Faculty Bibliography

Numerous studies support the role of dopamine in modulating aggression^1,2, but the exact neural mechanisms remain elusive. Here we show that dopaminergic cells in the ventral tegmental area (VTA) can bidirectionally modulate aggression in male mice in an experience-dependent manner. Although VTA dopaminergic cells strongly influence aggression in novice aggressors, they become ineffective in expert aggressors. Furthermore, eliminating dopamine synthesis in the VTA prevents the emergence of aggression in naive mice but leaves aggression intact in expert aggressors. VTA dopamine modulates aggression through the dorsal lateral septum (dLS), a region known for aggression control. Dopamine enables the flow of information from the hippocampus to the dLS by weakening local inhibition in novice aggressors. In expert aggressors, dLS local inhibition naturally weakens, and the ability of dopamine to modulate dLS cells diminishes. Overall, these results reveal a sophisticated role of dopamine in the rise of aggression in adult male mice.

In nearly all mammalian species, newborn pups are weak and vulnerable, relying heavily on care and protection from parents for survival. Thus, developmentally hardwired neural circuits are in place to ensure the timely expression of parental behaviors. Furthermore, several neurochemical systems, including estrogen, oxytocin, and dopamine, facilitate the emergence and expression of parental behaviors. However, stress can adversely affect these systems, impairing parental behaviors. In this review, we will summarize our current knowledge regarding the impact of stress on pup-directed behavior circuits that lead to infant neglect, abuse, and, in extreme cases, killing. We will discuss various stressors that influence parental behaviors at different life stages and how stress induces changes in the neurochemical systems that support parental care, ultimately leading to its poor performance.

Leptin is an adipose tissue hormone that maintains homeostatic control of adipose tissue mass by regulating the activity of specific neural populations controlling appetite and metabolism¹. Leptin regulates food intake by inhibiting orexigenic agouti-related protein (AGRP) neurons and activating anorexigenic pro-opiomelanocortin (POMC) neurons². However, whereas AGRP neurons regulate food intake on a rapid time scale, acute activation of POMC neurons has only a minimal effect^3-5. This has raised the possibility that there is a heretofore unidentified leptin-regulated neural population that rapidly suppresses appetite. Here we report the discovery of a new population of leptin-target neurons expressing basonuclin 2 (Bnc2) in the arcuate nucleus that acutely suppress appetite by directly inhibiting AGRP neurons. Opposite to the effect of AGRP activation, BNC2 neuronal activation elicited a place preference indicative of positive valence in hungry but not fed mice. The activity of BNC2 neurons is modulated by leptin, sensory food cues and nutritional status. Finally, deleting leptin receptors in BNC2 neurons caused marked hyperphagia and obesity, similar to that observed in a leptin receptor knockout in AGRP neurons. These data indicate that BNC2-expressing neurons are a key component of the neural circuit that maintains energy balance, thus filling an important gap in our understanding of the regulation of food intake and leptin action.

Winning increases the readiness to attack and the probability of winning, a widespread phenomenon known as the "winner effect." Here, we reveal a transition from target-specific to generalized aggression enhancement over 10 days of winning in male mice. This behavioral change is supported by three causally linked plasticity events in the ventrolateral part of the ventromedial hypothalamus (VMHvl), a critical node for aggression. Over 10 days of winning, VMHvl cells experience monotonic potentiation of long-range excitatory inputs, transient local connectivity strengthening, and a delayed excitability increase. Optogenetically coactivating the posterior amygdala (PA) terminals and VMHvl cells potentiates the PA-VMHvl pathway and triggers the same cascade of plasticity events observed during repeated winning. Optogenetically blocking PA-VMHvl synaptic potentiation eliminates all winning-induced plasticity. These results reveal the complex Hebbian synaptic and excitability plasticity in the aggression circuit during winning, ultimately leading to increased "aggressiveness" in repeated winners.

Gonadal hormones act throughout the brain ¹ , and neuropsychiatric disorders vary in symptom severity over the reproductive cycle, pregnancy, and perimenopause ^2-4 . Yet how hormones influence cognitive processes is unclear. Exogenous 17 β -estradiol modulates dopamine signaling in the nucleus accumbens core (NAcc) ^5,6 , which instantiates reward prediction errors (RPEs) for reinforcement learning ^7-16 . Here we show that endogenous 17 β -estradiol enhances RPEs and sensitivity to previous rewards by reducing dopamine reuptake proteins in the NAcc. Rats performed a task with different reward states; they adjusted how quickly they initiated trials across states, balancing effort against expected rewards. NAcc dopamine reflected RPEs that predicted and causally influenced initiation times. Elevated endogenous 17 β -estradiol increased sensitivity to reward states by enhancing dopaminergic RPEs in the NAcc. Proteomics revealed reduced dopamine transporter expression. Finally, knockdown of midbrain estrogen receptors suppressed reinforcement learning. 17 β -estradiol therefore controls RPEs via dopamine reuptake, mechanistically revealing how hormones influence neural dynamics for motivation and learning.

Distributed hypothalamic-midbrain neural circuits help orchestrate complex behavioral responses during social interactions. Given rapid advances in optical imaging, it is a fundamental question how population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. This paper aims to investigate the correspondence between MFP data and social behaviors. 
Approach: We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include a continuous-state linear dynamical system (LDS) and a discrete-state hidden semi-Markov model (HSMM). We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively. 
Main Results: Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states, and produce interpretable latent states. Our approach is also validated in computer simulations in the presence of known ground truth.
Significance: Overall, these analysis approaches provide a state-space framework to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks. 
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Norepinephrine (NE) is an essential biogenic monoamine neurotransmitter. The first-generation NE sensor makes in vivo, real-time, cell-type-specific and region-specific NE detection possible, but its low NE sensitivity limits its utility. Here, we developed the second-generation GPCR-activation-based NE sensors (GRAB_NE2m and GRAB_NE2h) with a superior response and high sensitivity and selectivity to NE both in vitro and in vivo. Notably, these sensors can detect NE release triggered by either optogenetic or behavioral stimuli in freely moving mice, producing robust signals in the locus coeruleus and hypothalamus. With the development of a novel transgenic mouse line, we recorded both NE release and calcium dynamics with dual-color fiber photometry throughout the sleep-wake cycle; moreover, dual-color mesoscopic imaging revealed cell-type-specific spatiotemporal dynamics of NE and calcium during sensory processing and locomotion. Thus, these new GRAB_NE sensors are valuable tools for monitoring the precise spatiotemporal release of NE in vivo, providing new insights into the physiological and pathophysiological roles of NE.

While brain regions function in coordination to mediate diverse behaviors, techniques allowing simultaneous monitoring of many deep brain regions remain limited. Here, we present a multi-fiber recording protocol that enables simultaneous recording of fluorescence signals from multiple brain regions in freely behaving mice. We describe steps for assembling a multi-fiber array and patch cord, implantation, and recording. We then detail procedures for data extraction and visualization. This protocol enables a comprehensive view of the neural activity at the network level. For complete details on the use and execution of this protocol, please refer to Guo et al.¹.

To survive in a complex social group, one needs to know who to approach and, more importantly, who to avoid. In mice, a single defeat causes the losing mouse to stay away from the winner for weeks¹. Here through a series of functional manipulation and recording experiments, we identify oxytocin neurons in the retrochiasmatic supraoptic nucleus (SOR^OXT) and oxytocin-receptor-expressing cells in the anterior subdivision of the ventromedial hypothalamus, ventrolateral part (aVMHvl^OXTR) as a key circuit motif for defeat-induced social avoidance. Before defeat, aVMHvl^OXTR cells minimally respond to aggressor cues. During defeat, aVMHvl^OXTR cells are highly activated and, with the help of an exclusive oxytocin supply from the SOR, potentiate their responses to aggressor cues. After defeat, strong aggressor-induced aVMHvl^OXTR cell activation drives the animal to avoid the aggressor and minimizes future defeat. Our study uncovers a neural process that supports rapid social learning caused by defeat and highlights the importance of the brain oxytocin system in social plasticity.

Distributed hypothalamic-midbrain neural circuits orchestrate complex behavioral responses during social interactions. How population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include continuous-state linear dynamical system (LDS) and discrete-state hidden semi-Markov model (HSMM). We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively. Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states. Overall, these analysis approaches provide an unbiased strategy to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.