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51 Publications

Showing 11-20 of 51 results
05/20/18 | Of what use is connectomics? A personal perspective on the connectome.
Meinertzhagen IA
The Journal of Experimental Biology. 2018 May 20;221(Pt 10):. doi: 10.1242/jeb.164954

The brain is a network of neurons and its biological output is behaviour. This is an exciting age, with a growing acknowledgement that the comprehensive compilation of synaptic circuits densely reconstructed in the brains of model species is now both technologically feasible and a scientifically enabling possibility in neurobiology, much as 30 years ago genomics was in molecular biology and genetics. Implemented by huge advances in electron microscope technology, especially focused ion beam-scanning electron microscope (FIB-SEM) milling (see Glossary), image capture and alignment, and computer-aided reconstruction of neuron morphologies, enormous progress has been made in the last decade in the detailed knowledge of the actual synaptic circuits formed by real neurons, in various brain regions of the fly It is useful to distinguish synaptic pathways that are major, with 100 or more presynaptic contacts, from those that are minor, with fewer than about 10; most neurites are both presynaptic and postsynaptic, and all synaptic sites have multiple postsynaptic dendrites. Work on has spearheaded these advances because cell numbers are manageable, and neuron classes are morphologically discrete and genetically identifiable, many confirmed by reporters. Recent advances are destined within the next few years to reveal the complete connectome in an adult fly, paralleling advances in the larval brain that offer the same prospect possibly within an even shorter time frame. The final amendment and validation of segmented bodies by human proof-readers remains the most time-consuming step, however. The value of a complete connectome in is that, by targeting to specific neurons transgenes that either silence or activate morphologically identified circuits, and then identifying the resulting behavioural outcome, we can determine the causal mechanism for behaviour from its loss or gain. More importantly, the connectome reveals hitherto unsuspected pathways, leading us to seek novel behaviours for these. Circuit information will eventually be required to understand how differences between brains underlie differences in behaviour, and especially to herald yet more advanced connectomic strategies for the vertebrate brain, with an eventual prospect of understanding cognitive disorders having a connectomic basis. Connectomes also help us to identify common synaptic circuits in different species and thus to reveal an evolutionary progression in candidate pathways.

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07/26/17 | Recent progress in the 3D reconstruction of Drosophila neural circuits.
Shinomiya K, Ito M
Decoding Neural Circuit Structure and Function:63-89. doi: 10.1007/978-3-319-57363-2_3

The brain of fruit fly Drosophila melanogaster has been used as a model system for functional analysis of neuronal circuits, including connectomics research, due to its modest size (~700 μm) and availability of abundant molecular genetics tools for visualizing neurons. Three-dimensional (3D) reconstruction of high-resolution images of neurons or circuits visualized with appropriate methods is a critical step for obtaining information such as morphology and connectivity patterns of neuronal circuits. In this chapter, we introduce methods for generating 3D reconstructed images with data acquired from confocal laser scanning microscopy (CLSM) or electron microscopy (EM) to analyze neuronal circuits found in the central nervous system (CNS) of the fruit fly. Comparisons of different algorithms and strategies for reconstructing neuronal circuits, using actual studies as references, will be discussed within this chapter.

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07/18/17 | A connectome of a learning and memory center in the adult Drosophila brain.
Takemura S, Aso Y, Hige T, Wong AM, Lu Z, Xu CS, Rivlin PK, Hess HF, Zhao T, Parag T, Berg S, Huang G, Katz WT, Olbris DJ, Plaza SM, Umayam LA, Aniceto R, Chang L, Lauchie S, et al
eLife. 2017 Jul 18;6:e26975. doi: 10.7554/eLife.26975

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB’s α lobe, using a dataset of isotropic 8-nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple en passant synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only six percent of KC>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC>DAN and DAN>MBON. DAN activation produces a slow depolarization of the MBON in these DAN>MBON synapses and can weaken memory recall.

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04/22/17 | The comprehensive connectome of a neural substrate for 'ON' motion detection in Drosophila.
Takemura S, Nern A, Chklovskii DB, Scheffer LK, Rubin GM, Meinertzhagen IA
eLife. 2017 Apr 22;6:. doi: 10.7554/eLife.24394

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's motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4'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.

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03/31/17 | Automatic tracing of ultra-volumes of neuronal images.
Peng H, Zhou Z, Meijering E, Zhao T, Ascoli GA, Hawrylycz M
Nature Methods. 2017 Mar 31;14(4):332-333. doi: 10.1038/nmeth.4233
01/31/17 | Multicut brings automated neurite segmentation closer to human performance.
Beier T, Pape C, Rahaman N, Prange T, Berg S, Bock DD, Cardona A, Knott GW, Plaza SM, Scheffer LK, Koethe U, Kreshuk A, Hamprecht FA
Nature Methods. 2017 Jan 31;14(2):101-102. doi: 10.1038/nmeth.4151
06/04/16 | Rapid and semi-automated extraction of neuronal cell bodies and nuclei from electron microscopy image stacks.
Holcomb PS, Morehead M, Doretto G, Chen P, Berg S, Plaza S, Spirou G
Methods in molecular biology (Clifton, N.J.). 2016;1427:277-90. doi: 10.1007/978-1-4939-3615-1_16

Connectomics-the study of how neurons wire together in the brain-is at the forefront of modern neuroscience research. However, many connectomics studies are limited by the time and precision needed to correctly segment large volumes of electron microscopy (EM) image data. We present here a semi-automated segmentation pipeline using freely available software that can significantly decrease segmentation time for extracting both nuclei and cell bodies from EM image volumes.

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12/17/15 | Ig superfamily ligand and receptor pairs expressed in synaptic partners in Drosophila.
Tan L, Zhang KX, Pecot MY, Nagarkar-Jaiswal S, Lee P, Takemura S, McEwen JM, Nern A, Xu S, Tadros W, Chen Z, Zinn K, Bellen HJ, Morey M, Zipursky SL
Cell. 2015 Dec 17;163(7):1756-69. doi: 10.1016/j.cell.2015.11.021

Information processing relies on precise patterns of synapses between neurons. The cellular recognition mechanisms regulating this specificity are poorly understood. In the medulla of the Drosophila visual system, different neurons form synaptic connections in different layers. Here, we sought to identify candidate cell recognition molecules underlying this specificity. Using RNA sequencing (RNA-seq), we show that neurons with different synaptic specificities express unique combinations of mRNAs encoding hundreds of cell surface and secreted proteins. Using RNA-seq and protein tagging, we demonstrate that 21 paralogs of the Dpr family, a subclass of immunoglobulin (Ig)-domain containing proteins, are expressed in unique combinations in homologous neurons with different layer-specific synaptic connections. Dpr interacting proteins (DIPs), comprising nine paralogs of another subclass of Ig-containing proteins, are expressed in a complementary layer-specific fashion in a subset of synaptic partners. We propose that pairs of Dpr/DIP paralogs contribute to layer-specific patterns of synaptic connectivity.

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12/11/15 | Efficient classifier training to minimize false merges in electron microscopy segmentation.
Parag T, Ciresan D, Giusti A
IEEE International Conference on Computer Vision. 2015:657-65
12/03/15 | Cortex commands the performance of skilled movement.
Guo J, Graves AR, Guo WW, Zheng J, Lee A, Rodríguez-González J, Li N, Macklin JJ, Phillips JW, Mensh BD, Branson K, Hantman AW
eLife. 2015 Dec 3;4:. doi: 10.7554/eLife.10774

Mammalian cerebral cortex is accepted as being critical for voluntary motor control, but what functions depend on cortex is still unclear. Here we used rapid, reversible optogenetic inhibition to test the role of cortex during a head-fixed task in which mice reach, grab, and eat a food pellet. Sudden cortical inhibition blocked initiation or froze execution of this skilled prehension behavior, but left untrained forelimb movements unaffected. Unexpectedly, kinematically normal prehension occurred immediately after cortical inhibition even during rest periods lacking cue and pellet. This 'rebound' prehension was only evoked in trained and food-deprived animals, suggesting that a motivation-gated motor engram sufficient to evoke prehension is activated at inhibition's end. These results demonstrate the necessity and sufficiency of cortical activity for enacting a learned skill.

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