title: A computationally efficient, exploratory approach to brain connectivity incorporating false discovery rate control, a priori knowledge, and group inference.
abstract: Graphical models appear well suited for inferring brain connectivity from fMRI data, as they can distinguish between direct and indirect brain connectivity. Nevertheless, biological interpretation requires not only that the multivariate time series are adequately modeled, but also that there is accurate error-control of the inferred edges. The PC(fdr) algorithm, which was developed by Li and Wang, was to provide a computationally efficient means to control the false discovery rate (FDR) of computed edges asymptotically. The original PC(fdr) algorithm was unable to accommodate a priori information about connectivity and was designed to infer connectivity from a single subject rather than a group of subjects. Here we extend the original PC(fdr) algorithm and propose a multisubject, error-rate-controlled brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity. In simulations, we show that the two proposed extensions can still control the FDR around or below a specified threshold. When the proposed approach is applied to fMRI data in a Parkinson's disease study, we find robust group evidence of the disease-related changes, the compensatory changes, and the normalizing effect of L-dopa medication. The proposed method provides a robust, accurate, and practical method for the assessment of brain connectivity patterns from functional neuroimaging data.
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
authors.forename: Aiping
authors.initials: A
authors.lastname: Liu
authors.forename: Junning
authors.initials: J
authors.lastname: Li
authors.forename: Z Jane
authors.initials: ZJ
authors.lastname: Wang
authors.forename: Martin J
authors.initials: MJ
authors.lastname: McKeown
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000378:metabolism / Q000473:pathology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003196:Computer Graphics; D003198:Computer Simulation; D005189:False Positive Reactions; D006801:Humans; D007980:Levodopa / Q000627:therapeutic use; D008279:Magnetic Resonance Imaging / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D015999:Multivariate Analysis; D010300:Parkinson Disease / Q000378:metabolism; D011336:Probability
chemical_list: D007980:Levodopa