White Matter Connectome and Behavior Relationships in Early Childhood
2023 Award: $27,044
Despite increasing research on the association between brain structure and cognition/behavior in adults, their relationship in early childhood remains largely unknown. We propose a new method to study how the brain’s networks relate to behavior in early childhood: connectome-based predictive modeling. Our prediction model will enable the early identification of children at high risk of developing psychiatric disorders.
Need/Problem: There is strong evidence that white matter connectome computed from diffusion MRI is associated with cognition or behavior in adults and older children. However, little is known about their relationship in early childhood.
Grant Summary: We will investigate how the brain’s networks relate to behavior. We will use the white matter connectome, which shows how different brain area connect to each other. We will develop a novel deep learning method to predict the individual subject’s behavioral score from the white matter connectome.
Goals and Projected Outcomes: We will predict individual attention and anxiety measures at ages 4 and 6 from their white matter connectome at age 1 via deep-learning prediction model. Our prediction model will enable the early identification of children at high risk of developing psychiatric illness. The outcome of this study will be the preliminary results for additional research proposal that will investigate more in-depth individual differences in brain-behavior in early childhood and their relationship to adolescence.