Frontiers in Neural Circuits最新文献

Despite the importance of postsynaptic inhibitory circuitry targeted by mid/long-range projections (e.g., top-down projections) in cognitive functions, its anatomical properties, such as laminar profile and neuron type, are poorly understood owing to the lack of efficient tracing methods. To this end, we developed a method that combines conventional adeno-associated virus (AAV)-mediated transsynaptic tracing with a distal-less homeobox (Dlx) enhancer-restricted expression system to label postsynaptic inhibitory neurons. We called this method "Dlx enhancer-restricted Interneuron-SpECific transsynaptic Tracing" (DISECT). We applied DISECT to a top-down corticocortical circuit from the secondary motor cortex (M2) to the primary somatosensory cortex (S1) in wild-type mice. First, we injected AAV1-Cre into the M2, which enabled Cre recombinase expression in M2-input recipient S1 neurons. Second, we injected AAV1-hDlx-flex-green fluorescent protein (GFP) into the S1 to transduce GFP into the postsynaptic inhibitory neurons in a Cre-dependent manner. We succeeded in exclusively labeling the recipient inhibitory neurons in the S1. Laminar profile analysis of the neurons labeled via DISECT indicated that the M2-input recipient inhibitory neurons were distributed in the superficial and deep layers of the S1. This laminar distribution was aligned with the laminar density of axons projecting from the M2. We further classified the labeled neuron types using immunohistochemistry and in situ hybridization. This post hoc classification revealed that the dominant top-down M2-input recipient neuron types were somatostatin-expressing neurons in the superficial layers and parvalbumin-expressing neurons in the deep layers. These results demonstrate that DISECT enables the investigation of multiple anatomical properties of the postsynaptic inhibitory circuitry.

Symptom provocation is a well-established component of psychiatric research and therapy. It is hypothesized that specific activation of those brain circuits involved in the symptomatic expression of a brain pathology makes the relevant neural substrate accessible as a target for therapeutic interventions. For example, in the treatment of obsessive-compulsive disorder (OCD), symptom provocation is an important part of psychotherapy and is also performed prior to therapeutic brain stimulation with transcranial magnetic stimulation (TMS). Here, we discuss the potential of symptom provocation to isolate neurophysiological biomarkers reflecting the fluctuating activity of relevant brain networks with the goal of subsequently using these markers as targets to guide therapy. We put forward a general experimental framework based on the rapid switching between psychiatric symptom states. This enable neurophysiological measures to be derived from EEG and/or TMS-evoked EEG measures of brain activity during both states. By subtracting the data recorded during the baseline state from that recorded during the provoked state, the resulting contrast would ideally isolate the specific neural circuits differentially activated during the expression of symptoms. A similar approach enables the design of effective classifiers of brain activity from EEG data in Brain-Computer Interfaces (BCI). To obtain reliable contrast data, psychiatric state switching needs to be achieved multiple times during a continuous recording so that slow changes of brain activity affect both conditions equally. This is achieved easily for conditions that can be controlled intentionally, such as motor imagery, attention, or memory retention. With regard to psychiatric symptoms, an increase can often be provoked effectively relatively easily, however, it can be difficult to reliably and rapidly return to a baseline state. Here, we review different approaches to return from a provoked state to a baseline state and how these may be applied to different symptoms occurring in different psychiatric disorders.

The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal's body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species.

Proprioceptive feedback is critically needed for locomotor control, but how this information is incorporated into central proprioceptive processing circuits remains poorly understood. Circuit organization emerges from the spatial distribution of synaptic connections between neurons. This distribution is difficult to discern in model systems where only a few cells can be probed simultaneously. Therefore, we turned to a relatively simple and accessible nervous system to ask: how are proprioceptors' input and output synapses organized in space, and what principles underlie this organization? Using the Drosophila larval connectome, we generated a map of the input and output synapses of 34 proprioceptors in several adjacent body segments (5-6 left-right pairs per segment). We characterized the spatial organization of these synapses, and compared this organization to that of other somatosensory neurons' synapses. We found three distinguishing features of larval proprioceptor synapses: (1) Generally, individual proprioceptor types display segmental somatotopy. (2) Proprioceptor output synapses both converge and diverge in space; they are organized into six spatial domains, each containing a unique set of one or more proprioceptors. Proprioceptors form output synapses along the proximal axonal entry pathway into the neuropil. (3) Proprioceptors receive few inhibitory input synapses. Further, we find that these three features do not apply to other larval somatosensory neurons. Thus, we have generated the most comprehensive map to date of how proprioceptor synapses are centrally organized. This map documents previously undescribed features of proprioceptors, raises questions about underlying developmental mechanisms, and has implications for downstream proprioceptive processing circuits.

The auditory cortex (AC) modulates the activity of upstream pathways in the auditory brainstem via descending (corticofugal) projections. This feedback system plays an important role in the plasticity of the auditory system by shaping response properties of neurons in many subcortical nuclei. The majority of layer (L) 5 corticofugal neurons project to the inferior colliculus (IC). This corticocollicular (CC) pathway is involved in processing of complex sounds, auditory-related learning, and defense behavior. Partly due to their location in deep cortical layers, CC neuron population activity patterns within neuronal AC ensembles remain poorly understood. We employed two-photon imaging to record the activity of hundreds of L5 neurons in anesthetized as well as awake animals. CC neurons are broader tuned than other L5 pyramidal neurons and display weaker topographic order in core AC subfields. Network activity analyses revealed stronger clusters of CC neurons compared to non-CC neurons, which respond more reliable and integrate information over larger distances. However, results obtained from secondary auditory cortex (A2) differed considerably. Here CC neurons displayed similar or higher topography, depending on the subset of neurons analyzed. Furthermore, specifically in A2, CC activity clusters formed in response to complex sounds were spatially more restricted compared to other L5 neurons. Our findings indicate distinct network mechanism of CC neurons in analyzing sound properties with pronounced subfield differences, demonstrating that the topography of sound-evoked responses within AC is neuron-type dependent.

Action selection is a capital feature of cognition that guides behavior in processes that range from motor patterns to executive functions. Here, the ongoing actions need to be monitored and adjusted in response to sensory stimuli to increase the chances of reaching the goal. As higher hierarchical processes, these functions rely on complex neural circuits, and connective loops found within the brain and the spinal cord. Successful execution of motor behaviors depends, first, on proper selection of actions, and second, on implementation of motor commands. Thus, pathological conditions crucially affecting the integrity and preservation of these circuits and their connectivity will heavily impact goal-oriented motor behaviors. Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are two neurodegenerative disorders known to share disease etiology and pathophysiology. New evidence in the field of ALS-FTD has shown degeneration of specific neural circuits and alterations in synaptic connectivity, contributing to neuronal degeneration, which leads to the impairment of motor commands and executive functions. This evidence is based on studies performed on animal models of disease, post-mortem tissue, and patient derived stem cells. In the present work, we review the existing evidence supporting pathological loss of connectivity and selective impairment of neural circuits in ALS and FTD, two diseases which share strong genetic causes and impairment in motor and executive functions.

We present a deep network-based model of the associative memory functions of the hippocampus. The proposed network architecture has two key modules: (1) an autoencoder module which represents the forward and backward projections of the cortico-hippocampal projections and (2) a module that computes familiarity of the stimulus and implements hill-climbing over the familiarity which represents the dynamics of the loops within the hippocampus. The proposed network is used in two simulation studies. In the first part of the study, the network is used to simulate image pattern completion by autoassociation under normal conditions. In the second part of the study, the proposed network is extended to a heteroassociative memory and is used to simulate picture naming task in normal and Alzheimer's disease (AD) conditions. The network is trained on pictures and names of digits from 0 to 9. The encoder layer of the network is partly damaged to simulate AD conditions. As in case of AD patients, under moderate damage condition, the network recalls superordinate words ("odd" instead of "nine"). Under severe damage conditions, the network shows a null response ("I don't know"). Neurobiological plausibility of the model is extensively discussed.

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

The anterior cingulate cortex (ACC) plays a crucial role in encoding, consolidating and retrieving memories related to emotionally salient experiences, such as aversive and rewarding events. Various studies have highlighted its importance for fear memory processing, but its circuit mechanisms are still poorly understood. Cortical layer 1 (L1) of the ACC might be a particularly important site of signal integration, since it is a major entry point for long-range inputs, which is tightly controlled by local inhibition. Many L1 interneurons express the ionotropic serotonin receptor 3a (5HT3aR), which has been implicated in post-traumatic stress disorder and in models of anxiety. Hence, unraveling the response dynamics of L1 interneurons and subtypes thereof during fear memory processing may provide important insights into the microcircuit organization regulating this process. Here, using 2-photon laser scanning microscopy of genetically encoded calcium indicators through microprisms in awake mice, we longitudinally monitored over days the activity of L1 interneurons in the ACC in a tone-cued fear conditioning paradigm. We observed that tones elicited responses in a substantial fraction of the imaged neurons, which were significantly modulated in a bidirectional manner after the tone was associated to an aversive stimulus. A subpopulation of these neurons, the neurogliaform cells (NGCs), displayed a net increase in tone-evoked responses following fear conditioning. Together, these results suggest that different subpopulations of L1 interneurons may exert distinct functions in the ACC circuitry regulating fear learning and memory.

This review presents a broad perspective of the Neuroscience of our days with special attention to how the brain generates our behaviors, emotions, and mental states. It describes in detail how unconscious and conscious processing of sensorimotor and mental information takes place in our brains. Likewise, classic and recent experiments illustrating the neuroscientific foundations regarding the behavioral and cognitive abilities of animals and, in particular, of human beings are described. Special attention is applied to the description of the different neural regulatory systems dealing with behavioral, cognitive, and emotional functions. Finally, the brain process for decision-making, and its relationship with individual free will and responsibility, are also described.