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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：
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
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/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

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.

项目总结