Degree
Doctor of Philosophy (PhD)
Department
Psychology - Cognitive and Brain Science
Document Type
Dissertation
Abstract
Traditional machine learning analyses are challenging with functional magnetic
resonance imaging (fMRI) data, not only because of the amount of data that needs to be
collected, adding a particular challenge for human fMRI research, but also due to the change in
hypothesis being addressed with various analytical techniques. Domain adaptation is a type of
transfer learning, a step beyond machine learning which allows for multiple related, but not
identical, data to contribute to a model, can be beneficial to overcome the limitation of data
needed but may address different hypothesis questions than anticipated given the analysis
computation. This dissertation assesses a novel domain adaptation package, PyKale, created for
cognitive fMRI data to determine the benefit and use it can have within cognitive research.
Date
3-30-2023
Recommended Citation
Burleigh, Lauryn Michelle, "Beyond Machine Learning: An fMRI Domain Adaptation Model for Multi-study Integration" (2023). LSU Doctoral Dissertations. 6076.
https://repository.lsu.edu/gradschool_dissertations/6076
Committee Chair
Cox, Christopher
DOI
10.31390/gradschool_dissertations.6076
Included in
Cognitive Psychology Commons, Cognitive Science Commons, Computer Sciences Commons, Statistical Models Commons