Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience

Parekh, Ruchi; Ascoli, Giorgio A.

doi:10.1016/j.neuron.2013.03.008

Cited by 199 publications

(146 citation statements)

References 182 publications

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“…In neuroscience, the DIADEM challenge (for digital reconstructions of axonal and dendritic morphology) addresses a similar need (Parekh and Ascoli, 2013).…”

Section: Rsml Promotes the Use Of A Central Repository For Root Archimentioning

confidence: 99%

Root System Markup Language: Toward a Unified Root Architecture Description Language

et al. 2015

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“…In neuroscience, the DIADEM challenge (for digital reconstructions of axonal and dendritic morphology) addresses a similar need (Parekh and Ascoli, 2013).…”

Section: Rsml Promotes the Use Of A Central Repository For Root Archimentioning

confidence: 99%

Root System Markup Language: Toward a Unified Root Architecture Description Language

et al. 2015

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“…The necessity for analysing neuronal morphology has led to the development of a variety of image analysis strategies that mainly differ in their level of accuracy and throughput (for an overview of tools, see Parekh et al 39 ; Fig. 2).…”

Section: Neuronal Morphologymentioning

confidence: 99%

Image Informatics Strategies for Deciphering Neuronal Network Connectivity

Detrez

Verstraelen

Gebuis

et al. 2016

Focus on Bio-Image Informatics

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Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphological plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular communication and the associated calcium-bursting behaviour. In vitro cultured neuronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies.2

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“…While this is still a major challenge for large animals such as primates (Helmstaedter 2013), acquisition of single-cell level connectomes for small animals, such as the Drosophila melanogaster (fruit fly), has seen rapid progress (Chiang et al 2011;Takemura et al 2013;Shinomiya et al 2011). Therefore, we suggest that the Drosophila is currently one of the best model animals for developing a high-resolution full brain computational model due to the availability of extensive neuron databases and neuroinformatics tools (Chiang et al 2011;Shinomiya et al 2011;Osumi-Sutherland et al 2012;Parekh and Ascoli 2013;Ukani et al fly brain may enable us to investigate how different subsystems in the brain integrate and how high-level behavior is carried out.…”

Section: Introductionmentioning

confidence: 99%

A single-cell level and connectome-derived computational model of the Drosophila brain

Huang

Wang

et al. 2018

Preprint

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Computer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits.Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible. Supported by FlyCircuit (http://www.flycircuit.tw), one of the largest Drosophila neuron image databases, we began a long-term project with the goal to construct a whole-brain spiking network model of the Drosophila brain. In this paper, we report the outcome of the first phase of the project. We developed the Flysim platform, which 1) identifies the polarity of each neuron arbor, 2) predicts connections between neurons, 3) translates morphology data from the database into physiology parameters for computational modeling, 4) reconstructs a brain-wide network model, which consists of 20,089 neurons and 1,044,020 synapses, and 5) performs computer simulations of the resting state. We compared the reconstructed brain network with a randomized brain network by shuffling the connections of each neuron. We found that the reconstructed brain can be easily stabilized by implementing synaptic short-term depression, while the randomized one exhibited seizure-like firing activity under the same treatment.Furthermore, the reconstructed Drosophila brain was structurally and dynamically more diverse than the randomized one and exhibited both Poisson-like and patterned firing activities. Despite being at its early stage of development, this single-cell level brain model allows us to study some of the fundamental properties of neural networks including network balance, critical behavior, long-term stability, and plasticity.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience

Cited by 199 publications

References 182 publications

Root System Markup Language: Toward a Unified Root Architecture Description Language

Root System Markup Language: Toward a Unified Root Architecture Description Language

Image Informatics Strategies for Deciphering Neuronal Network Connectivity

A single-cell level and connectome-derived computational model of the Drosophila brain

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