报告题目: Quantitative data-driven analysis of resting-state fMRI data
报告人职务：Professor, Center for Eye Disease and Development, Vision Science Graduate Program and School of Optometry, University of California at Berkeley; Adjunct Faculty, Proctor Foundation for Research in Ophthalmology, University of California at San Francisco
摘要: In recent years there has been a rapidly growing interest in the study of brain functional connectivity based on resting-state fMRI. The USA and EU countries have all started large-scale human brain connectone mapping projects involving thousands of volunteers, e.g., http://www.humanconnectomeproject.org. Besides the development of specific MRI hardware and fast data acquisition techniques, the development of advanced pre-processing pipeline, analysis and visualization tools are also very relevant and important to facilitate the identification of normal and abnormal brain functional connectivity networks. In this presentation, I will focus on the research activities of the medical physicist team from Karolinska University Hospital on exploring the use of data-driven methods to analyse resting-state fMRI data, including independent component analysis (ICA), hierarchical clustering analysis (HCA), and quantitative data-driven analysis (QDA) methods. Particularly, QDA applications in clinical diagnosis and intervention of neurological and psychiatric patients will be discussed.
报告人简介：Tie-Qiang Li, Professor, Department of Medical Radiation and Nuclear Medicine, Karolinska University Hospital; Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute. Tie-Qiang Li's main research interest is the development of imaging and spectroscopic techniques with potentials to improve the clinical diagnostics and the understandings of biological and physiological processes. Particularly, He is active in developing rapid data acquisition methods and novel post-processing techniques for MRI and exploring new imaging contrast mechanisms for neurological applications. In the last a few years, his team has made progress in developing methods and understanding the neurological mechanisms for non-invasive stimulation of the autonomic nervous system.