ArticleNeuroinformatics2015
Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on L-measure.
Article in Neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- DSM: Deep sequential model for complete neuronal morphology representation and feature extraction.Patterns (New York, N.Y.) · 2024Article
- Polymer Physics-Based Classification of Neurons.Neuroinformatics · 2023Article
- Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors.Journal of neuroscience research · 2023Article
- Classification of Activated Microglia by Convolutional Neural Networks.IEEE Biomedical Circuits and Systems Conference : healthcare technology : [proceedings]. IEEE Biomedical Circuits and Systems Conference · 2022Article
- Olfactory responses ofeLife · 2022Article
- Large scale similarity search across digital reconstructions of neural morphology.Neuroscience research · 2022Article
- Efficient metadata mining of web-accessible neural morphologies.Progress in biophysics and molecular biology · 2022Article
- Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks.Scientific reports · 2021Article
- A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination.Neuroinformatics · 2020Article
- Morphological Neuron Classification Based on Dendritic Tree Hierarchy.Neuroinformatics · 2019Article
- An open repository for single-cell reconstructions of the brain forest.Scientific data · 2018Article
- Binge alcohol alters exercise-driven neuroplasticity.Neuroscience · 2017Article
- Article
- Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors.Bioinformatics (Oxford, England) · 2015Article
- Towards the automatic classification of neurons.Trends in neurosciences · 2015Review
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Authors and funding
5 authors.
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Abstract
This paper presents a robust unsupervised harmonic co-clustering method for profiling arbor morphologies for ensembles of reconstructed brain cells (e.g., neurons, microglia) based on quantitative measurements of the cellular arbors. Specifically, this method can identify groups and sub-groups of cells with similar arbor morphologies, and simultaneously identify the hierarchical grouping patterns among the quantitative arbor measurements. The robustness of the proposed algorithm derives from use of the diffusion distance measure for comparing multivariate data points, harmonic analysis theory, and a Haar-like wavelet basis for multivariate data smoothing. This algorithm is designed to be practically usable, and is embedded into the actively linked three-dimensional (3-D) visualization and analytics system in the free and open source FARSIGHT image analysis toolkit for interactive exploratory population-scale neuroanatomic studies. Studies on synthetic datasets demonstrate its superiority in clustering data matrices compared to recent hierarchical clustering algorithms. Studies on heterogeneous ensembles of real neuronal 3-D reconstructions drawn from the NeuroMorpho database show that the proposed method identifies meaningful grouping patterns among neurons based on arbor morphology, and revealing the underlying morphological differences.
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