ArticleNeuroinformatics2017
Generating Neuron Geometries for Detailed Three-Dimensional Simulations Using AnaMorph.
Article in Neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Alpha_Mesh_Swc: automatic and robust surface mesh generation from the skeleton description of brain cells.Briefings in bioinformatics · 2025Article
- Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds for in silico modeling.Briefings in bioinformatics · 2024Article
- Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience.Briefings in bioinformatics · 2023Article
- Efficient Simulation of 3D Reaction-Diffusion in Models of Neurons and Networks.Frontiers in neuroinformatics · 2022Article
- Modeling Neurons in 3D at the Nanoscale.Advances in experimental medicine and biology · 2022Article
- Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation.Frontiers in neuroinformatics · 2022Article
- Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations.Bioinformatics (Oxford, England) · 2021Article
- NeuroBox: Computational Mathematics in Multiscale Neuroscience.Computing And Visualization In Science · 2019Article
- Multi-scale modeling toolbox for single neuron and subcellular activity under Transcranial Magnetic Stimulation.Brain stimulationArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generating realistic and complex computational domains for numerical simulations is often a challenging task. In neuroscientific research, more and more one-dimensional morphology data is becoming publicly available through databases. This data, however, only contains point and diameter information not suitable for detailed three-dimensional simulations. In this paper, we present a novel framework, AnaMorph, that automatically generates water-tight surface meshes from one-dimensional point-diameter files. These surface triangulations can be used to simulate the electrical and biochemical behavior of the underlying cell. In addition to morphology generation, AnaMorph also performs quality control of the semi-automatically reconstructed cells coming from anatomical reconstructions. This toolset allows an extension from the classical dimension-reduced modeling and simulation of cellular processes to a full three-dimensional and morphology-including method, leading to novel structure-function interplay studies in the medical field. The developed numerical methods can further be employed in other areas where complex geometries are an essential component of numerical simulations.
Indexed as
Identifiers
28447297What OpenQuestion holds
Registered trials
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.