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Activity Number: 509 - New Approaches to Modeling and Inference for Complex Space-Time Data
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #329701 Presentation
Title: A Scalable Multi-Resolution Spatio-Temporal Model for Brain Activation and Connectivity in fMRI Data
Author(s): Stefano Castruccio* and Hernando Ombao and Marc G Genton
Companies: University of Notre Dame and King Abdullah University of Science and Technology and King Abdullah University of Science and Technology
Keywords: big data; brain imaging; gaussian processes; spatio-temporal models

Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data, however, poses serious modeling and computational challenges. We introduce a multi-resolution and computationally efficient spatio-temporal model to estimate cognitive control related activation and whole-brain connectivity. The proposed model allows for testing voxel-specific activation while accounting for non-stationary local spatial dependence within anatomically defined regions, as well as regional dependence between-regions. The model is used in a motor-task fMRI study to investigate brain activation and connectivity related to regaining motor functionality following a stroke.

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

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