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Machine Learning Techniques For Improved Functional Brain Parcellation, Da Zhi
Machine Learning Techniques For Improved Functional Brain Parcellation, Da Zhi
Electronic Thesis and Dissertation Repository
Brain parcellation studies are fundamental for neuroscience as they serve as a bridge between anatomy and function, helping researchers interpret how functions are distributed across different brain regions. However, two substantial challenges exist in current imaging-based brain parcellation studies: large variations in the functional organization across individuals and the intrinsic spatial dependence which causes nearby brain locations to have a similar function. This thesis presents a series of projects aimed to tackle these challenges from different perspectives by using advanced machine learning techniques.
To handle the challenge of individual variability in building precise individual parcellations, Chapter 3 introduces a novel …