Typical and Atypical Development of Functional Human Brain Networks: Insights from Resting-State fMRI

Over the past several decades, structural MRI studies have provided remarkable insights into human brain development by revealing the trajectory of gray and white matter maturation from childhood to adolescence and adulthood. In parallel, functional MRI studies have demonstrated changes in brain activation patterns accompanying cognitive development. Despite these advances, studying the maturation of functional brain networks underlying brain development continues to present unique scientific and methodological challenges. Resting-state fMRI (rsfMRI) has emerged as a novel method for investigating the development of large-scale functional brain networks in infants and young children. We review existing rsfMRI developmental studies and discuss how this method has begun to make significant contributions to our understanding of maturing brain organization. In particular, rsfMRI has been used to complement studies in other modalities investigating the emergence of functional segregation and integration across short and long-range connections spanning the entire brain. We show that rsfMRI studies help to clarify and reveal important principles of functional brain development, including a shift from diffuse to focal activation patterns, and simultaneous pruning of local connectivity and strengthening of long-range connectivity with age. The insights gained from these studies also shed light on potentially disrupted functional networks underlying atypical cognitive development associated with neurodevelopmental disorders. We conclude by identifying critical gaps in the current literature, discussing methodological issues, and suggesting avenues for future research

Disambiguating the role of blood flow and global signal with partial information decomposition

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas

Controversies and progress on standardization of large-scale brain network nomenclature

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt

Correlation of Oxidative Stress with Serum Trace Element Levels and Antioxidant Enzyme Status in Beta Thalassemia Major Patients: A Review of the Literature

Beta thalassemia major is an inherited disease resulting from reduction or total lack of beta globin chains. Patients with this disease need repeated blood transfusion for survival. This may cause oxidative stress and tissue injury due to iron overload, altered antioxidant enzymes, and other essential trace element levels. The aim of this review is to scrutinize the relationship between oxidative stress and serum trace elements, degree of damage caused by oxidative stress, and the role of antioxidant enzymes in beta thalassemia major patients. The findings indicate that oxidative stress in patients with beta thalassemia major is mainly caused by tissue injury due to over production of free radical by secondary iron overload, alteration in serum trace elements and antioxidant enzymes level. The role of trace elements like selenium, copper, iron, and zinc in beta thalassemia major patients reveals a significant change of these trace elements. Studies published on the status of antioxidant enzymes like catalase, superoxide dismutase, glutathione, and glutathione S-transferase in beta thalassemia patients also showed variable results. The administration of selective antioxidants along with essential trace elements and minerals to reduce the extent of oxidative damage and related complications in beta thalassemia major still need further evaluation

Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network's Big Data.

The current article paper is aimed at assessing and comparing the seasonal check-in behavior of individuals in Shanghai, China, using location-based social network (LBSN) data and a variety of spatiotemporal analytic techniques. The article demonstrates the uses of location-based social network's data by analyzing the trends in check-ins throughout a three-year term for health purpose. We obtained the geolocation data from Sina Weibo, one of the biggest renowned Chinese microblogs (Weibo). The composed data is converted to geographic information system (GIS) type and assessed using temporal statistical analysis and spatial statistical analysis using kernel density estimation (KDE) assessment. We have applied various algorithms and trained machine learning models and finally satisfied with sequential model results because the accuracy we got was leading amongst others. The location cataloguing is accomplished via the use of facts about the characteristics of physical places. The findings demonstrate that visitors' spatial operations are more intense than residents' spatial operations, notably in downtown. However, locals also visited outlying regions, and tourists' temporal behaviors vary significantly while citizens' movements exhibit a more steady stable behavior. These findings may be used in destination management, metro planning, and the creation of digital cities

Data of chemical analysis and electrical properties of SnO2-TiO2 composite nanofibers

In this data article, we provide energy dispersive X-ray spectroscopy (EDX) spectra of the electrospun composite (SnO2-TiO2) nanowires with the elemental values measured in atomic and weight%. The linear sweep voltammetry data of composite and its component nanofibers are provided. The data collected in this article is directly related to our research article “Synergistic combination of electronic and electrical properties of SnO2 and TiO2 in a single SnO2-TiO2 composite nanowire for dye-sensitized solar cells

Occupational stress and addiction: Possible neurobiological elucidation of medical waste related individuals

Multiple factors contribute to the tendency to develop drug addictions, including social or psychological stressors. Most studies examining causes of and treatments for drug addiction have been conducted in Western developed nations. Here we used phenomenological research approach to explore the neurobiological explanation of drug addiction and to investigate attitudes towards drug use amongst individuals working with medical waste. Data were collected in Dhaka, the capital of Bangladesh, using a variety of techniques based on formal representative sampling for fixed populations and adaptive sampling for roaming populations. We found that over half of participants interviewed reported using illicit substances to cope with occupational stress. Self-reported disease symptoms related to stress were reported by most of the respondents. Working with horrifying waste contributes to increased stress among the participants. These results indicate that in the case of these workers, workplace stressors along with cultural and socio-economic context uniquely contribute to, and exacerbate, tendencies toward drug addiction