Sara Sanchez-Alonso, PhD, Postdoctoral Associate
Haskins Laboratories, Yale University

Predictive Modeling of Neurobehavioral Variation Across Development
A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A main challenge has been to develop models that enable prediction of both within-subject and between-subject variation. In this talk, I will present a conceptual and analytical framework that considers two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. I will illustrate this framework by considering variation across neural and behavioral measurements and concurrent alterations of state and trait variation across development. Specifically, I will discuss how changes across brain states can be characterized using fMRI data acquired during naturalistic viewing and resting-state paradigms in a developmental population. I will show how we can characterize a "core" neural pattern that is robustly associated with naturalistic viewing and also exhibits change across age. These findings, focused on naturalistic viewing, will provide a roadmap for quantifying state-specific functional neural organization across development.

Faculty host, Ariel Starr: abstarr@uw.edu

This free lecture is made possible in part by endowments by Professors Allen L. Edwards and Roger B. Loucks.