Network States in the Basolateral Amygdala Predicts Voluntary Alcohol Consumption.

Although most adults in the United States will drink alcohol in their life, only about 6% will go on to develop an alcohol use disorder (AUD). While a great deal of work has furthered our understanding of the cycle of addiction, it remains unclear why certain people transition to disordered drinking. Altered activity in regions implicated in AUDs, like the basolateral amygdala (BLA), has been suggested to play a role in the pathophysiology of AUDs, but how these networks contribute to alcohol misuse remains unclear. Our recent work demonstrated that alcohol can modulate BLA network states and that GABAergic parvalbumin (PV) interneurons are crucial modulators of network activity in the BLA. Further, our lab has demonstrated that δ subunit-containing GABA A receptors, which are modulated by alcohol, are highly expressed on PV interneurons in the BLA. These receptors on PV interneurons have also been shown to influence alcohol intake in a voluntary binge drinking paradigm and anxiety-like behavior in withdrawal. Therefore, we hypothesized that alcohol may impact BLA network states via δ subunit-containing GABA A receptors on PV interneurons to impact the extent of alcohol use. To test this hypothesis, we measured the impact of acute alcohol exposure on oscillatory states in the basolateral amygdala and then assessed the relationship to the extent of voluntary ethanol consumption in the Intermittent Access, Drinking-in-the-Dark-Multiple Scheduled Access, and Chronic Intermittent Ethanol exposure paradigms. Remarkably, we demonstrate that the average alcohol intake negatively correlates with δ subunit-containing GABA A receptor expression on PV interneurons and gamma power in the BLA after the first exposure to alcohol. These data implicate δ subunit-containing GABA A receptor expression on PV interneurons in the BLA in voluntary alcohol intake and suggest that BLA network states may serve as a useful biomarker for those at risk for alcohol misuse. Significance Statement: Oscillatory states in the BLA have been demonstrated to drive behavioral states involved in emotional processing, including negative valence processing. Given that negative emotional states/hyperkatifeia contribute to the cycle of AUDs, our previous work demonstrating the ability of alcohol to modulate BLA network states and thereby behavioral states suggests that this mechanism may influence alcohol intake. Here we demonstrate a relationship between the ability of alcohol to modulate oscillations in the BLA and future alcohol intake such that the extent to which alcohol influences BLA network states predict the extent of future voluntary alcohol intake. These findings suggest that individual variability in the sensitivity of the BLA network to alcohol influences voluntary alcohol consumption.

Mouse spontaneous behavior reflects individual variation rather than estrous state.

Behavior is shaped by both the internal state of an animal and its individual behavioral biases. Rhythmic variation in gonadal hormones during the estrous cycle is a defining feature of the female internal state, one that regulates many aspects of sociosexual behavior. However, it remains unclear whether estrous state influences spontaneous behavior and, if so, how these effects might relate to individual behavioral variation. Here, we address this question by longitudinally characterizing the open-field behavior of female mice across different phases of the estrous cycle, using unsupervised machine learning to decompose spontaneous behavior into its constituent elements.1,2,3,4 We find that each female mouse exhibits a characteristic pattern of exploration that uniquely identifies it as an individual across many experimental sessions; by contrast, estrous state only negligibly impacts behavior, despite its known effects on neural circuits that regulate action selection and movement. Like female mice, male mice exhibit individual-specific patterns of behavior in the open field; however, the exploratory behavior of males is significantly more variable than that expressed by females both within and across individuals. These findings suggest underlying functional stability to the circuits that support exploration in female mice, reveal a surprising degree of specificity in individual behavior, and provide empirical support for the inclusion of both sexes in experiments querying spontaneous behaviors.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.

Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.