Author ORCID Identifier
Date of Award
12-18-2024
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Advisor
Vince Calhoun
Abstract
Brain dynamism is a highly intricate phenomenon that encompasses dynamic patterns of neural activity. This complexity arises from the intricate network of neural interactions and necessitates sophisticated computational models to fully comprehend its essence across different brain regions. Currently, computational neuroscience lacks effective methods for mapping these dynamics across multiple 4D temporally evolving brain networks. Within this challenging landscape, we have harnessed the power of computer vision to address this issue, conceptualizing it as the weakly supervised spatiotemporal dense prediction of dynamic brain networks. To tackle this task, we have developed a novel framework called Scepter that encodes the spatiotemporal characteristics of functional magnetic resonance imaging (fMRI) data to predict dynamic brain networks. Each of these networks is represented as a 4D dense representation (dynamic map) that evolves over time and varies between individuals. Additionally, we introduce a strategy for generating prior information to serve as a form of weak supervision for training the model. This is essential due to the absence of a benchmark for addressing dynamic brain networks and the cost and inaccuracy associated with annotating fMRI data. Furthermore, our studies aim to address notable limitations in popular brain parcellation methods. The results of our experiments confirm the efficacy of our method, demonstrating its ability to generate plausible brain maps that are highly dynamic and consistent with previous findings in neuroscience. This advancement extends beyond the boundaries of conventional neuroscience research, ushering in a paradigm shift that promises to unlock new perspectives on the intricacies of brain function and connectivity.
Recommended Citation
Kazemivash, Behnam, "Spatiotemporal Dense Prediction of 4D Dynamic Brain Networks through Weak Supervision." Dissertation, Georgia State University, 2024.
https://scholarworks.gsu.edu/cs_diss/229
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