Detecting Functional Connectivity Changes in Neuroimages of Mesial Temporal Lobe Epilepsy and Type 2 Diabetes Using Machine Learning




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The purpose of this thesis is to detect the changes in functional connectivity that are detectable in neuroimaging data obtained for mesial temporal lobe epilepsy and type 2 diabetes using machine learning as a tool to uncover and characterize the changes. Thus this thesis is divided into two parts. In the first part, our experiments focus on the study of patients with refractory mesial temporal lobe epilepsy (MTLE). MTLE is known to cause memory deficits and the preferred treatment to suppress seizures, temporal lobe resection, can exacerbate difficulties in forming new episodic memory. Therefore, learning about the success or failure to form new memory in such patients is critical if we are to facilitate future development of new therapies based in cognitive biofeedback training or electrical neurostimulation-based therapies. To this end, we address many challenges in analyzing memory formation when MTLE brains are recorded using stereo-encephalography (sEEG) in a Free Recall test of episodic memory. Our contributions are threefold. First, we compute a rich measure of brain connectivity by computing the phase locking value statistic (synchrony) between pairs of regions, over hundreds of word memorization trials. Second, we leverage the rich information (over 400 values per pair of probed brain regions) to form consistent length feature vectors for classifier training. Third, we train and evaluate seven different types of classifier models and identify which ones achieve the highest accuracy and which brain features are most important for high accuracy.

In the second part, our experiments focus determining whether an association between Type 2 Diabetes (T2D) management and functional brain health in African American (AA) population exists, and if so quantifying that association. In these experiments we assess resting state functional magnetic resonance imaging (rs-fMRI) from adult African Americans with T2D. To form brain health measures, we construct a graph representation of brain connectivity and compute multiple local and global quantitative measures suitable for statistical analysis using graph theory. We then fit a regularized, linear support vector regression model to predict the brain health measure using 10-fold nested cross-validation and apply permutation testing to measure the statistical reliability of the fitted model. Our data supports an association between T2D management and brain health in African Americans. Moreover, we show details of how T2D impacts specific regions of the brain and its functional connectivity. Our findings demonstrate alterations in functional connectivity including the degree of the right hippocampus and degree of the left parahippocampal gyrus with diabetes and cardiovascular disease measures. The findings corroborate the association between CAC and hippocampal gray matter volume found in African Americans previously. This work gives additional impetus to T2 diabetics to control their glycemic load as the impact of diabetes may not be just on cardiac and kidney health but may extend to functional brain health as well.



Temporal lobe epilepsy, Non-insulin-dependent diabetes, Machine learning, Diagnostic imaging, Brain—Imaging, Episodic memory, Epilepsy—Surgery, Magnetic resonance imaging


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