Electrical connectomics: an innovative approach for dissecting brain mechanisms underlying behavioral states

项目概要： 了解大脑编码行为的机制是一个重大挑战 开发治疗神经和精神疾病的有效疗法。的 哺乳动物模型中神经连接的全脑测量的发展取得了进展 克服这一挑战的巨大潜力。在这里，我们提出了一种创新方法 在数量空前的互联大脑中收集和整合此类数据 用于阐明疾病中感觉处理改变的机制的区域。 许多神经和精神疾病都是由感觉引发或加剧的 刺激，但人们对这种感觉高/低的大脑连接性知之甚少。 敏化。感觉处理在以下病理学中起着重要作用： 自闭症谱系 疾病（ASD）、精神分裂症、纤维肌痛、注意力缺陷多动障碍（ADHD）、 感觉知觉障碍（SPD）和偏头痛。偏头痛尤其代表 令人信服的感觉过敏模型，因为对感觉刺激的反应是明确的，剂量- 依赖的、可测量的。在良好的环境中使用最先进的多部位体内记录 特征化的偏头痛模型，加上机器学习，我们会开发全网的 偏头痛背后的感觉超敏性电图。这些网络将 使用多种行为分析和偏头痛相关的方法验证了它们在偏头痛中的作用 药理操作。我们还将剖析其背后的机制 使用光遗传学电路研究偏头痛小鼠模型中的感觉超敏大脑状态 操作以及单细胞 RNA-Seq，目的是确定 特定的电路、细胞和分子达到这种状态。这种方法预计将大大 促进基于神经振荡的大脑网络在生物医学研究中的使用，以及 提供：1）一种快速识别可测试的感觉超敏大脑状态的工具 对于跨疾病共享的机制，2）脑电网络特征图，其中 作为关于感觉超敏大脑网络路径的强有力的假设 受到监管，3）深入了解特定细胞类型和分子对 大脑超敏感状态。总的来说，这项研究有望提供对病因学的见解 偏头痛和其他感觉过敏性疾病对大脑发育至关重要 针对这些疾病的基于网络的疗法。

Project Summary: Understanding the mechanisms by which the brain encodes behavior presents a major challenge for developing effective therapies with which to treat neurological and psychiatric disorders. The development of brain-wide measurements of neural connectivity in mammalian models holds great potential for overcoming this challenge. Here we propose an innovative approach for collecting and integrating such data across an unprecedented number of interconnected brain regions for use in elucidating the mechanisms by which sensory processing is altered in disease. A number of neurological and psychiatric disorders are triggered or exacerbated by sensory stimuli, yet little is understood about the brain connectivity underlying such sensory hyper/hypo- sensitization. Sensory processing plays a major role in the pathology of: autism spectrum disorders (ASD), schizophrenia, fibromyalgia, attention deficit hyperactivity disorder (ADHD), sensory perception disorders (SPDs), and migraine. Migraine in particular represents a compelling model of sensory hypersensitization, as the response to sensory stimuli is clear, dose- dependent, and measurable. Using state-of-the-art, multi-site in vivo recordings in a well- characterized migraine model, coupled with machine learning, we will develop network-wide electrical maps of the sensory hypersensitivity that underlies migraine. These networks will be validated for their roles in migraine using multiple behavioral assays and migraine-related pharmacological manipulations. We will additionally dissect the mechanisms underlying the sensory hypersensitivity brain state in a mouse model of migraine using optogenetic circuit manipulations as well as single-cell RNA-Seq, with the aim of identifying the contributions of specific circuits, cells, and molecules to this state. This approach is expected to substantially facilitate the use of neural oscillation-based brain networks in biomedical research, as well as provide: 1) a tool for rapid identification of a sensory hypersensitive brain state that can be tested for mechanisms shared across disorders, 2) a map of features of electrical brain networks, which serve as strong hypotheses regarding the routes whereby sensory hypersensitivity brain networks are regulated, and 3) insight into the contributions of specific cell types and molecules to the hypersensitive brain state. Collectively, this study is expected to provide insights into the etiology of migraine and other sensory hypersensitivity disorders that will be critical to developing brain network-based therapies for these diseases.