Approaches to extract, characterize, and interpret dynamic functional network connectivity in fMRI data
Eslampanah Sendi, Mohammad Sadegh
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The objective of this project is to develop new approaches for analyzing dynamic functional network connectivity (dFNC) and investigate the link between dFNC with cognitive score and symptom severity in different neurological disorders, including schizophrenia, major depressive disorder, and Alzheimer’s disease. Knowing that the brain is highly dynamic during the resting-state fMRI, even in the absence of external inputs, dFNC got much attention in recent years. However, there are still some gaps in the field. These gaps include the lack of an analytic pipeline analyzing big dFNC data, a pipeline uncovering hidden dynamics masked by the highly influential networks, a comprehensive toolbox extracting dFNC features, and a lack of understanding of the clinical benefit of dFNC results. In this Ph.D. proposal, we aim to address these potential gaps. This Ph.D. dissertation contributed to the field by developing new frameworks (methodological contributions) and identifying new biomarkers in brain disorders (clinical contributions). We proposed multiple frameworks to analyze both static (sFNC) and dynamic functional network connectivity (dFNC) for the former contributions. We developed a framework called iSparse k-means to analyze big dFNC data. We showed that this framework analyzes dFNC data 27 times faster than the conventional framework, but it does not need huge computational power. We also developed an analytic pipeline (toolbox), called “DyConX”, to estimate transient states and extract temporal features from dFNC. Also, we introduced some additional summary metrics to characterize dFNC. We validated these new features with the new toolbox in the largest dFNC analysis ever. Also, we introduced a new pipeline to uncover hidden dynamics of the brain network masked with highly active networks. Next, we proposed integrating our pipeline with an interpretable machine learning method to investigate the use of dynamic features to be useful as predictors (or biomarkers). We identified new dFNC biomarkers for the latter contributions in Alzheimer's disease, schizophrenia, and major depressive disorder. Additionally, we interpreted how dFNC information is linked with symptom severity in these neurological and neuropsychiatric disorders.