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Updated: Feb 18, 2026
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
The neuromorphological caudate-putaminal clustering of neostriate interneurons: Kohonen self-organizing maps and
Ivan Grbatinić1, Nebojša Milošević2, Bojana Krstonošić3
1Laboratory for digital image processing and analysis, Institute of Biophysics, Medical faculty, University of Belgrade, Belgrade, Serbia .
Aims:
The objective of this study is to investigate the possibility of the neuromorphotopological clustering of neostriate interneurons (NSIN) and their consequent classification into caudate (CIN) and putaminal neuron type (PIN), according to the nuclear localization of the neurons. It tends to discover whether these two topological neuron types are morphologically different.
Material And Methods:
The binary images of adult human NSIN are used for the purposes of the analysis. The total of the 46 neuromorphological parameters is used. They can be divided into the following classes: neuron surface/size, shape, compartmental length, dendritic branching, neuromorphological organization and complexity. The clustering is performed by an algorithm which consists of the steps of predictor extraction, multivariate cluster analysis set and cluster identification.
Results:
Unifactor analysis extracted as significant the following parameters: neurosoma/perikaryon size (AS), the size of a dendritic tree (ADT), the size of a dendritic field area (ADF), the size of an entire neuron field area (ANF), the size of a perineuronal space (APNS), the fractal dimension of a neuron (DN), the index of perikaryon asymmetry (MS), total dendritic length (L), standardized total dendritic length (Lst), standardized dendritic width (DWDTHst), dendritic centrifugal branching order (DCBO), branching polarization index (MDCBO), dendritic partial surface (DSP), the fractal dimension of a skeletonized neuron image (DS), the index of maximal complex density of a dendritic tree (NMAX) and standardized dendritic branching pattern complexity (CDF/ADFst). The cluster analysis set together with Kohonen self-organizing maps and backpropagation feed-forward artificial neural networks confirmed the classification on both unsupervised and supervised manner, respectively. As a final step, the cluster identification is performed by an assignment of each neuron to a particular cluster.
Conclusion:
NSIN can be classified neuromorphologically into CIN and PIN type. Differences are expected since the two nuclei have different functional roles in processing the information involved in volitional movement control.
