ArticleFrontiers in neuroanatomy2014
Statistical analysis and data mining of digital reconstructions of dendritic morphologies.
Article in Frontiers in neuroanatomy, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 71 citations in OpenAlex.
- Neuronal diversity and stereotypy at multiple scales through whole brain morphometry.Nature communications · 2024Article
- Accelerating the continuous community sharing of digital neuromorphology data.FASEB bioAdvances · 2024Article
- Multi-gate Weighted Fusion Network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders.Frontiers in neuroscience · 2024Review
- Full-Spectrum Neuronal Diversity and Stereotypy through Whole Brain Morphometry.Research square · 2023Article
- BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.Nature methods · 2023Article
- Cell morphologies in the nervous system: Glia steal the limelight.The Journal of comparative neurology · 2023Article
- Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors.Journal of neuroscience research · 2023Article
- Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test.Neuroinformatics · 2023Article
- A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes.Nature neuroscience · 2022Article
- Large scale similarity search across digital reconstructions of neural morphology.Neuroscience research · 2022Article
- Topological Sholl descriptors for neuronal clustering and classification.PLoS computational biology · 2022Article
- Efficient metadata mining of web-accessible neural morphologies.Progress in biophysics and molecular biology · 2022Article
- Quantitative neuronal morphometry by supervised and unsupervised learning.STAR protocols · 2021Article
- Cellular anatomy of the mouse primary motor cortex.Nature · 2021Article
- An imaging analysis protocol to trace, quantify, and model multi-signal neuron morphology.STAR protocols · 2021Article
- Modeling of Astrocyte Networks: Toward Realistic Topology and Dynamics.Frontiers in cellular neuroscience · 2021Article
- Retrieving similar substructures on 3D neuron reconstructions.Brain informatics · 2020Article
- A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination.Neuroinformatics · 2020Article
- Database of literature derived cellular measurements from the murine basal ganglia.Scientific data · 2020Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Neuronal morphology is diverse among animal species, developmental stages, brain regions, and cell types. The geometry of individual neurons also varies substantially even within the same cell class. Moreover, specific histological, imaging, and reconstruction methodologies can differentially affect morphometric measures. The quantitative characterization of neuronal arbors is necessary for in-depth understanding of the structure-function relationship in nervous systems. The large collection of community-contributed digitally reconstructed neurons available at NeuroMorpho.Org constitutes a "big data" research opportunity for neuroscience discovery beyond the approaches typically pursued in single laboratories. To illustrate these potential and related challenges, we present a database-wide statistical analysis of dendritic arbors enabling the quantification of major morphological similarities and differences across broadly adopted metadata categories. Furthermore, we adopt a complementary unsupervised approach based on clustering and dimensionality reduction to identify the main morphological parameters leading to the most statistically informative structural classification. We find that specific combinations of measures related to branching density, overall size, tortuosity, bifurcation angles, arbor flatness, and topological asymmetry can capture anatomically and functionally relevant features of dendritic trees. The reported results only represent a small fraction of the relationships available for data exploration and hypothesis testing enabled by sharing of digital morphological reconstructions.
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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.