@article {67715, title = {A framework for studying behavioral evolution by reconstructing ancestral repertoires.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Sep 02}, abstract = {

Although different animal species often exhibit extensive variation in many behaviors, typically scientists examine one or a small number of behaviors in any single study. Here, we propose a new framework to simultaneously study the evolution of many behaviors. We measured the behavioral repertoire of individuals from six species of fruit flies using unsupervised techniques and identified all stereotyped movements exhibited by each species. We then fit a Generalized Linear Mixed Model to estimate the intra- and inter-species behavioral covariances, and, by using the known phylogenetic relationships among species, we estimated the (unobserved) behaviors exhibited by ancestral species. We found that much of intra-specific behavioral variation has a similar covariance structure to previously described long-time scale variation in an individual{\textquoteright}s behavior, suggesting that much of the measured variation between individuals of a single species in our assay reflects differences in the status of neural networks, rather than genetic or developmental differences between individuals. We then propose a method to identify groups of behaviors that appear to have evolved in a correlated manner, illustrating how sets of behaviors, rather than individual behaviors, likely evolved. Our approach provides a new framework for identifying co-evolving behaviors and may provide new opportunities to study the mechanistic basis of behavioral evolution.

}, issn = {2050-084X}, doi = {10.7554/eLife.61806}, author = {Hern{\'a}ndez, Dami{\'a}n G and Rivera, Catalina and Cande, Jessica and Zhou, Baohua and Stern, David and Berman, Gordon J} } @article {64841, title = {Neural evolution of context-dependent fly song.}, journal = {Current Biology : CB}, volume = {29}, year = {2019}, month = {2019 Mar 08}, pages = {1089-99}, abstract = {

It is unclear where in the nervous system evolutionary changes tend to occur. To localize the source of neural evolution that has generated divergent behaviors, we developed a new approach to label and functionally manipulate homologous neurons across Drosophila species. We examined homologous descending neurons that drive courtship song in two species that sing divergent song types and localized relevant evolutionary changes in circuit function downstream of the intrinsic physiology of these descending neurons. This evolutionary change causes different species to produce divergent motor patterns in similar social contexts. Artificial stimulation of these descending neurons drives multiple song types, suggesting that multifunctional properties of song circuits may facilitate rapid evolution of song types.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2019.02.019}, author = {Ding, Yun and Lillvis, Joshua L and Cande, Jessica and Berman, Gordon J and Arthur, Benjamin J and Long, Xi and Xu, Min and Dickson, Barry J and Stern, David L} } @article {48976, title = {Optogenetic dissection of descending behavioral control in Drosophila.}, journal = {eLife}, year = {2018}, month = {2018}, pages = {e34275}, abstract = {

In most animals, the brain makes behavioral decisions that are transmitted by descending neurons to the nerve cord circuitry that produces behaviors. In insects, only a few descending neurons have been associated with specific behaviors. To explore how descending neurons control an insect{\textquoteright}s movements, we developed a novel method to systematically assay the behavioral effects of activating individual neurons on freely behaving terrestrial\ D. melanogaster. We calculated a two-dimensional representation of the entire behavior space explored by these flies and we associated descending neurons with specific behaviors by identifying regions of this space that were visited with increased frequency during optogenetic activation. Applying this approach across a large collection of descending neurons, we found that (1) activation of most of the descending neurons drove stereotyped behaviors, (2) in many cases multiple descending neurons activated similar behaviors, and (3) optogenetically-activated behaviors were often dependent on the behavioral state prior to activation.

}, doi = {10.7554/eLife.34275}, author = {Cande, Jessica and Namiki, Shigehiro and Qiu, Jirui and Korff, Wyatt and Card, Gwyneth M and Shaevitz, Joshua W and Stern, David L and Berman, Gordon J} } @article {48268, title = {Cilia-mediated Hedgehog signaling controls form and function in the mammalian larynx.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 Feb 08}, abstract = {

Acoustic communication is fundamental to social interactions among animals, including humans. In fact, deficits in voice impair the quality of life for a large and diverse population of patients. Understanding the molecular genetic mechanisms of development and function in the vocal apparatus is thus an important challenge with relevance both to the basic biology of animal communication and to biomedicine. However, surprisingly little is known about the developmental biology of the mammalian larynx. Here, we used genetic fate mapping to chart the embryological origins of the tissues in the mouse larynx, and we describe the developmental etiology of laryngeal defects in mice with disruptions in cilia-mediated Hedgehog signaling. In addition, we show that mild laryngeal defects correlate with changes in the acoustic structure of vocalizations. Together, these data provide key new insights in the molecular genetics of form and function in the mammalian vocal apparatus.

}, issn = {2050-084X}, doi = {10.7554/eLife.19153}, author = {Tabler, Jacqueline M and Rigney, Maggie M and Berman, Gordon J and Gopalakrishnan, Swetha and Heude, Eglantine and Al-Lami, Hadeel Adel and Yannakoudakis, Basil Z and Fitch, Rebecca D and Carter, Christopher M and Vokes, Steven A and Liu, Karen J and Tajbakhsh, Shahragim and Egnor, Se Roian and Wallingford, John B} } @article {47972, title = {An unsupervised method for quantifying the behavior of interacting individuals.}, journal = {Physical Biology}, volume = {14}, year = {2016}, month = {2017 Feb16}, pages = {1609.09345}, abstract = {

Behaviors involving the interaction of multiple individuals are complex and frequently crucial for an animal{\textquoteright}s survival. These interactions, ranging across sensory modalities, length scales, and time scales, are often subtle and difficult to characterize. Contextual effects on the frequency of behaviors become even more difficult to quantify when physical interaction between animals interferes with conventional data analysis, e.g. due to visual occlusion. We introduce a method for quantifying behavior in fruit fly interaction that combines high-throughput video acquisition and tracking of individuals with recent unsupervised methods for capturing an animal{\textquoteright}s entire behavioral repertoire. We find behavioral differences between solitary flies and those paired with an individual of the opposite sex, identifying specific behaviors that are affected by social and spatial context. Our pipeline allows for a comprehensive description of the interaction between two individuals using unsupervised machine learning methods, and will be used to answer questions about the depth of complexity and variance in fruit fly courtship.

}, doi = {10.1088/1478-3975/aa5c50}, url = {https://arxiv.org/abs/1609.09345v1}, author = {Klibaite, Ugne and Berman, Gordon J and Cande, Jessica and Stern, David L} }