The development of many human abilities, including cognitive, motor, sensory, and social functions, are intricately linked. Thus, a complete understanding of the development of one function relies upon understanding its interactions with the others. One way to study these interactions is to examine how different parts of the brain communicate to support these functions. Brain areas are organized into functional networks, and the complex interactions within and among these networks can reveal principles about the relationships among numerous human abilities in both typical and atypical development.
Our lab uses the latest tools of cognitive neuroscience to study the development of brain network organization and how it supports cognitive and sensorimotor development. We use multimodal MRI techniques to study typically and atypically developing populations to accomplish our goals.
Studying the whole brain, including the cortex, subcortex, and cerebellum, is important for fully understanding the many changes that occur over development and alterations seen in developmental disorders. Therefore, we use a variety of approaches, including resting-state fMRI and structural MRI, to interrogate whole-brain function and structure. For example, using resting-state fMRI we have identified an approach to best capture cortico-subcortical functional connectivity and have applied this approach to study its development.
The ability to make predictions about individuals could lead to improved outcomes. Therefore, we use whole-brain imaging data and multivariate machine learning techniques to make such predictions. We have shown successful prediction and classification of known characteristics of individuals, such as age and diagnosis, and our current work focuses on predicting unknown characteristics with the goal of improving outcomes for children.
Group-level studies are valuable, but we necessarily obscure individual differences that contribute to each individual’s uniqueness. Therefore, we also employ a highly-sampled individual-level approach in which we collect large amounts of high-quality imaging and behavioral data from each individual. Using this approach, we can examine individual-specific brain network organization, gaining insight into the distinctiveness that makes up each individual.
Tourette syndrome (TS) is a fairly common neurodevelopmental disorder characterized by motor and vocal tics. Though tics are the defining symptoms, TS often involves atypical sensory, cognitive, and social functioning as well. We use brain imaging, cognitive, and clinical assessments to interrogate the mechanisms underlying this complex disorder. We employ both cross sectional and longitudinal designs to understand its developmental trajectory with the goal of making clinically relevant predictions about individual children with TS.
One of the major obstacles for MRI research, particularly in pediatric populations, is excessive head movement in the scanner. Even small amounts of motion can introduce systematic distortions in the data. While data processing methods can mitigate some of these effects, the most effective approaches result in significant data loss. Thus, we aim to test and improve strategies for reducing head motion in the scanner. Our recent work has tested the effects of movie watching and real-time motion feedback on in-scanner head motion in children. We also employ real-time quality control using a newly developed software, Framewise Integrated Real-Time MRI Monitoring (FIRMM).