Abstract Number:
3472
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Xinyu Zhang (1), Kenneth Liao (2), Maureen McHugo (3), Anna Huang (2), Kristan Armstrong (2), Suzanne Avery (2), Stephan Heckers (2), Simon Vandekar (2)
Institutions:
(1) Vanderbilt University, Department of Biostatistics, N/A, (2) Vanderbilt University Medical Center, N/A, (3) University of Colorado Medicine, N/A
Co-Author(s):
First Author:
Xinyu Zhang
Vanderbilt University, Department of Biostatistics
Presenting Author:
Abstract Text:
Longitudinal data are increasingly prevalent in psychiatric neuroimaging as investigators aim to explore the relationships between biological factors and symptom variations on an individual level. This study addresses the complexity of longitudinal neuroimaging data to construct spatial confidence sets using a flexible semiparametric bootstrap joint (sPBJ) spatial extent inference (SEI) method. Our method involves robust estimation of the spatial covariance function based on the generalized estimating equation. We obtain more efficient effect size estimates by concurrently estimating the exchangeable working covariance and using the sPBJ bootstrap to determine the joint distribution of effect size across voxels. The bootstrap procedure is used to construct confidence sets for the effect size parameter. These confidence sets can identify the target and null regions of the image where the effect size is above or below given thresholds, respectively, with high probability. We evaluate the coverage of the proposed procedures using realistic simulations. This comprehensive approach, integrated into the pbj R package, offers a robust tool for analyzing repeated neuroimaging measurements.
Keywords:
Effect size|Confidence sets|Longitudinal neuroimaging data| | |
Sponsors:
Section on Statistics in Imaging
Tracks:
Brain Imaging
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