Dušica L. Marić

Morphology and classification of large neurons in the adult human dentate nucleus: A qualitative and quantitative analysis of 2D images

The dentate nucleus represents the most lateral of the four cerebellar nuclei that serve as major relay centres for fibres coming from the cerebellar cortex. Although many relevant findings regarding to the structure, neuronal morphology and cytoarchitectural development of the dentate nucleus have been presented so far, very little quantitative information has been collected on the types of large neurons in the human dentate nucleus. In the present study we qualitatively analyze our sample of large neurons according to their morphology and topology, and classify these cells into four types. Then, we quantify the morphology of such cell types taking into account seven morphometric parameters which describe the main properties of the cell soma, dendritic field and dendrite arborization. By performing appropriate statistics we prove out our classification of the large dentate neurons in the adult human. To the best of our knowledge, this study represents the first attempt of quantitative analysis of morphology and classification of the large neurons in the adult human dentate nucleus.

Application of fractal analysis to neuronal dendritic arborisation patterns of the monkey dentate nucleus.

The deep nuclei of the cerebellar cortex have not yet received adequate exploratory attention. An exception is represented by the pioneering work of Chan-Palay, published in 1977, on the dentate nucleus morphology. She has classified each individual cell in the dentatus of the monkey into one of six types. Although fractal analysis is presently the most prominent quantitative method for morphometric neuronal studies, no article referring to applications of this method to the analysis of cell types of the dentate nucleus has so far been published. In the present study we apply fractal analysis to this unsolved problem and calculate the fractal dimension for each dendritic arbour of a neuron. We will hereby prove that by application of fractal analysis to the dendritic arbours of these cells whilst ignoring other neuronal attributes allows for clear discrimination of only three cell types.

Morphology and cell classification of large neurons in the adult human dentate nucleus: a quantitative study.

The dentate nucleus represents the most lateral of the four cerebellar nuclei that serve as a major relay centres for fibres coming from the cerebellar cortex. Although many relevant findings regarding to the three-dimensional structure, the neuronal morphology and the cytoarchitectural development of the dentate nucleus have been presented so far, very little quantitative information has been collected to further explain several types of large neurons in the dentate nucleus. In this study we quantified the morphology of the large dentate neurons in the adult human taking, into account seven morphometric parameters that describe the main properties of the cell soma, the dendritic field and the dendritic branching pattern. Since the lateral cerebellar nucleus in the cat and other lower mammals is homologous to the dentate nucleus in primates and man, we have classified our sample of large neurons in accordance with the shape of the cell body, the dendritic arborization and their location within the dentate nucleus. By performing the appropriate statistical analysis, we have proved that our sample of human dentate neurons can be classified into four distinct types. In that sense, our quantitative analysis verifies the validity of previous qualitative conclusions concerning the large neurons in the developing human dentate nucleus. Furthermore, the present study represents the first attempt to perform a quantitative analysis and cell classification of the large projection neurons in the adult human dentate nucleus.

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

OBJECTIVES Topological (central vs. border neuron type) and morphological classification of adult human dentate nucleus neurons according to their quantified histomorphological properties using neural networks on real and virtual neuron samples. RESULTS In the real sample 53.1% and 14.1% of central and border neurons, respectively, are classified correctly with total of 32.8% of misclassified neurons. The most important result present 62.2% of misclassified neurons in border neurons group which is even greater than number of correctly classified neurons (37.8%) in that group, showing obvious failure of network to classify neurons correctly based on computational parameters used in our study. On the virtual sample 97.3% of misclassified neurons in border neurons group which is much greater than number of correctly classified neurons (2.7%) in that group, again confirms obvious failure of network to classify neurons correctly. Statistical analysis shows that there is no statistically significant difference in between central and border neurons for each measured parameter (p>0.05). Total of 96.74% neurons are morphologically classified correctly by neural networks and each one belongs to one of the four histomorphological types: (a) neurons with small soma and short dendrites, (b) neurons with small soma and long dendrites, (c) neuron with large soma and short dendrites, (d) neurons with large soma and long dendrites. Statistical analysis supports these results (p<0.05). CONCLUSION Human dentate nucleus neurons can be classified in four neuron types according to their quantitative histomorphological properties. These neuron types consist of two neuron sets, small and large ones with respect to their perykarions with subtypes differing in dendrite length i.e. neurons with short vs. long dendrites. Besides confirmation of neuron classification on small and large ones, already shown in literature, we found two new subtypes i.e. neurons with small soma and long dendrites and with large soma and short dendrites. These neurons are most probably equally distributed throughout the dentate nucleus as no significant difference in their topological distribution is observed.

On the classification of normally distributed neurons: an application to human dentate nucleus

One of the major goals in cellular neurobiology is the meaningful cell classification. However, in cell classification there are many unresolved issues that need to be addressed. Neuronal classification usually starts with grouping cells into classes according to their main morphological features. If one tries to test quantitatively such a qualitative classification, a considerable overlap in cell types often appears. There is little published information on it. In order to remove the above-mentioned shortcoming, we undertook the present study with the aim to offer a novel method for solving the class overlapping problem. To illustrate our method, we analyzed a sample of 124 neurons from adult human dentate nucleus. Among them we qualitatively selected 55 neurons with small dendritic fields (the small neurons), and 69 asymmetrical neurons with large dendritic fields (the large neurons). We showed that these two samples are normally and independently distributed. By measuring the neuronal soma areas of both samples, we observed that the corresponding normal curves cut each other. We proved that the abscissa of the point of intersection of the curves could represent the boundary between the two adjacent overlapping neuronal classes, since the error done by such division is minimal. Statistical evaluation of the division was also performed.

Mathematical modeling of the neuron morphology using two dimensional images.

In this study mathematical analyses such as the analysis of area and length, fractal analysis and modified Sholl analysis were applied on two dimensional (2D) images of neurons from adult human dentate nucleus (DN). Using mathematical analyses main morphological properties were obtained including the size of neuron and soma, the length of all dendrites, the density of dendritic arborization, the position of the maximum density and the irregularity of dendrites. Response surface methodology (RSM) was used for modeling the size of neurons and the length of all dendrites. However, the RSM model based on the second-order polynomial equation was only possible to apply to correlate changes in the size of the neuron with other properties of its morphology. Modeling data provided evidence that the size of DN neurons statistically depended on the size of the soma, the density of dendritic arborization and the irregularity of dendrites. The low value of mean relative percent deviation (MRPD) between the experimental data and the predicted neuron size obtained by RSM model showed that model was suitable for modeling the size of DN neurons. Therefore, RSM can be generally used for modeling neuron size from 2D images.

Neurons of the Human Dentate Nucleus: Box-Count Method in the Quantitative Analysis of Cell Morphology

The morphology of neurons from the human dentate nucleus was analyzed estimating the size and shape of the dendritic field, shape of the neuron, space-filling property and the degree of dendrite aberrations. Among them, the last three morphological properties were investigated using the most popular technique of fractal analysis: the box-count method. The box dimensions of binary images and dendritic field area were statistically investigated in order to test whether the binary box dimension can quantify the size of the neuron. The same analysis was carried out using the box dimension of outline images and image circularity. The parameters, presented in this study have proved to be a useful means for quantifying the morphology of dentate neurons as they provide a robust means of differentiating between neuron subtypes in the dentate nucleus. The findings of the present study are in accordance with previous qualitative data.

The translaminar neuromorphotopological clustering and classification of the dentate nucleus neurons

Thisstudy aims to determine whether dentate neurons can be translaminarlyneuromorphotopologically classified as ventrolateral or dorsomedial type. Adulthuman dentate interneuron 2D binary images are analyzed. The analysis isperformed on both real and virtual neuron samples and 29 parameters are used.They are divided into the classes: neuron surface, shape, length, branching andcomplexity. 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 Npd (p< 0:05), and from the virtual cell sample: the Adt (p< 0.05),Do (p< 0.001), Ms (p< 0.01), Dwdth (p< 0:001), Npd (p< 0:05), Nsd (p< 0.001), Nt/hod (p< 0.001), Nmax (p< 0.01), Ds (p< 0.001), Cdf (Nt/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.

Anatomical variations of the human occipital condyles

Occipital condyles, located at the inferior sides of lateral parts of occipital bone, are important structures that connect the cranium and the vertebral column. Their size, shape, location and, also, their congruence with superior articular facets of the atlas vertebra are of great importance for the stability of craniovertebral junctions. The progress in medical diagnostic and surgical techniques in the area of foramen magnum, requires the knowledge regarding anatomical aspects of this region. The purpose of this study was to evaluate the measurements of the occipital condyles, as well as to analyse the variations in the shape of the condyles and their position in relation to the foramen magnum. This study included 25 adult human skulls (11 male and 14 female) from the Osteological collection of the Department of Anatomy at Medical Faculty in Novi Sad. Nine parameters, which define morfological properties of the occipital condyles, were measured using Vernier caliper. Also, the shape and location of the condyles, as well as the narrowness of the foramen magnum were described. Our findings show that length of the left occipital condyle, as well as distance between the posterior top of the left occipital condyle and basion are statistically greater in male crania, comparing to female crania. According to the shape of the occipital condyles, we classified them in seven groups. Predominant type is oval shaped. The condylar foramen is bilaterally present in 36 % and the occipital condyles bilaterally protrude the foramen magnum in 40 % of analyzed crania. Morphometric analysis of the occipital condyles showed variations in their size, shape, presence of condylar canal and relation to the foramen magnum.