Neuro-Hotnet: A Graph Theoretic Approach for Brain FC Estimation

• Activity Number: 195 - Section on Statistics in Imaging Student Paper Award Winners
• Type: Topic Contributed
• Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Nathan Tung* and Eli Upfal and Jerome Sanes and Ani Eloyan
• Companies: Brown University and Brown University and Brown University and Brown University
• Keywords: algorithms; graphical models; diffusion kernels; functional brain networks; task-based functional connectivity

Abstract

There is increasing interest in the potential of multi-modal imaging to obtain more robust estimates of Functional Connectivity (FC) in high-dimensional settings. We develop novel algorithms that leverage a graphical random walk on diffusion tensor imaging data to define a new measure of structural influence that highlights connected components of interest. We then test for minimum subnetwork size and find the subnetwork topology using permutation testing before the discovered components are tested for significance. Simulations demonstrate that our method is comparable in power to other current methods with the advantages of simple implementation, greater speed, and equal or more robustness. To verify our approach, we analyze task-based fMRI data obtained from the Human Connectome Project database, which reveal novel insights into brain interactions during performance of a motor task. We expect that the transparency and flexibility of our approach will prove valuable as further understanding of the structure-function relationship informs the future of network estimation. Scalability will also become more important as neurological data become more granular and grow in dimension.