title: Probabilistic graphical models for effective connectivity extraction in the brain using FMRI data.
abstract: In this study using Bayesian network method to learn the structure of effective connectivity among brain regions involved in a functional MRI. The approach is exploratory in the sense that it does not require a priori model as in the earlier approaches, such as the Structural Equation Modeling or Dynamic Causal Modeling, which can only affirm or refute the connectivity of a previously known anatomical model or a hypothesized model. The conditional probabilities that render the interactions among brain regions in Bayesian networks represent the connectivity in the complete statistical sense. This method is applicable even when the number of regions involved in the cognitive network is large or unknown. In this study, we demonstrated the present approach using synthetic data and fMRI data collected in attention to motion in the visual system task.
authors.affiliation: Department of Biomedical Engineering, University Of Isfahan, Isfahan, Iran. mohammadali.safari@gmail.com
authors.forename: Mohammad
authors.initials: M
authors.lastname: Ali Safari
authors.forename: Majid
authors.initials: M
authors.lastname: Mohammadbeigi
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D009434:Neural Pathways / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*