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

We propose a novel way to characterize functional variability that combines insights from prior work. We model each subject's activation map as a weighted sum of group-level functional activation parcels that undergo a subject-specific deformation. Similar to Xu et al. [1], we define a hierarchical generative model, but instead of using a Gaussian mixture model to represent shapes, we represent each parcel as an image, which allows for complex shapes. By representing each subject's activation in terms of group-level parcels, our model maintains parcel correspondences across subjects, similar to [2]. Next, we assume that the template regions can deform to account for functional variability. This involves using groupwise registration similar to [3] that is guided by estimated group-level functional activation regions. We perform inference within the proposed model using an algorithm similar to expectation-maximization (EM) and illustrate our method on the language system, which is known to have significant functional variability [4].

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

[1] L. Xu, T.D. Johnson, T.E. Nichols, and D.E. Nee. "Modeling inter-subject variability in fMRI activation location: a bayesian hierarchical spatial model". Biometrics 65(4), 1041–1051, 2009.

[2] B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu, J.F. Mangin, J.B. Poline. "Structural analysis of fMRI data revisited: improving the sensitivity and reliability of fMRI group studies". IEEE Transactions in Medical Imaging 26(9), 1256–1269, 2007.

[3] M.R. Sabuncu, B.D. Singer, B. Conroy, R.E. Bryan, P.J. Ramadge, J.V. Haxby. "Function-based intersubject alignment of human cortical anatomy". Cerebral Cortex 20(1), 130–140, 2010.

[4] E. Fedorenko, P.J. Hsieh, A. Nieto-Casta ̃on, S. Whitfield-Gabrieli, N. Kanwisher. "New method for fMRI investigations of language: Defining ROIs functionally in individual subjects". Neurophysiology 104, 1177–1194, 2010.

Key Investigators

MIT: George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, and Polina Golland