Projects:ModelingFunctionalActivationPatterns
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Modeling Functional Activation Patterns
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 functional magnetic resonance imaging (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.
Description
Experiments
Conclusion
We developed a model that accounts for spatial variability of functional activation regions in the brain via deformations of weighted dictionary elements. Learning model parameters and estimating deformations yield correspondences of functional activation regions in the brain across subjects. We demonstrate our model in a language fMRI study, which contains substantial variability. We plan to validate the detected parcels using data from different fMRI language experiments.
Literature
Key Investigators
MIT: George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, and Polina Golland
