This proposal responds to an NIMH notice NOT-MH-18-036 aimed at the development and study of novel, computationally defined behavioral assays, and at applying theory and mathematical modeling to better capture the richness of complex, naturalistic behaviors. Specifically, we aim to develop novel computational tools for analyzing social behaviors in freely moving mice, and relating those identified behaviors to neural circuit activity in brain regions that govern the expression of those behaviors. Social behavior is affected in many human psychiatric disorders, such as autism, schizophrenia, and depression. We propose an interdisciplinary, collaborative approach to fill two major gaps that present a barrier to studies of social behavior: 1) the lack of quantitative and high-resolution descriptions of naturalistic social behaviors in freely moving animals, and 2) the difficulty of relating neural activity recorded in deep subcortical regions that govern such behaviors, such as the hypothalamus and extended amygdala, to animals' actions or to models of behavioral control. Our objective is to create a computational behavior analysis platform that integrates automated measurement of naturalistic social behavior, synchronous large-scale recording or imaging of neural activity, and apply these to a novel assay to investigate social behavioral decision-making. The central objective of this proposal is to extend our Mouse Action Recognition System (MARS) to create a platform that allows facile training of supervised and unsupervised behavior classifiers, quantitative correlation with simultaneously acquired neural recording or imaging data, and which can be flexibly adapted to additional behavior assays. The rationale for this approach is that fine-grained quantification of social behavior, and its correlation with neural recordings, is necessary to form and test theories of behavioral control by subcortical brain regions. While automated tracking and ?pose? estimation software such as DeepLabCut have made tracking of animals' body positions more feasible, the identification of social behaviors from pose data is a non-trivial problem, requiring a separate computational approach that takes into account the relative movements of multiple animals over time. To achieve our objective, we will broaden the palette of social behaviors MARS can detect using machine learning and generative models (Aim 1), develop methods to relate those behaviors to neural activity (Aim 2), and extend MARS to additional assays to study neural correlates of social decision-making. This contribution is significant because it will create a resource that will transform our ability to study micro- and meso-scale subcortical circuits controlling social behavior. The contribution is innovative because it combines expertise from circuit neuroscience and computer vision/machine learning to create new tools for understanding the link between neural activity and behavior, in a context that is relevant to understanding dysfunctions of neural circuits that underlie human psychiatric disorders.
The proposed research is relevant to public health because it develops new methods to investigate the neural circuits governing naturalistic social behaviors in animal models, and such behaviors are often disrupted in human psychiatric disorders such as autism, schizophrenia, and depression. Our objective is to use computer vision and machine learning algorithms (AI) to produce detailed and reproducible measurements capturing the richness of social behaviors, and to correlate those behaviors with simultaneously recorded neural activity in freely interacting animals. We apply these techniques in established and novel social behavior assays to test hypotheses about the neural mechanisms underlying social behavioral decision-making.
