Visiting Assistant Professor
Ph.D., University of Texas at Dallas, 2002
Brain Dynamics and Neural Source Modeling

Email: rrgreen@utdallas.edu
Phone: 972-883-6752
Office: JO 4.206

Overview

My research interests mainly deal with cognitive electrophysiology of the brain, specifically utilizing brain event-related potentials (ERPs) to study auditory and language processing. My research has shown that there are slight differences in processing dichotic speech stimuli in young adults; and I have extended this work to investigate effects of age-related hemispheric differences in the neural processing of dichotic information and linguistic processing.

Out of that body of research emerged one of my current research interests dealing with the role of selective attention, hemispheric asymmetry, and its role in processing cognitive information. Our findings support the notion that ERP topographic asymmetries may be dependent on specific cognitive task demands (e.g., diotic vs. dichotic modes of presentation). In addition, data suggested that later occurring ERP components may better reflect interaural advantages for complex tones than earlier components, and may, therefore, be a more sensitive indicator of hemispheric specialization.

Brain Dynamics and Neural Source Modeling

Unfortunately, ERPs are unable to localize the actual sources of where the electrical activity is originating in the brain. Understanding where these sources lie in the brain gives us a better grasp of the underlying cognitive processes. Over the last decade, much progress has been made in brain mapping methods that attempt to localize neuroelectric sources in the brain based on surface EEG or ERP recordings (referred to as the inverse problem).

During my post-doctoral fellowship at the University of Washington studying language function, I developed a special interest in methods dealing with this issue. I embarked on studying the validity and accuracy of source localization methods like Low Resolution Brain Electromagnetic Tomography (LORETA). LORETA is a neural source modeling technique whose purpose is to identify specific brain sources by utilizing complex spatial mathematics and accepted constraints based on brain anatomy and physiology. Using this method, we showed that LORETA can indeed properly localize language function (specifically syntax and semantics), but that proper experimental design and analysis are a vital component of source localization success. Overall, LORETA coupled with methods like ERPs, can provide valuable spatial information to complement temporal information, resulting in a dynamic spatiotemporal representation of brain activity (brain dynamics).