title: A new method for detecting causality in fMRI data of cognitive processing.
abstract: One of the most important achievements in understanding the brain is that the emergence of complex behavior is guided by the activity of brain networks. To fully apply this theoretical approach fully, a method is needed to extract both the location and time course of the activities from the currently employed techniques. The spatial resolution of fMRI received great attention, and various non-conventional methods of analysis have previously been proposed for the above-named purpose. Here, we briefly outline a new approach to data analysis, in order to extract both spatial and temporal activities from fMRI recordings, as well as the pattern of causality between areas. This paper presents a completely data-driven analysis method that applies both independent components analysis (ICA) and the Granger causality test (GCT), performed in two separate steps. First, ICA is used to extract the independent functional activities. Subsequently the GCT is applied to the independent component (IC) most correlated with the stimuli, to indicate its causal relation with other ICs. We therefore propose this method as a promising data-driven tool for the detection of cognitive causal relationships in neuroimaging data.
authors.affiliation: Department of Psychology 1, University of Rome "La Sapienza", Via dei Marsi, 78-00185, Rome, Italy. alessandro.londei@uniroma1.it
authors.forename: Alessandro
authors.initials: A
authors.lastname: Londei
authors.forename: Alessandro
authors.initials: A
authors.lastname: D'Ausilio
authors.forename: Demis
authors.initials: D
authors.lastname: Basso
authors.forename: Marta Olivetti
authors.initials: MO
authors.lastname: Belardinelli
mesh_terms: D000465:Algorithms; D016009:Chi-Square Distribution; D003071:Cognition / Q000502:physiology*; D003627:Data Interpretation, Statistical; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008959:Models, Neurological; D015233:Models, Statistical; D025341:Principal Component Analysis; D015203:Reproducibility of Results