A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster

Zheng, Zhihao; Lauritzen, J. Scott; Perlman, Eric; Robinson, Camenzind G.; Nichols, Matthew; Milkie, Daniel E.; Torrens, Omar; Price, J. W.; Fisher, Corey B.; Sharifi, Nadiya; Calle-Schuler, Steven A.; Kmecova, Lucia; Ali, Iqbal J.; Karsh, Bill; Trautman, Eric T.; Bogovic, John A.; Hanslovsky, Philipp; Jefferis, Gregory; Kazhdan, Michael; Khairy, Khaled; Saalfeld, Stephan; Fetter, Richard D.; Bock, Davi D.

doi:10.1016/j.cell.2018.06.019

Cited by 845 publications

(1,086 citation statements)

References 121 publications

(239 reference statements)

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“…In some cases these processes clearly involve different DANs whereas in others they appear to involve DANs that innervate the same MB compartments. More refined tools, connectomics (Zheng et al, 2018) and experiments should help reveal the full extent of functional heterogeneity.…”

Section: A Subset Of Rewarding Dans Code Learned Safetymentioning

confidence: 99%

Spaced training forms complementary long-term memories of opposite valence inDrosophila

Jacob

Waddell

2019

Preprint

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Forming long-term memory (LTM) in many cases requires repetitive experience spread over time. In Drosophila, aversive olfactory LTM is optimal following spaced training, multiple trials of differential odor conditioning with rest intervals. Studies often compare memory after spaced to that after massed training, same number of trials without interval. Here we show flies acquire additional information after spaced training, forming an aversive memory for the shock-paired odor and a 'safety-memory' for the explicitly unpaired odor. Safety-memory requires repetition, order and spacing of the training trials and relies on specific subsets of rewarding dopaminergic neurons. Co-existence of the aversive and safety memories can be measured as depression of odor-specific responses at different combinations of junctions in the mushroom body output network. Combining two particular outputs appears to signal relative safety. Learning a complementary safety memory thereby augments LTM performance after spaced training by making the odor preference more certain.

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Section: A Subset Of Rewarding Dans Code Learned Safetymentioning

confidence: 99%

Spaced training forms complementary long-term memories of opposite valence inDrosophila

Jacob

Waddell

2019

Preprint

View full text Add to dashboard Cite

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“…With recent technical advances, there has been a renaissance in the field. The first complete connectome, of an entire worm nervous system 3 has been updated and extended 4,5 , while progress in system automation [6][7][8][9][10][11] has enabled large scale studies of the nervous systems in the fly, fish, bird, and mammal 7,10,[12][13][14][15][16][17][18][19][20][21][22] . In some cases, like the fruit fly 10 , the dataset includes the totality of the adult brain, while in mammals the largest dataset (~0.07 mm 3 ) includes a fraction of a thalamic region 20 (incidentally the same structure, the dorsal lateral geniculate nucleus, was the target of one of the largest 20 th century reconstructions in the mammalian brain 23 ).…”

Section: Introductionmentioning

confidence: 99%

“…Our overarching strategy is to combine transmission electron microscopes (TEMs) with customized, precise sample handling and fast, high-resolution cameras to increase the net imaging rate 7,10,14,19 . Automation, system robustness, and integrated quality control all proved essential to collect data from a very large volume with an extremely low error rate.…”

Section: Introductionmentioning

confidence: 99%

A Petascale Automated Imaging Pipeline for Mapping Neuronal Circuits with High-throughput Transmission Electron Microscopy

Yin

Brittain

Borseth

et al. 2019

Preprint

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Serial-section electron microscopy is the method of choice for studying cellular structure and network connectivity in the brain. We have built a pipeline of parallel imaging using transmission electron automated microscopes (piTEAM) that scales this technology and enables the acquisition of petascale datasets containing local cortical microcircuits. The distributed platform is composed of multiple transmission electron microscopes that image, in parallel, different sections from the same block of tissue, all under control of a custom acquisition software (pyTEM) that implements 24/7 continuous autonomous imaging. The suitability of this architecture for large scale electron microscopy imaging was demonstrated by acquiring a volume of more than 1 mm 3 of mouse neocortex spanning four different visual areas. Over 26,500 ultrathin tissue sections were imaged, yielding a dataset of more than 2 petabytes. Our current burst imaging rate is 500 Mpixel/s (image capture only) per microscope and net imaging rate is 100 Mpixel/s (including stage movement, image capture, quality control, and post processing). This brings the combined burst acquisition rate of the pipeline to 3 Gpixel/s and the net rate to 600 Mpixel/s with six microscopes running acquisition in parallel, which allowed imaging a cubic millimeter of mouse visual cortex at synaptic resolution in less than 6 months.

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“…Research in visualization for neuroscience thus strives to develop novel visual methods for enabling neuroscientists to process, explore and filter data at multiple scales, from cellular to anatomical level. In this context, many efforts have been devoted to develop minable brain maps and to reconstruct the brain wiring and connections among individual neuron to infer networks inside and among functional areas [HCT*17,ZLP*18].…”

Section: Introductionmentioning

confidence: 99%

Interactive Volumetric Visual Analysis of Glycogen‐derived Energy Absorption in Nanometric Brain Structures

Agus

Calì

Al-Awami

et al. 2019

Computer Graphics Forum

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Digital acquisition and processing techniques are changing the way neuroscience investigation is carried out. Emerging applications range from statistical analysis on image stacks to complex connectomics visual analysis tools targeted to develop and test hypotheses of brain development and activity. In this work, we focus on neuroenergetics, a field where neuroscientists analyze nanoscale brain morphology and relate energy consumption to glucose storage in form of glycogen granules. In order to facilitate the understanding of neuroenergetic mechanisms, we propose a novel customized pipeline for the visual analysis of nanometric‐level reconstructions based on electron microscopy image data. Our framework supports analysis tasks by combining i) a scalable volume visualization architecture able to selectively render image stacks and corresponding labelled data, ii) a method for highlighting distance‐based energy absorption probabilities in form of glow maps, and iii) a hybrid connectivitybased and absorption‐based interactive layout representation able to support queries for selective analysis of areas of interest and potential activity within the segmented datasets. This working pipeline is currently used in a variety of studies in the neuroenergetics domain. Here, we discuss a test case in which the framework was successfully used by domain scientists for the analysis of aging effects on glycogen metabolism, extracting knowledge from a series of nanoscale brain stacks of rodents somatosensory cortex.

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A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster

Cited by 845 publications

References 121 publications

Spaced training forms complementary long-term memories of opposite valence inDrosophila

Spaced training forms complementary long-term memories of opposite valence inDrosophila

A Petascale Automated Imaging Pipeline for Mapping Neuronal Circuits with High-throughput Transmission Electron Microscopy

Interactive Volumetric Visual Analysis of Glycogen‐derived Energy Absorption in Nanometric Brain Structures

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