Dr. Rey is an assistant professor in the Department of Neurosurgery at the Medical College of Wisconsin and the Marquette-MCW Joint Department of Biomedical Engineering. He graduated in Electronic Engineering from the University of Buenos Aires, where he also obtained his PhD. He joined the University of Leicester (UK) in 2010 to perform his postdoctoral studies, and in 2012 he was awarded a Special Training Fellowship in Biomedical Informatics from the Medical Research Council (UK).
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Recording Neural Activity 24/7 at Different Scales in the Human Brain: A Unique Opportunity to Study Mechanisms Underlying Memory and Epilepsy
Epilepsy affects about 1% of the population worldwide, causing mortality, morbidity and reducing quality of life. When epilepsy becomes refractory (i.e., pharmacologically resistant), it is common to implant (macro) electrodes to record intracranial electroencephalogram (iEEG) in candidates identified for a surgical solution. In this talk, I will show you how we can apply modern technologies to simultaneously record from the human brain at different scales: from iEEG with clinical electrodes, to field potentials and single neuron activity from microwires protruding from the clinical probe. In fact, we are now able to record 24/7 for about a week, while the patient undergoes long-term monitoring to identify the epileptogenic zone (EZ), the area of the brain that is necessary for the appearance of seizures. During this time, the patient can engage in behavioral tasks while the neural signals are being recorded. In my lab, we are developing a research program that would allow to use this setup to answer important questions in clinical and cognitive neuroscience. The cognitive studies will allow us to unravel the building blocks of episodic memory and the associated neural mechanism, but will also provide us a platform to study other brain processes like perception, attention, language, and decision making. On the clinical research, we will search for new biomarkers to improve diagnosis and treatment in epilepsy. To achieve this, we rely on developing and applying methods to improve acquisition and analysis of neurophysiological data, based on concepts from signal processing and machine learning.