ArticleFrontiers in synaptic neuroscience2020
Dendritic Spines Shape Analysis-Classification or Clusterization? Perspective.
Article in Frontiers in synaptic neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers.
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97 citing papers in PubMed.
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- Synaptic spine head morphodynamics from graph grammar rules for actin dynamics.bioRxiv : the preprint server for biology · 2026Article
- 3D dendritic spines shape descriptors for efficient classification and morphology analysis in control and Alzheimer's disease modeling neurons.Bioinformatics (Oxford, England) · 2026Article
- Subplate neuron dendritic morphology during development in gyrencephalic and lissencephalic brains.Scientific reports · 2026Article
- Shared neuropathological features of mild traumatic brain injury and anesthesia.Frontiers in neurology · 2026Review
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- PKCγ-mediated Phosphorylation of Mtss1 Regulates the Dendritic Outgrowth and Spine Development of Cerebellar Purkinje Cells.Molecular neurobiology · 2025Article
37 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
Dendritic spines are small protrusions from the dendrite membrane, where contact with neighboring axons is formed in order to receive synaptic input. Changes in size, shape, and density of synaptic spines are associated with learning and memory, and observed after drug abuse in a variety of neurodegenerative, neurodevelopmental, and psychiatric disorders. Due to the preeminent importance of synaptic spines, there have been major efforts into developing techniques that enable visualization and analysis of dendritic spines in cultured neurons, in fixed slices and in intact brain tissue. The classification of synaptic spines into predefined morphological groups is a standard approach in neuroscience research, where spines are divided into fixed categories such as thin, mushroom, and stubby subclasses. This study examines accumulated evidence that supports the existence of dendritic spine shapes as a continuum rather than separated classes. Using new approaches and software tools we reflect on complex dendritic spine shapes, positing that understanding of their highly dynamic nature is required to perform analysis of their morphology. The study discusses and compares recently developed algorithms that rely on clusterization rather than classification, therefore enabling new levels of spine shape analysis. We reason that improved methods of analysis may help to investigate a link between dendritic spine shape and its function, facilitating future studies of learning and memory as well as studies of brain disorders.
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