Under the mentorship of Dr. Natasha Leporé, I conduct pediatric neuroradiology research within the CIBORG Lab, based out of Los Angeles Children’s Hospital. The CIBORG (Computational Imaging of Brain Organization Research Group) Lab works to advance pediatric neuroscience, utilizing machine learning and improving imaging technology and analysis to elucidate the relationship between preterm birth and structural neuroanatomy.

Currently, I am focused on writing/publishing three papers with the lab, the first of which uses magnetic resonance imaging data to explore the impact of preterm vs. full term neonatal delivery status, as well as APOE4 carrier status, on variations in the hippocampal anatomy of young children. The second paper aims to use surface tensor-based morphometry of the corpus callosum in children aged 1-5 years to investigate and expand our understanding of the normal development of the corpus callosum in young children. The final paper aims to study the effects of blindness on the morphology of the central sulcus as a means of identifying specific changes in somatotopic sensory-motor organization, within populations of congenitally blind, late-blind and sighted individuals. Through my work on these papers, in addition to processing and analyzing radiology data for the lab, I have gained proficiency in imaging analysis and segmentation programs such as ITK-SNAP, FreeSurfer Live, and MATLAB.

My role also encompasses personally facilitating the training of new research volunteers, along with the coordination and oversight of all work assignments done by lab volunteers. This requires constant and dynamic communication with both senior lab members and volunteers to ensure that projects are being assigned to and completed by the right people in a timely manner.

Headed by Dr. John Pearson, the Duke Pearson Lab conducts research in computational neuroscience ranging from behavioral modeling using deep neural networks, to human intraoperative, multi-electrode recordings and analyses. My work with the Pearson Lab provided generous experience in computationally tackling big data, a regular chance for patient interaction, and the opportunity to take ownership over the full research process, from the initial experimental design, through the data collection, to the subsequent data analysis.

I collected all human-subject data accrued through the lab’s two unique patient populations at the Duke University Hospital. Parkinson’s patients undergoing implantation of Deep Brain Stimulation (DBS) participate in intraoperative tasks aimed at gaining a better understanding of the involvement of the sub thalamic nucleus (STN) in impulsivity, conflict, and motivation. Epilepsy patients being considered for surgical resection of their epileptic focus are implanted with electrocorticography (ECog) grids to characterize and localize seizure activity, allowing our lab to conduct neurological research paradigms (including a theory of mind battery and a competitive game task), while collecting multi-electrode recordings of neural activity.

Additionally, I gained proficiency in coding in Python, a skill which I apply to both experimental design and data analysis. In our most recent project, a colleague and myself designed a dot-discrimination task, to be completed intraoperatively by Parkinson’s patients; after being primed with visual cues corresponding to the task’s varying levels of difficulty, patients are asked to discriminate directionality between several different coherences of dots on a computer screen. The study intends to expound the potential cognitive thresholding effects of STN by assessing how cued differential expectation to accurately discern an ensuing stimuli effects STN dynamics and behavior.

I also generated a data processing pipeline for ECog data collected from our epilepsy patient population, and applied Python, MNE, Jupyter Notebooks, and a variety of other computational tools to clean the data, and to employ complex analyses including the calculation of event related potentials, average power analyses, and time-frequency spectral analyses.

Under the mentorship of Dr. Ahmad Hariri, the research conducted through the Lab of Neurogenetics aims to use neuroimaging, molecular genetics, and behavioral data to understand the origins of variability in brain chemistry, as well as to explore the impacts of this variability on behaviorally and clinically relevant brain function.

Through the lab, I have worked on a meta-analysis exploring whether cognitive performance is a predictor of depression onset. The methodology included a systematic review and meta-analysis of existing literature, with a focus on longitudinal or epidemiological studies in which cognitive function had been assessed prior to depression onset. The analysis ultimately culminated in the calculation of an odds ratio of developing depression (or correlation coefficient if measures are dimensional) across studies, thereby establishing a quantitative relationship of any effect. The resulting article is currently being reviewed for publication.

My Graduation with Distinction project developed a genetic risk profile for Major Depressive Disorder (MDD), comprising of 9 genes previously identified for MDD risk that produce protein products involved in stress and/or reward neurocircuitry. Statistical analyses using linear regression models and interaction models were conducted to explore the ability of an individual’s genetic risk score to predict incidence of depressive symptomology. Additionally, the risk profile was tested against functioning of relevant neurobiological circuitry assessed via functional magnetic resonance imaging (fMRI). In particular, stress and reward-related functioning was tested. It was found that an individual’s genetic risk profile score predicts their level of depressive symptomology, as moderated by the experience of recent life stress. The results build off prior genetic findings to increase the predictive utility of depressive symptomatology, accounting for 1% of the variance in the total sample, and 4% of the variance in the male subsample, larger than the variance explained by any individual genetic variant.