제목: Regulation of smoking craving via real-time fMRI neurofeedback based on machine learning technique

연사: 김동율 박사, Postdoctoral associate, Fralin Biomedical Research Institute at VTC, Virginia Tech

날짜/시간: 9월 8일 (금) 12시 - 1시

초록:  In recent years, many studies have shown the feasibility of attaining volitional control over human brain activity using real-time functional magnetic resonance imaging (rtfMRI)-based neurofeedback (NF) paradigms based on blood-oxygenation-level-dependent (BOLD) signals. These rtfMRI-NF studies have commonly based NF signals on hemodynamic activity within regions-of-interest (ROIs). Of importance, these studies found alterations in connectivity and network patterns, extending beyond the modulated level of activity within targeted ROIs. In an effort to further improve rtfMRI-NF, this study used machine learning to estimate whole-brain information with the aim of investigating rtfMRI-NF for modulating individuals’ smoking craving-related brain patterns. This method was applied in chronic nicotine smokers and compared performance between group- and individual- classification accuracy related to modulating craving-related brain activity. The methodology and results of this study will be of interest in the emerging field of rtfMRI-NF and its applications to (pre-) clinical conditions or to basic research.