In recent years, in order to understand individual variability in brain network organization, researchers have begun using a technique known as Precision Functional Mapping (PFM) . PFM entails collecting hours of resting-state fMRI data from a small number participants to reliably characterize brain network topography in each individual. Empirical work using this approach has shown that adults display stable, individual-specific features of brain network topography that deviate from group averages and may be related to cognition. However, I am extending this work by applying PFM to an adolescent population. With these data, I characterize the individual-specific network topography of the developing brain. My team has collected resting-state fMRI, task-based fMRI, and movie-watching fMRI data from 8 teenagers between the ages of 13 and 16. In addition, our participants completed ecological momentary assessment (EMA) and salivary hormone sampling, as well as wearing an Oura ring to capture physiological variability in sleep and body temperature. Analyses are underway, and we hope to share these data and our results soon!

Previous research has established that adults often work harder and perform better when monetary rewards are at stake. However, less is known about how monetary rewards shape cognitive function during adolescence. My work looks at how monetary rewards influence performance on a working memory task in participants between the ages of 10 and 20. I am also completing cross-task analyses to investigate how the same participants perform in a cognitive flexibility task with an identical experimental design.

In Dr. Karen Berman's lab, I worked to better characterize the intricate link between the endocrine and nervous systems. To measure brain function, I used PET imaging to extract measures of resting regional cerebral blood flow (rCBF) as well as an MRI method known as Arterial Spin Labeling . Resting rCBF has been shown to exhibit robust sex differences, as well as abnormalities in several neuropsychiatric illnesses where in sex differences occur, such as major depression and schizophrenia. The goal of my project was to evaluate the effects of hormone condition on brain function using measures of rCBF extracted from PET resting scans of healthy subjects done at the NIH Clinical Center. Our subjects included men, naturally cycling women, and women using oral contraceptives.

During my time in Dr. Peter Bandettini's lab , I worked on a multitude of projects analyzing functional connectivity in resting-state fMRI and how to leverage connectivity information to enhance brain-behavior predictions. The goal of my first project was to investigate the source of individual variability in resting-state fMRI in healthy adults. We hypothesized that a potential source of variability during rest is 'ongoing cognition' that subjects engage in over the course of the scan (a, b). Ongoing cognition refers to everything from thinking about what to make for dinner to stressing about an upcoming meeting with your boss to daydreaming about a vacation and more. We used a publicly available dataset that consisted of multiple resting-state scans per subject as well as responses to a post-scan questionnaire that asked subjects to characterize their in-scanner experience. We found significant associations between distinct patterns of thought and functional connectivity patterns, highlighting the potential need to account for these effects when examining resting-state fMRI data.

Next, I sought to investigate how to leverage connectivity information to boost brain-behavior predictions. I employed a new technique to compute 'edge time series' from traditional ROI time series to capture the co-fluctuation between any given pair of nodes at each point in time across the whole scan. Then, to summarize these co-fluctuations overtime, we computed multiple summary metrics, including time-insensitive metrics, such as mean, and time-sensitive metrics, such as autocorrelation and dynamic entropy, to form new ROIxROI matrices for each subject and evaluated their predictive ability. We found that mean co-fluctuation, i.e. static functional connectivity, proves to be the best predictor of cognitive traits using this dataset , so far!