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，行为和认知表型相关的 精神障碍是由特定脑回路的扰动介导的，即一组强烈的 解剖学上和功能上连接的大脑结构。然而，目前没有 无偏见的计算方法，以全脑的方式， 并以高空间分辨率，然后将这些回路中的异常连接到特定的 患者表型。该提案的主要目标是开发和优化一个计算 这一办法将使人们有可能，第一次，并以前所未有的决议， 发现与精神障碍有关的功能性脑回路。该方法基于 对遗传学数据、超高分辨率表达和全脑 连接体数据-可用于相同的小鼠品系，并在一个共同的坐标系。一个 该方法的一个重要优点是它完全基于基因组和大脑范围的数据 因此不偏向于任何关于疾病病因学的先前假设。我们特别 提出目标1。识别主要受遗传影响的大脑回路和相关细胞类型 自闭症谱系障碍（ASD）和精神分裂症（SCZ）的损伤。我们将开发数据驱动 计算方法，以确定对解剖学上连接的功能性遗传偏见 大脑回路目标二。在几种ASD小鼠模型中对识别的电路进行实验测试 和SCZ。通过计算方法识别的功能电路将使用两个 独立的ASD小鼠模型和两种SCZ模型。电路的动力学将是 使用多站点光度成像进行探索。目标3。相关突变偏向大脑 区域、回路和具有特定ASD表型的细胞类型。广泛而深入地利用 表型人类数据与来自相同患者队列的遗传数据一起，我们将 将对脑细胞类型和回路的突变偏好与多个特定ASD相关联 表型