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Paper   IPM / Biological Sciences / 16197
School of Biological Sciences
  Title:   Dendritic Spines Taxonomy: The Functional and Structural Classification • Time-Dependent Probabilistic Model of Neuronal Activation
1.  Paulina Urban
2.  Vahid Rezaei Tabar
3.  Grzegorz Bokota
4.  Micha�? Denkiewicz
5.  Subhadip Basu
6.  Dariusz Plewczy�?ski
  Status:   Published
  Journal: Journal of Computational Biology
  Year:  2020
  Pages:   https://doi.org/10.1089/cmb.2018.0155
  Supported by:  IPM
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 distribution of variables that best fits all dendritic spine parameters.

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