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A Spatio‐Temporal Model for Functional Magnetic Resonance Imaging Data – with a View to Resting State Networks
Author(s) -
VEDEL JENSEN EVA B.,
THORARINSDOTTIR THORDIS L.
Publication year - 2007
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2006.00554.x
Subject(s) - functional magnetic resonance imaging , resting state fmri , inference , voxel , artificial intelligence , bayesian inference , computer science , bayesian probability , data set , set (abstract data type) , pattern recognition (psychology) , neuroscience , psychology , programming language
. Functional magnetic resonance imaging (fMRI) is a technique for studying the active human brain. During the fMRI experiment, a sequence of MR images is obtained, where the brain is represented as a set of voxels. The data obtained are a realization of a complex spatio‐temporal process with many sources of variation, both biological and technical. We present a spatio‐temporal point process model approach for fMRI data where the temporal and spatial activation are modelled simultaneously. It is possible to analyse other characteristics of the data than just the locations of active brain regions, such as the interaction between the active regions. We discuss both classical statistical inference and Bayesian inference in the model. We analyse simulated data without repeated stimuli both for location of the activated regions and for interactions between the activated regions. An example of analysis of fMRI data, using this approach, is presented.