title: Structurally-informed Bayesian functional connectivity analysis.
abstract: Functional connectivity refers to covarying activity between spatially segregated brain regions and can be studied by measuring correlation between functional magnetic resonance imaging (fMRI) time series. These correlations can be caused either by direct communication via active axonal pathways or indirectly via the interaction with other regions. It is not possible to discriminate between these two kinds of functional interaction simply by considering the covariance matrix. However, the non-diagonal elements of its inverse, the precision matrix, can be naturally related to direct communication between brain areas and interpreted in terms of partial correlations. In this paper, we propose a Bayesian model for functional connectivity analysis which allows estimation of a posterior density over precision matrices, and, consequently, allows one to quantify the uncertainty about estimated partial correlations. In order to make model estimation feasible it is assumed that the sparseness structure of the precision matrices is given by an estimate of structural connectivity obtained using diffusion imaging data. The model was tested on simulated data as well as resting-state fMRI data and compared with a graphical lasso analysis. The presented approach provides a theoretically solid foundation for quantifying functional connectivity in the presence of uncertainty.
authors.affiliation: Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; Radboud University Nijmegen, Institute for Computing and Information Sciences, Nijmegen, The Netherlands. Electronic address: mhinne@cs.ru.nl.
authors.forename: Max
authors.initials: M
authors.lastname: Hinne
authors.affiliation: Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
authors.forename: Luca
authors.initials: L
authors.lastname: Ambrogioni
authors.affiliation: Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
authors.forename: Ronald J
authors.initials: RJ
authors.lastname: Janssen
authors.affiliation: Radboud University Nijmegen, Institute for Computing and Information Sciences, Nijmegen, The Netherlands.
authors.forename: Tom
authors.initials: T
authors.lastname: Heskes
authors.affiliation: Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
authors.forename: Marcel A J
authors.initials: MA
authors.lastname: van Gerven
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009415:Nerve Net / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Bayesian inference; Functional connectivity; G-Wishart prior; Structural connectivity