Biological physics is the branch of science that applies the principles of physics, chemistry, and mathematics to the biological system to understand the fundamental biological processes such as how biomolecules, cells, tissues, and organs perform vital life functions. Scientists from diverse backgrounds use an experimental, computational, and theoretical approach to explore the mysteries of life. Medical physics applies knowledge of physics in medicine to diagnose and treat human disease using methods like magnetic resonance imaging and radiation treatments. Soft matter physics is an interdisciplinary field where scientists from different areas of science come together to understand the behavior and properties of soft materials like liquid crystals, colloids, polymers, gels, membranes, and cytoskeletons. The Biological/Medical/Soft matter physics session of the ANPA conference taking place on July 19-21, 2024, aims to bring the scientist together to present their findings, discuss their research and foster new collaborations. We invite you and your colleagues to submit abstracts for presentations and look forward to seeing you at the conference.
AI and computational neuroimaging for brain health and disorders
Imaging modalities, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), are among the most significant advancements in physics and engineering design. These non-invasive imaging techniques have transformed numerous sectors, including brain health and disorders. However, MRI and PET neuroimaging data are extremely complex and difficult to interpret. This makes it difficult to reach research and clinical outcomes that are important for clinical diagnosis, prognosis, and future precision medicine. To overcome these obstacles, modern machine learning and artificial intelligence (AI) technologies have shown considerable promise in describing brain systems using non-invasive neuroimaging data. In this talk, we will discuss various AI and computational neuroimaging approaches, as well as their promising applications in identifying novel brain mechanisms in health and disease, with a focus on schizophrenia, first-episode psychosis, autism, and Alzheimer’s disease. These findings indicate that physics-based imaging methods integrated with advanced AI methodologies provide a multidisciplinary perspective for uncovering novel mechanisms about brain health, which may contribute to the development of future computer-aided therapy efforts for brain disorders.
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