Digital Reconstructions of Neuronal Morphology: Three Decades of Research Trends

Halavi, Maryam; Hamilton, Kelly; Parekh, Ruchi; Ascoli, Giorgio A.

doi:10.3389/fnins.2012.00049

Cited by 120 publications

(77 citation statements)

References 51 publications

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“…Good curators cultivate three ‘p-virtues’: they are proactive, persistent, and patient in finding, requesting, and collating all data sets that investigators are willing to share. We regularly search the literature for any new publications describing neuronal morphology, promptly inviting the corresponding authors to deposit their digital reconstructions 17 . Our communications follow a systematic protocol with a set number of reminders.…”

Section: Recipes For Successful Data Sharingmentioning

confidence: 99%

Win–win data sharing in neuroscience

et al. 2017

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Section: Recipes For Successful Data Sharingmentioning

confidence: 99%

Win–win data sharing in neuroscience

et al. 2017

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“…Every month, NeuroMorpho.Org contacts authors of all papers with new morphologies identified by a literature survey to request that they share their data. The full list of papers with known morphologies and whether or not they are available in NeuroMorpho.Org is released on the repository's website [132]. …”

Section: Repositories and Standards: Code Data And Modelsmentioning

confidence: 99%

Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

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Objective Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. Methods Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. Results Building on these standard practices, model sharing sites and tools have been developed that fit into several categories: 1. standardized neural simulators, 2. shared computational resources, 3. declarative model descriptors, ontologies and standardized annotations; 4. model sharing repositories and sharing standards. Conclusion A number of complementary innovations have been proposed to enhance sharing, transparency and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. Significance Model management will become increasingly important as multiscale models become larger, more detailed and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.

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“…Here, we show that 3D reconstruction of the cerebral arterial vasculature can be achieved using existing methods originally developed for digital tracing and quantitative analysis of neuronal trees (Brown et al, 2005; Halavi et al, 2012; Scorcioni et al, 2008). Further, we demonstrate how the existing analysis methods can be used to study individual and population differences in cerebral vasculature at the level of the entire vasculature, specific arteries or single branches.…”

Section: Introductionmentioning

confidence: 99%

Digital reconstruction and morphometric analysis of human brain arterial vasculature from magnetic resonance angiography

et al. 2013

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Characterization of the complex branching architecture of cerebral arteries across a representative sample of the human population is important for diagnosing, analyzing, and predicting pathological states. Brain arterial vasculature can be visualized by magnetic resonance angiography (MRA). However, most MRA studies are limited to qualitative assessments, partial morphometric analyses, individual (or small numbers of) subjects, proprietary datasets, or combinations of the above limitations. Neuroinformatics tools, developed for neuronal arbor analysis, were used to quantify vascular morphology from 3 T time-of-flight MRA high-resolution (620 μm isotropic) images collected in 61 healthy volunteers (36/25 F/M, average age = 31.2 ± 10.7, range = 19–64 years). We present in-depth morphometric analyses of the global and local anatomical features of these arbors. The overall structure and size of the vasculature did not significantly differ across genders, ages, or hemispheres. The total length of the three major arterial trees stemming from the circle of Willis (from smallest to largest: the posterior, anterior, and middle cerebral arteries; or PCAs, ACAs, and MCAs, respectively) followed an approximate 1:2:4 proportion. Arterial size co-varied across individuals: subjects with one artery longer than average tended to have all other arteries also longer than average. There was no net right–left difference across the population in any of the individual arteries, but ACAs were more lateralized than MCAs. MCAs, ACAs, and PCAs had similar branch-level properties such as bifurcation angles. Throughout the arterial vasculature, there were considerable differences between branch types: bifurcating branches were significantly shorter and straighter than terminating branches. Furthermore, the length and meandering of bifurcating branches increased with age and with path distance from the circle of Willis. All reconstructions are freely distributed through a public database to enable additional analyses and modeling (cng.gmu.edu/brava).

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Digital Reconstructions of Neuronal Morphology: Three Decades of Research Trends

Cited by 120 publications

References 51 publications

Win–win data sharing in neuroscience

Win–win data sharing in neuroscience

Reproducibility in Computational Neuroscience Models and Simulations

Digital reconstruction and morphometric analysis of human brain arterial vasculature from magnetic resonance angiography

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