RESEARCH SUMMARY
The Hong Lab employs a multidisciplinary approach to identify the molecular and neural circuit mechanisms underlying normal social behaviors as well as their dysregulations in neuropsychiatric disorders. Social behaviors are essential for the survival and reproduction of animals. The control of social behavior is of particular importance in social species such as humans. Abnormalities in social behaviors are associated with several neuropsychiatric disorders, such as autism spectrum disorders and schizophrenia. Despite its importance, many fundamental questions regarding social behavior and its disorders still remain unanswered. We aim to understand how social behavior is regulated at the molecular and circuit level and how social behavior and social experience lead to molecular and circuit level changes in the brain.
We study these questions across molecular, circuit, and behavioral levels, by linking genes to circuits to behaviors. To do that, we take a multi-disciplinary approach and utilize a variety of experimental and computational technologies, including but not limited to optogenetics/chemogenetics, in vivo/vitro calcium imaging and electrophysiology, various genetic and molecular biology techniques, systems approaches such as next-generation sequencing and bioinformatics, and engineering and computational approaches such as machine learning and computer vision.
Summary of recent work:
We study these questions across molecular, circuit, and behavioral levels, by linking genes to circuits to behaviors. To do that, we take a multi-disciplinary approach and utilize a variety of experimental and computational technologies, including but not limited to optogenetics/chemogenetics, in vivo/vitro calcium imaging and electrophysiology, various genetic and molecular biology techniques, systems approaches such as next-generation sequencing and bioinformatics, and engineering and computational approaches such as machine learning and computer vision.
Summary of recent work:
1) Neural circuit mechanisms underlying social behaviors
Animals exhibit a broad range of innate behaviors that are essential for their survival and reproduction. These include solitary behaviors such as feeding, responses to predators or prey, and socialbehaviors among conspecifics. An important brain region implicated in the control of social behaviors is the medial amygdala (MeA), but little is known about how MeA exerts this control. Using cell-type specific functional manipulations, we identified two neuronal subpopulations in the posterior dorsal subdivision of MeA (MeApd) that separately control social behaviors and repetitive self-grooming (Hong et al, Cell 2014). Interestingly, we found that the GABAergic subpopulations promote social behaviors including attack, mounting, and social grooming, whereas glutamatergic subpopulations evoke repetitive self-grooming, a non-social behavior not previously associated with the MeA. Moreover, this glutamatergic subpopulation also inhibits social behaviors, independently of its effect to promote self-grooming. Conversely, the MeApd GABAergic subpopulation suppresses self-grooming, even in a non-social context. Together, these data suggest that social vs. repetitive non-social behaviors are controlled in an antagonistic manner by inhibitory vs. excitatory amygdala subpopulations (Fig. 1). These findings provide a framework for understanding circuit-level mechanisms underlying opponency between innate behaviors, with implications for their perturbation in neuropsychiatric disorders.
Animals exhibit a broad range of innate behaviors that are essential for their survival and reproduction. These include solitary behaviors such as feeding, responses to predators or prey, and socialbehaviors among conspecifics. An important brain region implicated in the control of social behaviors is the medial amygdala (MeA), but little is known about how MeA exerts this control. Using cell-type specific functional manipulations, we identified two neuronal subpopulations in the posterior dorsal subdivision of MeA (MeApd) that separately control social behaviors and repetitive self-grooming (Hong et al, Cell 2014). Interestingly, we found that the GABAergic subpopulations promote social behaviors including attack, mounting, and social grooming, whereas glutamatergic subpopulations evoke repetitive self-grooming, a non-social behavior not previously associated with the MeA. Moreover, this glutamatergic subpopulation also inhibits social behaviors, independently of its effect to promote self-grooming. Conversely, the MeApd GABAergic subpopulation suppresses self-grooming, even in a non-social context. Together, these data suggest that social vs. repetitive non-social behaviors are controlled in an antagonistic manner by inhibitory vs. excitatory amygdala subpopulations (Fig. 1). These findings provide a framework for understanding circuit-level mechanisms underlying opponency between innate behaviors, with implications for their perturbation in neuropsychiatric disorders.
2) Computer vision and machine learning technologies for automated tracking of social behaviors
A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactionsand their disorders such as autism. We developed a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice in their home cage (Hong et al, PNAS 2015). We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of BTBR mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics
A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactionsand their disorders such as autism. We developed a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice in their home cage (Hong et al, PNAS 2015). We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of BTBR mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics
References:
Hong W, Kim DW, Anderson DJ. (2014) Antagonistic control of social versus repetitive self-grooming behaviors by separable amygdala neuronal subsets. Cell. 158, 1348-1361. [ PDF | Previewed by Nature Review Neuroscience | Caltech News ]
Hong W, Kennedy A, Burgos-Artizzu XP, Zelikowsky M, Navonne SG, Perona P, Anderson DJ. (2015) Automated Measurement of Mouse Social Behaviors Using Depth Sensing, Video Tracking, and Machine Learning. Proc. Natl. Acad. Sci. USA. 112, E5351-5360. [ PDF ]
