Inferring functional connectivity in MRI using Bayesian network structure learning with a modified PC algorithm

• Published in: NeuroImage (Orlando, Fla.) Vol. 75; pp. 165 - 175
• Main Authors: Iyer, Swathi P., Shafran, Izhak, Grayson, David, Gates, Kathleen, Nigg, Joel T., Fair, Damien A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 15.07.2013; Elsevier; Elsevier Limited
• Subjects: Adult; Algorithms; Bayes Theorem; Bayesian network; Biological and medical sciences; Brain - physiology; Directed functional connectivity; Effective connectivity; Female; fMRI; Fundamental and applied biological sciences. Psychology; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Methods; Neural networks; Neural Pathways - physiology; PC algorithm; Simulation; Vertebrates: nervous system and sense organs; fMRI; Bayesian network; Directed functional connectivity; PC algorithm; Effective connectivity; Learning; Acquisition process; Nuclear magnetic resonance imaging; Functional imaging
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.02.054

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. •We use a Bayesian approach called PC algorithm to find effective connectivity.•The algorithm finds true connections and directions in Smith et al (2011) data.•The algorithm performs better than traditional correlation with the empirical data.