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

基本信息

批准号：
10264058
负责人：
JOSEPH A GOGOS
金额：
$ 76.05万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
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/10264058
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

项目摘要

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.

项目概要现在有明确的证据表明，行为和认知表型与精神疾病是由特定脑回路的扰动介导的，即一组强然而，目前还没有在解剖学和功能上相互连接的大脑结构。以全脑方式暗示疾病相关回路的无偏见计算方法并以高空间分辨率，然后将这些电路中的异常与特定的该提案的主要目标是开发和优化计算模型。这种方法将首次以前所未有的决心，发现与精神障碍有关的功能性大脑回路。遗传学数据、超高分辨率表达和全脑的协同分析连接组数据——可用于相同的小鼠品系，并在一个共同的坐标系中。该方法的重要优点是它完全基于基因组和大脑范围的数据因此，我们特别不偏向任何有关疾病病因学的先前假设。目标 1. 识别主要受遗传影响的大脑回路和相关细胞类型我们将开发数据驱动的治疗方法。识别对解剖学相关功能的遗传偏差的计算方法目标 2. 在几种 ASD 小鼠模型中对已识别的脑回路进行实验测试。通过计算方法确定的功能电路将使用两个进行测试。 ASD 的独立小鼠模型和 SCZ 的两个模型的电路动力学将是。使用多位点光度成像进行探索。目标 3：将突变偏差与大脑相关联。使用广泛和深入的方法来识别具有特定 ASD 表型的区域、回路和细胞类型。人类表型数据以及来自同一患者群体的遗传数据，我们将突变将脑细胞类型和回路的偏见与多种特定的自闭症谱系障碍相关联表型。