We will define personalized functional networks in multiple large-scale datasets, in order to define how the functional network topography evolves with age and is linked to executive function in youth. This project was selected for the NIH MERIT award for long-term funding support.

Association cortex undergoes protracted development throughout adolescence but the mechanisms underlying such plasticity remain sparsely described in humans. Here we will use large-scale neuroimaging data resources and advanced machine learning to describe how association cortex plasticity develops, and how individual differences in plasticity are linked to common psychiatric symptoms. This award was renewed for a second 5-year project period in 2023.

Most studies of brain imaging collect many image types, but evaluate them in isolation. This project seeks to develop new statistical methods for integrating high-dimensional imaging data. Specifically, in this award we seek to develop new techniques that describe the coupling between disparate imaging methods that provide complementary aspects of brain structure and function. This award was renewed for a second 5-year project period in 2022, focused on inter-modal coupling in brain networks.

In this project, we will develop next generation deep learning techniques to define personalized functional networks that can be used in large-scale datasets to predict major dimensions of psychopathology in youth.

This project will aggregate, process, curate, and publicly release many of the largest studies of brain development (n>11,000 scans). Specifically, we will harmonize clinical measures across studies to derive comparable dimensions of psychopathology, which are integrated with harmonized functional and structural imaging data. Together, this project will create a large new public resource to study normal brain maturation and developmental abnormalities associated with psychopathology.

In this BRAIN imitative project, we will develop reproducible and generalizable methods
for processing of neuroimaging data across modalities, populations, and species.

This study takes a data-science approach to understand affective instability in youth, leveraging large-scale repositories of imaging data.

This effort supports a series of comprehensive meta-analyses of pain neuroimaging in both healthy volunteers and those with chronic pain conditions. Specific projects include meta-analyses of brain responses to pain in healthy volunteers, differential functional responses to pain in patients with chronic pain, and neurostructural alterations associated with chronic pain.