Melvin Osei Opoku

Melvin Osei Opoku

Biography

Melvin Osei Opoku is a fourth-year undergraduate student at the University of Florida, majoring in biomedical engineering and a minor in electrical engineering with a strong interest in neuroscience and machine learning. Originally from Ghana, Melvin’s curiosity and hard work have propelled him from humble beginnings to becoming a passionate researcher. He has enriched his academic journey through impactful research experiences, including a summer internship atPrinceton University’s Murthy Lab, where he delved into connectomics, and work in Dr. Aysegul Gunduz’s Brain Mapping Lab, focusing on developing machine learning algorithms to enhance deep brain stimulation studies for Tourette syndrome. Currently, Melvin is a summer intern in the Lewis Lab at the Massachusetts Institute of Technology, investigating how brain metabolites fluctuate across sleep stages using concurrent EEG and MR spectroscopy. When he’s not immersed in research, Melvin enjoys creating educational content, playing soccer, running, watching movies, and mentoring peers. His ultimate goal is to contribute meaningfully to neuroscience research and inspire others through effective science communication.

Abstract

Characterizing Neurochemical Signatures of Sleep Stages via Simultaneous MRS-EEG

Sleep is crucial for maintaining healthy cognitive function, yet the neurochemical mechanisms underlying its different stages in the human brain remain poorly understood. This project aims to characterize how brain metabolites fluctuate across various sleep stages using simultaneous magnetic resonance spectroscopy (MRS) and electroencephalography (EEG). Specifically, we measured thirteen metabolites including GABA, glutamate, lactate, and creatine, in anterior cingulate cortex, hypothesizing distinct neurochemical signatures may characterize different arousal states. Utilizing a dataset of 68 subjects who underwent EEG-MRS scans, we segmented EEG data into 8-second windows aligned to MRS acquisition timing. Each artifact-free 8-second EEG window was classified into one of five sleep states: Awake and Alert, Awake and Drowsy, Transition into Sleep, Sleep, and Arousal from Sleep. The optimal length of MRS data needed to reliably quantify metabolites remains elusive, therefore we first ran a segmentation analysis to quantify this number. Our metrics were SNR and coefficient of variation of metabolite concentrations. Our findings will provide foundational insights into the biochemical dynamics of arousal stages, enhancing our understanding of healthy brain sleep physiology. This study bridges a significant gap by directly linking neurochemical profiles to physiological arousal states.