Brain Complexity and Psychiatric Disorders.

Objective: In recent years, researchers and neuroscientists have begun to use a variety of nonlinear techniques for analyzing neurophysiologic signals derived from fMRI, MEG, and EEG in order to describe the complex dynamical aspects of neural mechanisms. In this work, we first attempted to describe different algorithms to estimate neural complexity in a simple manner understandable for psychiatrists, psychologists, and neuroscientists. Then, we reviewed the findings of the brain complexity analysis in psychiatric disorders and their clinical implications. Method : A non-systematic comprehensive literature search was conducted for original studies on the complexity analysis of neurophysiological signals such as electroencephalogram, magnetoencephalogram, and blood-oxygen-level-dependent obtained from functional magnetic resonance imaging or functional near infrared spectroscopy. The search encompassed online scientific databases such as PubMed and Google Scholar. Results: Complexity measures mainly include entropy-based methods, the correlation dimension, fractal dimension, Lempel-Ziv complexity, and the Lyapunov exponent. There are important differences in the physical notions between these measures. Our literature review shows that dementia, autism, and adult ADHD exhibit less complexity in their neurophysiologic signals than healthy controls. However, children with ADHD, drug-naïve young schizophrenic patients with positive symptoms, and patients with mood disorders (i.e., depression and bipolar disorder) exhibit higher complexity in their neurophysiologic signals compared to healthy controls. In addition, contradictory findings still exist in some psychiatric disorders such as schizophrenia regarding brain complexity, which can be due to technical issues, large heterogeneity in psychiatric disorders, and interference of typical factors. Conclusion: In summary, complexity analysis may present a new dimension to understanding psychiatric disorders. While complexity analysis is still far from having practical applications in routine clinical settings, complexity science can play an important role in comprehending the system dynamics of psychiatric disorders.

An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges.

Objective: Automatic diagnosis of psychiatric disorders such as bipolar disorder (BD) through machine learning techniques has attracted substantial attention from psychiatric and artificial intelligence communities. These approaches mostly rely on various biomarkers extracted from electroencephalogram (EEG) or magnetic resonance imaging (MRI)/functional MRI (fMRI) data. In this paper, we provide an updated overview of existing machine learning-based methods for bipolar disorder (BD) diagnosis using MRI and EEG data. Method : This study is a short non-systematic review with the aim of describing the current situation in automatic diagnosis of BD using machine learning methods. Therefore, an appropriate literature search was conducted via relevant keywords for original EEG/MRI studies on distinguishing BD from other conditions, particularly from healthy peers, in PubMed, Web of Science, and Google Scholar databases. Results: We reviewed 26 studies, including 10 EEG studies and 16 MRI studies (including structural and functional MRI), that used traditional machine learning methods and deep learning algorithms to automatically detect BD. The reported accuracies for EEG studies is about 90%, while the reported accuracies for MRI studies remains below the minimum level for clinical relevance, i.e. about 80% of the classification outcome for traditional machine learning methods. However, deep learning techniques have generally achieved accuracies higher than 95%. Conclusion: Research utilizing machine learning applied to EEG signals and brain images has provided proof of concept for how this innovative technique can help psychiatrists distinguish BD patients from healthy people. However, the results have been somewhat contradictory and we must keep away from excessive optimistic interpretations of the findings. Much progress is still needed to reach the level of clinical practice in this field.

Breaking infodemic: discovering SARS-CoV-2 / Rompiendo la infodemia: descubriendo el SARS-CoV-2

By December 2019, multiple cases of unexplained pneumonia were reported in some hospitals in the city of Wuhan, China. Since then, it had been confirmed that it corresponded to an acute respiratory infection caused by a new coronavirus that spread quickly, becoming pandemic in a very short time. On the other hand, this pandemic forced confinement for months, something unprecedented. In that time, millions of people went online for entertainment, education, etc. Consequently, the use of the Internet increased, bringing, on the one hand, online education, and entertainment on the Internet, ensuring social distancing; and on the other hand, it brought new new risks to human life, among them rumors. In this way and given the large number of publications that could denote the level of misinformation about COVID-19 and the impact it could have on global public health, various scientific publications were analyzed and identified from a bibliometric point of view. Potential relationships between the descriptors obtained from the bibliometric search were identified. The results were conglomerated into 5 clusters: Cluster 1, related to studies on access to information provided on COVID-19; cluster 2 shows the list of studies that have been carried out on the information on the COVID-19 vaccine, cluster 3 analyzes the different responses given by conspiracy theories, rumors and misinformation about COVID-19, the Group 4 shows cross-sectional and longitudinal research on COVID-19 and the information it provides to the health sector, and cluster 5 represents studies on scientific production and communication that have contributed to global health during the pandemic(AU)

Para diciembre de 2019, se registraron múltiples casos de una neumonía inexplicables en algunos hospitales de la ciudad de Wuhan, China. Desde ese momento se había confirmado correspondía a una infección respiratoria aguda causada por un nuevo coronavirus que se propagó rápidamente haciéndose pandémico en muy poco tiempo. Por otra parte, esta pademia obligó a un confinamiento por meses, algo sin precedente. En ese tiempo, millones de personas se conectaron en línea para entretenimiento, educación, etc. En consecuencia, el uso de Internet aumentó trayendo, por una parte, educación online y entretenimiento en Internet asegurando el distanciamiento social; y por otra parte, trajo nuevos nuevos riesgos a la vida humana, entre ellos los rumores. En ese sentido, y ante la gran cantidad de publicaciones que podrían denotar el nivel de desinformación sobre el COVID-19 y el impacto que podría tener en la salud pública mundial, se analizaron e identificaron diversas publicaciones científicas desde el punto de vista bibliométrico. Se identificaron las relaciones potenciales entre los descriptores arrojados de la búsqueda bibliométrica. Los resultados se conglomeraron en 5 clúster: El clúster 1, relacionado con los estudios sobre el acceso a la información proporcionada sobre COVID-19; el clúster 2, muestra la relación de los estudios que se han realizado sobre la información de la vacuna COVID-19, el clúster 3, analiza las distintas respuestas que dan las teorías conspirativas, los rumores y la desinformación sobre el COVID-19, el grupo 4 muestra investigaciones transversales y longitudinales sobre el COVID-19 y la información que brinda al sector salud, y el clúster 5 representa los estudios sobre producción y comunicación científicas que han contribuido a la salud mundial durante la pandemia(AU)

Education in environmental health: scientific contributions of the last 20 years / Educación en salud ambiental: aportes científicos de los últimos 20 años

Man's attitudes about the environment have generated irreversible damage to the planet, emerging as an alternative to this problem Environmental Education, which aims to reorient social awareness towards a friendly and thoughtful culture. Through environmental education, we seek to make people aware of the problems of the natural and social environment from their school education in childhood to generate values, new attitudes, behaviors, and beliefs aimed at caring for the environment and learning new relationships between people. Likewise, to carry out these environmental education strategies, it is important to know some specific parameters, such as biological diversity and conservation, in addition to the conservation policies carried out by each nation. In this sense, in this work a bibliometric study was carried out based on high-impact scientific production and stipulated by ScienceDirect related to Environmental Education during a period of the last 20 years. The results were grouped into five clusters: "Environmental Education" OR "Education for Sustainable Development" OR "Education for Sustainability" OR "Education for Climate Change" OR "Eco citizenship". The union of all these clusters are connected and intertwined with each other. Them in a dependent way, which is a consequence of the study carried out(AU)

Las actitudes del hombre sobre el medio ambiente han generado daños irreversibles al planeta, surgiendo como alternativa para esta problemática la Educación Ambiental, que tiene como finalidad reorientar la conciencia social hacia una cultura amigable y reflexiva. Mediante la educación ambiental se busca concientizar a las personas sobre los problemas del ambiente natural y social desde su formación escolar en la niñez para generar valores, nuevas actitudes, comportamientos y creencias orientadas al cuidado del medio ambiente y el aprendizaje de nuevas relaciones entre las personas. Asimismo, para lleva a cabo estas estrategias de educación ambiental, es importante conocer algunos parámetros específicos, como la diversidad biológica y conservación, además de las políticas propias de conservación llevada a cabo por cada nación. En ese sentido, en este este trabajo se realizó un estudio bibliométrico basado en la producción científica de alto impacto y estipuladas por ScienceDirect relacionados con la Educación Ambiental durante un periodo de los últimos 20 años. Los resultados fueron agrupados en cinco clustes: "Environmental Education" OR "Education for Sustainable Development" OR "Education for Sustainability" OR "Education for Climate Change" OR "Eco citizenship". La unión de todos estos clústers se encuentran conectados y entrelados entre ellos de manera dependiente, lo cual es consecuencia del estudio realiz(AU)