@article {69975, title = {Statistical signature of subtle behavioral changes in large-scale assays.}, journal = {PLoS Comput Biol}, volume = {21}, year = {2025}, month = {2025 Apr 21}, pages = {e1012990}, abstract = {

The central nervous system can generate various behaviors, including motor responses, which we can observe through video recordings. Recent advances in gene manipulation, automated behavioral acquisition at scale, and machine learning enable us to causally link behaviors to their underlying neural mechanisms. Moreover, in some animals, such as the Drosophila melanogaster larva, this mapping is possible at the unprecedented scale of single neurons, allowing us to identify the neural microcircuits generating particular behaviors. These high-throughput screening efforts, linking the activation or suppression of specific neurons to behavioral patterns in millions of animals, provide a rich dataset to explore the diversity of nervous system responses to the same stimuli. However, important challenges remain in identifying subtle behaviors, including immediate and delayed responses to neural activation or suppression, and understanding these behaviors on a large scale. We here introduce several statistically robust methods for analyzing behavioral data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioral spaces aimed at detecting subtle deviations in behavior. 3) A generative model for larval behavioral sequences, providing a benchmark for identifying higher-order behavioral changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioral screen focused on responses to an air puff, analyzing data from 280 716 larvae across 569 genetic lines.

Preprint:\ https://www.biorxiv.org/content/10.1101/2024.05.03.591825v1

}, keywords = {Animals, Behavior, Animal, Computational Biology, Drosophila melanogaster, Larva, Machine Learning, Models, Neurological, Models, Statistical, Neurons}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1012990}, url = {https://doi.org/10.1371/journal.pcbi.1012990}, author = {Blanc, Alexandre and Laurent, Francois and Barbier-Chebbah, Alex and Van Assel, Hugues and Cocanougher, Benjamin T and Jones, Benjamin M W and Hague, Peter and Zlatic, Marta and Chikhi, Rayan and Vestergaard, Christian L and Jovanic, Tihana and Masson, Jean-Baptiste and Barre, Chloe} } @article {69526, title = {LarvaTagger: Manual and automatic tagging of drosophila larval behaviour.}, journal = {Bioinformatics}, year = {2024}, month = {2024 Jul 05}, abstract = {

MOTIVATION: As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data.We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth.

RESULTS: We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data.

AVAILABILITY: All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.

SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btae441}, author = {Laurent, Francois and Blanc, Alexandre and May, Lilly and G{\'a}ndara, Lautaro and Cocanougher, Benjamin T and Jones, Benjamin M W and Hague, Peter and Barre, Chloe and Vestergaard, Christian L and Crocker, Justin and Zlatic, Marta and Jovanic, Tihana and Masson, Jean-Baptiste} } @article {65301, title = {Identifying neural substrates of competitive interactions and sequence transitions during mechanosensory responses in Drosophila.}, journal = {PLoS Genetics}, volume = {16}, year = {2020}, month = {2020 Feb 14}, pages = {e1008589}, abstract = {

Nervous systems have the ability to select appropriate actions and action sequences in response to sensory cues. The circuit mechanisms by which nervous systems achieve choice, stability and transitions between behaviors are still incompletely understood. To identify neurons and brain areas involved in controlling these processes, we combined a large-scale neuronal inactivation screen with automated action detection in response to a mechanosensory cue in Drosophila larva. We analyzed behaviors from 2.9x105 larvae and identified 66 candidate lines for mechanosensory responses out of which 25 for competitive interactions between actions. We further characterize in detail the neurons in these lines and analyzed their connectivity using electron microscopy. We found the neurons in the mechanosensory network are located in different regions of the nervous system consistent with a distributed model of sensorimotor decision-making. These findings provide the basis for understanding how selection and transition between behaviors are controlled by the nervous system.

}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008589}, author = {Masson, Jean-Baptiste and Laurent, Francois and Cardona, Albert and Barre, Chloe and Skatchkovsky, Nicolas and Zlatic, Marta and Jovanic, Tihana} } @article {64785, title = {Neural substrates of Drosophila larval anemotaxis.}, journal = {Current Biology : CB}, volume = {29}, year = {2019}, month = {2019 Jan 29}, pages = {554-66}, abstract = {

Animals use sensory information to move toward more favorable conditions. Drosophila larvae can move up or down gradients of odors (chemotax), light (phototax), and temperature (thermotax) by modulating the probability, direction, and size of turns based on sensory input. Whether larvae can anemotax in gradients of mechanosensory cues is unknown. Further, although many of the sensory neurons that mediate taxis have been described, the central circuits are not well understood. Here, we used high-throughput, quantitative behavioral assays to demonstrate Drosophila larvae anemotax in gradients of wind speeds and to characterize the behavioral strategies involved. We found that larvae modulate the probability, direction, and size of turns to move away from higher wind speeds. This suggests that similar central decision-making mechanisms underlie taxis in somatosensory and other sensory modalities. By silencing the activity of single or very few neuron types in a behavioral screen, we found two sensory (chordotonal and multidendritic class III) and six nerve cord neuron types involved in anemotaxis. We reconstructed the identified neurons in an electron microscopy volume that spans the entire larval nervous system and found they received direct input from the mechanosensory neurons or from each other. In this way, we identified local interneurons and first- and second-order subesophageal zone (SEZ) and brain projection neurons. Finally, silencing a dopaminergic brain neuron type impairs anemotaxis. These findings suggest that anemotaxis involves both nerve cord and brain circuits. The candidate neurons and circuitry identified in our study provide a basis for future detailed mechanistic understanding of the circuit principles of anemotaxis.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2019.01.009}, author = {Jovanic, Tihana and Winding, Michael and Cardona, Albert and Truman, James W and Gershow, Marc and Zlatic, Marta} } @article {49001, title = {Neural substrates of navigational decision-making in Drosophila larva anemotaxis.}, journal = {bioRxiv}, year = {2018}, month = {2018 Jan 08}, pages = {244608}, abstract = {

Small animals navigate in the environment as a function of varying sensory information in order to reach more favorable environmental conditions. To achieve this Drosophila larvae alternate periods of runs and turns in gradients of light, temperature, odors and CO2. While the sensory neurons that mediate the navigation behaviors in the different sensory gradients have been described, where and how are these navigational strategies are implemented in the central nervous system and controlled by neuronal circuit elements is not well known. Here we characterize for the first time the navigational strategies of Drosophila larvae in gradients of air-current speeds using high-throughput behavioral assays and quantitative behavioral analysis. We find that larvae extend runs when facing favorable conditions and increase turn rate when facing unfavorable direction, a strategy they use in other sensory modalities as well. By silencing the activity of individual neurons and very sparse expression patterns (2 or 3 neuron types), we further identify the sensory neurons and circuit elements in the ventral nerve cord and brain of the larva required for navigational decisions during anemotaxis. The phenotypes of these central neurons are consistent with a mechanism where the increase of the turning rate in unfavorable conditions and decrease in turning rate in favorable conditions are independently controlled.

}, doi = {10.1101/244608}, author = {Jovanic, Tihana and Truman, James W and Gershow, Marc and Zlatic, Marta} } @article {47935, title = {Competitive disinhibition mediates behavioral choice and sequences in Drosophila.}, journal = {Cell}, volume = {167}, year = {2016}, month = {2016 Oct 5}, pages = {858-70}, abstract = {

Even a simple sensory stimulus can elicit distinct innate behaviors and sequences. During sensorimotor decisions, competitive interactions among neurons that promote distinct behaviors must ensure the selection and maintenance of one behavior, while suppressing others. The circuit implementation of these competitive interactions is still an open question. By combining comprehensive electron microscopy reconstruction of inhibitory interneuron networks, modeling, electrophysiology, and behavioral studies, we determined the circuit mechanisms that\ contribute to the Drosophila larval sensorimotor decision to startle, explore, or perform a sequence of the two in response to a mechanosensory stimulus. Together, these studies reveal that, early in sensory processing, (1) reciprocally connected feedforward inhibitory interneurons implement behavioral choice, (2) local feedback disinhibition provides positive feedback that consolidates and maintains the chosen behavior, and (3) lateral disinhibition promotes sequence transitions. The combination of these interconnected circuit motifs can implement both behavior selection and the serial organization of behaviors into a sequence.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2016.09.009}, author = {Jovanic, Tihana and Schneider-Mizell, Casey M and Shao, Mei and Masson, Jean-Baptiste and Denisov, Gennady and Fetter, Richard D and Mensh, Brett D and Truman, James W and Cardona, Albert and Zlatic, Marta} } @article {6262, title = {High-throughput analysis of stimulus-evoked behaviors in Drosophila larva reveals multiple modality-specific escape strategies.}, journal = {PLoS One}, volume = {8}, year = {2013}, month = {2013 Aug 20}, pages = {e71706}, abstract = {

All organisms react to noxious and mechanical stimuli but we still lack a complete understanding of cellular and molecular mechanisms by which somatosensory information is transformed into appropriate motor outputs. The small number of neurons and excellent genetic tools make Drosophila larva an especially tractable model system in which to address this problem. We developed high throughput assays with which we can simultaneously expose more than 1,000 larvae per man-hour to precisely timed noxious heat, vibration, air current, or optogenetic stimuli. Using this hardware in combination with custom software we characterized larval reactions to somatosensory stimuli in far greater detail than possible previously. Each stimulus evoked a distinctive escape strategy that consisted of multiple actions. The escape strategy was context-dependent. Using our system we confirmed that the nociceptive class IV multidendritic neurons were involved in the reactions to noxious heat. Chordotonal (ch) neurons were necessary for normal modulation of head casting, crawling and hunching, in response to mechanical stimuli. Consistent with this we observed increases in calcium transients in response to vibration in ch neurons. Optogenetic activation of ch neurons was sufficient to evoke head casting and crawling. These studies significantly increase our understanding of the functional roles of larval ch neurons. More generally, our system and the detailed description of wild type reactions to somatosensory stimuli provide a basis for systematic identification of neurons and genes underlying these behaviors.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0071706}, author = {Ohyama, Tomoko and Jovanic, Tihana and Denisov, Gennady and Dang, Tam C and Hoffmann, Dominik and Kerr, Rex A and Zlatic, Marta} }