Neurophysiological and behavioral synchronization in group-living and sleeping mice

From social effort to social behavior: An integrated neural model for social motivation

As humans rely on social groups for survival, social motivation is central to behavior and well-being. Here we define social motivation as the effort that initiates and directs behavior towards social outcomes, with the goal of satisfying our fundamental need for connection. We propose an integrated framework of social motivation which emphasizes the maintenance of optimal connection levels through effort exertion, regulating social approach and avoidance, which allow interpersonal synchrony. Together, these behaviors serve as basic building blocks of social behavior, and give rise to behaviors critical for collective living such as cooperation and empathy. We describe a neural model according to which social connection levels are monitored by the hypothalamus, while the anterior cingulate cortex and anterior insula respond to detected social deficiency. As adjustment is required, the social effort system - comprised of the thalamus and striatum - is activated. This system directs neural networks that permit interpersonal synchrony or, conversely, desynchronization, aiming to restore and maintain optimal connection by preventing isolation on the one hand, and exaggerated social closeness on the other hand. The proposed framework offers insights into disorders characterized by aberrant social motivation, potentially identifying neural dysfunctions that may inform novel interventions.

Food intake enhances hippocampal sharp wave-ripples

Effective regulation of energy metabolism is critical for survival. Metabolic control involves various nuclei within the hypothalamus, which receive information about the body's energy state and coordinate appropriate responses to maintain homeostasis, such as thermogenesis, pancreatic insulin secretion, and food-seeking behaviors. It has recently been found that the hippocampus, a brain region traditionally associated with memory and spatial navigation, is also involved in metabolic regulation. Specifically, hippocampal sharp wave-ripples (SWRs), which are high-frequency neural oscillations supporting memory consolidation and foraging decisions, have been shown to reduce peripheral glucose levels. However, whether SWRs are enhanced by recent feeding-when the need for glucose metabolism increases, and if so, whether feeding-dependent modulation of SWRs is communicated to other brain regions involved in metabolic regulation-remains unknown. To address these gaps, we recorded SWRs from the dorsal CA1 region of the hippocampus of mice during sleep sessions before and after consumption of meals of varying caloric values. We found that SWRs occurring during sleep are significantly enhanced following food intake, with the magnitude of enhancement being dependent on the caloric content of the meal. This pattern occurred under both food-deprived and ad libitum feeding conditions. Moreover, we demonstrate that GABAergic neurons in the lateral hypothalamus, which are known to regulate food intake, exhibit a robust SWR-triggered increase in activity. These findings identify the satiety state as a factor modulating SWRs and suggest that hippocampal-lateral hypothalamic communication is a potential mechanism by which SWRs could modulate peripheral metabolism and food intake.

Distinct Effects of Olanzapine Depot Treatment on Behavior and Muscarinic M1 Receptor Expression in the Triple-Hit Wisket Rat Model of Schizophrenia

This study aimed to characterize the triple-hit schizophrenia-like model rats (Wisket) by the assessment of (1) behavioral parameters in different test conditions (reward-based Ambitus test and HomeManner system) for a prolonged period, (2) cerebral muscarinic M1 receptor (M1R) expression, and (3) the effects of olanzapine treatment on these parameters. Wistar (control) and Wisket rats were injected for three consecutive weeks with olanzapine depot (100 mg/kg) and spent 4 weeks in large cages with environmental enrichment (HomeManner). The vehicle-treated Wisket rats spent longer time awake with decreased grooming activity compared to controls, without changes in their active social behavior (sniffing, playing, fighting) obtained in HomeManner. Olanzapine treatment decreased most of these parameters, only the passive social interaction (huddling during sleeping) enhanced mostly in the Wisket rats on the injection day, which recovered within 4 days. In the Ambitus test, vehicle-treated Wisket rats showed lower locomotor and exploratory activities and impaired cognition compared to control rats, deteriorating by olanzapine in both groups. In Wisket brain samples, the M1R mRNA expression was significantly lower in the cerebral cortex and elevated in the hippocampus, with no difference in the prefrontal cortex versus control. Olanzapine normalized the hippocampal M1R expression, but enhanced it in the prefrontal cortex. The triple-hit Wisket model rats had impaired behavioral characteristics in both acute reward-based test and undisturbed circumstances investigated for prolonged periods, and altered cerebral M1R expression. Chronic olanzapine treatment resulted deterioration of some parameters in control group, and could restore only few negative signs in model rats.

Calcium/calmodulin-dependent protein kinase II α and β differentially regulate mammalian sleep

While sleep is important, our understanding of its molecular mechanisms is limited. Over the last two decades, protein kinases including Ca2+/calmodulin-dependent protein kinase II (CaMKII) α and β have been implicated in sleep regulation. Of all the known mouse genetic mutants, the biggest changes in sleep is reported to be observed in adult mice with sgRNAs for Camk2b injected into their embryos: sleep is reduced by approximately 120 min (mins) over 24 h (hrs). We have reexamined the sleep phenotype in mice with either Camk2a or Camk2b gene knocked-out by conventional gene targetting. While the basal sleep is reduced in Camk2a knockout mice, it remains unaltered in Camk2b mutants. Knockout of either Camk2a or Camk2b reduces sleep rebound after deprivation, indicating their roles in sleep homeostasis. These results indicate the involvement of CaMKIIα in both basal sleep and sleep homeostasis while CaMKIIβ is mainly required physiologically for sleep homeostasis, serving as a stimulus for rigorous studies in the future.

The sociality of sleep in animal groups

Group-living animals sleep together, yet most research treats sleep as an individual process. Here, we argue that social interactions during the sleep period contribute in important, but largely overlooked, ways to animal groups' social dynamics, while patterns of social interaction and the structure of social connections within animal groups play important, but poorly understood, roles in shaping sleep behavior. Leveraging field-appropriate methods, such as direct and video-based observation, and increasingly common on-animal motion sensors (e.g., accelerometers), behavioral indicators can be tracked to measure sleep in multiple individuals in a group of animals simultaneously. Sleep proximity networks and sleep timing networks can then be used to investigate the collective dynamics of sleep in wild group-living animals.

Adolescent sleep and its disruption in depression and anxiety

Adolescence is a pivotal stage during development when one's personality, emotion, and behavioral traits are shaped to a great extent, and the underlying neural circuits undergo substantial developmental organizations. Dramatic and dynamic changes occur in sleep architecture throughout the postnatal developmental course. Insufficient sleep and disruption of sleep/wake coherence are prevalent among the adolescents worldwide, and even so in young patients with neuropsychiatric conditions. Although accumulating evidence has suggested a tight association between sleep disruption and depression/anxiety, the causal relationship remains largely unclear. More importantly, most of these studies focused on adult subjects, and little is known about the role of sleep during the development of mood and behavior. Here we review recent studies investigating the acute and chronic effects of adolescent sleep disruption on depression and anxiety both in humans and rodent models with focuses on the assessment methodology and age. By discussing the findings and unsolved problems, we hope to achieve a better understanding of the relationship between sleep and mental health in adolescents and provide insights for future research.

Neural basis of collective social behavior during environmental challenge

Humans and animals have a remarkable capacity to collectively coordinate their behavior to respond to environmental challenges. However, the underlying neurobiology remains poorly understood. Here, we found that groups of mice self-organize into huddles at cold ambient temperature during the thermal challenge assay. We found that mice make active (self-initiated) and passive (partner-initiated) decisions to enter or exit a huddle. Using microendoscopic calcium imaging, we found that active and passive decisions are encoded distinctly within the dorsomedial prefrontal cortex (dmPFC). Silencing dmPFC activity in some mice reduced their active decision-making, but also induced a compensatory increase in active decisions by non-manipulated partners, conserving the group's overall huddle time. These findings reveal how collective behavior is implemented in neurobiological mechanisms to meet homeostatic needs during environmental challenges.

DAMM for the detection and tracking of multiple animals within complex social and environmental settings

Accurate detection and tracking of animals across diverse environments are crucial for studying brain and behavior. Recently, computer vision techniques have become essential for high-throughput behavioral studies; however, localizing animals in complex conditions remains challenging due to intra-class visual variability and environmental diversity. These challenges hinder studies in naturalistic settings, such as when animals are partially concealed within nests. Moreover, current tools are laborious and time-consuming, requiring extensive, setup-specific annotation and training procedures. To address these challenges, we introduce the 'Detect-Any-Mouse-Model' (DAMM), an object detector for localizing mice in complex environments with minimal training. Our approach involved collecting and annotating a diverse dataset of single- and multi-housed mice in complex setups. We trained a Mask R-CNN, a popular object detector in animal studies, to perform instance segmentation and validated DAMM's performance on a collection of downstream datasets using zero-shot and few-shot inference. DAMM excels in zero-shot inference, detecting mice and even rats, in entirely unseen scenarios and further improves with minimal training. Using the SORT algorithm, we demonstrate robust tracking, competitive with keypoint-estimation-based methods. Notably, to advance and simplify behavioral studies, we release our code, model weights, and data, along with a user-friendly Python API and a Google Colab implementation.

DAMM for the detection and tracking of multiple animals within complex social and environmental settings

Accurate detection and tracking of animals across diverse environments are crucial for behavioral studies in various disciplines, including neuroscience. Recently, machine learning and computer vision techniques have become integral to the neuroscientist's toolkit, enabling high-throughput behavioral studies. Despite advancements in localizing individual animals in simple environments, the task remains challenging in complex conditions due to intra-class visual variability and environmental diversity. These limitations hinder studies in ethologically-relevant conditions, such as when animals are concealed within nests or in obscured environments. Moreover, current tools are laborious and time-consuming to employ, requiring extensive, setup-specific annotation and model training/validation procedures. To address these challenges, we introduce the 'Detect Any Mouse Model' (DAMM), a pretrained object detector for localizing mice in complex environments, capable of robust performance with zero to minimal additional training on new experimental setups. Our approach involves collecting and annotating a diverse dataset that encompasses single and multi-housed mice in various lighting conditions, experimental setups, and occlusion levels. We utilize the Mask R-CNN architecture for instance segmentation and validate DAMM's performance with no additional training data (zero-shot inference) and with few examples for fine-tuning (few-shot inference). DAMM excels in zero-shot inference, detecting mice, and even rats, in entirely unseen scenarios and further improves with minimal additional training. By integrating DAMM with the SORT algorithm, we demonstrate robust tracking, competitively performing with keypoint-estimation-based methods. Finally, to advance and simplify behavioral studies, we made DAMM accessible to the scientific community with a user-friendly Python API, shared model weights, and a Google Colab implementation.