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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
Abstract #322193
Title: Neuro-Hotnet: A Graph Theoretic Approach for Brain FC Estimation
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

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.

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

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