Frontiers in Neural Circuits最新文献

Descending neural pathways to the spinal cord plays vital roles in motor control. They are often damaged by brain injuries such as stroke and trauma, which lead to severe motor impairments. Due to the limited capacity for regeneration of neural circuits in the adult central nervous system, currently no essential treatments are available for complete recovery. Notably, accumulating evidence shows that residual circuits of the descending pathways are dynamically reorganized after injury and contribute to motor recovery. Furthermore, recent technological advances in cell-type classification and manipulation have highlighted the structural and functional diversity of these pathways. Here, we focus on three major descending pathways, namely, the corticospinal tract from the cerebral cortex, the rubrospinal tract from the red nucleus, and the reticulospinal tract from the reticular formation, and summarize the current knowledge of their structures and functions, especially in rodent models (mice and rats). We then review and discuss the process and patterns of reorganization induced in these pathways following injury, which compensate for lost connections for recovery. Understanding the basic structural and functional properties of each descending pathway and the principles of the induction and outcome of the rewired circuits will provide therapeutic insights to enhance interactive rewiring of the multiple descending pathways for motor recovery.

Functionally mature neural circuits are shaped during postnatal development by eliminating redundant synapses formed around birth. This process is known as synapse elimination and requires a proper balance of excitation and inhibition. Neuroligin-2 (NL2) is a postsynaptic cell adhesion molecule required for the formation, maintenance, and function of inhibitory synapses. However, how NL2 regulates synapse elimination during postnatal development is largely unknown. Here we report that the deletion of NL2 from Purkinje cells (PCs) in the cerebellum impairs the developmental elimination of redundant climbing fiber (CF) to PC synapses. In global NL2-knockout (KO) mice, GABAergic inhibition to PCs was attenuated and CF synapse elimination was impaired after postnatal day 10 (P10). These phenotypes were restored by the expression of NL2 into PCs of NL2-KO mice. Moreover, microRNA-mediated knockdown of NL2 specifically from PCs during development caused attenuated inhibition and impaired CF synapse elimination. In PCs innervated by "strong" and "weak" CFs, calcium transients elicited by "weak" CFs were enhanced in NL2-deficient PCs, suggesting that excess calcium signaling permits the survival of redundant "weak" CF synapses. We conclude that NL2 is crucial for maintaining inhibitory synaptic function and properly eliminating redundant CF synapses during postnatal development.

The spatiotemporal dynamics of resting-state brain activity can be characterized by switching between multiple brain states, and numerous techniques have been developed to extract such dynamic features from resting-state functional magnetic resonance imaging (fMRI) data. However, many of these techniques are based on momentary temporal correlation and co-activation patterns and merely reflect linear features of the data, suggesting that the dynamic features, such as state-switching, extracted by these techniques may be misinterpreted. To examine whether such misinterpretations occur when using techniques that are not based on momentary temporal correlation or co-activation patterns, we addressed Energy Landscape Analysis (ELA) based on pairwise-maximum entropy model (PMEM), a statistical physics-inspired method that was designed to extract multiple brain states and dynamics of resting-state fMRI data. We found that the shape of the energy landscape and the first-order transition probability derived from ELA were similar between real data and surrogate data suggesting that these features were largely accounted for by stationary and linear properties of the real data without requiring state-switching among locally stable states. To confirm that surrogate data were distinct from the real data, we replicated a previous finding that some topological properties of resting-state fMRI data differed between the real and surrogate data. Overall, we found that linear models largely reproduced the first order ELA-derived features (i.e., energy landscape and transition probability) with some notable differences.

Neurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of such sparse connectivity for a network's function? Surprisingly, it has been shown that sparse connectivity impairs information processing in artificial neural networks (ANNs). Does this imply that sparse connectivity also impairs information processing in biological neural networks? Although ANNs were originally inspired by the brain, conventional ANNs differ substantially in their structural network architecture from cortical networks. To disentangle the relevance of these structural properties for information processing in networks, we systematically constructed ANNs constrained by interpretable features of cortical networks. We find that in large and recurrently connected networks, as are found in the cortex, sparse connectivity facilitates time- and data-efficient information processing. We explore the origins of these surprising findings and show that conventional dense ANNs distribute information across only a very small fraction of nodes, whereas sparse ANNs distribute information across more nodes. We show that sparsity is most critical in networks with fixed excitatory and inhibitory nodes, mirroring neuronal cell types in cortex. This constraint causes a large learning delay in densely connected networks which is eliminated by sparse connectivity. Taken together, our findings show that sparse connectivity enables efficient information processing given key constraints from cortical networks, setting the stage for further investigation into higher-order features of cortical connectivity.

The prefrontal cortex (PFC) plays a key role in attention. In particular, neuronal activity in the ventromedial PFC (vmPFC) has been implicated in the preparatory attentional period that immediately precedes cue presentation. However, whether vmPFC neuronal activity during this preparatory period is also sensitive to changes in task demand and to the pro-cognitive effects of nicotine remained to be investigated. Here, we used in vivo electrophysiology to record vmPFC neuronal activity in rats during two distinct manipulations: a task manipulation that increased task demand by reducing the cue stimulus duration (from 1 to 0.5 s), and a pharmacological manipulation by administrating an acute nicotine injection (10 μg/inj, i.v.) before the session. We found that increasing task demand decreased attentional performances and vmPFC precue neuronal activity, but had no effect on gamma oscillations. In contrast, nicotine injection increased attention and gamma oscillations, but almost abolished vmPFC phasic precue responses. Together, these findings indicate the existence of two distinct neuronal processes operating at different timescales and suggests that allocation of attention could be achieved through multiple neuronal mechanisms within the vmPFC.

Experience-dependent neural plasticity enables the brain to adapt to diverse and dynamic environments by reshaping circuits. In the adult visual system, this plasticity can be elicited by repeated sensory stimuli; however, its temporal dynamics and underlying mechanisms remain unclear. Here, we investigated the regulation of visual response potentiation induced by repeated light flashes in the primary visual cortex of awake adult mice. Our findings revealed two distinct temporal phases of potentiation: a rapid phase occurring within seconds and a cumulative phase developing over hours to days. Notably, the identification of this rapid phase phenomenon adds to and refines the prevailing view that visual plasticity in the adult cortex is predominantly slow. Additionally, exposure to visual stimuli enhanced spontaneous slow-wave activity in the visual cortex during non-REM sleep. This plasticity was significantly impaired in Grin2a (NR2A) knockout mice, a model of schizophrenia, which mirrors visual plasticity deficits observed in human patients. The dual temporal characteristics of flash-evoked visual plasticity likely reflect multifaceted aspects of adult brain functionality, encompassing processes related to memory, learning, and neurological disorders. This model of visual plasticity in defined neural circuits provides a simplified yet robust and extensible framework for exploring the neural mechanisms underlying adaptive and maladaptive behavioral changes.

Optogenetics and chemogenetics are emerging neuromodulation techniques that have attracted significant attention in recent years. These techniques enable the precise control of specific neuronal types and neural circuits, allowing researchers to investigate the cellular mechanisms underlying depression. The advancement in these techniques has significantly contributed to the understanding of the neural circuits involved in depression; when combined with other emerging technologies, they provide novel therapeutic targets and diagnostic tools for the clinical treatment of depression. Additionally, these techniques have provided theoretical support for the development of novel antidepressants. This review primarily focuses on the application of optogenetics and chemogenetics in several brain regions closely associated with depressive-like behaviors in rodent models, such as the ventral tegmental area, nucleus accumbens, prefrontal cortex, hippocampus, dorsal raphe nucleus, and lateral habenula and discusses the potential and challenges of optogenetics and chemogenetics in future research. Furthermore, this review discusses the potential and challenges these techniques pose for future research and describes the current state of research on sonogenetics and odourgenetics developed based on optogenetics and chemogenetics. Specifically, this study aimed to provide reliable insights and directions for future research on the role of optogenetics and chemogenetics in the neural circuits of depressive rodent models.

There is evidence that stress factors and negative experiences in early in life may affect brain development leading to mental disorders in adulthood. At the early stage of postnatal ontogenesis, the central nervous system has high plasticity, which decreases with maturation. Most likely, this high plasticity is necessary for establishing synaptic connections between different types of neurons, regulating the strength of individual synapses, and ultimately forming properly functioning neuronal networks. The vast majority of studies have examined the effects of early-life stress (ELS) on gene expression or behavior and memory. However, the impact of ELS on functional synaptic development and on the plastic properties of excitatory and inhibitory synapses are currently much less understood. Based on data obtained in a few studies it has been suggested that ELS reduces long-term potentiation (LTP) at Schaffer collateral to CA1 pyramidal cell synapses in adulthood. Nevertheless, different groups have reported somewhat contradictory results. In this report we show that ELS differentially affects LTP at CA3 to CA1 pyramidal cell inputs, at synapses on apical dendrites LTP is reduced, while LTP at synapses formed by CA3 pyramidal cells on basal dendrites remains unaffected.