A new study from Carnegie Mellon University shows ultrasound can subtly prepare the brain to respond, rather than directly triggering activity. Combined with light electrical stimulation, it produces stronger, targeted effects for future therapies.

Learning French, reading the latest Andy Weir novel, hanging out with friends for St. Patrick’s Day — language is central to all these everyday activities. Seemingly effortless from childhood, language, it turns out, is quite complex, not constrained to one set of genes or one region in the brain. Cognitive neuroscientists are now using a diverse arsenal of tools, including novel genetic analyses and AI, to gain insights into both healthy and disordered communication across individuals, as will be presented at the annual meeting of the Cognitive Neuroscience Society (CNS) in Vancouver, British Columbia.

As artificial intelligence (AI) becomes more common in health care, from managing records to assisting with medication decisions, researchers at the Icahn School of Medicine at Mount Sinai are asking an important question: How well does AI hold up when the workload gets intense at health system scale? A new study, published in the March 9 online issue of npj Health Systems [https://doi.org/10.1038/s44401-026-00077-0], suggests that the answer depends less on the AI itself and more on how it’s designed. The investigators found that health care AI systems work far better when tasks are distributed among multiple specialized AI “agents”—software systems that can perform complex tasks, learn, and adapt—rather than relying on a single, all-purpose agent. This multi-agent approach kept performance steady even as demands increased, while dramatically reducing computing costs and delays, say the investigators.