Glycine Receptor Inhibition Differentially Affect Selected Neuronal Populations of the Developing Embryonic Cortex, as Evidenced by the Analysis of Spontaneous Calcium Oscillations

Abstract

The embryonic developing cerebral cortex is characterized by the presence of distinctive cell types such as progenitor pools, immature projection neurons and interneurons. Each of these cell types is diverse on itself, but they all take part of the developmental process responding to intrinsic and extrinsic cues that can affect their calcium oscillations. Importantly, calcium activity is crucial for controlling cellular events linked to cell cycle progression, cell fate determination, specification, cell positioning, morphological development and maturation. Therefore, in this work we measured calcium activity in control conditions and in response to neurotransmitter inhibition. Different data analysis methods were applied over the experimental measurements including statistical methods entropy and fractal calculations, and spectral and principal component analyses. We found that developing projection neurons are differentially affected by classic inhibitory neurotransmission as a cell type and at different places compared to migrating interneurons, which are also heterogeneous in their response to neurotransmitter inhibition. This reveals important insights into the developmental role of neurotransmitters and calcium oscillations in the forming brain cortex. Moreover, we present an improved analysis proposing a Gini coefficient-based inequality distribution and principal component analysis as mathematical tools for understanding the earliest patterns of brain activity.

Theil Entropy as a Non-Lineal Analysis for Spectral Inequality of Physiological Oscillations

“…Finally, for the study of spectral inequality, the Gini index has been used in biological signals [21]. Although the Gini index is easier to interpret, since it has values between zero and one and is easier to calculate, Theil entropy has certain advantages over Gini.…”

“…This phenomenon can be clearly appreciated with synchronic discharges observed in the EEG, however this is a more general effect susceptible to analysis in all biological signals. In particular, the application of the Gini coefficient to biological phenomena has recently been explored for the analysis of the electrocardiogram [16], in EEG [17], in the evaluation of diffusion magnetic resonance imaging [18], exploring the variability of the radionic characteristics of lung cancer lesions in non-contrast and contrast-enhanced chest computed tomography images [19], as an unbiased tool for the selection of genes in the analysis of gene expression [20], and in the study of oscillations of intracellular calcium in developing neurons [21]. In these cases, the Gini coefficient, applied to determine the spectral inequality of biological oscillations, has been used as a linear measure of the inequality of the distributions [16,17,21].…”

“…In particular, the application of the Gini coefficient to biological phenomena has recently been explored for the analysis of the electrocardiogram [16], in EEG [17], in the evaluation of diffusion magnetic resonance imaging [18], exploring the variability of the radionic characteristics of lung cancer lesions in non-contrast and contrast-enhanced chest computed tomography images [19], as an unbiased tool for the selection of genes in the analysis of gene expression [20], and in the study of oscillations of intracellular calcium in developing neurons [21]. In these cases, the Gini coefficient, applied to determine the spectral inequality of biological oscillations, has been used as a linear measure of the inequality of the distributions [16,17,21]. However, although the Gini coefficient and Theil entropy are used as indicators of inequality, they are very different when it comes to providing information on the origin of inequality by not setting the same criteria in the distributive analysis [22].…”

“…Previous work on the inequality of the frequency spectrum for biological oscillations [16,17,21] has been limited to describing the inequality of the spectrum without addressing its origin. Among the possible application of these inequality indices, one is to provide information about the causes of inequality, as well as clarifying the relative importance of the contributing elements.…”