Dynamic Causal Modeling (DCM): A Bayesian framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data
Date & Time: Tuesday 6 December 2022 - 14:30
Venue: Room M2.4, edificio Matematica, Dipartimento FIM, Modena
Speaker: Dr. Riccardo Maramotti, Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Universitą di Modena e Reggio Emilia
Title: Dynamic Causal Modeling (DCM): A Bayesian framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data
Abstract: In neuroscience, Dynamic Causal Modeling (DCM) is a method for making inferences about the actual connectivity between brain areas, with the aim of determining causal dependencies in the activation of different regions. From a mathematical point of view, the DCM structure is hierarchical. At the first level (subject-level) a connectivity model is defined for each subject, whose parameters represent the connections between regions and how these are modified by the experimental conditions. The parameters are estimated using variational techniques. At the second level (group-level), using a technique called Parametric Empirical Bayes (PEB), the models are combined in order to identify community and inter-subject differences. In addition to the main features of the method, an application in a recent study on fMRI (functional Magnetic Resonance Imaging) data will be mentioned in the seminar.
Host: Marco Prato (FIM) marco.prato@unimore.it
[Ultimo aggiornamento: 05/12/2022 10:44:02]