinal fluid perfusion and brain clearance

The accumulation of metabolic waste is a leading cause of numerous neurological disorders, yet we still have only limited knowledge of how the brain performs self-cleansing. Here we demonstrate that neural networks synchronize individual action potentials to create large-amplitude, rhythmic and self-perpetuating ionic waves in the interstitial fluid of the brain. These waves are a plausible mechanism to explain the correlated potentiation of the glymphatic flow1,2 through the brain parenchyma. Chemogenetic flattening of these high-energy ionic waves largely impeded cerebrospinal fluid infiltration into and clearance of molecules from the brain parenchyma. Notably, synthesized waves generated through transcranial optogenetic stimulation substantially potentiated cerebrospinal fluid-to-interstitial fluid perfusion. Our study demonstrates that neurons serve as master organizers for brain clearance. This fundamental principle introduces a new theoretical framework for the functioning of macroscopic brain waves.

Subcellular functions of tau mediates repair response and synaptic homeostasis in injury

Injury responses in terminally differentiated cells such as neurons is tightly regulated by pathways aiding homeostatic maintenance. Cancer patients subjected to neuronal injury in brain radiation experience cognitive declines similar to those seen in primary neurodegenerative diseases. Numerous studies have investigated the effect of radiation in proliferating cells of the brain, yet the impact in differentiated, post-mitotic neurons, especially the structural and functional alterations remain largely elusive. We identified that microtubule-associated tau is a critical player in neuronal injury response via compartmentalized functions in both repair-centric and synaptic regulatory pathways. Ionizing radiation-induced injury acutely induces increase in phosphorylated tau in the nucleus and directly interacts with histone 2AX (H2AX), a DNA damage repair (DDR) marker. Loss of tau significantly reduced H2AX after irradiation, indicating that tau may play an important role in neuronal DDR response. We also observed that loss of tau increases eukaryotic elongation factor levels after irradiation, the latter being a positive regulator of protein translation. This cascades into a significant increase in synaptic proteins, resulting in disrupted homeostasis. Consequently, novel object recognition test showed decrease in learning and memory in tau-knockout mice after irradiation, and electroencephalographic activity showed increase in delta and theta band oscillations, often seen in dementia patients. Our findings demonstrate tau's previously undefined, multifunctional role in acute responses to injury, ranging from DDR response in the nucleus to synaptic function within a neuron. Such knowledge is vital to develop therapeutic strategies targeting neuronal injury in cognitive decline for at risk and vulnerable populations.

Bioelectronic Medicine: a multidisciplinary roadmap from biophysics to precision therapies

Bioelectronic Medicine stands as an emerging field that rapidly evolves and offers distinctive clinical benefits, alongside unique challenges. It consists of the modulation of the nervous system by precise delivery of electrical current for the treatment of clinical conditions, such as post-stroke movement recovery or drug-resistant disorders. The unquestionable clinical impact of Bioelectronic Medicine is underscored by the successful translation to humans in the last decades, and the long list of preclinical studies. Given the emergency of accelerating the progress in new neuromodulation treatments (i.e., drug-resistant hypertension, autoimmune and degenerative diseases), collaboration between multiple fields is imperative. This work intends to foster multidisciplinary work and bring together different fields to provide the fundamental basis underlying Bioelectronic Medicine. In this review we will go from the biophysics of the cell membrane, which we consider the inner core of neuromodulation, to patient care. We will discuss the recently discovered mechanism of neurotransmission switching and how it will impact neuromodulation design, and we will provide an update on neuronal and glial basis in health and disease. The advances in biomedical technology have facilitated the collection of large amounts of data, thereby introducing new challenges in data analysis. We will discuss the current approaches and challenges in high throughput data analysis, encompassing big data, networks, artificial intelligence, and internet of things. Emphasis will be placed on understanding the electrochemical properties of neural interfaces, along with the integration of biocompatible and reliable materials and compliance with biomedical regulations for translational applications. Preclinical validation is foundational to the translational process, and we will discuss the critical aspects of such animal studies. Finally, we will focus on the patient point-of-care and challenges in neuromodulation as the ultimate goal of bioelectronic medicine. This review is a call to scientists from different fields to work together with a common endeavor: accelerate the decoding and modulation of the nervous system in a new era of therapeutic possibilities.

Whole-brain spatial organization of hippocampal single-neuron projectomes

Mapping single-neuron projections is essential for understanding brain-wide connectivity and diverse functions of the hippocampus (HIP). Here, we reconstructed 10,100 single-neuron projectomes of mouse HIP and classified 43 projectome subtypes with distinct projection patterns. The number of projection targets and axon-tip distribution depended on the soma location along HIP longitudinal and transverse axes. Many projectome subtypes were enriched in specific HIP subdomains defined by spatial transcriptomic profiles. Furthermore, we delineated comprehensive wiring diagrams for HIP neurons projecting exclusively within the HIP formation (HPF) and for those projecting to both intra- and extra-HPF targets. Bihemispheric projecting neurons generally projected to one pair of homologous targets with ipsilateral preference. These organization principles of single-neuron projectomes provide a structural basis for understanding the function of HIP neurons.

Preparation and Mechano-Functional Characterization of PEGylated Fibrin Hydrogels: Impact of Thrombin Concentration

Three-dimensional (3D) neuronal cultures grown in hydrogels are promising platforms to design brain-like neuronal networks in vitro. However, the optimal properties of such cultures must be tuned to ensure a hydrogel matrix sufficiently porous to promote healthy development but also sufficiently rigid for structural support. Such an optimization is difficult since it implies the exploration of different hydrogel compositions and, at the same time, a functional analysis to validate neuronal culture viability. To advance in this quest, here we present a combination of a rheological protocol and a network-based functional analysis to investigate PEGylated fibrin hydrogel networks with gradually higher stiffness, achieved by increasing the concentration of thrombin. We observed that moderate thrombin concentrations of 10% and 25% in volume shaped healthy networks, although the functional traits depended on the hydrogel stiffness, which was much higher for the latter concentration. Thrombin concentrations of 65% or higher led to networks that did not survive. Our results illustrate the difficulties and limitations in preparing 3D neuronal networks, and stress the importance of combining a mechano-structural characterization of a biomaterial with a functional one.

Zhi-Zi-Hou-Po decoction alleviates depressive-like behavior and promotes hippocampal neurogenesis in chronic unpredictable mild stress induced mice via activating the BDNF/TrkB/CREB pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Zhi-Zi-Hou-Po decoction (ZZHP), a traditional Chinese medicine (TCM) classic recipe, has been extensively applied for the remedy of depression. However, the underlying mechanism of ZZHP hasn't been fully elucidated and it needs to be further clarified.

AIM OF STUDY

The aim of the study is to uncover the mechanisms of ZZHP's effect on depression.

MATERIALS AND METHODS

C57BL/6 mice were employed to establish Chronic Unpredictable Mild Stress (CUMS) models. Behavioral tests were conducted for evaluating the antidepressant effects of ZZHP. Then, the monoamine neurotransmitters in the hippocampus through High Performance Liquid Chromatography Electrochemical Detection (HPLC-ECD) were utilized to assess the effect of ZZHP on the maintenance of monoamine neurotransmitter homeostasis. Immunofluorescence staining and Golgi staining were detected to analyze the effects of ZZHP on neuroplasticity in the hippocampus. Western Blot (WB) was utilized to examine the effects of ZZHP on BDNF/TrkB/CREB pathways. Finally, behavioral tests, WB and immunofluorescence staining were repeated after TrkB receptor antagonist was added to further confirm the underlying mechanism.

RESULTS

Our results shown that ZZHP attenuated depressive-like symptoms in CUMS mice. Moreover, ZZHP remarkably reversed the reduction and maintained the homeostasis of monoamine neurotransmitters in the hippocampus. Simultaneously, ZZHP protected neuronal synaptic plasticity and promoted hippocampal neurogenesis. Furthermore, ZZHP stimulated the BDNF/TrkB/CREB pathway in the hippocampus. The addition of TrkB receptor antagonist inhibited the antidepressant effects of ZZHP, suggesting that ZZHP could not work without triggering the BDNF/TrkB/CREB pathway.

CONCLUSION

This study demonstrates that ZZHP can alleviate depressive-like behavior and promote hippocampal neurogenesis in CUMS mice via activating the BDNF/TrkB/CREB pathway.

Structural and temporal dynamics analysis of neural circuit from 2002 to 2022: A bibliometric analysis

Background

In the pursuit of causal insights into neural circuit functionality, various interventions, including electrical, genetic, and pharmacological approaches, have been applied over recent decades. This study employs a comprehensive bibliometric perspective to explore the field of neural circuits.

Methods

Reviews and articles on neural circuits were obtained from the Web of Science Core Collection (WOSCC) database on Apr. 12, 2023. In this article, co-authorship analysis, co-occurrence analysis, citation analysis, bibliographic analysis, and co-citation analysis were used to clarify the authors, journals, institutions, countries, topics, and internal associations between them.

Results

More than 2000 organizations from 52 different countries published 3975 articles in the field of "neural circuit" were used to analysis. Luo liqun emerged as the most prolific author, and Deisseroth Karl garners the highest co-citations (3643). The Journal of Neuroscience leaded in publications, while Nature toped in citations. Chinese Academy of Science recorded the highest article count institutionally, with Stanford University ranking first with 14,350 citations. Since 2020, neurodynamic, anxiety-related mechanisms, and GABAergic neurons have gained prominence, shaping the trajectory of neural circuitry research.

Conclusions

Our investigation has discerned a paradigmatic reorientation towards neurodynamic processes, anxiety-related mechanisms, and GABAergic neurons within the domain of neural circuit research. This identification intimates a prospective trajectory for the field. In the future, it is imperative for research endeavors to accord priority to the translational application of these discernments, with the aim of materializing tangible clinical solutions.

Bibliometric analysis of hot literature on neural circuit research

Numerous brain diseases have been attributed to abnormalities in the connections of neural circuits. Exploration of neural circuits may give enlightenment in treating some intractable brain diseases. Here, we screened all publications on neural circuits in the Web of Science database from 2007 to 2022 and analyzed the research trends through VOSviewer, CiteSpace, Microsoft Excel 2019, and Origin. The findings revealed a consistent upward trend in research on neural circuits during this period. The United States emerged as the leading contributor, followed by China and Japan. Among the top 10 institutions with the largest number of publications, both the United States and China have a strong presence. Notably, the Chinese Academy of Sciences demonstrated the highest publication output, closely followed by Stanford University. In terms of influential authors, Karl Deisseroth stood out as one of the most prominent investigators. During this period, the majority of publications and citations on neural circuit research were found in highly influential journals including NEURON, NATURE JOURNAL OF NEUROSCIENCE, and so forth. Keyword clustering analysis highlighted the increasing focus on neural circuits and photogenetics in neuroscience research, and the reconstruction of neural circuits has emerged as a crucial research direction in brain science. In conclusion, over the past 15 years, the increasing high-quality publications have facilitated research development of neural circuits, indicating a promising prospect for investigations on neurological and psychiatric diseases.

Single-cell transcriptomics reveals the brain evolution of web-building spiders

Spiders are renowned for their efficient capture of flying insects using intricate aerial webs. How the spider nervous systems evolved to cope with this specialized hunting strategy and various environmental clues in an aerial space remains unknown. Here we report a brain-cell atlas of >30,000 single-cell transcriptomes from a web-building spider (Hylyphantes graminicola). Our analysis revealed the preservation of ancestral neuron types in spiders, including the potential coexistence of noradrenergic and octopaminergic neurons, and many peptidergic neuronal types that are lost in insects. By comparing the genome of two newly sequenced plesiomorphic burrowing spiders with three aerial web-building spiders, we found that the positively selected genes in the ancestral branch of web-building spiders were preferentially expressed (42%) in the brain, especially in the three mushroom body-like neuronal types. By gene enrichment analysis and RNAi experiments, these genes were suggested to be involved in the learning and memory pathway and may influence the spiders' web-building and hunting behaviour. Our results provide key sources for understanding the evolution of behaviour in spiders and reveal how molecular evolution drives neuron innovation and the diversification of associated complex behaviours.

In vivo labeling and quantitative imaging of neuronal populations using MRI

The study of neural circuits, which underlies perception, cognition, emotion, and behavior, is essential for understanding the mammalian brain, a complex organ consisting of billions of neurons. To study the structure and function of the brain, in vivo neuronal labeling and imaging techniques are crucial as they provide true physiological information that ex vivo methods cannot offer. In this paper, we present a new strategy for in vivo neuronal labeling and quantification using MRI. We demonstrate the efficacy of this method by delivering the oatp1a1 gene to the target neurons using rAAV2-retro virus. OATP1A1 protein expression on the neuronal membrane increased the uptake of a specific MRI contrast agent (Gd-EOB-DTPA), leading to hyperintense signals on T1W images of labeled neuronal populations. We also used dynamic contrast enhancement-based methods to obtain quantitative information on labeled neuronal populations in vivo.

Multilevel visual motion opponency in Drosophila

Inhibitory interactions between opponent neuronal pathways constitute a common circuit motif across brain areas and species. However, in most cases, synaptic wiring and biophysical, cellular and network mechanisms generating opponency are unknown. Here, we combine optogenetics, voltage and calcium imaging, connectomics, electrophysiology and modeling to reveal multilevel opponent inhibition in the fly visual system. We uncover a circuit architecture in which a single cell type implements direction-selective, motion-opponent inhibition at all three network levels. This inhibition, mediated by GluClα receptors, is balanced with excitation in strength, despite tenfold fewer synapses. The different opponent network levels constitute a nested, hierarchical structure operating at increasing spatiotemporal scales. Electrophysiology and modeling suggest that distributing this computation over consecutive network levels counteracts a reduction in gain, which would result from integrating large opposing conductances at a single instance. We propose that this neural architecture provides resilience to noise while enabling high selectivity for relevant sensory information.

Voltage-Mode Ferroelectric Synapse for Neuromorphic Computing

Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.

Targeted approaches to delineate neuronal morphology during early development

Understanding the developmental changes that affect neurons is a key step in exploring the assembly and maturation of neural circuits in the brain. For decades, researchers have used a number of labeling techniques to visualize neuronal morphology at different stages of development. However, the efficiency and accuracy of neuronal labeling technologies are limited by the complexity and fragility of neonatal brains. In this review, we illustrate the various labeling techniques utilized for examining the neurogenesis and morphological changes occurring during the early stages of development. We compare the advantages and limitations of each technique from different aspects. Then, we highlight the gaps remaining in our understanding of the structure of neurons in the neonatal mouse brain.

Application of the wholebrain calculation interactive framework to map whole-brain neural connectivity networks

The aim of this work was to develop a simple and feasible method of mapping the neural network topology of the mouse brain. Wild-type C57BL/6 J mice (n = 10) aged 8-10 weeks were injected with the cholera toxin subunit B (CTB) tracer in the anterior (NAcCA) and posterior (NAcCP) parts of the nucleus accumbens (NAc) core and in the medial (NAcSM) and lateral (NAcSL) parts of the NAc shell. The labeled neurons were reconstructed using the WholeBrain Calculation Interactive Framework. The NAcCA receives neuronal projections from the olfactory areas (OLF) and isocortex; the thalamus and isocortex project more fibers to the NAcSL, and the hypothalamus send more fiber projections to the NAcSM. Cell resolution can be automatically annotated, analyzed, and visualized using the WholeBrain Calculation Interactive Framework, making large-scale mapping of mouse brains at cellular and subcellular resolutions easier and more accurate.

Gap Junctions in the Brain: Hardwired but Functionally Versatile

Gap junctions between neurons of the brain are thought to be present in only certain cell types, and they mostly connect dendrites, somata, and axons. Synapses with gap junctions serve bidirectional metabolic and electrical coupling between connected neuronal compartments. Although plasticity of electrical synapses has been described, recent evidence of the presence of silent, but activatable, gap junctions suggests that electrical nodes in a neuronal circuit can be added or suppressed by changes in the synaptic microenvironment. This opens the possibility of reconfiguration of neuronal ensembles in response to activity. Moreover, the coexistence of gap junctions in a glutamatergic synapse may add electric and metabolic coupling to a neuronal aggregate and may serve to constitute primed ensembles within a higher-order neural network. The interaction of chemical with electrical synapses should be further explored to find, especially, emerging properties of neuronal ensembles. It will be worth to reexamine in a new light the "functional" implications of the "anatomic" concepts: "continuity" and "contiguity," which were championed by Golgi and Ramón y Cajal, respectively. In any case, exploring the versatility of the gap junctions will likely enrich the heuristic aspects of the neural and network postulates.

Brain-inspired neural circuit evolution for spiking neural networks

In biological neural systems, different neurons are capable of self-organizing to form different neural circuits for achieving a variety of cognitive functions. However, the current design paradigm of spiking neural networks is based on structures derived from deep learning. Such structures are dominated by feedforward connections without taking into account different types of neurons, which significantly prevent spiking neural networks from realizing their potential on complex tasks. It remains an open challenge to apply the rich dynamical properties of biological neural circuits to model the structure of current spiking neural networks. This paper provides a more biologically plausible evolutionary space by combining feedforward and feedback connections with excitatory and inhibitory neurons. We exploit the local spiking behavior of neurons to adaptively evolve neural circuits such as forward excitation, forward inhibition, feedback inhibition, and lateral inhibition by the local law of spike-timing-dependent plasticity and update the synaptic weights in combination with the global error signals. By using the evolved neural circuits, we construct spiking neural networks for image classification and reinforcement learning tasks. Using the brain-inspired Neural circuit Evolution strategy (NeuEvo) with rich neural circuit types, the evolved spiking neural network greatly enhances capability on perception and reinforcement learning tasks. NeuEvo achieves state-of-the-art performance on CIFAR10, DVS-CIFAR10, DVS-Gesture, and N-Caltech101 datasets and achieves advanced performance on ImageNet. Combined with on-policy and off-policy deep reinforcement learning algorithms, it achieves comparable performance with artificial neural networks. The evolved spiking neural circuits lay the foundation for the evolution of complex networks with functions.

A modular circuit architecture coordinates the diversification of courtship strategies in Drosophila

Identifying a mate is a central imperative for males of most species but poses the challenge of distinguishing a suitable partner from an array of potential male competitors or females of related species. Mate recognition systems are thus subject to strong selective pressures, driving the rapid coevolution of female sensory cues and male sensory preferences. Here we leverage the rapid evolution of female pheromones across the Drosophila genus to gain insight into how males coordinately adapt their detection and interpretation of these chemical cues to hone their mating strategies. While in some Drosophila species females produce unique pheromones that act to attract and arouse their conspecific males, the pheromones of most species are sexually monomorphic such that females possess no distinguishing chemosensory signatures that males can use for mate recognition. By comparing several close and distantly-related Drosophila species, we reveal that D. yakuba males have evolved the distinct ability to use a sexually-monomorphic pheromone, 7-tricosene (7-T), as an excitatory cue to promote courtship, a sensory innovation that enables D. yakuba males to court in the dark thereby expanding their reproductive opportunities. To gain insight into the neural adaptations that enable 7-T to act as an excitatory cue, we compared the functional properties of two key nodes within the pheromone circuits of D. yakuba and a subset of its closest relatives. We show that the instructive role of 7-T in D. yakuba arises from concurrent peripheral and central circuit changes: a distinct subpopulation of sensory neurons has acquired sensitivity to 7-T which in turn selectively signals to a distinct subset of P1 neurons in the central brain that trigger courtship behaviors. Such a modular circuit organization, in which different sensory inputs can independently couple to multiple parallel courtship control nodes, may facilitate the evolution of mate recognition systems by allowing males to take advantage of novel sensory modalities to become aroused. Together, our findings suggest how peripheral and central circuit adaptations can be flexibly linked to underlie the rapid evolution of mate recognition and courtship strategies across species.

Mechanosensitive Piezo1 channel in physiology and pathophysiology of the central nervous system

Since the discovery of the mechanosensitive Piezo1 channel in 2010, there has been a significant amount of research conducted to explore its regulatory role in the physiology and pathology of various organ systems. Recently, a growing body of compelling evidence has emerged linking the activity of the mechanosensitive Piezo1 channel to health and disease of the central nervous system. However, the exact mechanisms underlying these associations remain inadequately comprehended. This review systematically summarizes the current research on the mechanosensitive Piezo1 channel and its implications for central nervous system mechanobiology, retrospects the results demonstrating the regulatory role of the mechanosensitive Piezo1 channel on various cell types within the central nervous system, including neural stem cells, neurons, oligodendrocytes, microglia, astrocytes, and brain endothelial cells. Furthermore, the review discusses the current understanding of the involvement of the Piezo1 channel in central nervous system disorders, such as Alzheimer's disease, multiple sclerosis, glaucoma, stroke, and glioma.

Brain nuclei and neural circuits in neuropathic pain and brain modulation mechanisms of acupuncture: a review on animal-based experimental research

Neuropathic pain (NP) is known to be associated with abnormal changes in specific brain regions, but the complex neural network behind it is vast and complex and lacks a systematic summary. With the help of various animal models of NP, a literature search on NP brain regions and circuits revealed that the related brain nuclei included the periaqueductal gray (PAG), lateral habenula (LHb), medial prefrontal cortex (mPFC), and anterior cingulate cortex (ACC); the related brain circuits included the PAG-LHb and mPFC-ACC. Moreover, acupuncture and injurious information can affect different brain regions and influence brain functions via multiple aspects to play an analgesic role and improve synaptic plasticity by regulating the morphology and structure of brain synapses and the expression of synapse-related proteins; maintain the balance of excitatory and inhibitory neurons by regulating the secretion of glutamate, γ-aminobutyric acid, 5-hydroxytryptamine, and other neurotransmitters and receptors in the brain tissues; inhibit the overactivation of glial cells and reduce the release of pro-inflammatory mediators such as interleukins to reduce neuroinflammation in brain regions; maintain homeostasis of glucose metabolism and regulate the metabolic connections in the brain; and play a role in analgesia through the mediation of signaling pathways and signal transduction molecules. These factors help to deepen the understanding of NP brain circuits and the brain mechanisms of acupuncture analgesia.

Astrocytes adjust the dynamic range of cortical network activity to control modality-specific sensory information processing

Cortical neuron-astrocyte communication in response to peripheral sensory stimulation occurs in a topographic-, frequency-, and intensity-dependent manner. However, the contribution of this functional interaction to the processing of sensory inputs and consequent behavior remains unclear. We investigate the role of astrocytes in sensory information processing at circuit and behavioral levels by monitoring and manipulating astrocytic activity in vivo. We show that astrocytes control the dynamic range of the cortical network activity, optimizing its responsiveness to incoming sensory inputs. The astrocytic modulation of sensory processing contributes to setting the detection threshold for tactile and thermal behavior responses. The mechanism of such astrocytic control is mediated through modulation of inhibitory transmission to adjust the gain and sensitivity of responding networks. These results uncover a role for astrocytes in maintaining the cortical network activity in an optimal range to control behavior associated with specific sensory modalities.

Architectural organization of ∼1,500-neuron modular minicolumnar disinhibitory circuits in healthy and Alzheimer's cortices

Acquisition of neuronal circuit architectures, central to understanding brain function and dysfunction, remains prohibitively challenging. Here I report the development of a simultaneous and sequential octuple-sexdecuple whole-cell patch-clamp recording system that enables architectural reconstruction of complex cortical circuits. The method unveils the canonical layer 1 single bouquet cell (SBC)-led disinhibitory neuronal circuits across the mouse somatosensory, motor, prefrontal, and medial entorhinal cortices. The ∼1,500-neuron modular circuits feature the translaminar, unidirectional, minicolumnar, and independent disinhibition and optimize cortical complexity, subtlety, plasticity, variation, and redundancy. Moreover, architectural reconstruction uncovers age-dependent deficits at SBC-disinhibited synapses in the senescence-accelerated mouse prone 8, an animal model of Alzheimer's disease. The deficits exhibit the characteristic Alzheimer's-like cortical spread and correlation with cognitive impairments. These findings decrypt operations of the elementary processing units in healthy and Alzheimer's mouse cortices and validate the efficacy of octuple-sexdecuple patch-clamp recordings for architectural reconstruction of complex neuronal circuits.