ArticleJournal of neuroscience research2023
Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors.
Article in Journal of neuroscience research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Application of Machine Learning Models to Identify Differences in Neural Electrophysiological Properties Across Estrous Cycle Phases.Neuroinformatics · 2026Article
- Information flow drives localized morphological differences across neuronal and glial cell types.Frontiers in computational neuroscience · 2026Article
- Dendritome mapping reveals the spatial organization of striatal neuron morphology.Nature neuroscience · 2025Article
- Accelerating the continuous community sharing of digital neuromorphology data.FASEB bioAdvances · 2024Article
- Automatic identification of scientific publications describing digital reconstructions of neural morphology.Brain informatics · 2023Article
- Cell morphologies in the nervous system: Glia steal the limelight.The Journal of comparative neurology · 2023Article
- Circuit analysis of theNetwork neuroscience (Cambridge, Mass.) · 2023Article
- Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors.Journal of neuroscience research · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Neurons and glia are the two main cell classes in the nervous systems of most animals. Although functionally distinct, neurons and glia are both characterized by multiple branching arbors stemming from the cell bodies. Glial processes are generally known to form smaller trees than neuronal dendrites. However, the full extent of morphological differences between neurons and glia in multiple species and brain regions has not yet been characterized, nor is it known whether these cells can be reliably distinguished based on geometric features alone. Here, we show that multiple supervised learning algorithms deployed on a large database of morphological reconstructions can systematically classify neuronal and glial arbors with nearly perfect accuracy and precision. Moreover, we report multiple morphometric properties, both size related and size independent, that differ substantially between these cell types. In particular, we newly identify an individual morphometric measurement, Average Branch Euclidean Length that can robustly separate neurons from glia across multiple animal models, a broad diversity of experimental conditions, and anatomical areas, with the notable exception of the cerebellum. We discuss the practical utility and physiological interpretation of this discovery.
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Registered trials
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