@article {68628, title = {Finding the right type of cell.}, journal = {eLife}, volume = {12}, year = {2023}, month = {2023 Feb 16}, abstract = {

A new method allows researchers to automatically assign cells into different cell types and tissues, a step which is critical for understanding complex organisms.

}, issn = {2050-084X}, doi = {10.7554/eLife.86172}, author = {Scheffer, Louis K} } @article {68272, title = {neuPrint: An open access tool for EM connectomics.}, journal = {Frontiers in Neuroinformatics}, volume = {16}, year = {2022}, month = {2022 Jul 20}, pages = {896292}, abstract = {

Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled by downloading data, and performance was not a serious problem. But new and much larger reconstructions upend these assumptions. These networks now contain tens of thousands of neurons and tens of millions of connections, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. Allowing other scientists to make use of this data needs more than publication-it requires new tools that are publicly available, easy to use, and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. Neuprint contains two major components-a web interface and programmer APIs. The web interface is designed to allow any scientist worldwide, using only a browser, to quickly ask and answer typical biological queries about a connectome. The neuPrint APIs allow more computer-savvy scientists to make more complex or higher volume queries. NeuPrint also provides features for assessing reconstruction quality. Internally, neuPrint organizes connectome data as a graph stored in a neo4j database. This gives high performance for typical queries, provides access though a public and well documented query language Cypher, and will extend well to future larger connectomics databases. Our experience is also an experiment in open science. We find a significant fraction of the readers of the article proceed to examine the data directly. In our case preprints worked exactly as intended, with data inquiries and PDF downloads starting immediately after pre-print publication, and little affected by formal publication later. From this we deduce that many readers are more interested in our data than in our analysis of our data, suggesting that data-only papers can be well appreciated and that public data release can speed up the propagation of scientific results by many months. We also find that providing, and keeping, the data available for online access imposes substantial additional costs to connectomics research.

}, issn = {1662-5196}, doi = {10.3389/fninf.2022.896292}, author = {Plaza, Stephen M and Clements, Jody and Dolafi, Tom and Umayam, Lowell and Neubarth, Nicole N and Scheffer, Louis K and Berg, Stuart} } @article {67772, title = {A connectome is not enough - what is still needed to understand the brain of Drosophila?}, journal = {The Journal of Experimental Biology}, volume = {224}, year = {2021}, month = {2021 Nov 01}, abstract = {

Understanding the structure and operation of any nervous system has been a subject of research for well over a century. A near-term opportunity in this quest is to understand the brain of a model species, the fruit fly Drosophila melanogaster. This is an enticing target given its relatively small size (roughly 200,000 neurons), coupled with the behavioral richness that this brain supports, and the wide variety of techniques now available to study both brain and behavior. It is clear that within a few years we will possess a connectome for D. melanogaster: an electron-microscopy-level description of all neurons and their chemical synaptic connections. Given what we will soon have, what we already know and the research that is currently underway, what more do we need to know to enable us to understand the fly{\textquoteright}s brain? Here, we itemize the data we will need to obtain, collate and organize in order to build an integrated model of the brain of D. melanogaster.

}, issn = {1477-9145}, doi = {10.1242/jeb.242740}, author = {Scheffer, Louis K and Meinertzhagen, Ian A} } @article {68507, title = {A connectome and analysis of the adult Drosophila central brain.}, journal = {Elife}, volume = {9}, year = {2020}, month = {2020 Sep 07}, abstract = {

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly . Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly{\textquoteright}s brain.

}, keywords = {Animals, Brain, Connectome, Drosophila melanogaster, Female, Male, Neurons, Synapses}, issn = {2050-084X}, doi = {10.7554/eLife.57443}, author = {Scheffer, Louis K and Xu, C Shan and Januszewski, Michal and Lu, Zhiyuan and Takemura, Shin-ya and Hayworth, Kenneth J and Huang, Gary B and Shinomiya, Kazunori and Maitlin-Shepard, Jeremy and Berg, Stuart and Clements, Jody and Hubbard, Philip M and Katz, William T and Umayam, Lowell and Zhao, Ting and Ackerman, David and Blakely, Tim and Bogovic, John and Dolafi, Tom and Kainmueller, Dagmar and Kawase, Takashi and Khairy, Khaled A and Leavitt, Laramie and Li, Peter H and Lindsey, Larry and Neubarth, Nicole and Olbris, Donald J and Otsuna, Hideo and Trautman, Eric T and Ito, Masayoshi and Bates, Alexander S and Goldammer, Jens and Wolff, Tanya and Svirskas, Robert and Schlegel, Philipp and Neace, Erika and Knecht, Christopher J and Alvarado, Chelsea X and Bailey, Dennis A and Ballinger, Samantha and Borycz, Jolanta A and Canino, Brandon S and Cheatham, Natasha and Cook, Michael and Dreher, Marisa and Duclos, Octave and Eubanks, Bryon and Fairbanks, Kelli and Finley, Samantha and Forknall, Nora and Francis, Audrey and Hopkins, Gary Patrick and Joyce, Emily M and Kim, SungJin and Kirk, Nicole A and Kovalyak, Julie and Lauchie, Shirley A and Lohff, Alanna and Maldonado, Charli and Manley, Emily A and McLin, Sari and Mooney, Caroline and Ndama, Miatta and Ogundeyi, Omotara and Okeoma, Nneoma and Ordish, Christopher and Padilla, Nicholas and Patrick, Christopher M and Paterson, Tyler and Phillips, Elliott E and Phillips, Emily M and Rampally, Neha and Ribeiro, Caitlin and Robertson, Madelaine K and Rymer, Jon Thomson and Ryan, Sean M and Sammons, Megan and Scott, Anne K and Scott, Ashley L and Shinomiya, Aya and Smith, Claire and Smith, Kelsey and Smith, Natalie L and Sobeski, Margaret A and Suleiman, Alia and Swift, Jackie and Takemura, Satoko and Talebi, Iris and Tarnogorska, Dorota and Tenshaw, Emily and Tokhi, Temour and Walsh, John J and Yang, Tansy and Horne, Jane Anne and Li, Feng and Parekh, Ruchi and Rivlin, Patricia K and Jayaraman, Vivek and Costa, Marta and Jefferis, Gregory Sxe and Ito, Kei and Saalfeld, Stephan and George, Reed and Meinertzhagen, Ian A and Rubin, Gerald M and Hess, Harald F and Jain, Viren and Plaza, Stephen M} } @article {65899, title = {The connectome of the adult mushroom body provides insights into function.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Dec 14}, abstract = {

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory and activity regulation. Here we identify new components of the MB circuit in , including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. We find unexpected structure in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). We provide insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs. Our results provide a foundation for further theoretical and experimental work.

}, issn = {2050-084X}, doi = {10.7554/eLife.62576}, author = {Li, Feng and Lindsey, Jack W and Marin, Elizabeth C and Otto, Nils and Dreher, Marisa and Dempsey, Georgia and Stark, Ildiko and Bates, Alexander S and Pleijzier, Markus William and Schlegel, Philipp and Nern, Aljoscha and Takemura, Shin-ya and Eckstein, Nils and Yang, Tansy and Francis, Audrey and Braun, Amalia and Parekh, Ruchi and Costa, Marta and Scheffer, Louis K and Aso, Yoshi and Jefferis, Gregory Sxe and Abbott, Larry F and Litwin-Kumar, Ashok and Waddell, Scott and Rubin, Gerald M} } @article {51252, title = {Comparisons between the ON- and OFF-edge motion pathways in the brain.}, journal = {eLife}, volume = {8}, year = {2019}, month = {2019 Jan 09}, abstract = {

Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In , recent synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest an increasingly intricate motion model compared with the ubiquitous Hassenstein-Reichardt model, while our knowledge of OFF-pathway (T5) has been incomplete. Here we present a conclusive and comprehensive connectome that for the first time integrates detailed connectivity information for inputs to both T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. While the two pathways are likely evolutionarily linked and indeed exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.

}, issn = {2050-084X}, doi = {10.7554/eLife.40025}, author = {Shinomiya, Kazunori and Huang, Gary and Lu, Zhiyuan and Parag, Toufiq and Xu, C Shan and Aniceto, Roxanne and Ansari, Namra and Cheatham, Natasha and Lauchie, Shirley and Neace, Erika and Ogundeyi, Omotara and Ordish, Christopher and Peel, David and Shinomiya, Aya and Smith, Claire and Takemura, Satoko and Talebi, Iris and Rivlin, Patricia K and Nern, Aljoscha and Scheffer, Louis K and Plaza, Stephen M and Meinertzhagen, Ian A} } @article {65060, title = {The fly brain atlas.}, journal = {Annual Review of Cell and Developmental Biology}, volume = {35}, year = {2019}, month = {2019 Oct 6}, pages = {637-53}, abstract = {

The brain{\textquoteright}s synaptic networks endow an animal with powerfully adaptive biological behavior. Maps of such synaptic circuits densely reconstructed in those model brains, which can be examined and manipulated by genetic means, offer the best prospect for understanding the underlying biological bases of behavior. That prospect is now technologically feasible and a scientifically enabling possibility in neurobiology, much as genomics has been in molecular biology and genetics. In , two major advances are in electron microscopic technology, using focused ion beam-scanning electron microscopy (FIB-SEM) milling to capture and align digital images, and in computer-aided reconstruction of neuron morphologies. The last decade has witnessed enormous progress in detailed knowledge of the actual synaptic circuits formed by real neurons. Advances in various brain regions that heralded identification of the motion-sensing circuits in the optic lobe are now extending to other brain regions, with the prospect of encompassing the fly{\textquoteright}s entire nervous system, both brain and ventral nerve cord. Expected final online publication date for the Volume 35 is October 7, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

}, issn = {1530-8995}, doi = {10.1146/annurev-cellbio-100818-125444}, author = {Scheffer, Louis K and Meinertzhagen, Ian A} } @article {49493, title = {Analysis tools for large connectomes.}, journal = {Frontiers in Neural Circuits}, volume = {12}, year = {2018}, month = {2018}, pages = {85}, abstract = {

New reconstruction techniques are generating connectomes of unprecedented size. These must be analyzed to generate human comprehensible results. The analyses being used fall into three general categories. The first is interactive tools used during reconstruction, to help guide the effort, look for possible errors, identify potential cell classes, and answer other preliminary questions. The second type of analysis is support for formal documents such as papers and theses. Scientific norms here require that the data be archived and accessible, and the analysis reproducible. In contrast to some other "omic" fields such as genomics, where a few specific analyses dominate usage, connectomics is rapidly evolving and the analyses used are often specific to the connectome being analyzed. These analyses are typically performed in a variety of conventional programming language, such as Matlab, R, Python, or C++, and read the connectomic data either from a file or through database queries, neither of which are standardized. In the short term we see no alternative to the use of specific analyses, so the best that can be done is to publish the analysis code, and the interface by which it reads connectomic data. A similar situation exists for archiving connectome data. Each group independently makes their data available, but there is no standardized format and long-term accessibility is neither enforced nor funded. In the long term, as connectomics becomes more common, a natural evolution would be a central facility for storing and querying connectomic data, playing a role similar to the National Center for Biotechnology Information for genomes. The final form of analysis is the import of connectome data into downstream tools such as neural simulation or machine learning. In this process, there are two main problems that need to be addressed. First, the reconstructed circuits contain huge amounts of detail, which must be intelligently reduced to a form the downstream tools can use. Second, much of the data needed for these downstream operations must be obtained by other methods (such as genetic or optical) and must be merged with the extracted connectome.

}, issn = {1662-5110}, doi = {10.3389/fncir.2018.00085}, author = {Scheffer, Louis K} } @conference {49333, title = {Insights from Biology: low power circuits in the fruit fly.}, booktitle = {International Symposium on Low Power Electronics and Design}, year = {2018}, month = {2018 Jul 23-25}, publisher = {ACM}, organization = {ACM}, address = {Seattle, WA}, abstract = {

Fruit flies (Drosophila melanogaster) are small insects, with correspondingly small power budgets. Despite this, they perform sophisticated neural computations in real time. Careful study of these insects is revealing how some of these circuits work. Insights from these systems might be helpful in designing other low power circuits.

}, url = {https://dl.acm.org/citation.cfm?id=3241337}, author = {Scheffer, Louis K} } @article {49497, title = {A resource for the antennal lobe provided by the connectome of glomerulus VA1v.}, journal = {eLife}, volume = {7}, year = {2018}, month = {2018 Nov 01}, abstract = {

Using FIB-SEM we report the entire synaptic connectome of glomerulus VA1v of the right antennal lobe in . Within the glomerulus we densely reconstructed all neurons, including hitherto elusive local interneurons. The -positive, sexually dimorphic VA1v included \>11,140 presynaptic sites with ~38,050 postsynaptic dendrites. These connected input olfactory receptor neurons (ORNs, 51 ipsilateral, 56 contralateral), output projection neurons (18 PNs), and local interneurons (56 of \>150 previously reported LNs). ORNs are predominantly presynaptic and PNs predominantly postsynaptic; newly reported LN circuits are largely an equal mixture and confer extensive synaptic reciprocity, except the newly reported LN2V with input from ORNs and outputs mostly to monoglomerular PNs, however. PNs were more numerous than previously reported from genetic screens, suggesting that the latter failed to reach saturation. We report a matrix of 192 bodies each having 50 connections; these form 88\% of the glomerulus{\textquoteright} pre/postsynaptic sites.

}, issn = {2050-084X}, doi = {10.7554/eLife.37550}, author = {Horne, Jane Anne and Langille, Carlie and McLin, Sari and Wiederman, Meagan and Lu, Zhiyuan and Xu, C Shan and Plaza, Stephen M and Scheffer, Louis K and Hess, Harald F and Meinertzhagen, Ian A} } @article {48436, title = {The comprehensive connectome of a neural substrate for {\textquoteright}ON{\textquoteright} motion detection in Drosophila.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 Apr 22}, abstract = {

Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. We reconstructed a comprehensive connectome of the circuits of Drosophila{\textquoteright}s motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4{\textquoteright}s dendrite shafts, while inhibitory inputs localize to the bases. Consistent with our previous study, we reveal that Mi1 and Tm3 cells provide most synaptic contacts onto T4. We are, however, unable to reproduce the spatial offset between these cells reported previously. Our comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors.

}, issn = {2050-084X}, doi = {10.7554/eLife.24394}, author = {Takemura, Shin-ya and Nern, Aljoscha and Chklovskii, Dmitri B and Scheffer, Louis K and Rubin, Gerald M and Meinertzhagen, Ian A} } @article {48254, title = {Multicut brings automated neurite segmentation closer to human performance.}, journal = {Nature Methods}, volume = {14}, year = {2017}, month = {2017 Jan 31}, pages = {101-102}, issn = {1548-7105}, doi = {10.1038/nmeth.4151}, author = {Beier, Thorsten and Pape, Constantin and Rahaman, Nasim and Prange, Timo and Berg, Stuart and Bock, Davi D and Cardona, Albert and Knott, Graham W and Plaza, Stephen M and Scheffer, Louis K and Koethe, Ullrich and Kreshuk, Anna and Hamprecht, Fred A} } @article {48807, title = {Simulating extracted connectomes.}, journal = {bioRxiv}, year = {2017}, month = {2017 Aug 17}, abstract = {

Connectomes derived from volume EM imaging of the brain can generate detailed physical models of every neuron, and simulators such as NEURON or GENESIS are designed to work with such models. In principal, combining these technologies, plus transmitter and channel models, should allow detailed and accurate simulation of real neural circuits. Here we experiment with this combination, using a well-studied system (motion detection in Drosophila. Since simulation requires both the physical geometry (which we have) and the models of the synapses (which are not currently available), we built approximate synapses corresponding to their known and estimated function. Once we did so, we reproduced direction selectivity in T4 cells, one of the main functions of this neural circuit. This verified the basic functionality of both extraction and simulations, and provided a biologically relevant computation we could use in further experiments. We then compared models with different degrees of physical realism, from full detailed models down to models consisting of a single node, to examine the tradeoff of simulation resources required versus accuracy achieved. Our results show that much simpler models may be adequate, at least in the case of medulla neurons in Drosophila. Such models can be easily derived from fully detailed models, and result in simulations that are much smaller, much faster, and accurate enough for many purposes. Biologically, we show that a lumped neuron model reproduces the main motion detector operation, confirming the result of Gruntman, that dendritic compution is not required for this function.

}, doi = {10.1101/177113}, author = {Gornet, Jonathan and Scheffer, Louis K} } @article {47924, title = {Fully-automatic synapse prediction and validation on a large data set.}, journal = {Frontiers in Neural Circuits}, volume = {12}, year = {2016}, month = {2018 Oct 29}, pages = {87}, abstract = {

Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of synapses between neurons. As manual extraction of this information is very time-consuming, there has been extensive research effort to automatically segment the neurons to help guide and eventually replace manual tracing. Until recently, there has been comparatively less research on automatically detecting the actual synapses between neurons. This discrepancy can, in part, be attributed to several factors: obtaining neuronal shapes is a prerequisite first step in extracting a connectome, manual tracing is much more time-consuming than annotating synapses, and neuronal contact area can be used as a proxy for synapses in determining connections.
However, recent research has demonstrated that contact area alone is not a sufficient predictor of synaptic connection. Moreover, as segmentation has improved, we have observed that synapse annotation is consuming a more significant fraction of overall reconstruction time. This ratio will only get worse as segmentation improves, gating overall possible speed-up. Therefore, we address this problem by developing algorithms that automatically detect pre-synaptic neurons and their post-synaptic partners. In particular, pre-synaptic structures are detected using a Deep and Wide Multiscale Recursive Network, and post-synaptic partners are detected using a MLP with features conditioned on the local segmentation.
This work is novel because it requires minimal amount of training, leverages advances in image segmentation directly, and provides a complete solution for polyadic synapse detection. We further introduce novel metrics to evaluate our algorithm on connectomes of meaningful size. These metrics demonstrate that complete automatic prediction can be used to effectively characterize most connectivity correctly.

}, author = {Huang, Gary B. and Scheffer, Louis K and Plaza, Stephen M.} } @article {46940, title = {A common evolutionary origin for the ON- and OFF-edge motion detection pathways of the Drosophila visual system.}, journal = {Frontiers in Neural Circuits}, volume = {9}, year = {2015}, month = {2015}, pages = {33}, abstract = {

Synaptic circuits for identified behaviors in the Drosophila brain have typically been considered from either a developmental or functional perspective without reference to how the circuits might have been inherited from ancestral forms. For example, two candidate pathways for ON- and OFF-edge motion detection in the visual system act via circuits that use respectively either T4 or T5, two cell types of the fourth neuropil, or lobula plate (LOP), that exhibit narrow-field direction-selective responses and provide input to wide-field tangential neurons. T4 or T5 both have four subtypes that terminate one each in the four strata of the LOP. Representatives are reported in a wide range of Diptera, and both cell types exhibit various similarities in: (1) the morphology of their dendritic arbors; (2) their four morphological and functional subtypes; (3) their cholinergic profile in Drosophila; (4) their input from the pathways of L3 cells in the first neuropil, or lamina (LA), and by one of a pair of LA cells, L1 (to the T4 pathway) and L2 (to the T5 pathway); and (5) their innervation by a single, wide-field contralateral tangential neuron from the central brain. Progenitors of both also express the gene atonal early in their proliferation from the inner anlage of the developing optic lobe, being alone among many other cell type progeny to do so. Yet T4 receives input in the second neuropil, or medulla (ME), and T5 in the third neuropil or lobula (LO). Here we suggest that these two cell types were originally one, that their ancestral cell population duplicated and split to innervate separate ME and LO neuropils, and that a fiber crossing-the internal chiasma-arose between the two neuropils. The split most plausibly occurred, we suggest, with the formation of the LO as a new neuropil that formed when it separated from its ancestral neuropil to leave the ME, suggesting additionally that ME input neurons to T4 and T5 may also have had a common origin.

}, issn = {1662-5110}, doi = {10.3389/fncir.2015.00033}, author = {Shinomiya, Kazunori and Takemura, Shin-ya and Rivlin, Patricia K and Plaza, Stephen M and Scheffer, Louis K and Meinertzhagen, Ian A} } @article {47152, title = {Synaptic circuits and their variations within different columns in the visual system of Drosophila.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {112}, year = {2015}, month = {2015 Nov 3}, pages = {13711-6}, abstract = {

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly{\textquoteright}s compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla{\textquoteright}s neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, \<1\% of contacts are not part of a consensus circuit, and we classify those contacts that supplement (E+) or are missing from it (E-). Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form >=20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics.

}, issn = {1091-6490}, doi = {10.1073/pnas.1509820112}, author = {Takemura, Shin-ya and Xu, C Shan and Lu, Zhiyuan and Rivlin, Patricia K and Parag, Toufiq and Olbris, Donald J and Plaza, Stephen and Zhao, Ting and Katz, William T and Umayam, Lowell and Weaver, Charlotte and Hess, Harald F and Horne, Jane Anne and Nunez-Iglesias, Juan and Aniceto, Roxanne and Chang, Lei-Ann and Lauchie, Shirley and Nasca, Ashley and Ogundeyi, Omotara and Sigmund, Christopher and Takemura, Satoko and Tran, Julie and Langille, Carlie and Le Lacheur, Kelsey and McLin, Sari and Shinomiya, Aya and Chklovskii, Dmitri B and Meinertzhagen, Ian A and Scheffer, Louis K} } @article {45696, title = {A genetically specified connectomics approach applied to long-range feeding regulatory circuits.}, journal = {Nature Neuroscience}, volume = {17}, year = {2014}, month = {2014 Dec}, pages = {1830-9}, abstract = {

Synaptic connectivity and molecular composition provide a blueprint for information processing in neural circuits. Detailed structural analysis of neural circuits requires nanometer resolution, which can be obtained with serial-section electron microscopy. However, this technique remains challenging for reconstructing molecularly defined synapses. We used a genetically encoded synaptic marker for electron microscopy (GESEM) based on intra-vesicular generation of electron-dense labeling in axonal boutons. This approach allowed the identification of synapses from Cre recombinase-expressing or GAL4-expressing neurons in the mouse and fly with excellent preservation of ultrastructure. We applied this tool to visualize long-range connectivity of AGRP and POMC neurons in the mouse, two molecularly defined hypothalamic populations that are important for feeding behavior. Combining selective ultrastructural reconstruction of neuropil with functional and viral circuit mapping, we characterized some basic features of circuit organization for axon projections of these cell types. Our findings demonstrate that GESEM labeling enables long-range connectomics with molecularly defined cell types.

}, issn = {1546-1726}, doi = {10.1038/nn.3854}, author = {Atasoy, Deniz and Betley, J Nicholas and Li, Wei-Ping and Su, Helen H and Sertel, Sinem M and Scheffer, Louis K and Simpson, Julie H and Fetter, Richard D and Sternson, Scott M} } @article {46228, title = {Small sample learning of superpixel classifiers for EM segmentation- extended version.}, journal = {arXiv}, year = {2014}, month = {2014 Jun 6}, pages = {arXiv:1406.1774 [cs.CV]}, abstract = {

Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is "active semi-supervised" because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (\<20\%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

}, url = {http://arxiv.org/abs/1406.1774}, author = {Parag, Toufiq and Plaza, Stephen M and Scheffer, Louis K} } @article {6905, title = {Toward large-scale connectome reconstructions.}, journal = {Current Opinion in Neurobiology}, volume = {25C}, year = {2014}, month = {2014 Mar 2}, pages = {201-10}, abstract = {

Recent results have shown the possibility of both reconstructing connectomes of small but biologically interesting circuits and extracting from these connectomes insights into their function. However, these reconstructions were heroic proof-of-concept experiments, requiring person-months of effort per neuron reconstructed, and will not scale to larger circuits, much less the brains of entire animals. In this paper we examine what will be required to generate and use substantially larger connectomes, finding five areas that need increased attention: firstly, imaging better suited to automatic reconstruction, with excellent z-resolution; secondly, automatic detection, validation, and measurement of synapses; thirdly, reconstruction methods that keep and use uncertainty metrics for every object, from initial images, through segmentation, reconstruction, and connectome queries; fourthly, processes that are fully incremental, so that the connectome may be used before it is fully complete; and finally, better tools for analysis of connectomes, once they are obtained.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2014.01.019}, author = {Plaza, Stephen M and Scheffer, Louis K and Chklovskii, Dmitri B} } @article {6178, title = {A visual motion detection circuit suggested by Drosophila connectomics.}, journal = {Nature}, volume = {500}, year = {2013}, month = {2013 Aug 7}, pages = {175{\textendash}81}, abstract = {

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.

}, issn = {1476-4687}, doi = {doi:10.1038/nature12450}, url = {http://www.nature.com/nature/journal/v500/n7461/full/nature12450.html}, author = {Takemura, Shin-ya and Bharioke, Arjun and Lu, Zhiyuan and Nern, Aljoscha and Vitaladevuni, Shiv and Rivlin, Patricia K. and Katz, William T. and Olbris, Donald J. and Plaza, Stephen M and Winston, Philip and Zhao, Ting and Horne, Jane Anne and Fetter, Richard D and Takemura, Satoko and Blazek, Katerina and Chang, Lei-Ann and Ogundeyi, Omotara and Saunders, Mathew A. and Shapiro, Victor and Sigmund, Christopher and Rubin, Gerald M and Scheffer, Louis K and Meinertzhagen, Ian A. and Chklovskii, Dmitri B} } @article {6171, title = {Minimizing manual image segmentation turn-around time for neuronal reconstruction by embracing uncertainty.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e44448}, abstract = {

The ability to automatically segment an image into distinct regions is a critical aspect in many visual processing applications. Because inaccuracies often exist in automatic segmentation, manual segmentation is necessary in some application domains to correct mistakes, such as required in the reconstruction of neuronal processes from microscopic images. The goal of the automated segmentation tool is traditionally to produce the highest-quality segmentation, where quality is measured by the similarity to actual ground truth, so as to minimize the volume of manual correction necessary. Manual correction is generally orders-of-magnitude more time consuming than automated segmentation, often making handling large images intractable. Therefore, we propose a more relevant goal: minimizing the turn-around time of automated/manual segmentation while attaining a level of similarity with ground truth. It is not always necessary to inspect every aspect of an image to generate a useful segmentation. As such, we propose a strategy to guide manual segmentation to the most uncertain parts of segmentation. Our contributions include 1) a probabilistic measure that evaluates segmentation without ground truth and 2) a methodology that leverages these probabilistic measures to significantly reduce manual correction while maintaining segmentation quality.

}, keywords = {Animals, Automated, Computer-Assisted, Humans, Image Processing, Models, Neurons, Pattern Recognition, Theoretical, Uncertainty}, issn = {1932-6203}, doi = {10.1371/journal.pone.0044448}, author = {Plaza, Stephen M and Scheffer, Louis K and Saunders, Mathew} } @article {1234, title = {Semi-automated reconstruction of neural circuits using electron microscopy.}, journal = {Current Opinion in Neurobiology}, volume = {20}, year = {2010}, month = {2010 Oct}, pages = {667-75}, abstract = {

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience, and the focus of the nascent field of connectomics. Previously used to reconstruct the C. elegans wiring diagram, serial-section transmission electron microscopy (ssTEM) is a proven technique for the task. However, to reconstruct more complex circuits, ssTEM will require the automation of image processing. We review progress in the processing of electron microscopy images and, in particular, a semi-automated reconstruction pipeline deployed at Janelia. Drosophila circuits underlying identified behaviors are being reconstructed in the pipeline with the goal of generating a complete Drosophila connectome.

}, issn = {1873-6882}, doi = {10.1371/journal.pcbi.1001066}, author = {Chklovskii, Dmitri B and Vitaladevuni, Shiv and Scheffer, Louis K} }