Exploratory fMRI analysis without spatial normalization
Author(s)Lashkari, Danial; Golland, Polina
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We present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to fit the model to the data. The resulting algorithm finds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on a visual fMRI study.
Author Manuscript received 2010 March 11. 21st International Conference, IPMI 2009, Williamsburg, VA, USA, July 5-10, 2009. Proceedings
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Information Processing in Medical Imaging
Springer Berlin / Heidelberg
Lashkari, Danial, and Polina Golland. “Exploratory fMRI Analysis Without Spatial Normalization.” Information Processing in Medical Imaging. Ed. Jerry L. Prince, Dzung L. Pham, & Kyle J. Myers. LNCS Vol. 5636. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. 398–410.
Author's final manuscript