A new electrical biomarker has been identified in high resolution, intracranial electroencephalogram (iEEG) recordings, called a high frequency oscillation (HFO). Studies have suggested this biomarker has great promise to identify seizure networks and improve surgical outcomes for patients with refractory epilepsy. However, translation of HFOs to clinical practice is hampered by many factors such as spatial, temporal and inter-patient variation in HFO detection rates, false positive and false negative detections, and significant background noise. Big data approaches using large numbers of HFOs acquired from many patients are needed to quantify these effects and allow clinical usage of HFOs. This project details a plan in which the candidate's experience quantifying measurement and detection bias in massive high energy nuclear physics datasets will be combined with a multidisciplinary mentor team to address this problem. The combination of training in computational neuroscience, big data network analysis, and translational neural engineering research will be critical to approach this problem and provide a career trajectory for the candidate.
The specific aims of this proposal address three specific confounding factors: 1) the false negative HFO detection rate, 2) variations in HFO features not due to epilepsy, and 3) effects of the state of vigilance on HFOs. Each of these aims involve novel big data methods and/or applications generalizable to other situations: 1) estimating false positive detection rates using a combined experimental/simulated data approach, 2) clustering and classification of distributions of data points, rather than of the data points directly, and 3) a general disambiguation statistic to assess meaningful (rather than statistical) difference between distributions. The applicant's career goal is to become an academic researcher in the analysis and modeling of intracranial EEG data with a focus on translational epilepsy and sleep physiology research. With the rapid advancement in the resolution of clinical EEG, there is already a strong need for this type of research expertise. Thi grant will provide didactic coursework, formal research and methods training, and career guidance from an expert mentor team. The three mentors have appointments spanning Neurology, Anesthesiology, Mathematics, Statistics, Biomedical Engineering, and Electrical Engineering and Computer Science. The candidate will also build and mentor a research team and establish external collaborations. The University of Michigan is a premier research university with strong programs and training opportunities in biomedical and physical sciences, engineering, translational and academic research, and advanced research computing. This proposal makes extensive use of the University's large computer cluster. The mentor team and an external collaborator will provide candidate access to prerecorded, deidentified data from over 150 patients, estimated to have over 40 million HFOs. The environment and mentor team will provide the training, facilities, and data for the candidate to successfully complete the proposed goals.

Public Health Relevance

Recent advances in epilepsy research are generating very large datasets in the search for better ways to identify the region of brain responsible for generating seizures. A particular signa known as High Frequency Oscillations found in high resolution intracranial EEG shows great promise for identifying these regions, but clinicians are unable to use the signal due to confounding biases. This project combines big data processing expertise from particle physics, computer science and machine learning to address these confounds, providing a more accurate process to enable clinical translation of this new biomarker and potentially improve clinical outcomes.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Scientist Development Award - Research & Training (K01)
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Special Emphasis Panel (ZRG1)
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Shreffler, Carol A
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University of Michigan Ann Arbor
Schools of Medicine
Ann Arbor
United States
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Gliske, Stephen V; Irwin, Zachary T; Chestek, Cynthia et al. (2018) Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings. Nat Commun 9:2155
Garmire, Lana X; Gliske, Stephen; Nguyen, Quynh C et al. (2017) THE TRAINING OF NEXT GENERATION DATA SCIENTISTS IN BIOMEDICINE. Pac Symp Biocomput 22:640-645
Luna-Munguia, Hiram; Starski, Phillip; Chen, Wu et al. (2017) Control of in vivo ictogenesis via endogenous synaptic pathways. Sci Rep 7:1311
Gliske, Stephen V; Stacey, William C; Lim, Eugene et al. (2017) Emergence of Narrowband High Frequency Oscillations from Asynchronous, Uncoupled Neural Firing. Int J Neural Syst 27:1650049
Gliske, Stephen V; Irwin, Zachary T; Chestek, Cynthia et al. (2016) Effect of sampling rate and filter settings on High Frequency Oscillation detections. Clin Neurophysiol 127:3042-3050
Gliske, Stephen V; Stacey, William C; Moon, Kevin R et al. (2016) THE INTRINSIC VALUE OF HFO FEATURES AS A BIOMARKER OF EPILEPTIC ACTIVITY. Proc IEEE Int Conf Acoust Speech Signal Process 2016:6290-6294