Student: Jared Sullivan, Class of 2020, Concentration- Biomedical Engineering (A.B.)
Faculty: Yael Arbel, CCC-SLP, Ph.D., Associate Professor, Department of Communication Sciences and Disorders; Co-Director of the Cognitive Neuroscience Group, MGH Institute of Health Professions
Project Title: Signal analysis of Event Related Potentials (ERPs) in the study of typical and atypical feedback-based learning in children
Working in Dr. Yael Arbel’s lab as part of the Cognitive Neuroscience Group at the MGH Institute of Health Professions has been a very rewarding experience. As a Biomedical Engineering concentrator with a specific interest in neuroscience, I enjoyed learning about research applications of topics introduced in class, especially how Event Related Potentials (ERPs), which are extracted from the EEG, can be implemented to study differences in cognitive processes among individuals with typical and atypical learning abilities. Specifically, the research team that I worked on this summer aims to elucidate the extent to which children with Developmental Language Disorder (DLD) have an impaired ability to learn from feedback, by eliciting and measuring an Event Related Potential associated with feedback processing (i.e., the Feedback Related Negativity, or FRN which is elicited roughly 200-300 ms after a feedback stimulus is presented). This study tests both a group of children with DLD and a control group of typically developing children with a declarative learning task that incorporates feedback-based learning, and therefore will elicit the FRN component of the EEG signal. As part of this team, I worked to better capture the EEG signal of interest while eliminating background noise such as eye blinks or motion-related artifacts. We were able to use EEGLAB, a MATLAB based program, to optimize a data cleaning protocol that saved as much quality signal as possible, while eliminating unwanted noise. Along the way, I learned about statistical separation techniques like Independent Components Analysis (ICA) and Principal Components Analysis (PCA), as well as how to identify various noise artifacts based on component property plots. One significant side project that I worked on this summer was creating a MATLAB script to analyze, both visually and statistically, the results of a Serial Reaction Time (SRT) task that again was concerned with capturing cognitive differences between DLD and typically developing children, but this time by testing implicit sequence learning.
I hope to apply my research experience and knowledge gained from Dr. Yael Arbel’s lab to future neuroscience classes, as well as potentially to a medical career. The clinical nature of this research and its applications to the treatment and teaching of children with language disorders was very appealing to me. Thinking about some of the neuroscience topics taught in class from a research perspective with possible clinical benefits was certainly an experience that increased my interest in neuroscience. I am very grateful for Dr. Arbel and everyone in her lab for this opportunity, and I look forward to approaching my future studies and eventually my career options with this new understanding of the close relationship between scientific research and medicine.