@article {68921, title = {Network statistics of the whole-brain connectome of Drosophila}, journal = {Nature}, volume = {634}, year = {2024}, month = {2024 Oct 02}, pages = {153{\textendash}165}, abstract = {

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of\ information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30\% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex (https://codex.flywire.ai) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

}, doi = {10.1038/s41586-024-07968-y}, url = {https://doi.org/10.1038/s41586-024-07968-y}, author = {Albert Lin and Runzhe Yang and Sven Dorkenwald and Arie Matsliah and Amy R. Sterling and Philipp Schlegel and Szi-chieh Yu and Claire E. McKellar and Marta Costa and Katharina Eichler and Alexander Shakeel Bates and Nils Eckstein and Jan Funke and Gregory S.X.E. Jefferis and Mala Murthy} } @article {68774, title = {A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing}, journal = {bioRxiv}, year = {2023}, month = {2023 May 02}, abstract = {

The forthcoming assembly of the adult\ Drosophila melanogaster\ central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire\ Drosophila\ brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the\ Drosophila\ brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

}, doi = {10.1101/2023.05.02.539144}, url = {https://doi.org/10.1101/2023.05.02.539144}, author = {Philip K. Shiu and Gabriella R. Sterne and Nico Spiller and Romain Franconville and Andrea Sandoval and Joie Zhou and Neha Simha and Chan Hyuk Kang and Seongbong Yu and Jinseop S. Kim and Sven Dorkenwald and Arie Matsliah and Philipp Schlegel and Szi-chieh Yu and Claire E. McKellar and Amy Sterling and Marta Costa and Katharina Eichler and Gregory S.X.E. Jefferis and Mala Murthy and Alexander Shakeel Bates and Nils Eckstein and Jan Funke and Salil S. Bidaye and Stefanie Hampel and Andrew M. Seeds and Kristin Scott} } @conference {65837, title = {Microtubule Tracking in Electron Microscopy Volumes}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention}, year = {2020}, month = {2020 Sep 17}, abstract = {

We present a method for microtubule tracking in electron microscopy volumes. Our method first identifies a sparse set of voxels that likely belong to microtubules. Similar to prior work, we then enumerate potential edges between these voxels, which we represent in a candidate graph. Tracks of microtubules are found by selecting nodes and edges in the candidate graph by solving a constrained optimization problem incorporating biological priors on microtubule structure. For this, we present a novel integer linear programming formulation, which results in speed-ups of three orders of magnitude and an increase of 53\% in accuracy compared to prior art (evaluated on three 1 . 2 {\texttimes} 4 {\texttimes} 4{\textmu}m volumes of Drosophila neural tissue). We also propose a scheme to solve the optimization problem in a block-wise fashion, which allows distributed tracking and is necessary to process very large electron microscopy volumes. Finally, we release a benchmark dataset for microtubule tracking, here used for training, testing and validation, consisting of eight 30 x 1000 x 1000 voxel blocks (1 . 2 {\texttimes} 4 {\texttimes} 4{\textmu}m) of densely annotated microtubules in the CREMI data set (https://github.com/nilsec/micron).

}, author = {Nils Eckstein and Julia Buhmann and Matthew Cook and Jan Funke} }