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Neurodegenerative diseases like Alzheimer’s are a growing concern in the U.S., with over 7 million Americans living with Alzheimer’s disease today. By 2060, that number is expected to grow, affecting nearly 13 million people. These diseases are not only hard on individuals and families, but are costly, with more than $230 billion spent in the U.S. each year in caregiving alone. As the population ages, the need for new ways to detect and address the silent emergence of these diseases has never been more urgent. 

New artificial intelligence predictive models used in brain research may provide a way to better predict how a person’s brain ages over time, helping doctors recognize warning signs long before clinical symptoms surface. 

Supported by the U.S. National Science Foundation, a team of researchers led by Paul Bogdan, an associate professor in the University of Southern California Department of Electrical and Computer Engineering, has developed a cutting-edge AI system capable of generating a future MRI of a person’s brain from just a single scan. This technology opens the door to identifying subtle changes that may signal the earliest stages of neurodegenerative diseases — potentially years before traditional diagnostic methods could detect them.

To build the tool, the team combined two advanced AI techniques: a 3D diffusion model and a ControlNet, which allow the system to “control” or guide image generation

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RSS feed source: National Science Foundation

The U.S. National Science Foundation today announced a new funding opportunity to support research and technology development that will improve the next generation of wireless communication systems known as NextG.     In collaboration with industry, other government agencies, and international partners, the NSF Verticals-enabling Intelligent NEtwork  Systems (NSF VINES) program will invest up to $100 million to accelerate performance and capabilities of next-generation (NextG) advanced intelligent network systems  spanning the user-edge-core-cloud continuum. 

“NSF VINES will enhance U.S. competitiveness in advanced telecommunications technologies, including NextG wireless telecommunications and emerging potential NextG vertical industries, and prepare the American workforce for jobs available now and in the future,” said Brian Stone, performing the duties of the NSF Director.

“This important investment from NSF, in collaboration with industry and other government agencies, will help strengthen U.S. leadership and ensure the American people reap the benefits in areas such as self-driving cars, advanced manufacturing, energy infrastructure, and beyond,” said Dr. Lynne Parker, Principal Deputy Director of The White House Office of Science and Technology Policy. 

NSF VINES is in partnership with several major industry organizations and U.S. federal agencies, including Ericsson, Intel, Qualcomm, the U.S. Department of Homeland Security, U.S. Department of Defense Office of the Under Secretary for Research and Engineering, and U.S. Department of Commerce National Institute of Standards and Technology, as well as international partners from

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RSS feed source: National Science Foundation

Researchers supported by the U.S. National Science Foundation have provided a new understanding of how and where learning occurs in the brain. The two-part finding has implications for understanding and treating neurodegenerative diseases like Alzheimer’s and other dementias, which impact more than 7 million people in the United States and account for $384 billion in health and long-term care costs, as well as for enhancing neural networks.

“Identifying how the brain actually forms new connections and learns is a question at the frontier of neuroscience,” said Paul Forlano, program officer in the NSF Directorate for Biological Sciences. “Knowing that influences our understanding of how we interact with our environment and pick up on and respond to cues, which opens the door to a range of new fundamental and applied research.”

The researchers, led by Kishore Kuchibhotla, assistant professor at Johns Hopkins University, used brain imaging to determine when mice learned a new skill. The imaging reinforced previous work, showing that mice learned quickly and that those that continued to make errors weren’t still learning; they were experimenting. The difference between mistakes and testing the rules was evident in changes in the neural activity that the researchers saw in the mice.

Kuchibhotla said the distinction between the brain dynamics in learning and the dynamics involved in using that skill could be mimicked in having a memory

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