Two Dimensional Grayscale Images of the Aspiny Neurons from the Human Neostriatum: Monofractal and Gray Level Co-occurrence Matrix Analysis
European Journal of Biophysics
Volume 7, Issue 1, June 2019, Pages: 15-22
Received: Jun. 19, 2019;
Accepted: Jul. 23, 2019;
Published: Aug. 12, 2019
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Velicko Vranes, Department of Basic and Environmental Science, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic
Bojana Krstonošić, Department of Anatomy, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
Nebojša Tomislav Milošević, Department of Basic and Environmental Science, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic; Department of Biophysics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
The striatum (neostriatum) is one of the principal constituents of the basal nuclei. It is a complex structure which consists of a dorsal and the ventral components. According to the spine distribution and their density, neurons of the human striatum can be classified into two main types: spiny and aspiny cells. Further classification recognizes two groups of spiny, and three groups of aspiny neurons. The goal of this study was to analyze different morphometric properties of the digital images of the group IV and group V aspiny neurons, from the dorsal striatum of both cerebral hemispheres. In this study, a total of 175 two-dimensional images of aspiny neurons were analyzed. Image reconstruction and measurement was performed with the specialized, public software Image J. Four parameters of standard fractal analysis were quantified from these binary images. In addition, five textural parameters were obtained by analyzing grayscale images of the entire neuron. Results of both analyses show that six of nine parameters differed between the group IV and group V aspiny neurons. Moreover, in both groups of neurons, one parameter of the fractal and three of the texture analyses differed between the putamen and the caudate nucleus neurons. Thus, this study corroborates previous classification of aspiny neurons. Although they belong to the same aspiny group, different type of cells can qualify nerve signals in their own way. Therefore, this study supports the hypothesis that neuronal morphology differences can reflect their functional diversity and their role in communication.
Nebojša Tomislav Milošević,
Two Dimensional Grayscale Images of the Aspiny Neurons from the Human Neostriatum: Monofractal and Gray Level Co-occurrence Matrix Analysis, European Journal of Biophysics.
Vol. 7, No. 1,
2019, pp. 15-22.
Pisani A, Centonze D, Bernardi G, Calabresi P, Striatal synaptic plasticity: implications for motor learning and Parkinson’s disease. Mov Disord 20: 395–402, 2005.
Krstonošiċ B, Morphological Analysis of Two Dimensional Projection of Neurons in the Human Neostriatum. Dissertation, Medical faculty, University of Novi Sad, Serbia, Balkans, 2013.
Yin HH, Knowlton BJ, The role of the basal ganglia in habit formation. Nat Rev Neurosci 7: 464–476, 2006.
Bernácer J, Prensa L, Giménez-Amaya JM, Chemical architecture of the posterior striatum in the human brain. J Neural Transm 115: 67–75, 2008.
Steiner H, Tseng KY, Handbook of basal ganglia structure and function. Academic, London, 2010.
Chang HT, Wilson CJ, Kitai ST, A Golgi study of rat neostriatal neurons: light microscopic analysis. J Comp Neurol 208: 107–126, 1982.
Di Figlia M, Pasik T, Pasik P, Ultrastructure of Golgiimpregnated and gold-toned spiny and aspiny neurons in the monkey neostriatum. J Neurocytol 9: 471–492, 1980.
Leontovich TA (1998) Large neostriatal neurons in humans and their possible role in neuronal networks. Neurosci Behav Physiol 28: 252–259.
Braak H, Braak E, Neuronal types in the striatum of man. Cell Tissue Res 227: 319–342, 1982.
Graveland GA, Williams RS, DiFiglia M, A Golgi study of the human neostriatum: neurons and afferent fibers. J Comp Neurol 234: 317–333, 1985.
Krstonošić B, Milošević NT, Gudović R, Marić DL, Ristanović D, Neuronal images of the putamen in the adult human neostriatum: revised classification supported with qualitative and quantitative analysis. Anat Sci Int 87: 115–125, 2012.
Krstonošić B, Milošević NT, Marić DL, Babović SS, Quantitative analysis of spiny neurons in the adult human caudate nucleus: can it confirm the current qualitative ell classification? Acta Neurol Belg 115 (3): 273-280, 2015.
Rajković N, Krstnošić B, Milošević N, Box-counting method of 2D neuronal image: method modification and quantitative analysis demonstrated on images from the monkey and human brain. Comp Math Methods Med https://doi.org/10.1155/2017/8967902,2017.
Haralick R, Shanmugam K, Dinstein IH, Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3: 610–621, 1973.
Vujasinovic T, Pribic J, Kanjer K, Milošević NT, Tomasevic Z, Milovanovic Z, Nikolic-Vukosavljevic D, Radulovic M, Gray-level co-occurrence matrix texture analysis of breast tumor images in prognosis of distant metastasis risk. Microsc Microanal. 21 (3): 646-654, 2015.
Rajkovic N, Kolarevic D, Kanjer K, Milosevic NT, Nikolic- Vukosavljevic D, Radulovic M, Comparison of Monofractal, Multifractal and gray level Co-occurrence matrix algorithms in analysis of Breast tumor microscopic images for prognosis of distant metastasis risk. Biomed Microdevices https://doi.org/10.1007/s10544-016-0103-x,2016.
Đuričić GJ, Radulović M, Sopta JP, Nikitović M, Milošević NT, Fractal and gray level coocurence matrix computational analysis of primary ostosarcoma magnetic resonance images: predicts the chemotherapy response. F Onc. https://doi.org/10.3389/fonc.2017.00246,2017.
Cabrera J, Texture analyzer plugin. NIH, Image Processing and Analysis in Java, https://imagej.nih.gov/ij/plugins/texture.html,2006.
Miloševiċ NT, Fractal analysis of two dimensional images: parameters of the space-filling and shape, In Dumitrache I, Magda Florea A, Pop F, Dumitrascu A (Eds.) Proceedings of the 20th International Conference on Control Systems and Computer Science, Vol. 2: IAFA: Fractal Analysis of Medical Images, The Institute of Electrical and Electronics Engineers, Los Alamitos, pp 539–544, 2015.
Smith Jr. TG, Lange GD, Marks WB, Fractal methods and results in cellular morphology - dimensions, lacunarity and multifractals. J Neurosci Meth 69 (2): 123-136, 1996.
Fernández E, and Jelinek HF, Use of fractal theory in neuroscience: methods, advantages, and potential problems. Methods 24 (4): 309–321, 2001.
Miloševiċ NT, The morphology of brain neurons: box counting method in quantitative analysis of 2D image. In: Di Ieva A (Ed). The Fractal Geometry of the Brain, Springer-Verlag, Berlin, Germany, pp 109-127, 2016.
Karperien A, FracLac for ImageJ, version 2. 5. http://rsbinfonihgov/ij//fraclac/,2007.
R. H. Riffenburgh RH, Statistics in Medicine, Academic Press, London, UK, 1999.
Milošević NT, Krstonošić B, Elston GN, Rajković N, Box-count analysis of two dimensional images: methodology, analysis and classification. In: Dumitrache I, Magda-Florea A, Pop F (eds.), Proceedings of 19th International Conference on Control Systems and Computer Science, Vol. 2: Interdisciplinary approaches in fractal analysis, The Institute of Electrical and Electronics Engineers, Los Alamitos, CA, USA; pp 306-312, 2013.
Lecumberri A, Lopez-Janeiro A, Corral-Domenge C, Berancer J, Neuronal density and proportion of interneurons in the associative, sensorimotor and limbic human striatum. Brain Struct Funct 223 (4): 1615-1625, 2018.
Ramón y Cajal S, Textura del sistema nervioso del hombre y de los vertebrados [Texture of the nervous system of man and the vertebrates]. Springer, New York, 1899.
Haber S, Corticostriatal circuitry. Dialogues Clin Neurosci 18 (1): 7-21, 2016.
Bennett BD, Wilson CJ, Spontaneous activity of neostriatal cholinergic interneurons in vitro. J Neurosci 19: 5586-5596, 1999.
Kawaguchi Y, Physiological, morphological and histochemical characterization of three classes of interneurons in rat neostriatum. J Neurosci 13: 4908-4923, 1993.
Ramanathan S, Hanley JJ, Deniau JM, Bolam JP, Synaptic convergence of motor and somatosensory cortical afferents onto GABAergic interneurons in the rat striatum. J Neurosci 22: 8158-8169, 2001.
Chang HT, Kita H, Interneurons in the rat striatum: relationships between parvalbumin neurons and cholinergic neurons. Brain Res 574: 307-311, 1992.
Zaletel I, Milošević NT, Todorović V, Kovačević-Filipović M, Puškaš N, Fractal and gray level co-occurrence matrix texture analysis of senescent and non-senescent deciduous teeth stem cells: a pilot study. Fractal Geometry and Nonlinear Anal in Med and Biol. 2 (2): 1-6, 2016.
Milošević NT, Rajković N, Jelinek HF, Ristanović D, Richardson’s method of segment counting versus box-counting. In: Dumitrache I, Magda-Florea A, Pop F (eds.), Proceedings of 19th International Conference on Control Systems and Computer Science, Vol. 2: Interdisciplinary approaches in fractal analysis, The Institute of Electrical and Electronics Engineers, Los Alamitos, CA, USA; pp 299-305, 2013.
Milošević NT, Krstonošić B, Gudović R, Ristanović D, Fractal analysis of neuronal dendritic branching patterns in the human neostriatum: a revised classification scheme. In: Dobrescu R (Ed.), Proceedings CSCS-18, Vol. 2: Interdisciplinary approaches in fractal analysis, Editura Politehnica Press, Bucharest, Romania; pp 871-876, 2011.
Jelinek HF, Milošević NT, Karperien A, Krstonošić B, Box-Counting and Multifractal Analysis in Neuronal and Glial Classification. In: Dumitrache I (Ed.), Advances In Intelligent Control Systems and Computers Science, Springer-Verlag, Berlin-Heidelberg, pp 177-190, 2013.
Albregsten F, Statistical Texture Measures Computed from Gray Level Cooccurrence Matrices. Technical Note. Department of Informatics, University of Oslo, Norway, 1995.