@article {70012, title = {Sexually-dimorphic neurons in the Drosophila whole-brain connectome}, journal = {Research Square}, year = {2025}, month = {2025 Jun 26}, abstract = {

Sexual dimorphisms are present across brains. Male and female brains contain sets of cell types with differences in cell number, morphology, or synaptic connectivity between the two sexes. These differences are driven by differentially-expressed transcription factors, which set the stage for disparate sexual and social behaviors observed between males and females, such as courtship, aggression, receptivity, and mating. In the Drosophila brain, sexual dimorphisms result from differential expression of two transcription factors, Fruitless (Fru) and Doublesex (Dsx), and genetic reagents driven by enhancers for Fru and Dsx label sexually-dimorphic neurons in both male and female brains. The recent release of the first whole-brain connectome for Drosophila provides a unique opportunity to study the connectivity between these neurons as well as their integration into the larger brain network. Here, we identify 91 putative Fru or Dsx cell types, comprising ~1400 neurons, within the whole-brain connectome, using morphological similarity between electron microscopic (EM) reconstructions and light microscopic (LM) images of known Fru and Dsx neurons. We discover that while Fru and Dsx neurons are highly interconnected, each cell type typically receives more inputs from and sends more outputs to non-Fru/Dsx neurons. We characterize the connectivity in the Fru/Dsx networks to predict the function of cell types not previously characterized, we measure distances to the sensory periphery and uncover multisensory interactions, and we map connections to descending neurons that drive behavior. All Fru and Dsx labels reported here are shared within FlyWire Codex (codex.flywire.ai; gene==Fruitless or Doublesex); this work is a critical first step towards deciphering the neural basis of sexually-dimorphic behaviors and for making comparisons with future connectomes of the male brain.

}, doi = {10.21203/rs.3.rs-6881911/v1}, url = {https://doi.org/10.21203/rs.3.rs-6881911/v1}, author = {Deutsch, David and Matsliah, Arie and Wang, Kaiyu and Dorkenwald, Sven and Mondal, Arpita and Burke, Austin and Hebditch, James and Gager, Jay and Yu, Szi-Chieh and Sterling, Amy and McKellar, Amy and Schlegel, Philipp and Gerhard, Stephan and Sterne, Gabriella and Costa, Marta and Eichler, Katharina and Yin, Yijie and Jefferis, Gregory and Dickson, Barry and Seung, H Sebastian and Murthy, Mala} } @proceedings {65054, title = {Cellular level analysis of the locomotor neural circuits in Drosophila melanogaster.}, year = {2019}, month = {2019 Jul 6}, pages = {334-7}, author = {minegishi, ryo and Feng, Kai and Dickson, Barry} } @article {49002, title = {Persistent activity in a recurrent circuit underlies courtship memory in Drosophila.}, journal = {eLife}, volume = {7}, year = {2018}, month = {2018 Jan 11}, abstract = {

Recurrent connections are thought to be a common feature of the neural circuits that encode memories, but how memories are laid down in such circuits is not fully understood. Here we present evidence that courtship memory in Drosophila relies on the recurrent circuit between mushroom body gamma (MBg), M6 output, and aSP13 dopaminergic neurons. We demonstrate persistent neuronal activity of aSP13 neurons and show that it transiently potentiates synaptic transmission from MBγ\>M6 neurons. M6 neurons in turn provide input to aSP13 neurons, prolonging potentiation of MBγ\>M6 synapses over time periods that match short-term memory. These data support a model in which persistent aSP13 activity within a recurrent circuit lays the foundation for a short-term memory.

}, issn = {2050-084X}, doi = {10.7554/eLife.31425}, author = {Zhao, Xiaoliang and Lenek, Daniela and Dag, Ugur and Dickson, Barry and Keleman, Krystyna} } @article {6825, title = {Structure-based neuron retrieval across Drosophila brains.}, journal = {Neuroinformatics}, volume = {12}, year = {2014}, month = {2014 Jan 21}, pages = {423-34}, abstract = {

Comparing local neural structures across large sets of examples is crucial when studying gene functions, and their effect in the Drosophila brain. The current practice of aligning brain volume data to a joint reference frame is based on the neuropil. However, even after alignment neurons exhibit residual location and shape variability that, together with image noise, hamper direct quantitative comparison and retrieval of similar structures on an intensity basis. In this paper, we propose and evaluate an image-based retrieval method for neurons, relying on local appearance, which can cope with spatial variability across the population. For an object of interest marked in a query case, the method ranks cases drawn from a large data set based on local neuron appearance in confocal microscopy data. The approach is based on capturing the orientation of neurons based on structure tensors and expanding this field via Gradient Vector Flow. During retrieval, the algorithm compares fields across cases, and calculates a corresponding ranking of most similar cases with regard to the local structure of interest. Experimental results demonstrate that the similarity measure and ranking mechanisms yield high precision and recall in realistic search scenarios.

}, issn = {1559-0089}, doi = {10.1007/s12021-014-9219-4}, author = {Ganglberger, Florian and Schulze, Florian and Tirian, Laszlo and Novikov, Alexey and Dickson, Barry and B{\"u}hler, Katja and Langs, Georg} }