A groundbreaking new study conducted by researchers from Reichman University and other Israeli  institutions explored the psychological aftermath of the deadly terrorist attack at the Nova Festival in southern Israel. The attack, which took place on October 7, 2023, claimed the lives of nearly 400 people and left hundreds more physically and psychologically wounded. Three weeks after the massacre, the researchers administered in-person psychological questionnaires to 343 survivors aged 18–64, examining the participants’ mental state, the psychoactive substances they consumed before and during the festival, and the ways those substances may have affected their physical and psychological response to trauma.

In the context of global aging, maintaining brain health has become a public health priority. Brain age, estimated from MRI data, can deviate from chronological age, indicating accelerated aging. This study, using data from 16,972 UK Biobank participants, objectively measured physical activity (PA) via 7-day wrist-worn accelerometry and linked it to MRI-based brain age models. The analysis uncovered a U-shaped association between PA intensity and brain age gap (BAG), with both low and high levels of PA associated with increased BAG, while moderate MPA and VPA correlated with reduced BAG.

Further analysis revealed that BAG partially mediates the relationship between PA and both cognitive performance and brain disorders. Moderate activity levels were linked to lower BAG and better brain outcomes, driven by structural preservation in key brain regions like the cingulate cortex and striatum. This study, the first to demonstrate such nonlinear associations at scale using objective PA data, highlights the importance of calibrated exercise for brain health and lays the foundation for tailored intervention strategies.

Researchers have developed a cost-effective, AI-powered system for diagnosing nystagmus—a condition causing involuntary eye movements—using smartphone videos and cloud-based analysis. Unlike traditional methods like VNG, which are expensive and cumbersome, this deep learning model uses real-time facial landmark tracking to assess eye movement metrics remotely. A pilot study with 20 participants showed its accuracy closely matched traditional devices, highlighting its potential for telehealth use and broader clinical application.

A team led by University of Virginia School of Medicine researcher James Stone, MD, PhD, has received $3.2 million from the federal Department of Defense to enhance a critical tool for protecting the brain health of military personnel.