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Activity Number: 385 - SPEED: Statistical Methods and Applications in Medical Research, Risk Analysis, and Marketing Part 1
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Imaging
Abstract #322892
Title: Large-Scale Correlation Screening Under Dependence for Brain Functional Connectivity Inference
Author(s): HanĂ¢ LBATH* and Alexander Petersen and Sophie ACHARD
Companies: Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK and Brigham Young University and Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK
Keywords: correlation screening; network inference; multivariate time-series; resting-state fMRI; functional connectivity; brain imaging

Resting-state functional Magnetic Resonance Imaging (fMRI) is widely used to infer brain functional connectivity networks. Such networks correlate neural signals to connect brain regions, which consist in groups of dependent voxels. Previous work has focused on aggregating variables within predefined regions. However, it can be shown the presence of within-region correlations has noticeable impacts on inter-regional correlation detection, and thus edge identification. To alleviate them, we propose to leverage the large-scale correlation screening literature, and derive simple and practical characterizations of the mean number of correlation discoveries that flexibly incorporate intra-regional dependence structures. This novel approach for handling arbitrary intra-regional correlation is shown to improve false positive and true positive rates. A connectivity network inference framework is then presented. First, inter-regional correlation distributions are estimated. Then, correlation thresholds are constructed for each edge, with false discovery control that can be tailored to one's application. Finally, the proposed framework is implemented on a real-world dataset.

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

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