an empirical comparison of information-theoretic criteria in estimating the number of independent components of fmri data信息理论的实证比较标准在评估功能磁共振成像数据的独立组件的数量.pdfVIP

An Empirical Comparison of Information-Theoretic Criteria in Estimating the Number of Independent Components of fMRI Data 1 1 2 1,3 1 Mingqi Hui , Juan Li , Xiaotong Wen , Li Yao , Zhiying Long * 1 National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 2 J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States of America, 3 College of Information Science and Technology, Beijing Normal University, Beijing, China Abstract Background: Independent Component Analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components of fMRI data is critical to reduce over/under fitting. Although various methods based on Information Theoretic Criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data, the relative performance of different ITC in the context of the ICA model hasn’t been fully investigated, especially considering the properties of fMRI data. The present study explores and evaluates the performance of various ITC for the fMRI data with varied white noise levels, colored noise levels, temporal data sizes and spatial smoothness degrees. Methodology: Both simulated data and real fMRI data with varied Gaussian white noise levels, first-order auto-regressive (AR(1)) noise levels, temporal data sizes and spatial smoothness degrees were carried out to deeply explore and evaluate the performance of different traditional ITC. Principal Findings: Results indicate that the performance of ITCs depends on the noise level, temporal data size and spatial smoothness of fMRI data. 1) High white noise levels may lead to underestimation of all criteria and