Stride-level analysis of mouse open field behavior using deep-learning-based pose estimation

Gait and posture are often perturbed in many neurological, neuromuscular, and neuropsychiatric conditions. Rodents provide a tractable model for elucidating disease mechanisms and interventions. Here, we develop a neural-network-based assay that adopts the commonly used open field apparatus for mouse gait and posture analysis. We quantitate both with high precision across 62 strains of mice. We characterize four mutants with known gait deficits and demonstrate that multiple autism spectrum disorder (ASD) models show gait and posture deficits, implying this is a general feature of ASD. Mouse gait and posture measures are highly heritable and fall into three distinct classes. We conduct a genome-wide association study to define the genetic architecture of stride-level mouse movement in the open field. We provide a method for gait and posture extraction from the open field and one of the largest laboratory mouse gait and posture data resources for the research community.

Sheppard et al. present a method for gait and posture analysis in the common open field apparatus using neural-network-based pose estimation. They apply this high-throughput method to dissect the genetic architecture of mouse movement.

Natural sensory context drives diverse brain-wide activity during C. elegans mating

Natural goal-directed behaviors often involve complex sequences of many stimulus-triggered components. Understanding how brain circuits organize such behaviors requires mapping the interactions between an animal, its environment, and its nervous system. Here, we use brain-wide neuronal imaging to study the full performance of mating by the C. elegans male. We show that as mating unfolds in a sequence of component behaviors, the brain operates similarly between instances of each component but distinctly between different components. When the full sensory and behavioral context is taken into account, unique roles emerge for each neuron. Functional correlations between neurons are not fixed but change with behavioral dynamics. From individual neurons to circuits, our study shows how diverse brain-wide dynamics emerge from the integration of sensory perception and motor actions in their natural context.

Prefrontal GABAergic Interneurons Gate Long-Range Afferents to Regulate Prefrontal Cortex-Associated Complex Behaviors

Prefrontal cortical GABAergic interneurons (INs) and their innervations are essential for the execution of complex behaviors such as working memory, social behavior, and fear expression. These behavior regulations are highly dependent on primary long-range afferents originating from the subcortical structures such as mediodorsal thalamus (MD), ventral hippocampus (vHPC), and basolateral amygdala (BLA). In turn, the regulatory effects of these inputs are mediated by activation of parvalbumin-expressing (PV) and/or somatostatin expressing (SST) INs within the prefrontal cortex (PFC). Here we review how each of these long-range afferents from the MD, vHPC, or BLA recruits a subset of the prefrontal interneuron population to exert precise control of specific PFC-dependent behaviors. Specifically, we first summarize the anatomical connections of different long-range inputs formed on prefrontal GABAergic INs, focusing on PV versus SST cells. Next, we elaborate on the role of prefrontal PV- and SST- INs in regulating MD afferents-mediated cognitive behaviors. We also examine how prefrontal PV- and SST- INs gate vHPC afferents in spatial working memory and fear expression. Finally, we discuss the possibility that prefrontal PV-INs mediate fear conditioning, predominantly driven by the BLA-mPFC pathway. This review will provide a broad view of how multiple long-range inputs converge on prefrontal interneurons to regulate complex behaviors and novel future directions to understand how PFC controls different behaviors.

Model and Non-model Insects in Chronobiology

The fruit fly Drosophila melanogaster is an established model organism in chronobiology, because genetic manipulation and breeding in the laboratory are easy. The circadian clock neuroanatomy in D. melanogaster is one of the best-known clock networks in insects and basic circadian behavior has been characterized in detail in this insect. Another model in chronobiology is the honey bee Apis mellifera, of which diurnal foraging behavior has been described already in the early twentieth century. A. mellifera hallmarks the research on the interplay between the clock and sociality and complex behaviors like sun compass navigation and time-place-learning. Nevertheless, there are aspects of clock structure and function, like for example the role of the clock in photoperiodism and diapause, which can be only insufficiently investigated in these two models. Unlike high-latitude flies such as Chymomyza costata or D. ezoana, cosmopolitan D. melanogaster flies do not display a photoperiodic diapause. Similarly, A. mellifera bees do not go into "real" diapause, but most solitary bee species exhibit an obligatory diapause. Furthermore, sociality evolved in different Hymenoptera independently, wherefore it might be misleading to study the social clock only in one social insect. Consequently, additional research on non-model insects is required to understand the circadian clock in Diptera and Hymenoptera. In this review, we introduce the two chronobiology model insects D. melanogaster and A. mellifera, compare them with other insects and show their advantages and limitations as general models for insect circadian clocks.

Bacteria Use Collective Behavior to Generate Diverse Combat Strategies

Animals have evolved a wide diversity of aggressive behavior often based upon the careful monitoring of other individuals. Bacteria are also capable of aggression, with many species using toxins to kill or inhibit their competitors. Like animals, bacteria also have systems to monitor others during antagonistic encounters, but how this translates into behavior remains poorly understood. Here, we use colonies of Escherichia coli carrying colicin-encoding plasmids as a model for studying antagonistic behavior. We show that in the absence of threat, dispersed cells with low reproductive value produce colicin toxins spontaneously, generating efficient pre-emptive attacks. Cells can also respond conditionally to toxins released by clonemates via autoinduction or other genotypes via competition sensing. The strength of both pre-emptive and responsive attacks varies widely between strains. We demonstrate that this variability occurs easily through mutation by rationally engineering strains to recapitulate the diversity in naturally occurring strategies. Finally, we discover that strains that can detect both competitors and clonemates are capable of massive coordinated attacks on competing colonies. This collective behavior protects established colonies from competitors, mirroring the evolution of alarm calling in the animal world.

Emergent structure-function relations in emphysema and asthma

Structure-function relationships in the respiratory system are often a result of the emergence of self-organized patterns or behaviors that are characteristic of certain respiratory diseases. Proper description of such self-organized behavior requires network models that include nonlinear interactions among different parts of the system. This review focuses on 2 models that exhibit self-organized behavior: a network model of the lung parenchyma during the progression of emphysema that is driven by mechanical force-induced breakdown, and an integrative model of bronchoconstriction in asthma that describes interactions among airways within the bronchial tree. Both models suggest that the transition from normal to pathologic states is a nonlinear process that includes a tipping point beyond which interactions among the system components are reinforced by positive feedback, further promoting the progression of pathologic changes. In emphysema, the progressive destruction of tissue is irreversible, while in asthma, it is possible to recover from a severe bronchoconstriction. These concepts may have implications for pulmonary medicine. Specifically, we suggest that structure-function relationships emerging from network behavior across multiple scales should be taken into account when the efficacy of novel treatments or drug therapy is evaluated. Multiscale, computational, network models will play a major role in this endeavor.