Understanding the Network Vulnerability  Mechanism in Alzheimer’s Disease

▣ 연사(Speaker) :  Prof. Guorong Wu (UNC Chapel Hill)

▣ 일시(Date &Time) : 2022.2.17(목), 10:30 am ~

▣ 언어(Language) : 영어(English)

▣ Zoom URL : https://zoom.us/j/8978217407?pwd=d1pOZmF1OWlseEdZRVBpV3VuSkl3dz09

Abstract : Human brain is something of an enigma. Much is known about its physical structures, but quite how our brain managers to marshal its myriad components into a powerhouse capable of performing so many different tasks remains a mystery. We are now in the era of big data, which allows us to answer the biomedical questions today that we couldn’t answer before. As a computer scientist, this is the most exciting time in my entire career. In the last ten years, I have been collaborating with neurology, neuroscience, genetics, and imaging experts to understand the pathophysiological mechanism of Alzheimer’s disease (AD) and how AD-related genes affect the aging brains. Specifically, my lab is interested in establishing a neurobiological basis to quantify the structural/functional/behavior difference across individuals and discover reliable and putative biomarkers that will allow us to come up with personalized therapy and treatment for individuals. In this talk, I would like to share my experience of integrating the domain knowledge of neuroscience into the development of imaging-AI based computational tools for automated image analysis, image interpretation, and outcome prediction, with the focus on imaging biomarkers and the computer-assisted early diagnostic engine for AD. At the end of this talk, I will demonstrate the preliminary results of a recent research project where we aim to understand the selective mechanism of network vulnerability and resilience in AD using the state-of-the-art network analyses approaches across neuroimaging and genetics data.

Bio : Dr. Guorong Wu is an Assistant Professor in the Departments of Psychiatry, Computer Science, and Statistics at the University of North Carolina, Chapel Hill. His primary research interests are medical image analysis, big data mining, scientific data visualization, and computer-assisted diagnosis. He has been working on medical image analysis since he started his PhD study in 2003. In 2007, he received the Ph.D. degree in computer science and engineering from Shanghai Jiao Tong University, Shanghai, China. He has developed many image processing methods for brain magnetic resonance imaging (MRI), diffusion weighted imaging, breast dynamic contrast enhanced MRI (DCE-MRI), and computed tomography (CT) images. These cutting-edge computational methods have been successfully applied to early diagnosis of Alzheimer’s disease, infant brain development study, and image-guided lung cancer radiotherapy. Meanwhile, Dr. Wu leads a multi-discipline research team in UNC, which aims to translate the cutting-edge intelligent techniques to the imaging-based biomedical applications for the sake of boosting translational medicine. Dr. Wu has released more than 10 image analysis software packages to the medical imaging community, which count to more than 15,000 downloads since 2009. Dr. Wu is the recipient of NIH Career Development Award (K01). He also serves as the PI and Co-PI in several NIH and NSF grants.