Master of Sciences, Case Western Reserve University, 2019, EECS - Computer and Information Sciences

Epilepsy patients refractory to anti-epileptic medication are considered for surgery to disrupt the epileptogenic zone (EZ), integral to seizure signal generation. Accurate EZ characterization remains a challenge, leading to development of graph-theoretical analysis of brain connectivity using Stereotactic Electroencephalogram (SEEG) data. We describe a neuroinformatics tool called Neuro-Integrative Connectivity (NIC) workflow platform for large-scale EEG data analysis with applications in patient cohort studies. NIC is built on a scientific workflow system (Taverna) to support signal data processing, computation of signal coupling measures, and computation of global and local network analysis metrics. NIC captures provenance metadata to support scientific reproducibility. We performed a systematic evaluation of the platform using data from 5 patients and a task-based usability evaluation with 5 target users. Our results show that NIC is a scalable and intuitive tool for large-scale analysis of functional brain connectivity networks that can provide insights into epilepsy and related neurological disorders.

Committee: Satya Sahoo (Committee Chair); Chris Fietkiewicz (Committee Member); Mehmet Koyuturk (Committee Member); Guadalupe Fernandez-BacaVaca (Committee Member) Subjects: Bioinformatics; Biomedical Research; Computer Science; Health Care; Information Science; Medicine; Neurology

Doctor of Philosophy, The Ohio State University, 2010, Computer Science and Engineering

Recent advances in digital sensor technology and numerical simulations of real-world phenomena are resulting in the acquisition of unprecedented amounts of raw digital data. Terms like ‘data explosion' and ‘data tsunami' have come to describe the uncontrolled rate at which scientific datasets are generated by automated sources ranging from digital microscopes and telescopes to in-silico models simulating the complex dynamics of physical and biological processes. Scientists in various domains now have secure, affordable access to petabyte-scale observational data gathered over time, the analysis of which, is crucial to scientific discovery. The availability of commodity components have fostered the development of large distributed systems with high-performance computing resources to support the execution requirements of scientific data analysis applications. Increased levels of middleware support over the years have aimed to provide high scalability of application execution on these systems. However, the high-resolution, multi-dimensional nature of scientific datasets, and the complexity of analysis requirements present challenges to efficient application execution on such systems. Traditional brute-force analysis techniques to extract useful information from scientific datasets may no longer meet desired performance levels at extreme data scales. This thesis builds on a comprehensive study involving multi-dimensional data analysis applications at large data scales, and identifies a set of advanced factors or parameters to this class of applications which can be customized in domain-specific ways to obtain substantial improvements in performance. A useful property of these applications is their ability to operate at multiple performance levels based on a set of trade-off parameters, while providing different levels of quality-of-service (QoS) specific to the application instance. To avail the performance benefits brought about by such facto (open full item for complete abstract)

Committee: P Sadayappan PhD (Advisor); Joel Saltz MD, PhD (Committee Member); Gagan Agrawal PhD (Committee Member); Umit Catalyurek PhD (Committee Member) Subjects: Computer Science