@article {69126, title = {Ketamine modulates a norepinephrine-astroglial circuit to persistently suppress futility-induced passivity.}, journal = {bioRxiv}, year = {2023}, month = {2023 Dec 29}, abstract = {

Mood-altering compounds hold promise for the treatment of many psychiatric disorders, such as depression, but connecting their molecular, circuit, and behavioral effects has been challenging. Here we find that, analogous to effects in rodent learned helplessness models, ketamine pre-exposure persistently suppresses futility-induced passivity in larval zebrafish. While antidepressants are thought to primarily act on neurons, brain-wide imaging in behaving zebrafish showed that ketamine elevates intracellular calcium in astroglia for many minutes, followed by persistent calcium downregulation post-washout. Calcium elevation depends on astroglial α1-adrenergic receptors and is required for suppression of passivity. Chemo-/optogenetic perturbations of noradrenergic neurons and astroglia demonstrate that the aftereffects of glial calcium elevation are sufficient to suppress passivity by inhibiting neuronal-astroglial integration of behavioral futility. Imaging in mouse cortex reveals that ketamine elevates astroglial calcium through conserved pathways, suggesting that ketamine exerts its behavioral effects by persistently modulating evolutionarily ancient neuromodulatory systems spanning neurons and astroglia.

}, doi = {10.1101/2022.12.29.522099}, url = {https://doi.org/10.1101/2022.12.29.522099}, author = {Marc Duque and Alex B. Chen and Eric Hsu and Sujatha Narayan and Altyn Rymbek and Shahinoor Begum and Gesine Saher and Adam E. Cohen and David E. Olson and David A. Prober and Dwight E. Bergles and Mark C. Fishman and Florian Engert and Misha B. Ahrens} } @article {65884, title = {Probabilistic Models of Larval Zebrafish Behavior Reveal Structure on Many Scales}, journal = {Current Biology}, volume = {30}, year = {2020}, month = {01/2020}, pages = {70 - 82.e4}, abstract = {

Nervous systems have evolved to combine environmental information with internal state to select and generate adaptive behavioral sequences. To better understand these computations and their implementation in neural circuits, natural behavior must be carefully measured and quantified. Here, we collect high spatial resolution video of single zebrafish larvae swimming in a naturalistic environment and develop models of their action selection across exploration and hunting. Zebrafish larvae swim in punctuated bouts separated by longer periods of rest called interbout intervals. We take advantage of this structure by categorizing bouts into discrete types and representing their behavior as labeled sequences of bout types emitted over time. We then construct probabilistic models{\textemdash}specifically, marked renewal processes{\textemdash}to evaluate how bout types and interbout intervals are selected by the fish as a function of its internal hunger state, behavioral\ history, and the locations and properties of nearby prey. Finally, we evaluate the models by their predictive likelihood and their ability to generate realistic trajectories of virtual fish swimming through simulated environments. Our simulations capture multiple timescales of structure in larval zebrafish behavior and expose many ways in which hunger state influences their action selection to promote food seeking during hunger and safety during satiety.

}, keywords = {behavioral models, behavioral simulations, exploration, hunger, hunting, natural behavior, Zebrafish}, issn = {0960-9822}, doi = {https://doi.org/10.1016/j.cub.2019.11.026}, url = {http://www.sciencedirect.com/science/article/pii/S0960982219314654}, author = {Robert Evan Johnson and Scott Linderman and Thomas Panier and Caroline Lei Wee and Erin Song and Kristian Joseph Herrera and Andrew Miller and Florian Engert} } @article {64995, title = {Lamellar junctions in the endolymphatic sac act as a relief valve to regulate inner ear pressure.}, journal = {eLife}, year = {2018}, month = {2018 Jun 19}, doi = {10.7554/eLife.37131}, author = {Ian A. Swinburne and Kishore R. Mosaliganti and Srigokul Upadhyayula and Tsung-Li Liu and David G. C. Hildebrand and Tony Y.-C. Tsai and Anzhi Chen and Ebaa Al-Obeidi and Anna K. Fass and Samir Malhotra and Florian Engert and Jeff W. Lichtman and Tom Kirchhausen and Sean G. Megason and Eric Betzig} } @conference {65885, title = {Point process latent variable models of larval zebrafish behavior}, booktitle = {NeurIPS}, year = {2018}, month = {12/2018}, abstract = {

A fundamental goal of systems neuroscience is to understand how neural activity gives rise to natural behavior. In order to achieve this goal, we must first build comprehensive models that offer quantitative descriptions of behavior. We develop a new class of probabilistic models to tackle this challenge in the study of larval zebrafish, an important model organism for neuroscience. Larval zebrafish locomote via sequences of punctate swim bouts--brief flicks of the tail--which are naturally modeled as a marked point process. However, these sequences of swim bouts belie a set of discrete and continuous internal states, latent variables that are not captured by standard point process models. We incorporate these variables as latent marks of a point process and explore various models for their dynamics. To infer the latent variables and fit the parameters of this model, we develop an amortized variational inference algorithm that targets the collapsed posterior distribution, analytically marginalizing out the discrete latent variables. With a dataset of over 120,000 swim bouts, we show that our models reveal interpretable discrete classes of swim bouts and continuous internal states like hunger that modulate their dynamics. These models are a major step toward understanding the natural behavioral program of the larval zebrafish and, ultimately, its neural underpinnings.

}, url = {http://papers.nips.cc/paper/8289-point-process-latent-variable-models-of-larval-zebrafish-behavior}, author = {Anuj Sharma and Robert Johnson and Florian Engert and Scott W. Linderman} }