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The use of antidepressants during pregnancy benefits the mother's well-being, but the effects of such substances on neurodevelopment remain poorly understood. Moreover, the consequences of early exposure to antidepressants may not be immediately apparent at birth. In utero exposure to selective serotonin reuptake inhibitors (SSRIs) has been related to developmental abnormalities, including a reduced white matter volume. Several reports have observed an increased incidence of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) after prenatal exposure to SSRIs such as sertraline, the most widely prescribed SSRI. The advent of human-induced pluripotent stem cell (hiPSC) methods and assays now offers appropriate tools to test the consequences of such compounds for neurodevelopment in vitro. In particular, hiPSCs can be used to generate cerebral organoids - self-organized structures that recapitulate the morphology and complex physiology of the developing human brain, overcoming the limitations found in 2D cell culture and experimental animal models for testing drug efficacy and side effects. For example, single-cell RNA sequencing (scRNA-seq) and electrophysiological measurements on organoids can be used to evaluate the impact of antidepressants on the transcriptome and neuronal activity signatures in developing neurons. While the analysis of large-scale transcriptomic data depends on dimensionality reduction methods, electrophysiological recordings rely on temporal data series to discriminate statistical characteristics of neuronal activity, allowing for the rigorous analysis of the effects of antidepressants and other molecules that affect the developing nervous system, especially when applied in combination with relevant human cellular models such as brain organoids.

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The enormous amount of currently available data demands efforts to extract meaningful information. For this purpose, different measurements are applied, including Shannon's entropy, permutation entropy, and the Lempel-Ziv complexity. These methods have been used in many applications, such as pattern recognition, series classification, and several other areas (e.g., physical, financial, and biomedical). Data in these applications are often presented in binary series with temporal correlations. Herein, we compare the measures of information entropy in binary series conveying short- and long-range temporal correlations characterized by the Hurst exponent H. Combining numerical and analytical approaches, we scrutinize different methods that were not efficient in detecting temporal correlations. To surpass this limitation, we propose a measure called the binary permutation index (BPI). We will demonstrate that BPI efficiently discriminates patterns embedded in the series, offering advantages over previous methods. Subsequently, we collect stock market time series and rain precipitation data as well as perform in vivo electrophysiological recordings in the hippocampus of an experimental animal model of temporal lobe epilepsy, in which the BPI application in both public open source and experimental data is demonstrated. An index is proposed to evaluate information entropy, allowing the ability to discriminate randomness and extract meaningful information in binary time series.

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Shannon's concept of information is related to predictability. In a binary series, the value of information relies on the frequency of 0's and 1's, or how it is expected to occur. However, information entropy does not consider the bias in randomness related to autocorrelation. In fact, it is possible for a binary temporal series to carry both short- and long-term memories related to the sequential distribution of 0's and 1's. Although the Hurst exponent measures the range of autocorrelation, there is a lack of mathematical connection between information entropy and autocorrelation present in the series. To fill this important gap, we combined numerical simulations and an analytical approach to determine how information entropy changes according to the frequency of 0's and 1's and the Hurst exponent. Indeed, we were able to determine how predictability depends on both parameters. Our findings are certainly useful to several fields when binary times series are applied, such as neuroscience to econophysics.

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Epilepsy is a disorder of the brain characterized by the predisposition to generate recurrent unprovoked seizures, which involves reshaping of neuronal circuitries based on intense neuronal activity. In this review, we first detailed the regulation of plasticity-associated genes, such as ARC, GAP-43, PSD-95, synapsin, and synaptophysin. Indeed, reshaping of neuronal connectivity after the primary, acute epileptogenesis event increases the excitability of the temporal lobe. Herein, we also discussed the heterogeneity of neuronal populations regarding the number of synaptic connections, which in the theoretical field is commonly referred as degree. Employing integrate-and-fire neuronal model, we determined that in addition to increased synaptic strength, degree correlations might play essential and unsuspected roles in the control of network activity. Indeed, assortativity, which can be described as a condition where high-degree correlations are observed, increases the excitability of neural networks. In this review, we summarized recent topics in the field, and data were discussed according to newly developed or unusual tools, as provided by mathematical graph analysis and high-order statistics. With this, we were able to present new foundations for the pathological activity observed in temporal lobe epilepsy.

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Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states.

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Anoxia is one of the most prevalent causes of neonatal morbidity and mortality, especially in preterm neonates, constituting an important public health problem due to permanent neurological sequelae observed in patients. Oxygen deprivation triggers a series of simultaneous cascades, culminating in cell death mainly located in more vulnerable metabolic brain regions, such as the hippocampus. In the process of cell death by oxygen deprivation, cytosolic calcium plays crucial roles. Intracellular inositol 1,4,5-trisphosphate receptors (IP3Rs) are important regulators of cytosolic calcium levels, although the role of these receptors in neonatal anoxia is completely unknown. This study focused on the functional role of inositol 1,4,5-trisphosphate receptor type 1 (IP3R1) in rat hippocampus after neonatal anoxia. Quantitative real-time PCR revealed a decrease of IP3R1 gene expression 24 hours after neonatal anoxia. We detected that IP3R1 accumulates specially in CA1, and this spatial pattern did not change after neonatal anoxia. Interestingly, we observed that anoxia triggers translocation of IP3R1 to nucleus in hippocampal cells. We were able to observe that anoxia changes distribution of IP3R1 immunofluorescence signals, as revealed by cluster size analysis. We next examined the role of IP3R1 in the neuronal cell loss triggered by neonatal anoxia. Intrahippocampal injection of non-specific IP3R1 blocker 2-APB clearly reduced the number of Fluoro-Jade C and Tunel positive cells, revealing that activation of IP3R1 increases cell death after neonatal anoxia. Finally, we aimed to disclose mechanistics of IP3R1 in cell death. We were able to determine that blockade of IP3R1 did not reduced the distribution and pixel density of activated caspase 3-positive cells, indicating that the participation of IP3R1 in neuronal cell loss is not related to classical caspase-mediated apoptosis. In summary, this study may contribute to new perspectives in the investigation of neurodegenerative mechanisms triggered by oxygen deprivation.

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The cytoskeleton (CSK) is a tensed fiber framework that supports, shapes and stabilizes the cell. The CSK is in a constant state of remodeling, moreover, which is an active non-equilibrium thermodynamic process. We report here that cytoskeletal remodeling involves reconfigurations that are not only sudden but also are transmitted to great distances within the cell in a fashion reminiscent of quakes in the Earth's crust. Remarkably, these events in the cell conform both qualitatively and quantitatively to empirical laws typical of earthquakes, including hierarchical fault structures, cumulative energy distributions following the Gutenberg-Richter law, and rate of after-shocks following Omori's law. While it is well-established that remodeling and stabilization of the cytoskeleton are non-equilibrium process, these new unanticipated observations establish that these processes are also remarkably non-local and strongly cooperative.

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