Evidence map›Paper›PMID 29360104›Full record

ArticleScientific data2018

Design and implementation of multi-signal and time-varying neural reconstructions.

Sumit Nanda, Hanbo Chen, Ravi Das, Shatabdi Bhattacharjee, Hermann Cuntz, Benjamin Torben-Nielsen, Hanchuan Peng, Daniel N Cox, Erik De Schutter, Giorgio A Ascoli

Abstract read
In one paragraph

Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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  12. Efficient metadata mining of web-accessible neural morphologies.Progress in biophysics and molecular biology · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Sumit NandaCenter for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA.
Hanbo ChenAllen Institute for Brain Science, Seattle, WA 98109, USA.ORCID 0000-0002-8094-1761
Ravi DasNeuroscience Institute, Georgia State University, Atlanta, GA 30303, USA.
Shatabdi BhattacharjeeNeuroscience Institute, Georgia State University, Atlanta, GA 30303, USA.
Hermann CuntzErnst Strüngmann Institute (ESI), Frankfurt/Main D-60528, Germany.ORCID 0000-0001-5445-0507
Benjamin Torben-NielsenUniversity of Hertfordshire, Hatfield AL10 9AB, UK.
Hanchuan PengAllen Institute for Brain Science, Seattle, WA 98109, USA.
Daniel N CoxNeuroscience Institute, Georgia State University, Atlanta, GA 30303, USA.ORCID 0000-0001-9191-9212
Erik De SchutterOkinawa Institute of Technology, Okinawa 904-0495, Japan.ORCID 0000-0001-8618-5138
Giorgio A AscoliCenter for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA.

Funding

Generation and Description of Neuronal Morphology and ConnectivityR01NS039600 · NINDS · GEORGE MASON UNIVERSITY · PI ASCOLI, GIORGIO A · 1999 to 2024
$6.9M
Cytoskeletal mechanisms of dendrite arbor shape developmentR01NS086082 · NINDS · GEORGIA STATE UNIVERSITY · PI ASCOLI, GIORGIO A, COX, DANIEL N · 2013 to 2023
$3.1M
NINDS NIH HHS R01 NS039600NINDS NIH HHS R01 NS086082
6 · The paper itself

Abstract

Several efficient procedures exist to digitally trace neuronal structure from light microscopy, and mature community resources have emerged to store, share, and analyze these datasets. In contrast, the quantification of intracellular distributions and morphological dynamics is not yet standardized. Current widespread descriptions of neuron morphology are static and inadequate for subcellular characterizations. We introduce a new file format to represent multichannel information as well as an open-source Vaa3D plugin to acquire this type of data. Next we define a novel data structure to capture morphological dynamics, and demonstrate its application to different time-lapse experiments. Importantly, we designed both innovations as judicious extensions of the classic SWC format, thus ensuring full back-compatibility with popular visualization and modeling tools. We then deploy the combined multichannel/time-varying reconstruction system on developing neurons in live Drosophila larvae by digitally tracing fluorescently labeled cytoskeletal components along with overall dendritic morphology as they changed over time. This same design is also suitable for quantifying dendritic calcium dynamics and tracking arbor-wide movement of any subcellular substrate of interest.

Indexed as

DrosophilaNeuronsSoftwareAnimalsDatasets as TopicImage Processing, Computer-AssistedImaging, Three-Dimensional

Identifiers

PMID29360104
PMCPMC5779069

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.