Bayesian networks in neuroscience: a survey

Concha Bielza, Pedro Larrañaga
2014 Frontiers in Computational Neuroscience  
These authors have contributed equally to this work Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be
more » ... arned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind -morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope -molecular, cellular, structural, functional, cognitive and medical-of the brain aspects to be studied. Frontiers in Computational Neuroscience October 2014 | Volume 8 | Article 131 | 1 COMPUTATIONAL NEUROSCIENCE X i is c.i. of ND(X i ) given Pa(X i ), i = 1, . . . , n, FIGURE 1 | Hypothetical example of a BN modeling the risk of dementia. Frontiers in Computational Neuroscience October 2014 | Volume 8 | Article 131 | 2 FIGURE 2 | Example of dynamic BN structure with three variables X 1 , X 2 , and X 3 and three time slices. (A) The prior network. (B) The transition network, with first-order Markov assumption. (C) The dynamic BN unfolded in time for three time slices.
doi:10.3389/fncom.2014.00131 pmid:25360109 pmcid:PMC4199264 fatcat:2ip7hztt4fexdj5cw4a2gpmgbu