For a given cognitive task such as language processing, the location of corresponding functional regions in the brain may vary across subjects relative to anatomy. We present a probabilistic generative model that accounts for such variability as observed in fMRI data. We relate our approach to sparse coding that estimates a basis consisting of functional regions in the brain. Individual fMRI data is represented as a weighted sum of these functional regions that undergo deformations. We demonstrate the proposed method on a language fMRI study. Our method identified activation regions that agree with known literature on language processing and established correspondences among activation regions across subjects, producing more robust group-level effects than anatomical alignment alone. © 2012 Springer-Verlag.
CITATION STYLE
Chen, G. H., Fedorenko, E. G., Kanwisher, N. G., & Golland, P. (2012). Deformation-invariant sparse coding for modeling spatial variability of functional patterns in the brain. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7263 LNAI, pp. 68–75). https://doi.org/10.1007/978-3-642-34713-9_9
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