A0938
Title: Nonparametric methods for analysis of brain cortical gradients
Authors: Andrew Chen - University of Pennsylvania (United States) [presenting]
Abstract: Recent methodological advances describe the topological organization of the brain cortex as a continuous map called brain cortical gradients. These gradients are consistent with seminal research on brain functional organization, well-studied neurodevelopmental trajectories, and key multimodal brain metrics. However, statistical methods for the analysis of brain cortical gradients are limited and current approaches either ignore population variability or key properties of gradient data. Brain cortical gradients and methods for deriving gradients are first introduced. Then, the unique properties of this novel data type and the limitations of existing approaches are discussed. Finally, nonparametric hypothesis testing methods appropriate for gradient data are proposed. Application to the Philadelphia Neurodevelopmental Cohort reveals that the proposed methods can capture neurodevelopmental changes in gradients and differences between demographic groups. Potential extensions and statistical frameworks are explored for further methodological developments.
