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This study found that anxiety induced by a suspenseful story increased communication between the brain’s salience, default mode, and central executive networks, with the central executive network acting as a bridge during peak tension. These findings suggest that anxiety alters large-scale brain connectivity patterns and may help inform future diagnostic tools and personalized treatments for anxiety disorders.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and is difficult to diagnose in young children. Here magnetoencephalography was used to compare the brain activity in patients with ASD to patients in a control group. The results show that patients with ASD have a high level of activity in different areas of the brain than those in the control group.

The authors looked at the effects of commonly used anesthetics in rodents on brain activity (specifically quasi-periodic patterns). Understanding effects on brain activity is important for researchers to understand when choosing rodent models for disease.

The authors compare neuroimaging datasets to identify potential new biomarkers for earlier detection of Alzheimer's disease.

Here, seeking to understand the effects of social media in relation to social media fatigue and/or overload in recent years, the authors used various linear models to assess the results of a survey of 27 respondents. Their results showed that increased duration of use of social media did not necessarily lead to fatigue, suggesting that quality may be more important than quantity. They also considered the purpose of an individual's social media usage as well as their engagement behavior during the COVID-19 pandemic.

In this study, the impact of shutting down schools on the emotional aspects of high school students was analyzed using survey responses.

The authors design and test an easy-to-use and cost-effective mobile app-based alert system to help senior citizens rapidly communicate with caregivers in emergencies or when in need of assistance.

The authors investigate the ability of machine learning models to developing new drug-like molecules by learning desired chemical properties versus simply generating molecules that similar to those in the training set.