“School of Biological”
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| Paper IPM / Biological / 15492 |
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| Abstract: | |||||||||||||
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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, 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 contains two sets of spines: the active set and the control set. The first population was stimulated with long-term potentiation, and the other one in its resting state was used as a control population. We propose one equation describing the distribution of variables which best fits all dendritic spine parameters.
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