Magnetic Resonance Imaging (MRI) has been extensively utilized in brain studies. Diffusion MRI (dMRI) measures brain microstructure and functional MRI (fMRI) reveals neural activity in vivo. Neuroimaging studies can be performed from various spatial perspectives such as voxel, region, connectivity between a pair of regions, and connectome which is a network consisting of brain regions as nodes and connectivity as edges. In a brain, information is processed by the combined interactions of neurons, ensembles of neurons, and collaborating brain regions, which form a special (small-world) topological structure. Network analysis offers tools for characterizing and studying the topological structure of brain networks. In addition to the network analysis established using fMRI or dMRI separately, joint analysis has shown favorable benefits in leveraging the advantages from dMRI and fMRI. However, it is difficult to combine information from dMRI and fMRI and create a joint network. This thesis presents solutions for three problems based on an interdisciplinary framework combining domain knowledge, neuroimaging techniques, signal processing, graph theory, machine learning and statistical analysis. First, a joint model is proposed to create function-specific structural networks, i.e., joint networks, from both dMRI and fMRI simultaneously. Function-specific structural networks inherit the detailed neuron connectome from dMRI and the functional specificity from fMRI, which potentially can improve the statistical power and the limitation of small sample size in clinical applications. Secondly, anatomical features including connectivity and network topological measures established from dMRI data are analyzed using statistical tools, along-track analysis and machine learning techniques to reveal alterations in brain network for adolescents with major depressive disorder (MDD). Last, wavelet-filtered functional connectivity and network topology features are extracted from fMRI data to characterize the correlation of neural activity between brain regions. The functional features are analyzed using statistical tools and false discovery rate control to discover neurological responders to selective serotonin reuptake inhibitors (SSRIs) and neurological correlations with clinical improvement in treating depression. The identified features add new knowledge to the current understanding of the underlying mechanisms of adolescent MDD and the responses to SSRIs and may be further developed and utilized in monitoring disease progression and effectiveness of therapy. Applications in MDD show how network analysis, signal processing and machine learning are utilized to reveal spatial, temporal and frequency information in brain activity, connectivity and network topology.
University of Minnesota Ph.D. dissertation. September 2018. Major: Electrical/Computer Engineering. Advisors: Keshab Parhi, Christophe Lenglet. 1 computer file (PDF); ix, 163 pages.
Approaches to Anatomical and Functional Brain Connectivity Analysis with Applications to Adolescent Major Depressive Disorder.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.