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Targeting EEG/LFP Synchrony with Neural Nets
Yitong Li · michael Murias · samantha Major · geraldine Dawson · Kafui Dzirasa · Lawrence Carin · David Carlson

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #142

We consider the analysis of Electroencephalography (EEG) and Local Field Potential (LFP) datasets, which are “big” in terms of the size of recorded data but rarely have sufficient labels required to train complex models (e.g., conventional deep learning methods). Furthermore, in many scientific applications, the goal is to be able to understand the underlying features related to the classification, which prohibits the blind application of deep networks. This motivates the development of a new model based on {\em parameterized} convolutional filters guided by previous neuroscience research; the filters learn relevant frequency bands while targeting synchrony, which are frequency-specific power and phase correlations between electrodes. This results in a highly expressive convolutional neural network with only a few hundred parameters, applicable to smaller datasets. The proposed approach is demonstrated to yield competitive (often state-of-the-art) predictive performance during our empirical tests while yielding interpretable features. Furthermore, a Gaussian process adapter is developed to combine analysis over distinct electrode layouts, allowing the joint processing of multiple datasets to address overfitting and improve generalizability. Finally, it is demonstrated that the proposed framework effectively tracks neural dynamics on children in a clinical trial on Autism Spectrum Disorder.

Author Information

Yitong Li (Duke University)
michael Murias (Duke University)
samantha Major
geraldine Dawson (Duke University)
Kafui Dzirasa (Duke University)

Kafui Dzirasa completed a PhD in Neurobiology at Duke University. His research interests focus on understanding how changes in the brain produce neurological and mental illness, and his graduate work has led to several distinctions including: the Somjen Award for Most Outstanding Dissertation Thesis, the Ruth K. Broad Biomedical Research Fellowship, the UNCF·Merck Graduate Science Research Fellowship, and the Wakeman Fellowship. Kafui obtained an MD from the Duke University School of Medicine in 2009, and he completed residency training in General Psychiatry in 2016. Kafui received the Charles Johnson Leadership Award in 2007, and he was recognized as one of Ebony magazine’s 30 Young Leaders of the Future in February 2008. He has also been awarded the International Mental Health Research Organization Rising Star Award, the Sydney Baer Prize for Schizophrenia Research, and his laboratory was featured on CBS 60 Minutes in 2011. In 2016, he was awarded the inaugural Duke Medical Alumni Emerging Leader Award and the Presidential Early Career Award for Scientists and Engineers: The Nation’s highest award for scientists and engineers in the early stages of their independent research careers. In 2017, he was recognized as 40 under 40 in Health by the National Minority Quality Forum, and the Engineering Alumni of the Year from UMBC. He was induced into the American Society for Clinical Investigation in 2019. Kafui has served as an Associate Scientific Advisor for the journal Science Translational Medicine, and he was a member of the Congressional-mandated Next Generation Research Initiative. He currently serves on the Editorial Advisory Board for TEDMED, and the NIH Director’s guiding committee for the BRAIN Initiative. Kafui is an Associate Professor at Duke University with appointments in the Departments of Psychiatry and Behavioral Sciences, Neurobiology, Biomedical Engineering, and Neurosurgery. His ultimate goal is to combine his research, medical training, and community experience to improve outcomes for diverse communities suffering from Neurological and Psychiatric illness.

Lawrence Carin (Duke University)
David Carlson (Duke University)

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