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Activity Number: 311 - Inference, Prediction, and Statistical Learning for Functional Data
Type: Topic-Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: SSC (Statistical Society of Canada)
Abstract #317513
Title: Functional Random Effects Modeling of Brain Shape and Connectivity
Author(s): Eardi Lila* and John Aston
Companies: University of Washington and University of Cambridge
Keywords: Functional data; Geometric statistics; Shape analysis; Functional connectivity

We introduce a novel functional data analysis approach that jointly models brain shape and connectivity, which are two complex aspects of the brain that have been classically studied independently. We adopt a Riemannian modeling approach to account for the non-Euclidean geometry of the space of shapes and that of connectivity that allows us to constrain the predictions to be valid estimates. In order to disentangle genetic sources of variability from those driven by unique environmental factors, we embed a functional random effects model in the Riemannian framework. We apply the proposed model to the Human Connectome Project dataset in order to explore spontaneous co-variation between brain shape and connectivity in young healthy individuals.

Authors who are presenting talks have a * after their name.

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