Microscopic pictures of human dentate nucleus border neurons: parameters of nonlinear quantitative analysis and examination of age distribution
Abstract
Introduction: Dentate nucleus represents a cluster of neurons located deep in white matter of cerebellum. Neurons from dentate nucleus, as the biggest and the most lateral from deep cerebellar nuclei, are classified into two groups by its morphology and function. The group of big neurons are further classified into four subgroups and one of them are border neurons.
Aim: This study investigates possibility to discriminate inner and outer neurons from small sample of border neurons, using quantitative and nonlinear parameters of the image analysis. In addition, this study investigates relations between nonlinear parameters and the age of neurons.
Material and methods: The small sample of 2D images of dentate nucleus border neurons have been used for this study: 16 images of inner and 12 images of outer neurons. Their morphology was quantified by 7 parameters which investigate the neuron area, dendritic length, number of primary dendrites, space-filling property, shape, dendritic complexity/tortuosity and neurons inhomogeneity/rotational invariance.
Results: The results have showed that means of three quantitative parameters, as well as the mean of one nonlinear parameter, are statistically equal for inner and outer border neurons. In contrast to this, three parameters of fractal analysis are statistically different between two types of border neurons. In addition, means of four nonlinear parameters does not change when the age of neurons increases.
Conclusion: Our results corroborate previous findings and conclusions: border neurons of the dentate nuclei can be classified into inner and outer type. Moreover, our study promotes hypothesis that morphology of neurons from the human dentate nucleus does not change with the age.
Key words: age, border neurons, classification, dentate nucleus, nonlinear analysis
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