A Brain Module for Scalable Control of Complex, Multi-motor Threat Displays

Abstract

Summary Threat displays are a universal feature of agonistic interactions. Whether threats are part of a continuum of aggressive behaviors, or separately controlled, remains unclear. We analyze threats in Drosophila and show they are triggered by male cues and visual motion, and comprised of multiple motor elements that can be flexibly combined. We isolate a cluster of ~3 neurons whose activity is necessary for threat displays but not for other aggressive behaviors, and whose artificial activation suffices to evoke naturalistic threats in solitary flies, suggesting that the neural control of threats is modular with respect to other aggressive behaviors. Artificially evoked threats suffice to repel opponents from a resource, in the absence of contact aggression. Depending on its level of artificial activation, this neural threat module can evoke different motor elements in a threshold-dependent manner. Such scalable modules may represent fundamental “building blocks” of neural circuits that mediate complex multi-motor behaviors.

The Drosophila Split Gal4 System for Neural Circuit Mapping

“…The Split Gal4 method was introduced precisely to permit such refinement of a Gal4 expression pattern and its considerable utility in this regard has been demonstrated in numerous behavioral screens conducted with the collections of GMR and/or VT Gal4 lines. Among these are studies that have successfully identified and/or characterized neural substrates of: grooming (Hampel et al, 2015 , 2017 ), walking (Bidaye et al, 2014 ; Robie et al, 2017 ; Sen et al, 2017 , 2019 ), gap-crossing (Triphan et al, 2016 ), male aggression (Hoopfer et al, 2015 ; Watanabe et al, 2017 ; Duistermars et al, 2018 ; Jung et al, 2020 ), female mating receptivity (Feng et al, 2014 ), egg-laying (Shao et al, 2019 ; Wang et al, 2020 ), circadian rhythms (Guo et al, 2017 ; Liang et al, 2019 ; Sekiguchi et al, 2019 ) and sleep (Liu et al, 2016 ). Increasingly, the Split Gal4 method is being integrated into powerful circuit-mapping pipelines that employ high-throughput screening methods in which behavioral analysis is facilitated by machine learning and other computational approaches (Dankert et al, 2009 ; Anderson and Perona, 2014 ; Robie et al, 2017 ; Cande et al, 2018 ).…”

Versatile Method to measure locomotion

“…We chose the widely used, multi-platform FlyTracker MatLab software [15, 17, 20] that does not require any expensive equipment. Essentially anyone with any camera and a computer can run the software and perform these experiments.…”

How to Build a Behavior

“… How are complicated behavioral sequences executed? In this issue of Neuron , Duistermars et al (2018) deconstruct neural control schemes underlying threats in flies, finding a small collection of neurons in which varying levels of activation lead to the performance of different movements. …”

“…So how do we proceed? In this issue of Neuron , Duistermars et al use aggressive threat displays in the vinegar fly Drosophila melanogaster as a means of uncovering how the nervous system controls behavior at a variety of scales, from the movements of individual appendages to the performance of a complicated behavioral sequence (Duistermars et al, 2018). Aggression is a common behavior across many animals, as it can assist in defending or acquiring resources such as territory, food, or potential mates.…”

“…To understand such motor sequences, though, one first needs to precisely and quantitatively define the behaviors one wishes to study (Berman, 2018). To achieve this goal, Duistermars et al (2018) use supervised machine learning techniques to identify threat bouts between pairs of male flies. Additionally, manually defined thresholds on a fly’s wing angle, as well as its body-frame velocity and angular velocity, are used to define subsections of an individual bout, resulting in a set of five motor elements: wing pumps, wing elevations, charges, turns, and periods of immobility.…”