Literature DB >> 21811443

On the fractal nature of nervous cell system.

Gabriele Angelo Losa1, Antonio Di Ieva, Fabio Grizzi, Gionata De Vico.   

Abstract

Entities:  

Year:  2011        PMID: 21811443      PMCID: PMC3143723          DOI: 10.3389/fnana.2011.00045

Source DB:  PubMed          Journal:  Front Neuroanat        ISSN: 1662-5129            Impact factor:   3.856


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In a detailed study entitled “Morphological development of thick – tufted layer V pyramidal cells in the rat somatosensory cortex,” an international team of scientists (Romand et al., 2011) reported a series of results pertaining to an analytical investigation of the morphological development of thick-tufted layer V pyramidal cells (also called the principal cells) in the rat somatosensory cortex. At the end of the Introduction Section, the Authors stated “all compartments of a TTL5 cell undergo different developmental changes, supporting the notion that multiple functional compartments receive different inputs and may integrate distinct signal transduction systems.” Following on a careful reading of this stimulating report a main question rose which concerned the epistemic view adopted by the Authors and in turn the analytical procedure chosen for investigating neural cells from an highly organized system, privileging in fact the recourse to “conventional” morphometry. These morphometric approaches are usually termed conventional because being based on single scale measuring which may suite well for evaluating biological objects assumed to be or arbitrary approximated to regular Euclidean structures, but inappropriate to quantitatively describe the morphology of thick-tufted layer V pyramidal cells, characterized by complex functional properties and irregular morphological features. Therefore an objective estimation could be reached only by applying the principles and rules of the Fractal geometry proposed by the mathematician Mandelbrot (1982) in the early 1980s. The Authors specified that most neural parameters, including lengths and diameters of individual segments, surface area, branch angles, and other cellular elements were “subjectively classified” and thereafter analyzed either from reconstructed figures or obtained from unrealistic representations. Another incongruous sentence was found in the Somatic Development Section: “Somata were subjectively classified into three formats according to shape: triangular, round, and oval. Although three shapes were found at all ages, somata of TTL5 neurons appeared to be mostly triangular or round at P7 and predominantly triangular thereafter.” It is by far evident from Figures 1 and 3 of Romand et al. (2011) that somata, dendrites and axons are neither round or triangular bodies, nor linear segments, but appeared as irregularly shaped anatomical entities susceptible to be adequately investigated by the “non-conventional” fractal morphometry. Suffices it to mention that, during the last two decades, several studies have been performed on brain tissue and nervous system cells by adopting fractal concepts and methods, which has enabled to quantitatively elucidate most developmental, morphological, and spatial pattern avoiding arbitrary approximation or smoothing of cellular shapes and structures. (Smith and Bejar, 1994; Smith et al., 1996; Bernard et al., 2001; Grizzi and Chiriva-Internati, 2005; Milosevic and Ristanovic, 2006; Ristanovic et al., 2006; Di Ieva et al., 2007; Jelinek et al., 2008; Di Ieva, 2011). Therefore, it may not be surprising that the Authors, despite a huge investigative effort, were obliged to recognize a frank blank, honestly admitted, when they were trying to interpret the data in the light of Methodological considerations (Page 20), with the words: “Variations in results across different studies can be due to many methodological factors such as differences in the staining procedure, the section thickness, the measuring, and analyzing method, the cell selection criterion, the sample size, and the cortical area. These differences make it difficult to directly compare results between different studies.” Proper considerations indeed, but not unpublished, because they evoked considerations much similar reported as far as thirty years ago by a Swiss Group (Paumgartner et al., 1981) in a pioneer study which clearly demonstrated the influence of resolution scale, i.e., objective magnification, on the estimates of geometric irregular features of liver cell membranes, or in other words the role of resolution scale at which the measurements were performed. The large observed discrepancy was consistently annulled while the variations reported by different investigators could be explained by taking into account the “resolution effect” according to the concepts of the Fractal geometry, such as the irregularity, the statistical self-similarity, the scale invariance of form, the occurrence of repetitive morphologic determinants and the fractal, i.e., non-integer dimension, rather than the trivial methodological factors called upon to explain estimate variations across different studies. Biologic structures with irregular shape and complex morphology should not be approximated to ideal geometric objects, since far from the real pictures, while a single scale of measurements should not be adopted a priori if an objective morphological description of complex objects has to be achieved (Losa and Nonnenmacher, 1996). It should be pointed out that fractal and conventional morphometric approaches, built up on distinct epistemological principles, may set the understanding of the biologic reality at different level. The former describes the morphological complexity within an experimental interval of observation scales that obviously encompasses the Euclidean dimension, while the latter proceeds at a primary level, i.e., by reducing cellular shapes and tissue structures to monotone elements which could be described by means of deterministic rules. Nevertheless, fractal and conventional morphometry may represent complementary analytical/quantitative tools to elucidate the diversity of morphological patterns and functional parameters which characterize neural cells and brain structures.
  10 in total

1.  Identification of living oligodendrocyte developmental stages by fractal analysis of cell morphology.

Authors:  F Bernard; J L Bossu; S Gaillard
Journal:  J Neurosci Res       Date:  2001-09-01       Impact factor: 4.164

2.  Fractality of dendritic arborization of spinal cord neurons.

Authors:  Nebojsa T Milosević; Dusan Ristanović
Journal:  Neurosci Lett       Date:  2005-12-20       Impact factor: 3.046

Review 3.  Self-similarity and fractal irregularity in pathologic tissues.

Authors:  G A Losa; T F Nonnenmacher
Journal:  Mod Pathol       Date:  1996-03       Impact factor: 7.842

Review 4.  Fractal methods and results in cellular morphology--dimensions, lacunarity and multifractals.

Authors:  T G Smith; G D Lange; W B Marks
Journal:  J Neurosci Methods       Date:  1996-11       Impact factor: 2.390

5.  Comparative fractal analysis of cultured glia derived from optic nerve and brain demonstrate different rates of morphological differentiation.

Authors:  T G Smith; T N Behar
Journal:  Brain Res       Date:  1994-01-21       Impact factor: 3.252

6.  Resolution effect on the stereological estimation of surface and volume and its interpretation in terms of fractal dimensions.

Authors:  D Paumgartner; G Losa; E R Weibel
Journal:  J Microsc       Date:  1981-01       Impact factor: 1.758

7.  Angioarchitectural heterogeneity in human glioblastoma multiforme: a fractal-based histopathological assessment.

Authors:  Antonio Di Ieva; Fabio Grizzi; Camillo Sherif; Christian Matula; Manfred Tschabitscher
Journal:  Microvasc Res       Date:  2010-12-28       Impact factor: 3.514

8.  Fractal dimension as a quantitator of the microvasculature of normal and adenomatous pituitary tissue.

Authors:  Antonio Di Ieva; Fabio Grizzi; Giorgia Ceva-Grimaldi; Carlo Russo; Paolo Gaetani; Enrico Aimar; Daniel Levi; Patrizia Pisano; Flavio Tancioni; Giancarlo Nicola; Manfred Tschabitscher; Nicola Dioguardi; Riccardo Rodriguez Y Baena
Journal:  J Anat       Date:  2007-09-03       Impact factor: 2.610

9.  Morphological development of thick-tufted layer v pyramidal cells in the rat somatosensory cortex.

Authors:  Sandrine Romand; Yun Wang; Maria Toledo-Rodriguez; Henry Markram
Journal:  Front Neuroanat       Date:  2011-02-17       Impact factor: 3.856

10.  The complexity of anatomical systems.

Authors:  Fabio Grizzi; Maurizio Chiriva-Internati
Journal:  Theor Biol Med Model       Date:  2005-07-19       Impact factor: 2.432

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1.  Fractal anatomy of the hippocampal formation.

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Journal:  Surg Radiol Anat       Date:  2018-08-07       Impact factor: 1.246

Review 2.  Fractal dimension of chromatin: potential molecular diagnostic applications for cancer prognosis.

Authors:  Konradin Metze
Journal:  Expert Rev Mol Diagn       Date:  2013-09       Impact factor: 5.225

3.  Mathematical model of neuronal morphology: prenatal development of the human dentate nucleus.

Authors:  Katarina Rajković; Goran Bačić; Dušan Ristanović; Nebojša T Milošević
Journal:  Biomed Res Int       Date:  2014-06-05       Impact factor: 3.411

4.  Box-Counting Method of 2D Neuronal Image: Method Modification and Quantitative Analysis Demonstrated on Images from the Monkey and Human Brain.

Authors:  Nemanja Rajković; Bojana Krstonošić; Nebojša Milošević
Journal:  Comput Math Methods Med       Date:  2017-05-08       Impact factor: 2.238

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