@article {70047, title = {Distributed control circuits across a brain-and-cord connectome}, journal = {bioRxiv}, year = {2025}, month = {2025 Aug 02}, abstract = {

Just as genomes revolutionized molecular genetics, connectomes (maps of neurons and synapses) are transforming neuroscience. To date, the only species with complete connectomes are worms\ and sea squirts\ (103-104 synapses). By contrast, the fruit fly is more complex (108 synaptic connections), with a brain that supports learning and spatial memory and an intricate ventral nerve cord analogous to the vertebrate spinal cord. Here we report the first adult fly connectome that unites the brain and ventral nerve cord, and we leverage this resource to investigate principles of neural control. We show that effector cells (motor neurons, endocrine cells and efferent neurons targeting the viscera) are primarily influenced by local sensory cells in the same body part, forming local feedback loops. These local loops are linked by long-range circuits involving ascending and descending neurons organized into behavior-centric modules. Single ascending and descending neurons are often positioned to influence the voluntary movements of multiple body parts, together with endocrine cells or visceral organs that support those movements. Brain regions involved in learning and navigation supervise these circuits. These results reveal an architecture that is distributed, parallelized and embodied (tightly connected to effectors), reminiscent of distributed control architectures in engineered systems.

}, doi = {10.1101/2025.07.31.667571}, url = {https://www.biorxiv.org/content/early/2025/08/02/2025.07.31.667571}, author = {Bates, Alexander Shakeel and Phelps, Jasper S. and Kim, Minsu and Yang, Helen H. and Matsliah, Arie and Ajabi, Zaki and Perlman, Eric and Delgado, Kevin M. and Osman, Mohammed Abdal Monium and Salmon, Christopher K. and Gager, Jay and Silverman, Benjamin and Renauld, Sophia and Collie, Matthew F. and Fan, Jingxuan and Pacheco, Diego A. and Zhao, Yunzhi and Patel, Janki and Zhang, Wenyi and Serratosa Capdevilla, Laia and Roberts, Ruair{\'\i} J.V. and Munnelly, Eva J. and Griggs, Nina and Langley, Helen and Moya-Llamas, Borja and Maloney, Ryan T. and Yu, Szi-Chieh and Sterling, Amy R. and Sorek, Marissa and Kruk, Krzysztof and Serafetinidis, Nikitas and Dhawan, Serene and St{\"u}rner, Tomke and Klemm, Finja and Brooks, Paul and Lesser, Ellen and Jones, Jessica M. and Pierce-Lundgren, Sara E. and Lee, Su-Yee and Luo, Yichen and Cook, Andrew P. and McKim, Theresa H. and Kophs, Emily C. and Falt, Tjalda and Negr{\'o}n Morales, Alexa M. and Burke, Austin and Hebditch, James and Willie, Kyle P. and Willie, Ryan and Popovych, Sergiy and Kemnitz, Nico and Ih, Dodam and Lee, Kisuk and Lu, Ran and Halageri, Akhilesh and Bae, J. Alexander and Jourdan, Ben and Schwartzman, Gregory and Demarest, Damian D. and Behnke, Emily and Bland, Doug and Kristiansen, Anne and Skelton, Jaime and Stocks, Tom and Garner, Dustin and Salman, Farzaan and Daly, Kevin C. and Hernandez, Anthony and Kumar, Sandeep and The BANC-FlyWire Consortium and Dorkenwald, Sven and Collman, Forrest and Suver, Marie P. and Fenk, Lisa M. and Pankratz, Michael J. and Jefferis, Gregory S.X.E. and Eichler, Katharina and Seeds, Andrew M. and Hampel, Stefanie and Agrawal, Sweta and Zandawala, Meet and Macrina, Thomas and Adjavon, Diane-Yayra and Funke, Jan and Tuthill, John C. and Azevedo, Anthony and Seung, H. Sebastian and de Bivort, Benjamin L. and Murthy, Mala and Drugowitsch, Jan and Wilson, Rachel I. and Lee, Wei-Chung Allen} } @article {70155, title = {Sexual dimorphism in the complete connectome of the Drosophila male central nervous system}, journal = {bioRxiv}, year = {2025}, month = {2025 Oct 09}, abstract = {

Sex differences in behaviour exist across the animal kingdom, typically under strong genetic regulation. In Drosophila, previous work has shown that fruitless and doublesex transcription factors identify neurons driving sexually dimorphic behaviour. However, the organisation of dimorphic neurons into functional circuits remains unclear.We now present the connectome of the entire Drosophila male central nervous system. This contains 166,691 neurons spanning the brain and ventral nerve cord, fully proofread and comprehensively annotated including fruitless and doublesex expression and 11,691 cell types. By comparison with a previous female brain connectome, we provide the first comprehensive description of the differences between male and female brains to synaptic resolution. Of 7,319 cross-matched cell types in the central brain, 114 are dimorphic with an additional 262 male- and 69 female-specific (totalling 4.8\% of neurons in males and 2.4\% in females).This resource enables analysis of full sensory-to-motor circuits underlying complex behaviours as well as the impact of dimorphic elements. Sex-specific and dimorphic neurons are concentrated in higher brain centres while the sensory and motor periphery are largely isomorphic. Within higher centres, male-specific connections are organised into hotspots defined by male-specific neurons or the presence of male-specific arbours on neurons that are otherwise similar between sexes. Numerous circuit switches reroute sensory information to form conserved, antagonistic circuits controlling opposing behaviours.

}, doi = {10.1101/2025.10.09.680999}, url = {https://www.biorxiv.org/content/early/2025/10/09/2025.10.09.680999}, author = {Berg, Stuart and Beckett, Isabella R and Costa, Marta and Schlegel, Philipp and Januszewski, Micha{\l} and Marin, Elizabeth C and Nern, Aljoscha and Preibisch, Stephan and Qiu, Wei and Takemura, Shin-ya and Fragniere, Alexandra MC and Champion, Andrew S and Adjavon, Diane-Yayra and Cook, Michael and Gkantia, Marina and Hayworth, Kenneth J and Huang, Gary B and Katz, William T and K{\"a}mpf, Florian and Lu, Zhiyuan and Ordish, Christopher and Paterson, Tyler and St{\"u}rner, Tomke and Trautman, Eric T and Whittle, Catherine R and Burnett, Laura E and Hoeller, Judith and Li, Feng and Loesche, Frank and Morris, Billy J and Pietzsch, Tobias and Pleijzier, Markus W and Silva, Valeria and Yin, Yijie and Ali, Iris and Badalamente, Griffin and Bates, Alexander Shakeel and Bogovic, John and Brooks, Paul and Cachero, Sebastian and Canino, Brandon S and Chaisrisawatsuk, Bhumpanya and Clements, Jody and Crowe, Arthur and de Haan Vicente, In{\^e}s and Dempsey, Georgia and Don{\`a}, Erika and dos Santos, M{\'a}rcia and Dreher, Marisa and Dunne, Christopher R and Eichler, Katharina and Finley-May, Samantha and Flynn, Miriam A and Hameed, Imran and Hopkins, Gary Patrick and Hubbard, Philip M and Kiassat, Ladann and Kovalyak, Julie and Lauchie, Shirley A and Leonard, Meghan and Lohff, Alanna and Longden, Kit D and Maldonado, Charli A and Mitletton, Myrto and Moitra, Ilina and Moon, Sung Soo and Mooney, Caroline and Munnelly, Eva J and Okeoma, Nneoma and Olbris, Donald J and Pai, Anika and Patel, Birava and Phillips, Emily M and Plaza, Stephen M and Richards, Alana and Rivas Salinas, Jennifer and Roberts, Ruair{\'\i} Jv and Rogers, Edward M and Scott, Ashley L and Scuderi, Louis A and Seenivasan, Pavithraa and Serratosa Capdevila, Laia and Smith, Claire and Svirskas, Rob and Takemura, Satoko and Tastekin, Ibrahim and Thomson, Alexander and Umayam, Lowell and Walsh, John J and Whittome, Holly and Xu, C Shan and Yakal, Emily A and Yang, Tansy and Zhao, Arthur and George, Reed and Jain, Viren and Jayaraman, Vivek and Korff, Wyatt and Meissner, Geoffrey W and Romani, Sandro and Funke, Jan and Knecht, Christopher and Saalfeld, Stephan and Scheffer, Louis K and Waddell, Scott and Card, Gwyneth M and Ribeiro, Carlos and Reiser, Michael B and Hess, Harald F and Rubin, Gerald M and Jefferis, Gregory Sxe} } @article {69689, title = {Quantitative Attributions with Counterfactuals}, journal = {bioRxiv}, year = {2024}, month = {2024 Dec 02}, abstract = {

We address the problem of explaining the decision process of deep neural network classifiers on images, which is of particular importance in biomedical datasets where class-relevant differences are not always obvious to a human observer. Our proposed solution, termed quantitative attribution with counterfactuals (QuAC), generates visual explanations that highlight class-relevant differences by attributing the classifier decision to changes of visual features in small parts of an image. To that end, we train a separate network to generate counterfactual images (i.e., to translate images between different classes). We then find the most important differences using novel discriminative attribution methods. Crucially, QuAC allows scoring of the attribution and thus provides a measure to quantify and compare the fidelity of a visual explanation. We demonstrate the suitability and limitations of QuAC on two datasets: (1) a synthetic dataset with known class differences, representing different levels of protein aggregation in cells and (2) an electron microscopy dataset of D. melanogaster synapses with different neurotransmitters, where QuAC reveals so far unknown visual differences. We further discuss how QuAC can be used to interrogate mispredictions to shed light on unexpected inter-class similarities and intra-class differences.

}, doi = {10.1101/2024.11.26.625505}, url = {https://doi.org/10.1101/2024.11.26.625505}, author = {Adjavon, Diane-Yayra and Eckstein, Nils and Bates, Alexander S. and Jefferis, Gregory S.X.E. and Funke, Jan} }