Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Alexis Battle, Gal Chechik, Daphne Koller


We present a probabilistic model applied to the fMRI video rating prediction task of the Pittsburgh Brain Activity Interpretation Competition (PBAIC) [2]. Our goal is to predict a time series of subjective, semantic ratings of a movie given functional MRI data acquired during viewing by three subjects. Our method uses conditionally trained Gaussian Markov random fields, which model both the relationships between the subjects' fMRI voxel measurements and the ratings, as well as the dependencies of the ratings across time steps and between subjects. We also employed non-traditional methods for feature selection and regularization that exploit the spatial structure of voxel activity in the brain. The model displayed good performance in predicting the scored ratings for the three subjects in test data sets, and a variant of this model was the third place entrant to the 2006 PBAIC.