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Ph.D. Proposal Oral Exam - Mohammad Eslampanah Sendi

Event Details

Friday, March 12, 2021

3:00pm - 4:59pm


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Event Details

Title:  Approaches to Extract, Characterize, and Interpret Dynamic Functional Network Connectivity in fMRI Data


Dr. Calhoun, Advisor     

Dr. Inan, Chair

Dr. Anderson

Abstract: The objective of the proposed research  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, 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. Some of these gaps include the lack of an analytic pipeline extracting features from dFNC, lack of knowledge about the reproducibility of results in healthy subjects and patients, lack of understanding about the clinical benefit of dFNC result. In this Ph.D. proposal, we aim to address these potential gaps. We propose to develop an analytic pipeline (toolbox), called “DyConX”, to estimate transient states and extract temporal features from dFNC. Also, we would introduce some additional summary metrics to characterize dFNC. Next, we propose integrating our pipeline with an interpretable machine learning method to investigate the use of dynamic features to be useful as predictors (or biomarkers). We will evaluate the results in the context of mental and neurological disorders. Finally, we plan to examine the reproducibility of dFNC state across multiple resting-state fMRI sessions in healthy subjects and explore whether demographic (such as age and gender) and cognitive variables impact state reproducibility. We also plan to investigate whether brain states' reproducibility can be a useful indicator of disease progression and evaluate this in the context of neurological disorders. Our ultimate goal is to develop approaches that can better leverage dynamic connectivity information to advance our understanding of brain disorders with potential clinical implications.

Last revised March 8, 2021