Discovery and analysis of brain circuits and cell types affected in autism and schizophrenia

发现和分析受自闭症和精神分裂症影响的大脑回路和细胞类型

• 批准号: 10100970
• 负责人: JOSEPH A GOGOS
• 金额: $ 80.32万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10100970
• 关键词: Affect; Amaze; American; Anatomy; Behavioral; Biological; Biological Models; Brain; Brain imaging; Brain region; Cognitive; Collaborations; Collection; Computer Analysis; Computing Methodologies; Data; Development; Disease; Distant; Dreams; Economics; Etiology; Fiber; Foundations; Genetic; Genome; Genotype; Goals; Grant; Human; Image; Institution; Knowledge; Large-Scale Sequencing; Mediating; Mental disorders; Modeling; Mouse Strains; Mus; Mutation; Neurons; Neurosciences; Patients; Phenotype; Photometry; Publications; Recording of previous events; Research; Resolution; Schizophrenia; Sensory; Sex Differences; Site; Societies; Source; Structure; System; Systems Biology; Testing; Time; Universities; Validation; Variant; autism spectrum disorder; base; brain cell; cell type; cohort; connectome data; genetic analysis; genetic variant; human data; insight; mouse model; neuropsychiatric disorder; prevent; psychogenetics; single-cell RNA sequencing; skills; social; ultra high resolution

PROJECT SUMMARY There is now unequivocal evidence that the behavioral and cognitive phenotypes associated with psychiatric disorders are mediated by perturbations to specific brain circuits, i.e. sets of strongly anatomically and functionally connected brain structures. However, there are currently no unbiased computational approaches to implicate disease-related circuits, in a brain-wide fashion and at a high spatial resolution, and then to connect abnormalities in these circuits to specific patient phenotypes. The main goal of the proposal is to develop and optimize a computational approach which will make it possible, for the first time and at an unprecedented resolution, to discover functional brain circuits involved in mental disorders. The proposed approach is based on synergistic analyses of genetics data, ultra-high-resolution expression and brain-wide connectome data – available for the same mouse strain, and in a common coordinate system. An important virtue of the approach is that it is based exclusively on genome- and brain-wide data and therefore is not biased towards any prior hypothesis about disorders' etiology. We specifically propose Aim 1. Identify brain circuits and associated cell types primarily affected by genetic insults in autism spectrum disorder (ASD) and schizophrenia (SCZ). We will develop data-driven computational approaches to identify genetic biases towards anatomically connected functional brain circuits. Aim 2. Experimentally test the identified circuits in several mouse models of ASD and SCZ. Functional circuits identified by the computational approach will be tested using two independent mouse models of ASD and two models of SCZ. The dynamics of the circuits will be explored using multi-site photometric imaging. Aim 3. Correlate mutation biases towards brain regions, circuits, and cell types with specific ASD phenotypes. Using extensive and deep phenotypic human data together with genetic data from the same patient cohorts, we will correlate mutation biases towards brain cell types and circuits with multiple specific ASD phenotypes.

项目摘要 现在有明确的证据表明，行为和认知表型与 精神疾病是通过对特定脑电路的扰动来介导的，即强烈 在解剖和功能上连接的大脑结构。但是，目前没有 公正的计算方法，以脑部的方式暗示与疾病相关的电路 并以高空间分辨率，然后将这些电路中的异常连接到特定 患者表型。该提案的主要目标是开发和优化计算 这将使这可能是第一次，并以前所未有的决议， 发现参与精神障碍的功能性脑回路。提出的方法是基于 关于遗传学数据的协同分析，超高分辨率表达和大脑 Connectome数据 - 可用于相同的鼠标应变和公共坐标系。一个 该方法的重要优点是它仅基于基因组和大脑数据 因此，对疾病病因的任何先前的假设都没有偏见。我们具体 提案目标1。确定主要受遗传影响的脑电路和相关细胞类型 自闭症谱系障碍（ASD）和精神分裂症（SCZ）的侮辱。我们将开发数据驱动 计算方法以识别针对解剖连接功能的遗传偏见 脑电路。 AIM 2。在几种ASD的小鼠模型中实验测试已识别的电路 和SCZ。计算方法确定的功能电路将使用两个测试 独立的ASD小鼠模型和两个SCZ模型。电路的动态将是 使用多站点光度成像进行探索。目标3。相关突变偏见与大脑 具有特定ASD表型的区域，电路和细胞类型。使用广泛而深 表型人类数据以及来自同一患者队列的遗传数据，我们将 与多个特定ASD的脑细胞类型和电路相关的突变偏置 表型。