Victoria Bajela

Victoria Bajela

Biography

Victoria O. Bajela—known to friends and colleagues as Ore—is a rising senior at Case WesternReserve University majoring in Data Science. Born and raised in Nigeria, Ore’s journey into research has been shaped by a deep awareness of how unequal access to technology and education limits opportunity, especially in underrepresented communities. This experience fuels her passion for building intelligent systems that are both impactful and inclusive. Her early research focused on extracting novel fatomics features from medical imaging data to build time-to-event models for clinical prediction. Currently, she is an intern in Dr. Laura Lewis’s lab at MIT, where she investigates how dopamine-sensitive brain activity fluctuates across arousal states using receptor-informed fMRI, EEG, and PET imaging. Her work aims to reveal how neuro modulatory signals shape brain dynamics and how these biological principles can inspire more adaptive, brain-like AI systems. Ore thrives at the intersection of data science, health, and human behavior, and is particularly excited by problems that require both analytical depth and creativity. Her work spans applications from medical imaging to economics, reflecting a drive to use data to understand complex, real-world systems. Outside of research, she mentors fellow students, champions women in STEM, and enjoys exploring how art and science intertwine—whether through poetry, music, or a well-structured algorithm.

Abstract

Investigating how dopamine-sensitive signals vary with brain arousal states using Receptor-Informed fMRI

Dopamine is a key neuromodulator involved in motivation, movement, and arousal, but its role in natural sleep-wake transitions in humans remains unclear. While animal studies show dopamine fluctuates with arousal, few have examined these dynamics in healthy humans. This project analyzes functional MRI (fMRI) data using positron emission tomography (PET) -derived dopamine receptor and transporter density maps to assess dopaminergic contributions to the fMRI signal across spontaneous arousal transitions. Using a dataset combining simultaneous PET, fMRI, and EEG recordings, we evaluated blood oxygen level-dependent (BOLD) signal changes during sleep and wake states in healthy participants, instructed to press a button with each breath to monitor behavioral responsiveness. To isolate dopaminergic effects from global signal fluctuations, we used the Neuromaps Python toolbox to align BOLD data with PET-based dopamine maps. We then computed average fMRI time courses weighted by dopamine receptor and transporter density and analyzed these signals in relation to behavioral responsiveness. By integrating neurochemical and neuroimaging data, this work offers new insights into dopamine’s functional role in brain state transitions and advances interpretation of the BOLD signal. Understanding how dopamine modulates these transitions may improve treatment for disorders involving both dopamine dysregulation and sleep disturbances, such as Parkinson’s disease and depression.