Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 306 - Statistical Challenges in Large-Scale Imaging Studies
Type: Invited
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #320514
Title: Enhancing the Reliability of Brain Connectomics in Neuroimaging Studies
Author(s): Ying Guo*
Companies: Emory University
Keywords: neuroimaging; brain network; connectome; reliablity; reproducibility; blind source separation

Brain connectomics using neuroimaging has become increasing important to advance understanding of neural circuitry among healthy as well as diseased human brains. There are major challenges in brain connectome research including the high dimensionality of brain networks, unknown neural circuits underlying the observed connectivity, and the large number of brain connections leading to spurious findings. We present statistical methods that aim to improve the reliability and reproducibility in brain connectome research. The methods provide fully data-driven decomposition of observed network measures to reveal underlying neural circuits. We tackle the challenges in brain connectiomics with statistical strategies such as low-rank factorization, novel angle-based sparsity regularization and an automatic method for adaptively selecting the tuning parameters. We propose a computationally efficient iterative Node-Rotation algorithm to solve the non-convex optimization problems. By applying the methods to large-scale neuroimaging studies, we obtain more reliable findings and discover biologically insightful connectivity traits that are not revealed with the existing method.

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

Back to the full JSM 2022 program