Development of NeuronBank: A Federation of Customizable Knowledge Bases of Neuronal Circuitry

Calin-Jageman, Robert; Dhawan, Akshaye; Yang, Hong Yul; Wang, Hsiu-Chung; Tian, Hao; Phoungphol, Piyaphol; Frederick, C.; Balasooriya, Janaka; Chen, Yan; Prasad, Sushil K.; Sunderraman, Rajshekhar; Zhu, Ying; Katz, Paul S.

doi:10.1109/services.2007.29

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…4). An ultimate goal is for the algorithms provided here to contribute to a NeuronBank-like database, such as that created for individually identified invertebrate neurons (Calin-Jageman et al, 2007).…”

Section: Presentation Of Resultsmentioning

confidence: 99%

“…The impact of neuronal heterogeneity on the information carrying capacity of such systems has recently been investigated (Shamir & Sompolinsky, 2006). With regard to unique, individually identifiable neurons, Calin-Jageman et al (2007) have attempted to codify diverse experimental data from invertebrate animals. Studies of individually identified neurons have led to major insights into the operations of neural circuits in marine snails, crustaceans, leeches and insects (see, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Further research, into these objects' anatomical and phenotypic features would be needed to more conclusively compare manual vs. algorithmic performance. Eventually, automation may offer superior performance in classification while also helping tie data from diverse sources into a common, template-driven frameworkan objective currently being pursued in invertebrate nervous systems (Calin-Jageman et al, 2007) and, in a more general sense, in vertebrate animals (Dyhrfjeld-Johnsen et al, 2005;Bota & Swanson, 2007;Martone et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

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Automated identification of neurons in 3D confocal datasets from zebrafish brainstem

Kamali

Day

Brooks

et al. 2009

Journal of Microscopy

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SummaryMany kinds of neuroscience data are being acquired regarding the dynamic behaviour and phenotypic diversity of nerve cells. But as the size, complexity and numbers of 3D neuroanatomical datasets grow ever larger, the need for automated detection and analysis of individual neurons takes on greater importance. We describe here a method that detects and identifies neurons within confocal image stacks acquired from the zebrafish brainstem. The first step is to create a template that incorporates the location of all known neurons within a population -in this case the population of reticulospinal cells. Once created, the template is used in conjunction with a sequence of algorithms to determine the 3D location and identity of all fluorescent neurons in each confocal dataset. After an image registration step, neurons are segmented within the confocal image stack and subsequently localized to specific locations within the brainstem template -in many instances identifying neurons as specific, individual reticulospinal cells. This image-processing sequence is fully automated except for the initial selection of three registration points on a maximum projection image. In analysing confocal image stacks that ranged considerably in image quality, we found that this method correctly identified on average ∼80% of the neurons (if we assume that manual detection by experts constitutes 'ground truth'). Because this identification can be generated approximately 100 times faster than manual identification, it offers a considerable time savings for the investigation of zebrafish reticulospinal neurons. In addition to its cell identification function, this protocol might also be integrated with stereological techniques to enhance quantification of neurons in larger databases. Our focus has been on zebrafish brainstem systems, but the Correspondence to: Donald M. O'Malley.

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Section: Presentation Of Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Automated identification of neurons in 3D confocal datasets from zebrafish brainstem

Kamali

Day

Brooks

et al. 2009

Journal of Microscopy

View full text Add to dashboard Cite

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“…In an effort to create a tool that could begin to address some of the needs mentioned, we embarked on the creation of a neural circuit atlas called NeuronBank (Calin-Jageman et al, 2007a ). Here, we will explain the fundamental organization of NeuronBank and demonstrate some of its functionality using the Tritonia nervous system as an example.…”

Section: Introductionmentioning

confidence: 99%

NeuronBank: a tool for cataloging neuronal circuitry

Katz

Calin-Jageman

Dhawan

et al. 2010

Front. Syst. Neurosci.

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The basic unit of any nervous system is the neuron. Therefore, understanding the operation of nervous systems ultimately requires an inventory of their constituent neurons and synaptic connectivity, which form neural circuits. The presence of uniquely identifiable neurons or classes of neurons in many invertebrates has facilitated the construction of cellular-level connectivity diagrams that can be generalized across individuals within a species. Homologous neurons can also be recognized across species. Here we describe , a web-based tool that we are developing for cataloging, searching, and analyzing neuronal circuitry within and across species. Information from a single species is represented in an individual branch of NeuronBank. Users can search within a branch or perform queries across branches to look for similarities in neuronal circuits across species. The branches allow for an extensible ontology so that additional characteristics can be added as knowledge grows. Each entry in NeuronBank generates a unique accession ID, allowing it to be easily cited. There is also an automatic link to a Wiki page allowing an encyclopedic explanation of the entry. All of the 44 previously published neurons plus one previously unpublished neuron from the mollusc, Tritonia diomedea, have been entered into a branch of NeuronBank as have 4 previously published neurons from the mollusc, Melibe leonina. The ability to organize information about neuronal circuits will make this information more accessible, ultimately aiding research on these important models.

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Semiautomatic neuron segmentation and identification in zebrafish brainstem

Kamali¹

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Development of NeuronBank: A Federation of Customizable Knowledge Bases of Neuronal Circuitry

Cited by 3 publications

References 24 publications

Automated identification of neurons in 3D confocal datasets from zebrafish brainstem

Automated identification of neurons in 3D confocal datasets from zebrafish brainstem

NeuronBank: a tool for cataloging neuronal circuitry

Semiautomatic neuron segmentation and identification in zebrafish brainstem

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