Abstract
Inferring the position of functionally active regions from a multi-subject fMRI dataset involves the comparison of the individual data and the inference of a common activity model. While voxel-based analyzes, e.g. Random Effect statistics, are widely used, they do not model each individual activation pattern. Here, we develop a new procedure that extracts structures individually and compares them at the group level. For inference about spatial locations of interest, a Dirichlet Process Mixture Model is used. Finally, inter-subject correspondences are computed with Bayesian Network models. We show the power of the technique on both simulated and real datasets and compare it with standard inference techniques.
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Thirion, B. et al. (2007). High Level Group Analysis of FMRI Data Based on Dirichlet Process Mixture Models. In: Karssemeijer, N., Lelieveldt, B. (eds) Information Processing in Medical Imaging. IPMI 2007. Lecture Notes in Computer Science, vol 4584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73273-0_40
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DOI: https://doi.org/10.1007/978-3-540-73273-0_40
Publisher Name: Springer, Berlin, Heidelberg
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