Journal of Integrative Neuroscience (May 2018)
Translaminar neuromorphotopological clustering and classification of dentate nucleus neurons
Abstract
This study aims to determine whether dentate neurons can be translaminarly neuromorphotopologically classified as ventrolateral or dorsomedial type. Adult human dentate interneuron 2D binary images are analyzed. The analysis is performed on both real and virtual neuron samples and 29 parameters are used. They are divided into the classes: neuron surface, shape, length, branching and complexity. Clustering is performed by an algorithm that employs predictor extraction (matrix attractor analysis/non-negative matrix factorization and cluster analysis of predictor factors - separate unifactor analysis/Student's $ t $-test and MANOVA) and multivariate cluster analysis (cluster analysis, principal component analysis, factor analysis with pro/varimax rotation, Fisher's linear discriminant analysis and feed-forward backpropagation artificial neural networks). The separate unifactor analysis extracted as significant the following predictors from the natural cell sample: the $ N_{pd}(p < 0.05) $, and from the virtual cell sample: the $ Adt $ ($ p < $ 0.05), $ D_o (p < 0.001) $, $ M_s (p < 0.01) $, $ D_{wdth} (p < 0.001) $, $ N_{pd} (p < 0.05) $, $ N_{sd}(p < 0.001) $, $ N_{t/hod} (p < 0.001) $, $ N_{\max} (p < 0.01)$, $ D_s (p < 0.001) $, $C_{df} (N_{t/hod})_{st} (p < 0.05) $. For the multidimensional analysis, with the exception of the Fisher's linear discriminant analysis which gave a false positive result, all other analyses rejected the translaminar dentate neuron classification. Thus, dentate neurons cannot be classified into ventrolateral/dorsomedial neuromorphotopological subtypes. Although some differences were found to exist, they are not sufficient to carry this classification. The methods of multidimensional statistical analysis are again shown to be the best for such kinds of analysis.
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