Research Interests
My interdisciplinary research program aims to understand all the different ways our brains pay attention by integrating ideas and perspectives across cognitive, clinical, computational, and developmental neuroscience. I leverage cutting-edge data science tools in both large-scale open-source datasets and smaller- scale deeply phenotyped samples. In my initial training in cognitive neuroscience, I investigated neurophysiological signatures of visual and auditory attention. I then applied these cognitive neuroscience methods and findings from healthy adults to clinical populations during my doctoral work, characterizing differences in attention in depression and anxiety. Motivated by findings that childhood experiences can profoundly impact neurocognitive functioning, I sought post-doctoral training in developmental neuroscience, where I currently investigate how childhood environments and experiences shape functional brain network development and cognition. See below for some examples of my projects:
Your Brain is One of a Kind!
Development of Functional Brain Organization
Most human fMRI research uses standardized network atlases to locate functional brain regions, based on the assumption that there exists a direct correspondence between brain structure and function that's the same across every person. However, networks of brain regions (especially those that support cognition!) can vary dramatically across individuals in their size, shape, and spatial locations across the cortex. Leveraging cutting-edge machine learning tools, I have defined personalized atlases of functional brain networks in thousands of individuals and found that individual differences in the spatial organization of these networks is associated with individual differences in cognition.
Effects of experience on neurocognitive development in children and adolescents
Each of us has a unique set of experiences throughout childhood and adolescence that shapes how our brains develop and forms the foundation for how we think. Stressful experiences in particular can lead to differences in cognition and increase risk for psychopathology and risk-taking behaviors in youth. To understand how experience shapes the development of brain regions supporting cognition, I am leveraging innovative new methodologies in supervised and unsupervised machine learning in large datasets of youth.
How Does the Brain Pay Attention?
Neural Mechanisms of Goal-Directed Attention
Sometimes our attention is guided by salient features of the environment, as bright objects or loud noises grab our attention automatically. Other times, we intentionally guide our attention toward whatever is relevant to our goals. What parts of the brain mediate these different forms of attention? In this collaborative project with the Vision and Perception Neuroscience Lab, we are working on separating goal-directed from stimulus-driven attention in fMRI signals. We use these novel computational techniques in combination with machine learning to uncover neural mechanisms of goal-directed attention
Moving Beyond "Mood"
Attention Impairments in Major Depression
Concentration difficulties are a diagnostic criterion for major depression and are associated with poorer quality of life, but we know relatively little about the specific types of attention that are impaired and their neural underpinnings. Leveraging knowledge from cognitive neuroscience, I conducted studies to better understand what specific attention sub-functions (e.g., selective, sustained, divided) go awry in depression and their neural correlates.
Attention Impairment in Depression & Anxiety
Which types of goal-directed attention are impaired in depression and anxiety? What are the neural mechanisms that become impaired to produce these deficits? To answer these questions, I have developed novel tasks to test various subtypes of goal-directed attention in a controlled manner. I will be testing these attention functions in individuals with depression/anxiety and healthy controls using functional magnetic resonance imaging (fMRI), and electro-encephalography (EEG).
Neural Oscillations and Attention
Characterizing the Roles of Alpha & Theta Oscillations in Multisensory Attention
Alpha oscillations are a particular frequency of brain waves that help us selectively attend to important sensory information while filtering out distraction. Our results show that alpha oscillations are sustained over time as we continuously ignore distraction. We also showed that theta oscillations, a different frequency of brain waves, tend to increase when we're dividing attention among multiple senses (e.g., visual and auditory), a separate function from theta's role in short-term memory.
Musicians' Brains Bind Sounds to Lights
Audio-Visual Sequence Integration in Trained Musicians
In my previous work with Dr. Robert Sekuler at Brandeis University, I studied how our brains integrate auditory and visual signals. We found that musicians tend to automatically bind together auditory tunes and visual light patterns. This automatic binding of sensory signals occurs in musicians' brains even when they're trying to pay attention to visual patterns and ignore distracting auditory tunes.
Computational Modeling of Electro-Encephalography (EEG) Signals
Electro-encephalography (EEG) is a tool that can be used to measure neural activity from outside the skull. I'm interested in ways that we can use EEG signals to better understand various conditions and make predictions about future outcomes. Here are a few examples:
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Using EEG signals and measures of early life stress to predict whether attention impairments will or won't alleviate with anti-depressant treatment
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Determining the genetic heritability of EEG oscillations associated with wellbeing
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Developing a machine learning algorithm to predict how various symptoms of depression (e.g. energy loss, appetite changes) change with anti-depressant treatment
Attention Matters!
How can the study of attention inform classroom learning?
Attention is the gateway between information and learning, yet there is much we do not know about how instructors orchestrate attention in classrooms. Working with Dr. Kimberly Tanner at SFSU and Dr. Ido Davidesco at UConn, we developed a framework for understanding attention in the classroom to explore how different approaches to the same active-learning strategy might vary in how effectively they direct attention.
