Unsupervised Learning of Brain States from fMRI Data

摘要：

The use of multivariate pattern recognition for the analysis of neural representations encoded in fMRI data has become a significant research topic, with wide applications in neuroscience and psychology. A popular approach is to learn a mapping from the data to the observed behavior. However, identifying the instantaneous cognitive state without reference to external conditions is a relatively unexplored problem and could provide important insights into mental processes. In this paper, we present preliminary but promising results from the application of an unsupervised learning technique to identify distinct brain states. The temporal ordering of the states were seen to be synchronized with the experimental conditions, while the spatial distribution of activity in a state conformed with the expected functional recruitment.

作者: F.Janoos R.Machiraju S.Sammet M.V.Knopp I.A .Morocz

作者单位: Dept.of Computer Science, The Ohio State University, USA Dept.of Radiology, The Ohio State Universit Dept.of Radiology, The Ohio State University, USA Dept.of Radiology, Harvard Medical School, USA

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 201-208

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）