Functional connectivity of a brain-scale neural circuit for motion perception

用于运动感知的大脑规模神经回路的功能连接

• 批准号: 10524593
• 负责人: Eva Aimable Naumann
• 金额: $ 193.25万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-17 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10524593
• 关键词: 3-Dimensional; Address; Area; BRAIN initiative; Behavior; Behavioral; Biological Assay; Biological Models; Brain; Brain Diseases; Brain region; Calcium; Cell Nucleus; Cells; Collaborations; Complex; Computer software; Cues; Data; Data Analyses; Development; Distant; Experimental Models; Eye; Frequencies; Genetic; Goals; ImProv; Image; Individual; Interneurons; Knowledge; Link; Locomotion; Maps; Measures; Medial; Methodology; Methods; Mission; Modeling; Molecular; Molecular Profiling; Motion; Motion Perception; Motor; Motor Neurons; Motor output; Movement; Neurons; Neurotransmitters; Opsin; Optics; Output; Pattern; Population; Preparation; Property; Research; Resolution; Rest; Reticular Formation; Role; Shapes; Signal Transduction; Speed; Spinal; Stimulus; Stream; Synapses; System; Tail; Technology; Testing; Time; Training; Update; Visual; Visual Motion; Visualization; Work; Zebrafish; base; brain behavior; computer framework; data streams; experimental study; functional group; in vivo; innovation; insight; molecular marker; neural circuit; neural network architecture; neural patterning; neuropsychiatric disorder; optogenetics; postsynaptic; recurrent neural network; relating to nervous system; response; theories; therapy development; treatment strategy; two photon microscopy; two-photon; visual information; visual motor; visual processing

Abstract The transformation of visual cues into appropriate behavior requires the collaboration of diverse neurons across distant brain areas. A fundamental gap in our knowledge about these visuomotor transformations is understanding how these neurons are functionally connected, shaping neural response dynamics that give rise to behavioral output. This gap is due to the inaccessibility of mammalian model systems, in which simultaneous in vivo observation and manipulations across the brain is impossible as well as a lack of real-time computational frameworks that can capture these dynamics. Here, we plan to investigate the brain-scale functional connectivity underlying the visually guided optomotor response (OMR) in the genetically and optically accessible larval zebrafish. Our previous computational brain-scale models generate concrete predictions for circuit composition and connectivity strength between functional cell classes and behavior but fail to capture the individual neural dynamics of this system. Therefore, to generate realistic dynamic models and test these predictions, we propose leveraging integrated methods combining streaming data analysis, volumetric two-photon microscopy, holographic optogenetic manipulation, and training of multi-regional recurrent neural networks (RNNs). Using patterned photostimulation of single and groups of functionally and molecularly identified neurons, while simultaneously recording activity from other hypothesized downstream neurons, we will infer excitability, sign, and synaptic strength from the network's response. In Aim 1, we will first define neurons both functionally and by their neurotransmitter type across the brain including the pretectum, a conserved visual processing area. In Aim 2, we will train biologically constrained RNNs to predict functional connectivity between these neurons, which we will iteratively test and validate by photostimulating automatically selected neural targets while recording resulting neural activity across the pretectum, orchestrated by our streaming analysis software (improv). Next, we will use these integrated methods to map and model the functional connectivity of pretectal neurons with specific, identifiable premotor spinal projection neurons hypothesized to orchestrate specific behavioral aspects. In Aim 3, we will develop online, gradient-based RNN training of recorded neurons to permit real-time testing and refinement of the predicted brain-wide connectivity leading to behavior in individual zebrafish. These computationally integrated experiments will generate predictive dynamic models of how signals from each eye are transformed into behavior. Together, this research will apply innovative computational and all-optical technologies to decode the temporal neural dynamics underlying complex sensorimotor processing, promising essential insights for the development of treatment strategies for neuropsychiatric disorders that are manifested in the neural connectivity across multiple brain areas.

摘要 将视觉线索转化为适当的行为需要不同神经元的协作。 遥远的大脑区域我们对这些视觉转换的认识存在一个根本性的空白， 了解这些神经元在功能上是如何连接的，塑造神经反应动力学， 到行为输出。这一差距是由于哺乳动物模型系统的不可及性，其中同时 在体内观察和操纵整个大脑是不可能的，以及缺乏实时计算 可以捕捉这些动态的框架。在这里，我们计划研究大脑规模的功能连接， 潜在的视觉引导的optomotor反应（OMR）在遗传和光学可访问的幼虫 斑马鱼我们以前的计算大脑规模模型产生具体的预测电路组成 以及功能细胞类别和行为之间的连接强度，但未能捕获单个神经元 这个系统的动态。因此，为了生成真实的动态模型并测试这些预测，我们建议 利用结合流数据分析，体积双光子显微镜， 全息光遗传学操作和多区域递归神经网络（RNN）的训练。使用 单个和多组功能和分子识别的神经元的模式化光刺激， 同时记录来自其他假设的下游神经元的活动，我们将推断兴奋性，信号， 和突触强度的关系。在目标1中，我们将首先从功能和 通过他们大脑中的神经递质类型，包括前顶盖，一个保守的视觉处理区域。在 目标2，我们将训练生物约束的RNN来预测这些神经元之间的功能连接， 我们将通过光刺激自动选择的神经目标来迭代测试和验证， 通过我们的流分析软件， （即兴）。接下来，我们将使用这些综合方法来绘制和模拟前顶盖的功能连接 神经元与特定的，可识别的运动前脊髓投射神经元假设编排特定的 行为方面。在目标3中，我们将开发记录神经元的在线、基于梯度的RNN训练， 实时测试和改进预测的大脑连接，导致个体行为 斑马鱼这些计算集成的实验将产生预测性的动态模型， 每一只眼睛都被转化为行为。总之，这项研究将应用创新的计算和 全光学技术来解码复杂感觉运动背后的时间神经动力学 处理，有前途的基本见解的发展，治疗策略的神经精神 这些疾病表现在多个大脑区域的神经连接上。