Our bodies have built-in neural reflexes that continuously monitor organ function and maintain physiological homeostasis. While the field of bioelectronic medicine and neuromodulation has mainly been focused on the stimulation of neural circuits to treat various conditions, recent studies have started to investigate the possibility of leveraging the sensory arm of these reflexes to diagnose disease states. To accomplish this, neural signals emanating from the body’s built-in biosensors and propagating through peripheral nerves must be recorded and decoded in order to identify the presence or levels of relevant biomarkers of disease. The process of acquiring these signals poses several technical challenges related to the neural interfaces, surgical techniques and data processing frameworks needed to record and analyze them. However, these challenges can be addressed with a rigorous experimental approach and new advances in implantable electrodes, signal processing and machine learning methods. In this workshop, we’ll try to provide an overview of the latest advances in these efforts. Successfully decoding peripheral nerve activity related to disease states will not only enable the development of real-time diagnostic devices, but also help advancing truly closed-loop neuromodulation technologies.
Dr. Theo Zanos received his Bachelor of Engineering degree in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in Greece in 2004, his Master of Science and his Doctorate in Biomedical Engineering from the University of Southern California, Viterbi School of Engineering in 2006 and 2009 respectively. His thesis, supervised by Dr. Vasilis Marmarelis, focused on developing machine learning and system identification approaches for Multi-Input Multi-Output hippocampal neural circuits. In 2009, Dr. Zanos was recruited as a postdoctoral fellow by Dr. Christopher Pack to work at the Montreal Neurological Institute (MNI), McGill, in Montreal, Canada, combining high-channel-count primate electrophysiology with machine-learning based neural data analysis methods to relate neural activity to behavior and cognition. In 2016, Dr. Zanos accepted a position as an Assistant Professor at the Feinstein Institute for Medical Research, where he joined the Center for Bioelectronic Medicine as the head of the Neural and Data Science lab. The same year he was also appointed as Assistant Professor in the Department of Molecular Medicine at the Zucker School of Medicine, at Hofstra University. His recent research is focused on developing neural data analysis tools and neurophysiology recording and stimulating methods, to understand and modulate central and peripheral neuronal circuit function, in order to develop devices that interface with the nervous system and diagnose and treat disease. Dr. Zanos has authored 19 peer-reviewed publications in journals like Neuron, PNAS, Journal of Neuroscience and others and his research has been featured in PBS, Scientific American and other media outlets. He is the Principal Investigator of the GE-funded Real-Time Diagnostics and Monitoring project and has been awarded the Excellence in Research Award in 2018, the Jean Timmins Award in 2012 and the Center of Excellence in Commercialization and Research Award in 2010.