Abstract
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.
Primary Advisor
S.Nkurunziza
Program Reader
M.Belalia
Degree Name
Master of Science
Department
Mathematics and Statistics
Document Type
Major Research Paper
Convocation Year
2023
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.