3D Digital Modeling of the Developing Drosophila Brain

发育中的果蝇大脑的 3D 数字建模

• 批准号: 8417738
• 负责人: VOLKER HARTENSTEIN
• 金额: $ 31.86万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-01 至 2015-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8417738
• 关键词: Address; Adult; Animals; Atlases; Autistic Disorder; Axon; Behavior Control; Bioinformatics; Biological Neural Networks; Brain; Brain Part; Caliber; Cognition; Communities; Complex; Computer software; Data; Data Set; Dendrites; Development; Disease; Drosophila genus; Electron Microscopy; Electronics; Elements; Embryo; Exhibits; Fascicle; Funding; Genes; Genetic; Grant; Image; Individual; Learning; Light; Maps; Memory; Mental disorders; Modeling; Mushroom Bodies; Neurites; Neurobiology; Neurons; Pharmacological Treatment; Population; Prefrontal Cortex; Process; Relative (related person); Research; Schizophrenia; Staging; Stereotyping; Surface; Synapses; Technology; Testing; Time; Vertebral column; area striata; base; data mining; digital; digital models; improved; insight; interest; knowledge base; motor control; mutant; nerve stem cell; neural circuit; neuroblast; neuronal cell body; public health relevance; reconstruction; relating to nervous system; software development; tool

DESCRIPTION (provided by applicant): Brains contain large number of neurons whose connections, formed by axonal and dendritic processes, are the structural underpinning of electrical circuits that control behavior. The analysis of circuits is of great importance. All acts of fine motor control, memory formation and cognition can only be understood if the circuitry within the brain compartments dealing with these functions is known. Likewise, the insight into psychiatric disease mechanisms and their pharmacological treatment requires brain circuitry to be known in detail. For example, recent findings suggest that diseases like autism or schizophrenia can be understood in terms of abnormalities in the micro- circuitry of the prefrontal cortex. We propose in this grant to develop and utilize bioinformatics tools that enable us to address circuitry in the Drosophila brain. The Drosophila central brain is formed by a stereotyped set of approximately 100 paired lineages, each one derived from one neuroblast. Neurons of one lineage form processes that spread within discrete compartments of the brain. Lineages thereby represent the most appropriate structural/developmental units of brain macro-circuitry. Reconstructing the projection of all lineages means to have generated an accurate map of Drosophila brain circuitry at the level of neuron populations ("macro-circuitry"). We propose to generate this map, presented in a standardized electronic format that is accessible to the neurobiology community. In addition, we will reconstruct circuitry at the level of individual synapses ("micro-circuitry"), which requires electron microscopy (EM). We have developed the software required for the automated recording, registration and navigation of large EM image data sets. We will further improve and use these tools to generate a digital EM dataset that, for the first time, encompasses the entire brain of an animal with a sizeable number of structurally complex neurons. Our software allows us to efficiently reconstruct the neural networks encountered in different parts of the brain. We anticipate that we will be able to learn structural principles about neural network that have general application.

描述（由申请人提供）：大脑包含大量神经元，其连接由轴突和树突过程形成，是控制行为的电路的结构基础。 电路分析是非常重要的。 只有知道了处理这些功能的脑区中的回路，才能理解精细运动控制、记忆形成和认知的所有行为。 同样，深入了解精神疾病的机制及其药物治疗需要详细了解大脑回路。 例如，最近的研究结果表明，像自闭症或精神分裂症这样的疾病可以用前额叶皮层微电路的异常来理解。 我们在这项资助中提出开发和利用生物信息学工具，使我们能够解决果蝇大脑中的电路。 果蝇的中央脑是由大约100对谱系的定型集合形成的，每一对谱系都来自一个神经母细胞。 一个谱系的神经元形成在大脑的离散隔室中扩散的过程。 因此，谱系代表了大脑宏观回路最合适的结构/发育单位。 重建所有谱系的投影意味着在神经元群体水平上生成果蝇脑回路的准确地图（“宏回路”）。 我们建议生成这张地图，以标准化的电子格式呈现，神经生物学社区可以访问。 此外，我们将在单个突触的水平上重建电路（“微电路”），这需要电子显微镜（EM）。 我们已经开发了大型EM图像数据集的自动记录，配准和导航所需的软件。 我们将进一步改进和使用这些工具来生成数字EM数据集，该数据集首次涵盖了具有大量结构复杂神经元的动物的整个大脑。 我们的软件使我们能够有效地重建大脑不同部位的神经网络。 我们预计，我们将能够学习具有普遍应用的神经网络的结构原理。