Search

Your search keyword '"Shirley Lauchie"' showing total 5 results
Start Over Author "Shirley Lauchie" Topic drosophila melanogaster
5 results on '"Shirley Lauchie"'

1. A connectome and analysis of the adult Drosophila central brain

Author

Temour Tokhi, Tom Dolafi, Nneoma Okeoma, Tanya Wolff, Philip M Hubbard, Kazunori Shinomiya, Madelaine K Robertson, Gerald M. Rubin, Gregory S.X.E. Jefferis, Christopher J Knecht, Laramie Leavitt, Alia Suleiman, Satoko Takemura, Christopher Ordish, Jody Clements, Ian A. Meinertzhagen, Alexander Shakeel Bates, Takashi Kawase, Samantha Finley, Nicholas Padilla, Jackie Swift, C. Shan Xu, Stuart Berg, Tyler Paterson, Ashley L Scott, Erika Neace, Shirley Lauchie, Sean M Ryan, Emily M Joyce, Shin-ya Takemura, Tim Blakely, Michael A Cook, Christopher Patrick, Bryon Eubanks, Audrey Francis, Robert Svirskas, William T. Katz, Eric T. Trautman, Caroline Mooney, Ting Zhao, Nicole A Kirk, Megan Sammons, Brandon S Canino, Reed A. George, Louis K. Scheffer, Jolanta A. Borycz, Jon Thomson Rymer, Natasha Cheatham, Dagmar Kainmueller, Gary B. Huang, Khaled Khairy, Nicole Neubarth, Elliott E Phillips, John A. Bogovic, Neha Rampally, Larry Lindsey, Viren Jain, David G. Ackerman, Jane Anne Horne, Kelli Fairbanks, Lowell Umayam, Jens Goldammer, Emily M Phillips, Donald J. Olbris, Feng Li, Emily A Manley, Philipp Schlegel, Hideo Otsuna, Marta Costa, Stephen M. Plaza, Omotara Ogundeyi, Samantha Ballinger, Charli Maldonado, Kelsey Smith, Gary Patrick Hopkins, Vivek Jayaraman, Emily Tenshaw, Julie Kovalyak, Peter H. Li, Tansy Yang, Masayoshi Ito, Miatta Ndama, Claire Smith, Michał Januszewski, Alanna Lohff, SungJin Kim, Anne K Scott, Kei Ito, Iris Talebi, Jeremy Maitlin-Shepard, Nora Forknall, Marisa Dreher, Harald F. Hess, Sari McLin, Patricia K. Rivlin, Dennis A Bailey, Kenneth J. Hayworth, Octave Duclos, Caitlin Ribeiro, John J. Walsh, Zhiyuan Lu, Dorota Tarnogorska, Ruchi Parekh, Aya Shinomiya, Stephan Saalfeld, Margaret A Sobeski, Natalie L Smith, Chelsea X Alvarado, Scheffer, Louis K [0000-0002-3289-6564], Xu, C Shan [0000-0002-8564-7836], Januszewski, Michal [0000-0002-3480-2744], Lu, Zhiyuan [0000-0002-4128-9774], Takemura, Shin-ya [0000-0003-2400-6426], Huang, Gary B [0000-0002-9606-3510], Shinomiya, Kazunori [0000-0003-0262-6421], Maitlin-Shepard, Jeremy [0000-0001-8453-7961], Hubbard, Philip M [0000-0002-6746-5035], Katz, William T [0000-0002-9417-6212], Ackerman, David [0000-0003-0172-6594], Blakely, Tim [0000-0003-0995-5471], Bogovic, John [0000-0002-4829-9457], Kainmueller, Dagmar [0000-0002-9830-2415], Khairy, Khaled A [0000-0002-9274-5928], Li, Peter H [0000-0001-6193-4454], Trautman, Eric T [0000-0001-8588-0569], Bates, Alexander S [0000-0002-1195-0445], Goldammer, Jens [0000-0002-5623-8339], Wolff, Tanya [0000-0002-8681-1749], Svirskas, Robert [0000-0001-8374-6008], Schlegel, Philipp [0000-0002-5633-1314], Knecht, Christopher J [0000-0002-5663-5967], Alvarado, Chelsea X [0000-0002-5973-7512], Bailey, Dennis A [0000-0002-4675-8373], Borycz, Jolanta A [0000-0002-4402-9230], Canino, Brandon S [0000-0002-8454-865X], Cook, Michael [0000-0002-7892-6845], Dreher, Marisa [0000-0002-0041-9229], Eubanks, Bryon [0000-0002-9288-2009], Fairbanks, Kelli [0000-0002-6601-4830], Finley, Samantha [0000-0002-8086-206X], Forknall, Nora [0000-0003-2139-7599], Francis, Audrey [0000-0003-1974-7174], Joyce, Emily M [0000-0001-5794-6321], Kovalyak, Julie [0000-0001-7864-7734], Lauchie, Shirley A [0000-0001-8223-9522], Lohff, Alanna [0000-0002-1242-1836], McLin, Sari [0000-0002-9120-1136], Patrick, Christopher M [0000-0001-8830-1892], Phillips, Elliott E [0000-0002-4918-2058], Phillips, Emily M [0000-0001-7615-301X], Robertson, Madelaine K [0000-0002-1764-0245], Rymer, Jon Thomson [0000-0002-4271-6774], Ryan, Sean M [0000-0002-8879-6108], Sammons, Megan [0000-0003-4516-5928], Shinomiya, Aya [0000-0002-6358-9567], Smith, Natalie L [0000-0002-8271-9873], Swift, Jackie [0000-0003-1321-8183], Takemura, Satoko [0000-0002-2863-0050], Talebi, Iris [0000-0002-0173-8053], Tarnogorska, Dorota [0000-0002-7063-6165], Walsh, John J [0000-0002-7176-4708], Yang, Tansy [0000-0003-1131-0410], Horne, Jane Anne [0000-0001-9673-2692], Parekh, Ruchi [0000-0002-8060-2807], Jayaraman, Vivek [0000-0003-3680-7378], Costa, Marta [0000-0001-5948-3092], Jefferis, Gregory SXE [0000-0002-0587-9355], Ito, Kei [0000-0002-7274-5533], Saalfeld, Stephan [0000-0002-4106-1761], Rubin, Gerald M [0000-0001-8762-8703], Hess, Harald F [0000-0003-3000-1533], Plaza, Stephen M [0000-0001-7425-8555], Apollo - University of Cambridge Repository, Takemura, Shin-Ya [0000-0003-2400-6426], and Jefferis, Gregory Sxe [0000-0002-0587-9355]

Subjects
Male, Computer science, computational biology, 0302 clinical medicine, Drosophila Proteins, Research article, Biology (General), Neurons, Cognitive science, 0303 health sciences, biology, D. melanogaster, General Neuroscience, connectome, Brain, systems biology, graph properties, General Medicine, Human brain, Drosophila melanogaster, medicine.anatomical_structure, Connectome, Medicine, Drosophila, Female, synapse detecton, Insight, Function and Dysfunction of the Nervous System, cell types, Research Article, Computational and Systems Biology, brain regions, Connectomes, QH301-705.5, Ubiquitin-Protein Ligases, Science, connectome reconstuction methods, Small mammal, Central region, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, medicine, Animals, 030304 developmental biology, General Immunology and Microbiology, biology.organism_classification, synapse detection, Synapses, 030217 neurology & neurosurgery, Neuroscience
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 Drosophila melanogaster. 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’s brain., eLife digest Animal brains of all sizes, from the smallest to the largest, work in broadly similar ways. Studying the brain of any one animal in depth can thus reveal the general principles behind the workings of all brains. The fruit fly Drosophila is a popular choice for such research. With about 100,000 neurons – compared to some 86 billion in humans – the fly brain is small enough to study at the level of individual cells. But it nevertheless supports a range of complex behaviors, including navigation, courtship and learning. Thanks to decades of research, scientists now have a good understanding of which parts of the fruit fly brain support particular behaviors. But exactly how they do this is often unclear. This is because previous studies showing the connections between cells only covered small areas of the brain. This is like trying to understand a novel when all you can see is a few isolated paragraphs. To solve this problem, Scheffer, Xu, Januszewski, Lu, Takemura, Hayworth, Huang, Shinomiya et al. prepared the first complete map of the entire central region of the fruit fly brain. The central brain consists of approximately 25,000 neurons and around 20 million connections. To prepare the map – or connectome – the brain was cut into very thin 8nm slices and photographed with an electron microscope. A three-dimensional map of the neurons and connections in the brain was then reconstructed from these images using machine learning algorithms. Finally, Scheffer et al. used the new connectome to obtain further insights into the circuits that support specific fruit fly behaviors. The central brain connectome is freely available online for anyone to access. When used in combination with existing methods, the map will make it easier to understand how the fly brain works, and how and why it can fail to work correctly. Many of these findings will likely apply to larger brains, including our own. In the long run, studying the fly connectome may therefore lead to a better understanding of the human brain and its disorders. Performing a similar analysis on the brain of a small mammal, by scaling up the methods here, will be a likely next step along this path.

Published
2020

2. A Connectome and Analysis of the Adult Drosophila Central Brain

Author

C. Shan Xu, Jackie Swift, Miatta Ndama, Philipp Schlegel, SungJin Kim, Khaled Khairy, Christopher Ordish, Omotara Ogundeyi, Kelli Fairbanks, Kenneth J. Hayworth, Samantha Finley, Natasha Cheatham, Nora Forknall, Laramie Leavitt, Temour Tokhi, Nicole A Kirk, Shin-ya Takemura, Nneoma Okeoma, Robert Svirskas, Kazunori Shinomiya, Madelaine K Robertson, Caitlin Ribeiro, Christopher J Knecht, Emily M Joyce, Margaret A Sobeski, Ruchi Parekh, Alia Suleiman, Shirley Lauchie, Sean M Ryan, Iris Talebi, Harald F. Hess, Christopher Patrick, William T. Katz, Stephen M. Plaza, Dagmar Kainmueller, Feng Li, Natalie L Smith, Michał Januszewski, Satoko Takemura, Chelsea X Alvarado, Michael A Cook, Sari McLin, Tom Dolafi, Hideo Otsuna, Jeremy Maitin-Shepard, Kei Ito, Viren Jain, Donald J. Olbris, Tanya Wolff, Takashi Kawase, Tyler Paterson, Patricia K. Rivlin, Jolanta A. Borycz, Ashley L Scott, Claire Smith, Nicholas Padilla, Gary Patrick Hopkins, Vivek Jayaraman, Emily Tenshaw, Zhiyuan Lu, Stuart Berg, Dorota Tarnogorska, Samantha Ballinger, Audrey Francis, Julie Kovalyak, Ting Zhao, Anne K Scott, Alanna Lohff, Caroline Mooney, Brandon S Canino, Gary B. Huang, Jon Thomson Rymer, Marisa Dreher, Jody Clements, Nicole Neubarth, Larry Lindsey, John A. Bogovic, David G. Ackerman, Jane Anne Horne, Louis K. Scheffer, Elliott E Phillips, Lowell Umayam, Jens Goldammer, Eric T. Trautman, Emily A Manley, Charli Maldonado, Peter H. Li, Octave Duclos, John J. Walsh, Stephan Saalfeld, Reed A. George, Gerald M. Rubin, Philip M Hubbard, Ian A. Meinertzhagen, Emily M Phillips, Masayoshi Ito, Erika Neace, Kelsey Smith, Bryon Eubanks, Neha Rampally, Tim Blakely, Tansy Yang, Dennis A Bailey, Megan Sammons, and Aya Shinomiya

Subjects
0303 health sciences, Cell type, biology, Computer science, biology.organism_classification, Synapse, 03 medical and health sciences, 0302 clinical medicine, Connectome, Biological neural network, Drosophila melanogaster, Function and Dysfunction of the Nervous System, Neuroscience, 030217 neurology & neurosurgery, 030304 developmental biology
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 Drosophila melanogaster. 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’s brain.

Published
2020
Full Text
View/download PDF

3. A Connectome of the Adult Drosophila Central Brain

Author

Audrey Francis, Ting Zhao, Feng Li, Megan Sammons, Madelaine K Robertson, SungJin Kim, Tyler Paterson, Philipp Schlegel, Chelsea X Alvarado, Viren Jain, Brandon S Canino, Omotara Ogundeyi, Nora Forknall, Dagmar Kainmueller, Tansy Yang, Natasha Cheatham, Neha Rampally, Caitlin Ribeiro, Kimothy L. Smith, Emily M Phillips, Ruchi Parekh, Jackie Swift, Donald J. Olbris, Takashi Kawase, Jon Thomson Rymer, Zhiyuan Lu, Nicholas Padilla, Christopher Ordish, Dorota Tarnogorska, Nicole Neubarth, Aya Shinomiya, Miatta Ndama, Samantha Finley, Stuart Berg, Erika Neace, Bryon Eubanks, John A. Bogovic, David G. Ackerman, Robert Svirskas, Sari McLin, Emily A Manley, Jane Anne Horne, Michael A Cook, Samantha Ballinger, Michał Januszewski, Jeremy Maitin-Shepard, Caroline Mooney, Nicole A Kirk, Shin-ya Takemura, Iris Talebi, Temour Tokhi, Kei K. Ito, Khaled Khairy, Stephen M. Plaza, Julie Kovalyak, Patricia K. Rivlin, Emily M Joyce, Kelli Fairbanks, Philip M Hubbard, Charli Maldonado, Nneoma Okeoma, Hideo Otsuna, Laurence F. Lindsey, Tim Blakely, Gerald M. Rubin, Alanna Lohff, William T. Katz, Anne K Scott, Mutsumi Ito, Peter H. Li, Ian A. Meinertzhagen, Natalie L Smith, Gary B. Huang, Dennis A Bailey, Reed A. George, Kenneth J. Hayworth, Tom Dolafi, Marisa Dreher, Tanya Wolff, Kazunori Shinomiya, Harald F. Hess, E.T. Troutman, Christopher J Knecht, Gary Patrick Hopkins, Alia Suleiman, Vivek Jayaraman, Emily Tenshaw, Octave Duclos, John J. Walsh, Stephan Saalfeld, Louis K. Scheffer, Elliott E Phillips, Lowell Umayam, Jens Goldammer, Sobeski, Jody Clements, Ashley L Scott, Shirley Lauchie, Sean M Ryan, Christopher Patrick, Jolanta A. Borycz, Claire Smith, C.S. Xu, and Laramie Leavitt

Subjects
Cell type, Computer science, Cell, Machine learning, computer.software_genre, Synapse, 03 medical and health sciences, 0302 clinical medicine, medicine, Biological neural network, 030304 developmental biology, Structure (mathematical logic), 0303 health sciences, biology, business.industry, Motor control, biology.organism_classification, Associative learning, medicine.anatomical_structure, Mushroom bodies, Identity (object-oriented programming), Connectome, Artificial intelligence, Drosophila melanogaster, Function and Dysfunction of the Nervous System, business, computer, 030217 neurology & neurosurgery
Abstract

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

Published
2020
Full Text
View/download PDF

4. Comparisons between the ON- and OFF-edge motion pathways in the Drosophila brain

Author

Natasha Cheatham, Iris Talebi, Aya Shinomiya, C. Shan Xu, Claire Smith, Omotara Ogundeyi, David Peel, Kazunori Shinomiya, Shirley Lauchie, Erika Neace, Namra Ansari, Louis K. Scheffer, Roxanne Aniceto, Patricia K. Rivlin, Toufiq Parag, Zhiyuan Lu, Ian A. Meinertzhagen, Gary B. Huang, Aljoscha Nern, Christopher Ordish, Satoko Takemura, and Stephen M. Plaza

Subjects
Computer science, QH301-705.5, neuroanatomy, Science, Models, Neurological, Motion Perception, General Biochemistry, Genetics and Molecular Biology, Motion (physics), Synapse, medicine, motion detection, Image Processing, Computer-Assisted, Animals, Biology (General), Crosses, Genetic, Neurons, optic lobe, General Immunology and Microbiology, biology, electron microscopy, D. melanogaster, General Neuroscience, connectome, Homozygote, Optic Lobe, Nonmammalian, Brain, Motion detection, General Medicine, Dendrites, biology.organism_classification, Lobe, medicine.anatomical_structure, Drosophila melanogaster, Receptive field, Synapses, Connectome, Medicine, visual system, Female, Photoreceptor Cells, Invertebrate, Neuroscience, Neuroanatomy, Research Article
Abstract

Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In Drosophila melanogaster, recently discovered synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest a motion model that is increasingly intricate when compared with the ubiquitous Hassenstein-Reichardt model. By contrast, 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 the 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. Although the two pathways are probably evolutionarily linked and exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.

Published
2019

5. Synaptic circuits and their variations within different columns in the visual system of Drosophila

Author

Ting Zhao, C. Shan Xu, Aya Shinomiya, Omotara Ogundeyi, Roxanne Aniceto, Stephen M. Plaza, Toufiq Parag, Dmitri B. Chklovskii, Shin-ya Takemura, William T. Katz, Juan Nunez-Iglesias, Ian A. Meinertzhagen, Sari McLin, Patricia K. Rivlin, Kelsey Le Lacheur, Shirley Lauchie, Julie Tran, Ashley Nasca, Jane Anne Horne, Christopher Sigmund, Lei Ann Chang, Louis K. Scheffer, Lowell Umayam, Satoko Takemura, Carlie Langille, Donald J. Olbris, Charlotte Weaver, Zhiyuan Lu, and Harald F. Hess

Subjects
Connectomics, Multidisciplinary, Connection (vector bundle), Compound eye, Biology, Biological Sciences, Synapse, medicine.anatomical_structure, Drosophila melanogaster, Postsynaptic potential, Synapses, Neuropil, medicine, Biological neural network, Animals, Neuroscience, Vision, Ocular, Electronic circuit
Abstract

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly'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'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

Published
2015

Catalog

Books, media, physical & digital resources
See catalog results