title: Inferring functional connectivity in MRI using Bayesian network structure learning with a modified PC algorithm.
abstract: Resting state functional connectivity MRI (rs-fcMRI) is a popular technique used to gauge the functional relatedness between regions in the brain for typical and special populations. Most of the work to date determines this relationship by using Pearson's correlation on BOLD fMRI timeseries. However, it has been recognized that there are at least two key limitations to this method. First, it is not possible to resolve the direct and indirect connections/influences. Second, the direction of information flow between the regions cannot be differentiated. In the current paper, we follow-up on recent work by Smith et al. (2011), and apply PC algorithm to both simulated data and empirical data to determine whether these two factors can be discerned with group average, as opposed to single subject, functional connectivity data. When applied on simulated individual subjects, the algorithm performs well determining indirect and direct connection but fails in determining directionality. However, when applied at group level, PC algorithm gives strong results for both indirect and direct connections and the direction of information flow. Applying the algorithm on empirical data, using a diffusion-weighted imaging (DWI) structural connectivity matrix as the baseline, the PC algorithm outperformed the direct correlations. We conclude that, under certain conditions, the PC algorithm leads to an improved estimate of brain network structure compared to the traditional connectivity analysis based on correlations.
authors.affiliation: Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA.
authors.forename: Swathi P
authors.initials: SP
authors.lastname: Iyer
authors.affiliation: Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
authors.forename: Izhak
authors.initials: I
authors.lastname: Shafran
authors.affiliation: Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; Center for Neuroscience, University of California - Davis, Davis, CA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Grayson
authors.affiliation: Virginia Polytechnic Institute and State University, Psychology Department, USA; Virginia Polytechnic Institute and State University, Arlington Innovation Center, USA.
authors.forename: Kathleen
authors.initials: K
authors.lastname: Gates
authors.affiliation: Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA.
authors.forename: Joel T
authors.initials: JT
authors.lastname: Nigg
authors.affiliation: Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA; Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA. Electronic address: faird@ohsu.edu.
authors.forename: Damien A
authors.initials: DA
authors.lastname: Fair
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009434:Neural Pathways / Q000502:physiology*