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

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.