Neurons from the adult human dentate nucleus: Neural networks in the neuron classification

Grbatinić, Ivan; Marić, Dušica L.; Milošević, Nebojša T.

doi:10.1016/j.jtbi.2015.01.024

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…In our previous study [10], we determined two neuromorphotopologically different clusters (types) of dentate nucleus (DN) neurons (Figure 1), namely, the external (EBC) and internal border neurons (IBC), differing in terms of both their morphology and topology. The topological cluster of central neurons, neuromorphologically, can be divided into the EBC and IBC histotypes [11]. In our other, analogous study, dealing with the histological organization of the neostriatum, we discovered three histomorphologically different clusters: the two caudate clusters (I and II) and putaminal cluster [12].…”

Section: Introductionmentioning

confidence: 50%

Computational RSM Modeling of Neuromorphofunctional Relations of Dentate Nuclear Neurons and Dentatostriate Inter-Cluster Mapping with the Dentatostriate Neural Network Reconstruction: RLSR/PCR Regression and Canonical Correlation Analysis

Grbatinić

Krstonošić

Marić

et al. 2019

ABNE

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Aim:The aim of this study is to find relational connections (interdependence) between the two most general categorical aspects of a neuron, i.e., between the form (morphology) and its function, using as a model for this task dentate nucleus neurons. Furthermore, the configuration of the dentatostriate nucleotopic inter-cluster mapping of the dentatostriate neural network is investigated in order to determine mutual, inter-neuronal, neuromorphofunctional remote influence, i.e. the neuromorphofunctional relations at the level of a neural network. (Semi) virtual dentate and neostriate adult human neuronal samples were used. Materials and methods:Neuromorphological parameters of each neuron have been directly measured, i.e. experimentally determined, whereas the corresponding neurofunctional parameters have been theoretically obtained. The neuromorphological parameters determine the following properties of a neuron: neuron shape, compartmental length and size/ surface, dendritic branching, complexity and organization of neuronal morphology. The group of neurofunctional parameters determines functional aspects of action potential (AV/AP), as well as neurofunctional properties of the perikaryodendritic compartment of a neuron. Data analysis is performed using response surface (RSM) modeling, along with partial least-squares (PLSR) and principal component regression analysis (PCR), accompanied by canonical and Pearson correlation analysis. A stepwise algorithm formulates the complete data analysis. Results:Obtained RSM models represent response-predictor relations, where a neuromorphological/functional response parameter is expressed as a function in terms of parameters of other category (morphology/function). Additionally, RSM modeling is also used to decipher the symmetry of the dentatostriate inter-cluster neural network by the corresponding inter-cluster inter-nuclear mapping, using so-called integral parameters/variables, obtained on a computational, theoretical manner. The obtained network is a fully connected, symmetric, Hopfield neural network. Conclusion:Neuronal morphology and function are definitely interrelated and depend on each other. By intensity, however, this interconnectedness can be treated as mild to moderate. It is determined by elementary neuromorphofunctional relations, observed at the macroscopic, phenomenological level, i.e. only through measured

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Section: Introductionmentioning

confidence: 50%

Computational RSM Modeling of Neuromorphofunctional Relations of Dentate Nuclear Neurons and Dentatostriate Inter-Cluster Mapping with the Dentatostriate Neural Network Reconstruction: RLSR/PCR Regression and Canonical Correlation Analysis

Grbatinić

Krstonošić

Marić

et al. 2019

ABNE

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“…A previous study dealing with the neuromorphotopological classification of DN neurons in the central and border zones [12] showed that these DN neurons could not be histologically classified as two topological neuron subtypes. This led to the further conclusion that the histologically observed DN neuron distribution is uniform throughout the whole gray nuclear mass.…”

Section: Discussionmentioning

confidence: 97%

“…The real VLL sample comprised 152 (45.1%) neurons while the virtual DML sample contained 185 (54.9%) neurons. In this study, a virtual DN sample using the NARX/NAR neural network [12] was employed due to the potentially small sample size for the defined task and to validate results for larger (virtual) samples. Thus, the virtual sample is actually semi-virtual as it contained both real and computationally formed (virtual) neuron data.…”

Section: The Dn Neuron Samplesmentioning

confidence: 99%

Translaminar neuromorphotopological clustering and classification of the dentate nucleus neurons

Grbatinić

Milošević

Marić

2018

J. Integr. Neurosci.

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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: theFor 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. Keywords Dentate neurons; 2D binary image; translaminar neuromorphotopological clustering/classification based upon un/supervised learning techniques; parameter; multidimensional analysis; Fisher's linear discriminant analysis; multidimensional approach 106 Journal of Integrative Neuroscience by introducing matrix attractor analysis, the second, methodological, aim is to further develop the previously described [6] classification algorithm. Materials and methodsThe adult human dentate nucleus (DN) is characterized by two phylogenetically different parts, the younger ventrolateral lamina (VLL) and older dorsomedial lamina (DML) (Fig. 1).

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“…Veliki neuroni su dalje podeljeni u četiri podgrupe (4). Prvu grupu čine centralni neuroni, kružnog oblika i sa velikim brojem dendrita koji se cirkularno odvajaju od some.…”

Section: Uvodunclassified

Microscopic images of the human dentate nucleus border neurons: Parameters of the nonlinear quantitative analysis and examination of age distribution

Milovanović¹,

Stevanović²,

Milošević³

2018

Medicinski podmladak

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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 large 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.

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Neurons from the adult human dentate nucleus: Neural networks in the neuron classification

Cited by 13 publications

References 19 publications

Computational RSM Modeling of Neuromorphofunctional Relations of Dentate Nuclear Neurons and Dentatostriate Inter-Cluster Mapping with the Dentatostriate Neural Network Reconstruction: RLSR/PCR Regression and Canonical Correlation Analysis

Computational RSM Modeling of Neuromorphofunctional Relations of Dentate Nuclear Neurons and Dentatostriate Inter-Cluster Mapping with the Dentatostriate Neural Network Reconstruction: RLSR/PCR Regression and Canonical Correlation Analysis

Translaminar neuromorphotopological clustering and classification of the dentate nucleus neurons

Microscopic images of the human dentate nucleus border neurons: Parameters of the nonlinear quantitative analysis and examination of age distribution

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