The application of functional magnetic resonance imaging (fMRI) has greatly improved our comprehension of the human brain and behaviour. However, after anatomical alignment, there remains large inter-individual variability in brain anatomy and functional localization, which is one of the obstacles to conducting group studies and performing group-level inference. This major paper addresses this problem by applying a new method (Bayesian Functional Registration) to decrease misalignment in functional brain systems between people by spatially transforming each subject’s functional data into a common reference map. The proposed approach allows us to assess differences in brain function across subjects. It also creates a framework that integrates feature- and intensity-based data and enables inference of the transformation parameters using posterior samples. Next, we evaluate the method using the data from a study of the correspondence of categorical and feature-based representations of music in the human brain. Finally, the proposed approach shows an increased sensitivity for group-level inference compared with the standard method, which uses the registration estimation toolbox in Matlab.
Master of Science
Mathematics and Statistics
Major Research Paper
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