@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 {68297, title = {A single-cell transcriptomic atlas of complete insect nervous systems across multiple life stages.}, journal = {Neural Development}, volume = {17}, year = {2022}, month = {2022 Aug 24}, pages = {8}, abstract = {

Molecular profiles of neurons influence neural development and function but bridging the gap between genes, circuits, and behavior has been very difficult. Here we used single cell RNAseq to generate a complete gene expression atlas of the Drosophila larval central nervous system composed of 131,077 single cells across three developmental stages (1\ h, 24\ h and 48\ h after hatching). We identify 67 distinct cell clusters based on the patterns of gene expression. These include 31 functional mature larval neuron clusters, 1 ring gland cluster, 8 glial clusters, 6 neural precursor clusters, and 13 developing immature adult neuron clusters. Some clusters are present across all stages of larval development, while others are stage specific (such as developing adult neurons). We identify genes that are differentially expressed in each cluster, as well as genes that are differentially expressed at distinct stages of larval life. These differentially expressed genes provide promising candidates for regulating the function of specific neuronal and glial types in the larval nervous system, or the specification and differentiation of adult neurons. The cell transcriptome Atlas of the Drosophila larval nervous system is a valuable resource for developmental biology and systems neuroscience and provides a basis for elucidating how genes regulate neural development and function.

}, keywords = {Animals, Drosophila, Gene Expression Regulation, Developmental, Larva, Neuroglia, Neurons, Transcriptome}, issn = {1749-8104}, doi = {10.1186/s13064-022-00164-6}, author = {Corrales, Marc and Cocanougher, Benjamin T and Kohn, Andrea B and Wittenbach, Jason D and Long, Xi S and Lemire, Andrew and Cardona, Albert and Singer, Robert H and Moroz, Leonid L and Zlatic, Marta} } @article {67793, title = {Circuits for integrating learned and innate valences in the insect brain.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Nov 10}, abstract = {

Animal behavior is shaped both by evolution and by individual experience. Parallel brain pathways encode innate and learned valences of cues, but the way in which they are integrated during action-selection is not well understood. We used electron microscopy to comprehensively map with synaptic resolution all neurons downstream of all Mushroom Body output neurons (encoding learned valences) and characterized their patterns of interaction with Lateral Horn neurons (encoding innate valences) in larva. The connectome revealed multiple types that receive convergent Mushroom Body and Lateral Horn inputs. A subset of these receives excitatory input from positive-valence MB and LH pathways and inhibitory input from negative-valence MB pathways. We confirmed functional connectivity from LH and MB pathways and behavioral roles of two of these neurons. These neurons encode integrated odor value and bidirectionally regulate turning. Based on this we speculate that learning could potentially skew the balance of excitation and inhibition onto these neurons and thereby modulate turning. Together, our study provides insights into the circuits that integrate learned and innate valences to modify behavior.

}, issn = {2050-084X}, doi = {10.7554/eLife.62567}, author = {Eschbach, Claire and Fushiki, Akira and Winding, Michael and Afonso, Bruno and Andrade, Ingrid V and Cocanougher, Benjamin T and Eichler, Katharina and Gepner, Ruben and Si, Guangwei and Valdes-Aleman, Javier and Fetter, Richard D and Gershow, Marc and Jefferis, Gregory Sxe and Samuel, Aravinthan Dt and Truman, James W and Cardona, Albert and Zlatic, Marta} } @article {65088, title = {MuscleViz: Free open-source software for muscle weakness visualization.}, journal = {Journal of Neuromuscular Diseases}, volume = {6}, year = {2019}, month = {2019 May 21}, pages = {263-266}, abstract = {

Muscle strength testing is routine in clinical practice. Here we provide an aid to the documentation and visual conceptualization of those results - MuscleViz: a free, open-source application for visualizing the results of muscle strength testing. Its use in clinical settings streamlines the communication of physical examination findings. The tool is also useful for presenting patient data in case reports or case series. A push towards free, open-source software has benefitted other areas of science; we believe a similar effort dedicated to the development of clinical tools is worth pursuing.

}, issn = {2214-3602}, doi = {10.3233/JND-190385}, author = {Wittenbach, Jason D and Cocanougher, Benjamin T and Yun, Pomi and Foley, A Reghan and B{\"o}nnemann, Carsten G} }