Hypocretin/orexin neurons encode social discrimination and exhibit a sex-dependent necessity for social interaction

Abstract

Intraspecies social interactions are integral for survival and maintenance of society among all mammalian species. Yet, our understanding of the neural systems and mechanisms involved in the establishment of social connectedness are limited. Since their initial discovery as regulators of sleep/wakefulness and appetite in the brain, the hypocretin/orexin neurons have also been shown to play an essential role in modulating energy homeostasis, motivated and emotional behavior. These neurons are located exclusively in the hypothalamus which, regulates complex and goal-directed behaviors. The hypothalamus also plays an important role in the modulation of social behavior by encoding internal states. However, our understanding of the role of hypocretin neurons in social behavior is currently limited. To address this knowledge gap, we infused AAV encoding GCaMP6s into the lateral hypothalamus of female and male HcrtIRES-Cre mice and performed fiber photometry to record the activity of hypocretin neuron population during social interaction. We then applied optogenetic inhibition of hypocretin neurons to determine the necessity of these neurons for social behavior. Our results indicate that hypocretin neurons exhibit a robust increase in activity in response to social interaction in both female and male mice. We demonstrate the hypocretin neuron population is differentially activated during interaction between familiar and stranger conspecifics. The optogenetic inhibition of hypocretin neuron activity during social behavior leads to a reduction in the amount of time mice are engaged in social interaction in males but not in females. Together, these data implicate the lateral hypothalamus hypocretin neurons as a key regulator within the larger network of neural systems involved in social behavior.

Using deep learning to study emotional behavior in rodent models

“…Supervised approaches employ a classification model trained on human-annotated data, which is then applied to new data ( Figure 1A ). SimBA (Nilsson et al, 2020 ) uses features extracted from DLC or SLEAP pose data as input to a random forest classifier to label mouse behaviors, such as sniffing (social behavior; Dawson et al, 2022 ), freezing (fear conditioning; Hon et al, 2022 ), and pup retrieval (Winters et al, 2022 ). DLCAnalyzer (Sturman et al, 2020 ) also includes capabilities for supervised behavior classification and has been used to track head dips in the EPM (Grimm et al, 2021 ) and head angle in the open-field test (von Ziegler et al, 2022 ).…”