Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available

Synaptic transmission parallels neuromodulation in a central food-intake circuit

NeuromedinU is a potent regulator of food intake and activity in mammals. In Drosophila, neurons producing the homologous neuropeptide hugin regulate feeding and locomotion in a similar manner. Here, we use EM-based reconstruction to generate the entire connectome of hugin-producing neurons in the Drosophila larval CNS. We demonstrate that hugin neurons use synaptic transmission in addition to peptidergic neuromodulation and identify acetylcholine as a key transmitter. Hugin neuropeptide and acetylcholine are both necessary for the regulatory effect on feeding. We further show that subtypes of hugin neurons connect chemosensory to endocrine system by combinations of synaptic and peptide-receptor connections. Targets include endocrine neurons producing DH44, a CRH-like peptide, and insulin-like peptides. Homologs of these peptides are likewise downstream of neuromedinU, revealing striking parallels in flies and mammals. We propose that hugin neurons are part of an ancient physiological control system that has been conserved at functional and molecular level.SFB 645 and 704, DFG Cluster of Excellence ImmunoSensation, DFG grant PA 787, HHMI Janeli

The complete connectome of a learning and memory center in an insect brain

Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting associative memory has not been previously available. Here we reconstruct one such circuit at synaptic resolution, the $\textit{Drosophila}$ larval mushroom body. We find that most Kenyon cells integrate random combinations of inputs but that a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections found between output neurons could enhance the selection of learned behaviours. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory centre.AL-K was supported by NIH grant #F32DC014387. AL-K and LFA were supported by the Simons Collaboration on the Global Brain. LFA was also supported by the Gatsby, Mathers and Kavli Foundations. CEP and YP were supported by the DARPA XDATA program (AFRL contract FA8750-12-2-0303) and the NSF BRAIN EAGER award DBI-1451081. KE and AST thank the Deutsche Forschungsgemeinschaft, TH1584/1-1, TH1584/3- 1; the Swiss National Science Foundation, 31003A 132812/1; the Baden Wurttemberg Stiftung; Zukunftskolleg of the University of ¨ Konstanz and DAAD. BG and TS thank the Deutsche Forschungsgemeinschaft, CRC 779, GE 1091/4-1; the European Commission, FP7-ICT MINIMAL. We thank the Fly EM Project Team at HHMI Janelia for the gift of the EM volume, the Janelia Visiting Scientist program, the HHMI visa office, and HHMI Janelia for funding

The complete connectome of a learning and memory center in an insect brain

Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting associative memory has not been previously available. Here we reconstruct one such circuit at synaptic resolution, the $\textit{Drosophila}$ larval mushroom body. We find that most Kenyon cells integrate random combinations of inputs but that a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections found between output neurons could enhance the selection of learned behaviours. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory centre.AL-K was supported by NIH grant #F32DC014387. AL-K and LFA were supported by the Simons Collaboration on the Global Brain. LFA was also supported by the Gatsby, Mathers and Kavli Foundations. CEP and YP were supported by the DARPA XDATA program (AFRL contract FA8750-12-2-0303) and the NSF BRAIN EAGER award DBI-1451081. KE and AST thank the Deutsche Forschungsgemeinschaft, TH1584/1-1, TH1584/3- 1; the Swiss National Science Foundation, 31003A 132812/1; the Baden Wurttemberg Stiftung; Zukunftskolleg of the University of ¨ Konstanz and DAAD. BG and TS thank the Deutsche Forschungsgemeinschaft, CRC 779, GE 1091/4-1; the European Commission, FP7-ICT MINIMAL. We thank the Fly EM Project Team at HHMI Janelia for the gift of the EM volume, the Janelia Visiting Scientist program, the HHMI visa office, and HHMI Janelia for funding

Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete drosophila brain

Trabajo presentado en la 21st International Conference Medical Image Computing and Computer Assisted Intervention, celebrada en Granada, del 16 al 20 de septiembre de 2018Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available.Peer reviewe

Patterns and distribution of presynaptic and postsynaptic elements within serial electron microscopic reconstructions of neuronal arbors from the medicinal leech Hirudo verbana

Microscale connectomics involves the large-scale acquisition of high resolution serial electron micrographs from which neuronal arbors can be reconstructed and synapses detected. In addition to connectivity information, these datasets are also rich with structural information including vesicle types, number of postsynaptic partners at a given presynaptic site, and spatial distribution of synaptic inputs and outputs. Using serial blockface scanning electron microscopy (SBEM), we collected two volumes of serial EM data from ganglia of the medicinal leech. In the first volume, we sampled a small fraction of the neuropil belonging to an adult ganglion. From this dataset we measured the proportion of arbors that contain vesicles, the types of vesicles contained, and developed criteria to identify synapses and measure the number of apparent postsynaptic partners in apposition to presynaptic boutons. In the second dataset, we sampled an entire juvenile ganglion, which included the somata and arbors of all the neurons. We used this dataset to put our findings from mature tissue into the context of fully reconstructed arbors and to explore the spatial distribution of synaptic inputs and outputs on these arbors. We observed that some neurons segregate their arbors into input-only and mixed input/output zones, that other neurons contained exclusively mixed input/output zones, and that still others contained only input zones. These results provide a groundwork for future behavioral studies