@article {70035, title = {Cell type-specific contributions to a persistent aggressive internal state in female Drosophila}, journal = {Elife}, volume = {12}, year = {2025}, month = {2025 Jul 25}, abstract = {

Persistent internal states are important for maintaining survival-promoting behaviors, such as aggression. In female\ Drosophila melanogaster, we have previously shown that individually activating either aIPg or pC1d cell types can induce aggression. Here we investigate further the individual roles of these cholinergic, sexually dimorphic cell types, and the reciprocal connections between them, in generating a persistent aggressive internal state. We find that a brief 30-second optogenetic stimulation of aIPg neurons was sufficient to promote an aggressive internal state lasting at least 10 minutes, whereas similar stimulation of pC1d neurons did not. While we previously showed that stimulation of pC1e alone does not evoke aggression, persistent behavior could be promoted through simultaneous stimulation of pC1d and pC1e, suggesting an unexpected synergy of these cell types in establishing a persistent aggressive state. Neither aIPg nor pC1d show persistent neuronal activity themselves, implying that the persistent internal state is maintained by other mechanisms. Moreover, inactivation of pC1d did not significantly reduce aIPg-evoked persistent aggression, arguing that the aggressive state did not depend on pC1d-aIPg recurrent connectivity. Our results suggest the need for alternative models to explain persistent female aggression.

Preprint:\ https://www.biorxiv.org/content/10.1101/2023.06.07.543722v2

}, keywords = {Aggression, Animals, Behavior, Animal, Drosophila melanogaster, Female, Neurons, Optogenetics}, issn = {2050-084X}, doi = {10.7554/eLife.88598}, url = {https://doi.org/10.7554/elife.88598}, author = {Chiu, Hui and Robie, Alice A and Branson, Kristin and Vippa, Tanvi and Epstein, Samantha and Rubin, Gerald M and Anderson, David J and Schretter, Catherine E} } @article {69658, title = {The Fly Disco: Hardware and software for optogenetics and fine-grained fly behavior analysis}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 04}, abstract = {

In the fruit fly, Drosophila melanogaster, connectome data and genetic tools provide a unique opportunity to study complex behaviors including navigation, mating, aggression, and grooming in an organism with a tractable nervous system of 140,000 neurons. Here we present the Fly Disco, a flexible system for high quality video collection, optogenetic manipulation, and fine-grained behavioral analysis of freely walking and socializing fruit fly groups. The data collection hardware and software automates the collection of videos synced to programmable optogenetic stimuli. Key pipeline features include behavioral analysis based on trajectories of 21 keypoints and optogenetic-specific summary statistics and data visualization. We created the multifly dataset for pose estimation that includes 9701 examples enriched in complex behaviors. All hardware designs, software, and the multifly dataset are freely available.

}, doi = {10.1101/2024.11.04.621948}, url = {https://doi.org/10.1101/2024.11.04.621948}, author = {Robie, Alice A and Taylor, Adam L and Schretter, Catherine E and Kabra, Mayank and Branson, Kristin} } @article {65816, title = {Cell types and neuronal circuitry underlying female aggression in Drosophila.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Nov 03}, abstract = {

Aggressive social interactions are used to compete for limited resources and are regulated by complex sensory cues and the organism{\textquoteright}s internal state. While both sexes exhibit aggression, its neuronal underpinnings are understudied in females. Here, we identify a population of sexually dimorphic aIPg neurons in the adult central brain whose optogenetic activation increased, and genetic inactivation reduced, female aggression. Analysis of GAL4 lines identified in an unbiased screen for increased female chasing behavior revealed the involvement of another sexually dimorphic neuron, pC1d, and implicated aIPg and pC1d neurons as core nodes regulating female aggression. Connectomic analysis demonstrated that aIPg neurons and pC1d are interconnected and suggest that aIPg neurons may exert part of their effect by gating the flow of visual information to descending neurons. Our work reveals important regulatory components of the neuronal circuitry that underlies female aggressive social interactions and provides tools for their manipulation.

}, issn = {2050-084X}, doi = {10.7554/eLife.58942}, author = {Schretter, Catherine E and Aso, Yoshi and Robie, Alice A and Dreher, Marisa and Dolan, Michael-John and Chen, Nan and Ito, Masayoshi and Yang, Tansy and Parekh, Ruchi and Branson, Kristin M and Rubin, Gerald M} } @article {48160, title = {Machine vision methods for analyzing social interactions.}, journal = {The Journal of Experimental Biology}, volume = {220}, year = {2017}, month = {2017 Jan 01}, pages = {25-34}, abstract = {

Recent developments in machine vision methods for automatic, quantitative analysis of social behavior have immensely improved both the scale and level of resolution with which we can dissect interactions between members of the same species. In this paper, we review these methods, with a particular focus on how biologists can apply them to their own work. We discuss several components of machine vision-based analyses: methods to record high-quality video for automated analyses, video-based tracking algorithms for estimating the positions of interacting animals, and machine learning methods for recognizing patterns of interactions. These methods are extremely general in their applicability, and we review a subset of successful applications of them to biological questions in several model systems with very different types of social behaviors.

}, issn = {1477-9145}, doi = {10.1242/jeb.142281}, author = {Robie, Alice A and Seagraves, Kelly M and Egnor, S E Roian and Branson, Kristin} } @article {48646, title = {Mapping the neural substrates of behavior.}, journal = {Cell}, volume = {170}, year = {2017}, month = {2017-07-13}, pages = {393-406}, abstract = {

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

{\textbullet}We developed machine-vision methods to broadly and precisely quantify fly behavior{\textbullet}We measured effects of activating 2,204 genetically targeted neuronal populations{\textbullet}We created whole-brain maps of neural substrates of locomotor and social behaviors{\textbullet}We created resources for exploring our results and enabling further investigation

Machine-vision analyses of large behavior and neuroanatomy data reveal whole-brain maps of regions associated with numerous complex behaviors.

}, doi = {10.1016/j.cell.2017.06.032}, url = {http://dx.doi.org/10.1016/j.cell.2017.06.032}, author = {Robie, Alice A and Hirokawa, Jonathan and Edwards, Austin W and Umayam, Lowell A and Lee, Allen and Phillips, Mary L and Card, Gwyneth M and Korff, Wyatt and Rubin, Gerald M and Simpson, Julie H and Reiser, Michael B and Branson, Kristin M} } @article {6954, title = {Genome-wide identification of Drosophila Hb9 targets reveals a pivotal role in directing the transcriptome within eight neuronal lineages, including activation of nitric oxide synthase and Fd59a/Fox-D.}, journal = {Developmental Biology}, volume = {388}, year = {2014}, month = {2014 Apr 1}, pages = {117-33}, abstract = {

Hb9 is a homeodomain-containing transcription factor that acts in combination with Nkx6, Lim3, and Tail-up (Islet) to guide the stereotyped differentiation, connectivity, and function of a subset of neurons in Drosophila. The role of Hb9 in directing neuronal differentiation is well documented, but the lineage of Hb9(+) neurons is only partly characterized, its regulation is poorly understood, and most of the downstream genes through which it acts remain at large. Here, we complete the lineage tracing of all embryonic Hb9(+) neurons (to eight neuronal lineages) and provide evidence that hb9, lim3, and tail-up are coordinately regulated by a common set of upstream factors. Through the parallel use of micro-array gene expression profiling and the Dam-ID method, we searched for Hb9-regulated genes, uncovering transcription factors as the most over-represented class of genes regulated by Hb9 (and Nkx6) in the CNS. By a nearly ten-to-one ratio, Hb9 represses rather than activates transcription factors, highlighting transcriptional repression of other transcription factors as a core mechanism by which Hb9 governs neuronal determination. From the small set of genes activated by Hb9, we characterized the expression and function of two - fd59a/foxd, which encodes a transcription factor, and Nitric oxide synthase. Under standard lab conditions, both genes are dispensable for Drosophila development, but Nos appears to inhibit hyper-active behavior and fd59a appears to act in octopaminergic neurons to control egg-laying behavior. Together our data clarify the mechanisms through which Hb9 governs neuronal specification and differentiation and provide an initial characterization of the expression and function of Nos and fd59a in the Drosophila CNS.

}, issn = {1095-564X}, doi = {10.1016/j.ydbio.2014.01.029}, author = {Lacin, Haluk and Rusch, Jannette and Yeh, Raymond T and Fujioka, Miki and Wilson, Beth A and Zhu, Yi and Robie, Alice A and Mistry, Hemlata and Wang, Ting and Jaynes, James B and Skeath, James B} } @article {45811, title = {Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila.}, journal = {eLife}, volume = {4}, year = {2014}, month = {12/2014}, abstract = {

Animals discriminate stimuli, learn their predictive value and use this knowledge to modify their behavior. In Drosophila, the mushroom body (MB) plays a key role in these processes. Sensory stimuli are sparsely represented by \~{}2000 Kenyon cells, which converge onto 34 output neurons (MBONs) of 21 types. We studied the role of MBONs in several associative learning tasks and in sleep regulation, revealing the extent to which information flow is segregated into distinct channels and suggesting possible roles for the multi-layered MBON network. We also show that optogenetic activation of MBONs can, depending on cell type, induce repulsion or attraction in flies. The behavioral effects of MBON perturbation are combinatorial, suggesting that the MBON ensemble collectively represents valence. We propose that local, stimulus-specific dopaminergic modulation selectively alters the balance within the MBON network for those stimuli. Our results suggest that valence encoded by the MBON ensemble biases memory-based action selection.

}, issn = {2050-084X}, doi = {10.7554/eLife.04580}, author = {Aso, Yoshinori and Sitaraman, Divya and Ichinose, Toshiharu and Kaun, Karla R and Vogt, Katrin and Belliart-Gu{\'e}rin, Ghislain and Placais, Pierre-Yves and Robie, Alice A and Yamagata, Nobuhiro and Schnaitmann, Christopher and Rowell, William J and Johnston, Rebecca M and Ngo, Teri-T B and Chen, Nan and Korff, Wyatt and Nitabach, Michael N and Heberlein, Ulrike and Preat, Thomas and Branson, Kristin M and Tanimoto, Hiromu and Rubin, Gerald M} } @article {5763, title = {JAABA: interactive machine learning for automatic annotation of animal behavior.}, journal = {Nature Methods}, volume = {10}, year = {2013}, month = {2012 Dec}, pages = {64-7}, abstract = {

We present a machine learning{\textendash}based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

}, issn = {1548-7091}, author = {Kabra, Mayank and Robie, Alice A and Rivera-Alba, Marta and Branson, Steven and Branson, Kristin} } @article {5036, title = {A simple strategy for detecting moving objects during locomotion revealed by animal-robot interactions.}, journal = {Current Biology}, volume = {22}, year = {2012}, month = {2012 Jun 20}, pages = {1344-50}, abstract = {

An important role of visual systems is to detect nearby predators, prey, and potential mates [1], which may be distinguished in part by their motion. When an animal is at rest, an object moving in any direction may easily be detected by motion-sensitive visual circuits [2, 3]. During locomotion,\ however, this strategy is compromised because the observer must detect a moving object within the pattern of\ optic flow created by its own motion through the stationary\ background. However, objects that move creating back-to-front (regressive) motion may be unambiguously distinguished from stationary objects because forward locomotion creates only front-to-back (progressive) optic flow. Thus, moving animals should exhibit an enhanced sensitivity to regressively moving objects. We explicitly tested this hypothesis by constructing a simple fly-sized robot that was programmed to interact with a real fly. Our measurements indicate that whereas walking female flies freeze in response to a regressively moving object, they ignore a progressively moving one. Regressive motion salience also explains observations of behaviors exhibited by pairs of walking flies. Because the assumptions underlying the regressive motion salience hypothesis are general, we suspect that the behavior we have observed in Drosophila may be widespread among eyed, motile organisms.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2012.05.024}, author = {Zabala, Francisco and Polidoro, Peter and Robie, Alice A and Branson, Kristin and Perona, Pietro and Dickinson, Michael H} } @article {5758, title = {High-throughput ethomics in large groups of Drosophila.}, journal = {Nature Methods}, volume = {6}, year = {2009}, month = {2009 Jun}, pages = {451-7}, abstract = {

We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting in a planar arena. Our system includes machine-vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly or as a vector that concisely captures the statistical properties of all behaviors displayed in a given period. We found that behavioral differences between individuals were consistent over time and were sufficient to accurately predict gender and genotype. In addition, we found that the relative positions of flies during social interactions vary according to gender, genotype and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.

}, keywords = {Animal, Animals, Artificial Intelligence, Automated, Behavior, Computer-Assisted, Drosophila melanogaster, Humans, Image Interpretation, Monitoring, Movement, Pattern Recognition, Physiologic, Posture, Social Behavior}, issn = {1548-7105}, doi = {10.1038/nmeth.1328}, author = {Branson, Kristin and Robie, Alice A and Bender, John and Perona, Pietro and Dickinson, Michael H} }