RESEARCH SUMMARY
We live in a world that is largely socially constructed, and we are constantly involved in and fundamentally influenced by a broad array of complex social interactions. Social behaviors among conspecifics, either conflictive or cooperative, are exhibited by all sexually reproducing animal species and are essential for the health, survival, and reproduction of animals. Conversely, impairment in social function is a prominent feature of several neuropsychiatric disorders, such as autism spectrum disorders and schizophrenia. Despite the importance of social behaviors, many fundamental questions remain unanswered (see Chen and Hong. Neuron. 2018). How is social sensory information processed and integrated in the nervous system? How are different social behavioral decisions selected and modulated in brain circuits? The central goal of the lab is to provide a systems-level understanding of the neural circuit mechanisms underlying social behaviors as well as their dysregulation in psychiatric disorders.
RESEARCH AREAS
1) Neural circuit basis of social behavior
General review
Chen P, Hong W†. (2018) Neural circuit mechanisms of social behavior. Neuron. 2018. 98, 16-30. [ PDF ]
Chen P, Hong W†. (2018) Neural circuit mechanisms of social behavior. Neuron. 2018. 98, 16-30. [ PDF ]
A multi-brain framework to study social interaction
Social interactions involve complex decision-making tasks that are shaped by dynamic, mutual feedback between participants. An open question is whether and how emergent properties may arise across brains of socially interacting individuals to influence social decisions. By simultaneously performing microendoscopic calcium imaging in pairs of socially interacting mice, we find that animals exhibit interbrain correlations of neural activity in the prefrontal cortex that are dependent on ongoing social interaction. Activity synchrony arises from two neuronal populations that separately encode one’s own behaviors and those of the social partner. Strikingly, interbrain correlations predict future social interactions as well as dominance relationships in a competitive context. Together, our study provides conclusive evidence for interbrain synchrony in rodents, uncovers how synchronization arises from activity at the single-cell level, and presents a role for interbrain neural activity coupling as a property of multi-animal systems in coordinating and sustaining social interactions between individuals.
Kingsbury L, Huang S, Wang J, Gu K, Golshani P, Wu YE, Hong W†. Correlated neural activity and encoding of behavior across brains of socially interacting animals. Cell. 2019. doi: 10.1016/j.cell.2019.5. 022. [ PDF ]
Social interactions involve complex decision-making tasks that are shaped by dynamic, mutual feedback between participants. An open question is whether and how emergent properties may arise across brains of socially interacting individuals to influence social decisions. By simultaneously performing microendoscopic calcium imaging in pairs of socially interacting mice, we find that animals exhibit interbrain correlations of neural activity in the prefrontal cortex that are dependent on ongoing social interaction. Activity synchrony arises from two neuronal populations that separately encode one’s own behaviors and those of the social partner. Strikingly, interbrain correlations predict future social interactions as well as dominance relationships in a competitive context. Together, our study provides conclusive evidence for interbrain synchrony in rodents, uncovers how synchronization arises from activity at the single-cell level, and presents a role for interbrain neural activity coupling as a property of multi-animal systems in coordinating and sustaining social interactions between individuals.
Kingsbury L, Huang S, Wang J, Gu K, Golshani P, Wu YE, Hong W†. Correlated neural activity and encoding of behavior across brains of socially interacting animals. Cell. 2019. doi: 10.1016/j.cell.2019.5. 022. [ PDF ]
Neural basis of parenting behavior
The medial amygdala (MeA), located downstream of sensory organs involved in pheromonal detection, is a sexually dimorphic brain region involved in many innate social behaviors. Contrary to traditional views, activation of GABAergic neurons in the MeA promotes parental behavior in females, while activation of this population in males differentially promotes parental vs. infanticidal behaviors in an activity level-dependent manner. Through single-cell transcriptomic analysis, we found that molecular sex differences in the MeA are specifically represented in GABAergic neurons. These results establish crucial roles for the MeA as a key node in the neural circuitry underlying pup-directed behaviors and provide important insight into the connection between sex differences across transcriptomes, cells, and circuits in regulating sexually dimorphic behavior.
Chen PB, Hu RK, Wu YE, Pan L, Huang S, Micevych PE, Hong W†. Sexually dimorphic control of parenting behavior by the medial amygdala. Cell. 2019. 176, 1206-1221. [ PDF ]
The medial amygdala (MeA), located downstream of sensory organs involved in pheromonal detection, is a sexually dimorphic brain region involved in many innate social behaviors. Contrary to traditional views, activation of GABAergic neurons in the MeA promotes parental behavior in females, while activation of this population in males differentially promotes parental vs. infanticidal behaviors in an activity level-dependent manner. Through single-cell transcriptomic analysis, we found that molecular sex differences in the MeA are specifically represented in GABAergic neurons. These results establish crucial roles for the MeA as a key node in the neural circuitry underlying pup-directed behaviors and provide important insight into the connection between sex differences across transcriptomes, cells, and circuits in regulating sexually dimorphic behavior.
Chen PB, Hu RK, Wu YE, Pan L, Huang S, Micevych PE, Hong W†. Sexually dimorphic control of parenting behavior by the medial amygdala. Cell. 2019. 176, 1206-1221. [ PDF ]
Neural basis of aggressive behavior
Using cell-type specific functional manipulations, we identified two neuronal subpopulations in the MeA that separately control social behaviors and repetitive self-grooming (Hong et al. Cell. 2014). Interestingly, the GABAergic subpopulations promote social behaviors including aggression, whereas glutamatergic subpopulations evoke repetitive self-grooming, a nonsocial behavior not previously associated with the MeA. Furthermore, social vs. repetitive nonsocial behaviors are controlled in an antagonistic manner by GABAergic vs. glutamatergic subpopulations. These findings provide a framework for understanding circuit-level mechanisms underlying opponency between innate behaviors.
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 ]
Using cell-type specific functional manipulations, we identified two neuronal subpopulations in the MeA that separately control social behaviors and repetitive self-grooming (Hong et al. Cell. 2014). Interestingly, the GABAergic subpopulations promote social behaviors including aggression, whereas glutamatergic subpopulations evoke repetitive self-grooming, a nonsocial behavior not previously associated with the MeA. Furthermore, social vs. repetitive nonsocial behaviors are controlled in an antagonistic manner by GABAergic vs. glutamatergic subpopulations. These findings provide a framework for understanding circuit-level mechanisms underlying opponency between innate behaviors.
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 ]
2) Molecular and behavioral tools to study social behavior
Act-seq: a single-cell sequencing approach to identify active neuronal populations
To interrogate neuronal cell type diversity in brain circuits controlling social behavior, we have recently successfully applied scRNA-Seq to uncover the cell type composition in the MeA and have identified multiple transcriptionally distinct neuronal subpopulations (Wu et al, Neuron 2017). Furthermore, we have developed a new scRNA-Seq method, which allows us to systematically map molecularly defined neuronal subpopulations that are activated by specific behaviors and/or experiences. These advances have provided powerful tools for revealing the molecular identify of behaviorally relevant neuronal cell types and abundant molecular markers to facilitate further functional probing of specific neuronal subpopulations in the medial amygdala.
Wu YE, Pan L, Zuo Y, Li X, Hong W†. (2017) Detecting Activated Cell Populations Using Single-Cell RNA-Seq. Neuron. 96, 313-329. [ PDF | Highlighted by Neuron | UCLA News ]
To interrogate neuronal cell type diversity in brain circuits controlling social behavior, we have recently successfully applied scRNA-Seq to uncover the cell type composition in the MeA and have identified multiple transcriptionally distinct neuronal subpopulations (Wu et al, Neuron 2017). Furthermore, we have developed a new scRNA-Seq method, which allows us to systematically map molecularly defined neuronal subpopulations that are activated by specific behaviors and/or experiences. These advances have provided powerful tools for revealing the molecular identify of behaviorally relevant neuronal cell types and abundant molecular markers to facilitate further functional probing of specific neuronal subpopulations in the medial amygdala.
Wu YE, Pan L, Zuo Y, Li X, Hong W†. (2017) Detecting Activated Cell Populations Using Single-Cell RNA-Seq. Neuron. 96, 313-329. [ PDF | Highlighted by Neuron | UCLA News ]
Using depth sensing to track and analyze social behavior
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. 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.
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 ]
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. 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.
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 ]