Directed functional connectivity using dynamic graphical models

Graphical models for functional connectivity

A lack of functional integration has been proposed as a signature of many degenerative and developmental disorders, including schizophrenia and autism (Just et al., 2004). These connectivity patterns are often assessed with ad-hoc techniques based on pairwise correlations. The procedures may lead to a misleading characterization of the underlying neural connectivity pattern, because these techn...

Recombination analysis using directed graphical models.

In Strimmer and Moulton (2000), we described a method for computing the likelihood of a set of sequences assuming a phylogenetic network as an evolutionary hypothesis. That approach relied on converting a given graph into a directed graphical model or stochastic network from which all desired probability distributions could be derived. In particular, we investigated how to compute likelihoods u...

Directed graphical models

for some functions f and g. Probabilistic graphical models are a way of representing conditional independence assumptions using graphs. Nodes represent random variables and lack of edges represent conditional independence assumptions, in a way which we will define below. There are many kinds of graphical model, but the two most popular are Bayesian (belief) networks1, which are based on directe...

Graphical Models of Functional MRI Data for Assessing Brain Connectivity

Dynamic functional connectivity measures using fcMRI

Introduction: Communication between brain areas and the formation of brain networks is key to understanding how the brain functions; furthermore perturbed functional connectivity (fc) within networks is thought to be responsible for some pathologies. Therefore it is important to better understand the processes underlying fc. In many fcMRI studies, fc is measured from the temporal correlation be...