Dendritic Spines Taxonomy: The Functional and Structural Classification • Time-Dependent Probabilistic Model of Neuronal Activation

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Paulina UrbanDariusz Plewczynski

Abstract

Categorizing spines into four subpopulations, stubby, mushroom, thin, or filopodia, is one of the common approaches in morphological analysis. Most cellular models describing synaptic plasticity, long-term potentiation (LTP), and long-term depression associate synaptic strength with either spine enlargement or spine shrinkage. Unfortunately, although we have a lot of available software with automatic spine segmentation and feature extraction methods, at present none of them allows for automatic and unbiased distinction between dendritic spine subpopulations, or for the detailed computational models of spine behavior. Therefore, we propose structural classification based on two different mathematical approaches: unsupervised construction of spine shape taxonomy based on arbitrary features (SpineTool) and supervised classification exploiting convolution kernels theory (2dSpAn). We compared two populations of spines in a form of static and dynamic data sets gathered at three time points. The dynamic data contain two sets of spines: the active set and the control set. The first population was stimulated with LTP, and the other population in its resting state was used as a control population. We propose one equation describing the d...Continue Reading

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Citations

Mar 17, 2020·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Paulina UrbanDariusz Plewczynski
Sep 13, 2021·The European Journal of Neuroscience·Ashby KissoondoyalDorota A Crawford

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Methods Mentioned

BETA
PCA

Software Mentioned

DeclareMathSizes
SpineTools
R
Python
Minitab
2dSpAn
Spine Magic
Scipy stats
SpineTool

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