Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Accounting for multiple impacts of the Common agricultural policies in rural areas: an analysis using a Bayesian networks approach

In evaluating the potential effects of the reforms of the Common Agricultural Policy, a particularly challenging issue is the representation of the complexity of rural systems either in a static or dynamic framework. In this paper we use Bayesian networks, to the best knowledge of the authors, basically ignored by the literature on rural development. The objective of this paper is to discuss the potential use of Bayesian Networks tools to represent the multiple determinants and impacts of the Common Agricultural Policies in rural areas across Europe. The analysis shows the potential use of BNs in terms of representation of the multiple linkages between different components of rural areas and farming systems, though its use as a simulation tool still requires further improvements.Bayesian Networks (BNs), farm-household, multiple outcomes, Agricultural and Food Policy, Q1, Q18,