title: Quantifying uncertainty in brain network measures using Bayesian connectomics.
abstract: The wiring diagram of the human brain can be described in terms of graph measures that characterize structural regularities. These measures require an estimate of whole-brain structural connectivity for which one may resort to deterministic or thresholded probabilistic streamlining procedures. While these procedures have provided important insights about the characteristics of human brain networks, they ultimately rely on unwarranted assumptions such as those of noise-free data or the use of an arbitrary threshold. Therefore, resulting structural connectivity estimates as well as derived graph measures fail to fully take into account the inherent uncertainty in the structural estimate. In this paper, we illustrate an easy way of obtaining posterior distributions over graph metrics using Bayesian inference. It is shown that this posterior distribution can be used to quantify uncertainty about graph-theoretical measures at the single subject level, thereby providing a more nuanced view of the graph-theoretical properties of human brain connectivity. We refer to this model-based approach to connectivity analysis as Bayesian connectomics.
authors.affiliation: Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Nijmegen, Netherlands.
authors.forename: Ronald J
authors.initials: RJ
authors.lastname: Janssen
authors.affiliation: Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Nijmegen, Netherlands ; Machine Learning Group, Institute for Computing and Information Sciences, Radboud University Nijmegen Nijmegen, Netherlands.
authors.forename: Max
authors.initials: M
authors.lastname: Hinne
authors.affiliation: Machine Learning Group, Institute for Computing and Information Sciences, Radboud University Nijmegen Nijmegen, Netherlands.
authors.forename: Tom
authors.initials: T
authors.lastname: Heskes
authors.affiliation: Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Nijmegen, Netherlands.
authors.forename: Marcel A J
authors.initials: MA
authors.lastname: van Gerven
keywords: Bayesian inference; connectomics; diffusion weighted imaging; graph theory