A1314
Title: A mixed model approach for estimating regional functional connectivity from voxel-level BOLD signals
Authors: Wendy Meiring - University of California Santa Barbara (United States) [presenting]
Ruobin Liu - University of California, Santa Barbara (United States)
Chao Zhang - Yeshiva University (United States)
Chau Tran - University of California Davis (United States)
Sophie Achard - CNRS LJK (France)
Alexander Petersen - Brigham Young University (United States)
Abstract: Resting-state brain functional connectivity quantifies the synchrony between activity patterns of different brain regions. In functional magnetic resonance imaging (fMRI), each region comprises a set of spatially contiguous voxels at which blood-oxygen-level-dependent signals are acquired. The ubiquitous Correlation of Averages (CA) estimator, and other similar metrics, are computed from spatially aggregated signals within each region, and remain the quantifications of inter-regional connectivity most used by neuroscientists despite their bias that stems from intra-regional Correlation and measurement error. Linear mixed-effects models are used to isolate different sources of variability in the voxel-level signals, including inter-regional and intra-regional Correlation and measurement error. A novel computational pipeline, focused on subject-level inter-regional Correlation parameters of interest, is developed to address the challenges of applying maximum (or restricted maximum) likelihood estimation to such structured, high-dimensional spatiotemporal data. Simulations demonstrate reliability of these Correlation estimates, their large sample standard error approximations, and their superiority relative to CA. The method is applied to public fMRI data, including subjects from a Human Connectome Project test-retest database. Concordance between inter-regional Correlation estimates for test-retest scans of the same subject is higher for the proposed method compared to CA.
