title: A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data.
abstract: The Independent Component Analysis (ICA)-linear non-Gaussian acyclic model (LiNGAM), an algorithm that can be used to estimate the causal relationship among non-Gaussian distributed data, has the potential value to detect the effective connectivity of human brain areas. Under the assumptions that (a): the data generating process is linear, (b) there are no unobserved confounders, and (c) data have non-Gaussian distributions, LiNGAM can be used to discover the complete causal structure of data. Previous studies reveal that the algorithm could perform well when the data points being analyzed is relatively long. However, there are too few data points in most neuroimaging recordings, especially functional magnetic resonance imaging (fMRI), to allow the algorithm to converge. Smith's study speculates a method by pooling data points across subjects may be useful to address this issue (Smith et al., 2011). Thus, this study focus on validating Smith's proposal of pooling data points across subjects for the use of LiNGAM, and this method is named as pooling-LiNGAM (pLiNGAM). Using both simulated and real fMRI data, our current study demonstrates the feasibility and efficiency of the pLiNGAM on the effective connectivity estimation.
authors.affiliation: College of Information Science and Technology, Beijing Normal University Beijing, China.
authors.forename: Lele
authors.initials: L
authors.lastname: Xu
authors.affiliation: College of Information Science and Technology, Beijing Normal University Beijing, China.
authors.forename: Tingting
authors.initials: T
authors.lastname: Fan
authors.affiliation: College of Information Science and Technology, Beijing Normal University Beijing, China ; State Key Laboratories of Transducer Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences Shanghai, China ; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China.
authors.forename: Xia
authors.initials: X
authors.lastname: Wu
authors.affiliation: Department of Mathematics and Statistics, Banner Good Samaritan PET Center, Banner Alzheimer's Institute, Arizona State University Phoenix, AZ, USA.
authors.forename: KeWei
authors.initials: K
authors.lastname: Chen
authors.affiliation: College of Information Science and Technology, Beijing Normal University Beijing, China.
authors.forename: Xiaojuan
authors.initials: X
authors.lastname: Guo
authors.affiliation: College of Information Science and Technology, Beijing Normal University Beijing, China.
authors.forename: Jiacai
authors.initials: J
authors.lastname: Zhang
authors.affiliation: College of Information Science and Technology, Beijing Normal University Beijing, China ; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China.
authors.forename: Li
authors.initials: L
authors.lastname: Yao
keywords: causal structure; effective connectivity; functional magnetic resonance imaging (fMRI); group analysis; linear non-Gaussian acyclic model (LiNGAM); pooling-LiNGAM (pLiNGAM)