@article {65832, title = {Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Jul 1}, pages = {771-4}, abstract = {

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and requires only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: 96\% of edges between connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.

}, doi = {10.1038/s41592-021-01183-7}, author = {Buhmann, Julia and Sheridan, Arlo and Gerhard, Stephan and Krause, Renate and Nguyen, Tri and Heinrich, Larissa and Schlegel, Philipp and Lee, Wei-Chung Allen and Wilson, Rachel and Saalfeld, Stephan and Jefferis, Gregory and Bock, Davi and Turaga, Srinivas and Cook, Matthew and Funke, Jan} } @article {65317, title = {Neural circuitry linking mating and egg laying in Drosophila females.}, journal = {Nature}, volume = {579}, year = {2020}, month = {2020 Mar 02}, pages = {101-105}, abstract = {

Mating and egg laying are tightly cooordinated events in the reproductive life of\ all oviparous females. Oviposition is typically rare in virgin females but is initiated after copulation. Here we identify the neural circuitry that links egg laying to mating status in Drosophila melanogaster. Activation of female-specific oviposition descending neurons (oviDNs) is necessary and sufficient for egg laying, and is equally potent in virgin and mated females. After mating, sex peptide-a protein from the male seminal fluid-triggers many behavioural and physiological changes in the female, including the onset of egg laying. Sex peptide is detected by sensory neurons in the uterus, and silences these neurons and their postsynaptic ascending neurons in the abdominal ganglion. We show that these\ abdominal ganglion neurons directly activate the female-specific pC1 neurons. GABAergic (γ-aminobutyric-acid-releasing) oviposition inhibitory neurons (oviINs) mediate feed-forward inhibition from pC1 neurons to both oviDNs and their major excitatory input, the oviposition excitatory neurons (oviENs). By attenuating the abdominal ganglion inputs to pC1 neurons and oviINs, sex peptide disinhibits oviDNs to enable egg laying after mating. This circuitry thus coordinates the two key events in female reproduction: mating and egg laying.

}, issn = {1476-4687}, doi = {10.1038/s41586-020-2055-9}, author = {Wang, Fei and Wang, Kaiyu and Forknall, Nora and Patrick, Christopher and Yang, Tansy and Parekh, Ruchi and Bock, Davi and Dickson, Barry J} } @article {65847, title = {Spatial readout of visual looming in the central brain of Drosophila.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Nov 18}, abstract = {

Visual systems can exploit spatial correlations in the visual scene by using retinotopy. However, retinotopy is often lost, such as when visual pathways are integrated with other sensory modalities. How is spatial information processed outside of strictly visual brain areas? Here, we focused on visual looming responsive LC6 cells in , a population whose dendrites collectively cover the visual field, but whose axons form a single glomerulus-a structure without obvious retinotopic organization-in the central brain. We identified multiple cell types downstream of LC6 in the glomerulus and found that they more strongly respond to looming in different portions of the visual field, unexpectedly preserving spatial information. Through EM reconstruction of all LC6 synaptic inputs to the glomerulus, we found that LC6 and downstream cell types form circuits within the glomerulus that enable spatial readout of visual features and contralateral suppression-mechanisms that transform visual information for behavioral control.

}, issn = {2050-084X}, doi = {10.7554/eLife.57685}, author = {Morimoto, Mai M and Nern, Aljoscha and Zhao, Arthur and Rogers, Edward M and Wong, Allan and Isaacson, Mathew D and Bock, Davi and Rubin, Gerald M and Reiser, Michael B} } @article {64934, title = {Functional and anatomical specificity in a higher olfactory centre.}, journal = {Elife}, volume = {8}, year = {2019}, month = {2019 May 21}, abstract = {

Most sensory systems are organized into parallel neuronal pathways that process distinct aspects of incoming stimuli. In the insect olfactory system, second order projection neurons target both the mushroom body, required for learning, and the lateral horn (LH), proposed to mediate innate olfactory behavior. Mushroom body neurons form a sparse olfactory population code, which is not stereotyped across animals. In contrast, odor coding in the LH remains poorly understood. We combine genetic driver lines, anatomical and functional criteria to show that the LH has ~1400 neurons and \>165 cell types. Genetically labeled LHNs have stereotyped odor responses across animals and on average respond to three times more odors than single projection neurons. LHNs are better odor categorizers than projection neurons, likely due to stereotyped pooling of related inputs. Our results reveal some of the principles by which a higher processing area can extract innate behavioral significance from sensory stimuli.

}, issn = {2050-084X}, doi = {10.7554/eLife.44590}, author = {Frechter, Shahar and Bates, Alexander Shakeel and Tootoonian, Sina and Dolan, Michael-John and Manton, James D and Jamasb, Arian Rokkum and Kohl, Johannes and Bock, Davi and Jefferis, Gregory Sxe} } @article {65040, title = {Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron.}, journal = {eLife}, volume = {8}, year = {2019}, month = {2 Jul 2019}, abstract = {

Serotonin plays different roles across networks within the same sensory modality. Previously, we used whole-cell electrophysiology in\ Drosophila\ to show that serotonergic neurons innervating the first olfactory relay are inhibited by odorants (Zhang and Gaudry, 2016). Here we show that network-spanning serotonergic neurons segregate information about stimulus features, odor intensity and identity, by using opposing coding schemes in different olfactory neuropil. A pair of serotonergic neurons (the CSDns) innervate the antennal lobe and lateral horn, which are first and second order neuropils. CSDn processes in the antennal lobe are inhibited by odors in an identity independent manner. In the lateral horn, CSDn processes are excited in an odor identity dependent manner. Using functional imaging, modeling, and EM reconstruction, we demonstrate that antennal lobe derived inhibition arises from local GABAergic inputs and acts as a means of gain control on branch specific inputs that the CSDns receive within the lateral horn.

}, doi = {10.7554/eLife.46839}, url = {https://elifesciences.org/articles/46839https://cdn.elifesciences.org/articles/46839/elife-46839-v1.pdfhttps://cdn.elifesciences.org/articles/46839/elife-46839-v1.xml}, author = {Zhang, Xiaonan and Coates, Kaylynn and Dacks, Andrew M and Gunay, Cengiz and Lauritzen, J Scott and Li, Feng and Calle-Schuler, Steven A and Bock, Davi and Gaudry, Quentin} } @article {49434, title = {Communication from learned to innate olfactory processing centers is required for memory retrieval in Drosophila.}, journal = {Neuron}, volume = {100}, year = {2018}, month = {2018 Sep 19}, pages = {651-68}, abstract = {

The behavioral response to a sensory stimulus may depend on both learned and innate neuronal representations. How these circuits interact to produce appropriate behavior is unknown. In Drosophila, the lateral horn (LH) and mushroom body (MB) are thought to mediate innate and learned olfactory behavior, respectively, although LH function has not been tested directly. Here we identify two LH cell types (PD2a1 and PD2b1) that receive input from an MB output neuron required for recall of aversive olfactory memories. These neurons are required for aversive memory retrieval and modulated by training. Connectomics data demonstrate that PD2a1 and PD2b1 neurons also receive direct input from food odor-encoding neurons. Consistent with this, PD2a1 and PD2b1 are also necessary for unlearned attraction to some odors, indicating that these neurons have a dual behavioral role. This provides a circuit mechanism by which learned and innate olfactory information can interact in identified neurons to produce appropriate behavior.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2018.08.037}, author = {Dolan, Michael-John and Belliart-Gu{\'e}rin, Ghislain and Bates, Alexander Shakeel and Frechter, Shahar and Lampin-Saint-Amaux, Aur{\'e}lie and Aso, Yoshinori and Roberts, Ruair{\'\i} J V and Schlegel, Philipp and Wong, Allan and Hammad, Adnan and Bock, Davi and Rubin, Gerald M and Preat, Thomas and Placais, Pierre-Yves and Jefferis, Gregory S X E} } @article {2165, title = {Accelerating feature based registration using the Johnson-Lindenstrauss Lemma.}, journal = {Medical Image Computing and Computer-Assisted Intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {12}, year = {2009}, month = {2009}, pages = {632-9}, abstract = {

We introduce an efficient search strategy to substantially accelerate feature based registration. Previous feature based registration algorithms often use truncated search strategies in order to achieve small computation times. Our new accelerated search strategy is based on the realization that the search for corresponding features can be dramatically accelerated by utilizing Johnson-Lindenstrauss dimension reduction. Order of magnitude calculations for the search strategy we propose here indicate that the algorithm proposed is more than a million times faster than previously utilized naive search strategies, and this advantage in speed is directly translated into an advantage in accuracy as the fast speed enables more comparisons to be made in the same amount of time. We describe the accelerated scheme together with a full complexity analysis. The registration algorithm was applied to large transmission electron microscopy (TEM) images of neural ultrastructure. Our experiments demonstrate that our algorithm enables alignment of TEM images with increased accuracy and efficiency compared to previous algorithms.

}, keywords = {Algorithms, Animals, Artificial Intelligence, Automated, Computer-Assisted, Electron, Ferrets, Geniculate Bodies, Image Enhancement, Image Interpretation, Imaging, Microscopy, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Three-Dimensional}, author = {Akselrod-Ballin, Ayelet and Bock, Davi and Reid, R Clay and Warfield, Simon K} } @article {2167, title = {Alignment of large image series using cubic B-splines tessellation: application to transmission electron microscopy data.}, journal = {Medical Image Computing and Computer-Assisted Intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {10}, year = {2007}, month = {2007}, pages = {710-7}, abstract = {

3D reconstruction from serial 2D microscopy images depends on non-linear alignment of serial sections. For some structures, such as the neuronal circuitry of the brain, very large images at very high resolution are necessary to permit reconstruction. These very large images prevent the direct use of classical registration methods. We propose in this work a method to deal with the non-linear alignment of arbitrarily large 2D images using the finite support properties of cubic B-splines. After initial affine alignment, each large image is split into a grid of smaller overlapping sub-images, which are individually registered using cubic B-splines transformations. Inside the overlapping regions between neighboring sub-images, the coefficients of the knots controlling the B-splines deformations are blended, to create a virtual large grid of knots for the whole image. The sub-images are resampled individually, using the new coefficients, and assembled together into a final large aligned image. We evaluated the method on a series of large transmission electron microscopy images and our results indicate significant improvements compared to both manual and affine alignment.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Electron, Image Enhancement, Image Interpretation, Imaging, Microscopy, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Three-Dimensional, Transmission}, author = {Dauguet, Julien and Bock, Davi and Reid, R Clay and Warfield, Simon K} }