| R-fMRI spontaneous low frequency fluctuations: making measurements more robust and a theoretical framework of the underlying electrophysiological “mechanisms” |
Speaker: Chao-Gan Yan, 严超赣, Ph.D. Research Scientist, Nathan Kline Institute for Psychiatric Research Research Assistant Professor, New York University Child Study Center Host: Yu-Feng Zang, 臧玉峰, M.D. 10am, August 9, 2014; 2014年8月9日，上午10点 杭州师范大学附属医院7号楼5楼会议室（暂定） |  |
| As a research tool to investigate ongoing brain activity in basic, translational and clinical neuroscience studies, the use of resting-state fMRI (R-fMRI) has grown rapidly due to its sensitivity to developmental, aging and pathological processes, ease of data collection in challenging populations, and amenability to aggregation across studies and sites. Although R-fMRI has substantial potential to support novel clinical applications, it confronts significant methodological challenges. Here I would like to present our work on addressing the impact of head motion on R-fMRI measures, and how standardization methods make R-fMRI measures more robust in combining data across sites. Furthermore, as the fMRI BOLD signal is not a direct measure of neuronal electrical activity, the underlying mechanistic understanding of ongoing slow brain activity remains unclear. I will present my recent pilot work on bridging R-fMRI and electrophysiological methods, and propose a theoretical framework, modulation of low frequency fluctuations (MoLFF), to tap into the underlying electrophysiological “mechanisms” of spontaneous low frequency fluctuations and R-fMRI measures. |
| About the Speaker: Dr. Chao-Gan Yan is a research scientist at the Nathan Kline Institute for Psychiatric Research, and a research assistant professor at the Department of Child and Adolescent Psychiatry / NYU Langone Medical Center Child Study Center, New York University. His main research interests are focused on computational methods for R-fMRI, the electrophysiological significance of R-fMRI measures and developing a mechanistic understanding of ongoing low frequency fluctuations. Dedicated to the field of R-fMRI, he has published 28 peer-reviewed journal articles, achieving an h-index of 21 (http://scholar.google.com/citations?user=lJQ9B58AAAAJ). His prior work focused on addressing the methodological issues related R-fMRI measures. He is currently developing a theoretical framework on the electrophysiological “mechanisms” underlying R-fMRI entitled: modulation of low frequency fluctuations (MoLFF). To facilitate the application of R-fMRI to brain disorder studies, Dr. Yan created a user-friendly pipeline called Data Processing Assistant for Resting-State fMRI (DPARSF) and leads the maintenance group in updating the Resting-state fMRI Data Analysis Toolkit (REST). He also initiated the R-fMRI Network (rfmri.org) for connecting researchers, sharing resources and supporting tools, and created multimedia courses on R-fMRI data processing (http://rfmri.org/Course). He is an academic reviewer for multiple journals including Journal of Neuroscience, Human Brain Mapping, NeuroImage, PLoS ONE, Brain Connectivity, Neuroinformatics and Frontiers in Neuroscience. |