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

项目总结 现在有明确的证据表明，行为和认知表型与 精神障碍是通过对特定大脑回路的扰动来调节的，即强烈的 在解剖上和功能上相连的大脑结构。然而，目前还没有 无偏见的计算方法，以全脑的方式牵连疾病相关回路 在高空间分辨率下，然后将这些电路中的异常连接到特定的 病人的表型。该提案的主要目标是开发和优化一种计算 这将使我们能够第一次以前所未有的决心 发现与精神障碍有关的大脑功能回路。建议的方法是基于 关于遗传学数据、超高分辨率表达和全脑的协同分析 连接组数据-可用于相同的鼠标品系，并在公共坐标系中。一个 该方法的重要优点是它完全基于全基因组和全脑数据 因此，并不偏向于任何关于疾病病因的先前假设。我们特别指出 提出目标1.确定主要受基因影响的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)中的侮辱。我们将发展数据驱动 识别解剖学上相关功能的遗传偏向的计算方法 大脑回路。目的2.在几种ASD小鼠模型上对已识别的电路进行实验测试 和SCZ。通过计算方法确定的功能电路将使用两个 独立的ASD小鼠模型和SCZ的两个模型。赛道的动态将是 使用多点光度成像进行了探索。目标3.与大脑相关的突变偏向 具有特定ASD表型的区域、电路和细胞类型。广泛而深刻地使用 表型人类数据和来自相同患者队列的基因数据，我们将 与多个特定ASD相关的突变偏向脑细胞类型和回路 表型。

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.