@article {69489, title = {Comparative connectomics of the descending and ascending neurons of the Drosophila nervous system: stereotypy and sexual dimorphism}, journal = {bioRxiv}, year = {2024}, month = {2024 Jun 06}, abstract = {

In most complex nervous systems there is a clear anatomical separation between the nerve cord, which contains most of the final motor outputs necessary for behaviour, and the brain. In insects, the neck connective is both a physical and information bottleneck connecting the brain and the ventral nerve cord (VNC, spinal cord analogue) and comprises diverse populations of descending (DN), ascending (AN) and sensory ascending neurons, which are crucial for sensorimotor signalling and control.Integrating three separate EM datasets, we now provide a complete connectomic description of the ascending and descending neurons of the female nervous system of Drosophila and compare them with neurons of the male nerve cord. Proofread neuronal reconstructions have been matched across hemispheres, datasets and sexes. Crucially, we have also matched 51\% of DN cell types to light level data defining specific driver lines as well as classifying all ascending populations.We use these results to reveal the general architecture, tracts, neuropil innervation and connectivity of neck connective neurons. We observe connected chains of descending and ascending neurons spanning the neck, which may subserve motor sequences. We provide a complete description of sexually dimorphic DN and AN populations, with detailed analysis of circuits implicated in sex-related behaviours, including female ovipositor extrusion (DNp13), male courtship (DNa12/aSP22) and song production (AN hemilineage 08B). Our work represents the first EM-level circuit analyses spanning the entire central nervous system of an adult animal.

}, doi = {10.1101/2024.06.04.596633}, url = {https://www.biorxiv.org/content/early/2024/06/06/2024.06.04.596633}, author = {St{\"u}rner, Tomke and Brooks, Paul and Serratosa Capdevila, Laia and Morris, Billy J. and Javier, Alexandre and Fang, Siqi and Gkantia, Marina and Cachero, Sebastian and Beckett, Isabella R. and Champion, Andrew S. and Moitra, Ilina and Richards, Alana and Klemm, Finja and Kugel, Leonie and Namiki, Shigehiro and Cheong, Han S.J. and Kovalyak, Julie and Tenshaw, Emily and Parekh, Ruchi and Schlegel, Philipp and Phelps, Jasper S. and Mark, Brandon and Dorkenwald, Sven and Bates, Alexander S. and Matsliah, Arie and Yu, Szi-Chieh and McKellar, Claire E. and Sterling, Amy and Seung, Sebastian and Murthy, Mala and Tuthill, John and Lee, Wei-Chung A. and Card, Gwyneth M. and Costa, Marta and Jefferis, Gregory S.X.E. and Eichler, Katharina} } @article {69439, title = {Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster}, journal = {Cell}, volume = {187}, year = {2024}, month = {2024 May 09}, pages = {2574-2594.e23}, abstract = {

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87\% for individual synapses, 94\% for neurons, and 91\% for known cell types across a D.\ melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

}, keywords = {Animals, Brain, Connectome, Drosophila melanogaster, gamma-Aminobutyric Acid, Microscopy, Electron, Neural Networks, Computer, Neurons, Neurotransmitter Agents, Synapses}, issn = {1097-4172}, doi = {10.1016/j.cell.2024.03.016}, author = {Eckstein, Nils and Bates, Alexander Shakeel and Champion, Andrew and Du, Michelle and Yin, Yijie and Schlegel, Philipp and Lu, Alicia Kun-Yang and Rymer, Thomson and Finley-May, Samantha and Paterson, Tyler and Parekh, Ruchi and Dorkenwald, Sven and Matsliah, Arie and Yu, Szi-Chieh and McKellar, Claire and Sterling, Amy and Eichler, Katharina and Costa, Marta and Seung, Sebastian and Murthy, Mala and Hartenstein, Volker and Jefferis, Gregory S X E and Funke, Jan} } @article {3846, title = {Learning to agglomerate superpixel hierarchies.}, journal = {Neural Information Processing Systems}, volume = {24}, year = {2011}, month = {2011}, pages = {648-56}, abstract = {

An agglomerative clustering algorithm merges the most similar pair of clusters at every iteration. The function that evaluates similarity is traditionally handdesigned, but there has been recent interest in supervised or semisupervised settings in which ground-truth clustered data is available for training. Here we show how to train a similarity function by regarding it as the action-value function of a reinforcement learning problem. We apply this general method to segment images by clustering superpixels, an application that we call Learning to Agglomerate Superpixel Hierarchies (LASH). When applied to a challenging dataset of brain images from serial electron microscopy, LASH dramatically improved segmentation accuracy when clustering supervoxels generated by state of the boundary detection algorithms. The naive strategy of directly training only supervoxel similarities and applying single linkage clustering produced less improvement.

}, author = {Jain, Viren and Turaga, Srinivas and Briggman, Kevin and Helmstaedter, Moritz N. and Denk, Winfried and Seung, Sebastian} }