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Title: Windowing Effects and Adaptive Change Point Detection of Dynamic Functional Connectivity in the Brain
Committee:
Dr. Chin-Hui Lee, ECE, Chair , Advisor
Dr. Shella Keilholz, BME, Co-Advisor
Dr. Mark Clements, ECE
Dr. Xiaoping Hu, BME
Dr. Ghassan Al-Regib, ECE
Dr. Omer Inan, ECE
Abstract:
Evidence of networks in the resting-brain reflecting the spontaneous brain activity is the most significant discovery to understand intrinsic brain functionality. Moreover, subsequent detection of dynamics in these networks can be milestone in differentiating the normal and disordered brain functions. However, capturing the correct dynamics is a challenging task since no ‘ground truths’ are present for comparison of the results. Despite the absence of ground truths, studies have analyzed these dynamics using the existing methods and some of them have developed new algorithms too. One of the most commonly used method for this purpose is sliding window correlation. However, the result of the sliding window correlation is dependent on many parameters and without the ground truth there is no way of validating the results. In addition, most of the new algorithms are complicated, computationally expensive, and/or focus on just one aspect on these dynamics. This study applies the algorithms and concepts from signal processing, image processing, video processing, information theory, and machine learning to analyze the results of the sliding window correlation and develop a novel algorithm to detect change points of these networks adaptively. The findings in this study are beneficial to the field of neuro dynamics from various aspects and can be divided into three parts: 1) Analyzing the extent of variability in well-defined networks of rodents and humans with sliding window correlation applying concepts from information theory and machine learning domains. 2) Analyzing the performance of sliding window correlation using simulated networks as ground truths for best parameters’ selection, and mathematical formulation of the analysis by processing the signals in time and Fourier domains. 3) Development of novel algorithm based on image similarity measures, used in image and video processing, to capture the change points of these networks adaptively.
