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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.
Highlights
Connectomics refers to the field of research that aims to unravel the connectivity pattern between distinct neural populations within a nervous system (Sporns et al, 2005; Behrens and Sporns, 2012; Seung, 2012; Sporns, 2012)
Connectomics strongly relies on non-invasive mapping of anatomical pathways between brain regions using diffusion weighted imaging (DWI) (Behrens and Sporns, 2012)
In order to demonstrate the merits of Bayesian connectomics, we use diffusion imaging data collected for twenty subjects
Summary
Connectomics refers to the field of research that aims to unravel the connectivity pattern between distinct neural populations within a nervous system (Sporns et al, 2005; Behrens and Sporns, 2012; Seung, 2012; Sporns, 2012). The resulting structural connectivity estimates have been used to subdivide brain regions into functionally coherent clusters via the notion of connectivitybased parcellation (Beckmann et al, 2009; Cloutman and Ralph, 2012; Mars et al, 2012). Variability in structural connectivity has been related to individual differences (de Schotten et al, 2011; Catani et al, 2012). It has become commonplace to summarize structural networks in terms of a wide variety of graph-theoretical measures (Stam and Reijneveld, 2007; Bullmore and Sporns, 2009; Rubinov and Sporns, 2010; van den Heuvel and Sporns, 2011), each reflecting different aspects of network topology
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