A0943
Title: Robust and scalable distributed learning for surface-based imaging regression with applications to neuroimaging
Authors: Yang Long - George Mason University (United States) [presenting]
Zhiling Gu - Yale (United States)
Guannan Wang - College of William & Mary (United States)
Lily Wang - George Mason University (United States)
Abstract: High-dimensional medical imaging data are rapidly expanding, yet their complex structure and measurement errors pose significant challenges for reliable scientific discovery. A robust distributed image-on-scalar regression (R-DISR) framework is proposed that integrates spatially varying coefficient models with triangulated spherical spline smoothing via domain decomposition. This approach is designed to handle heavy-tailed noise and measurement errors while achieving near-linear computational speedup and minimizing communication overhead in distributed computing environments. It is rigorously established that the R-DISR estimators attain the same convergence rate as full-sample global estimators and their asymptotic distributions are derived. Moreover, a weighted bootstrap procedure is developed to construct simultaneous confidence corridors for the spatially varying coefficient functions. Extensive simulation studies demonstrate the method's finite-sample performance, and its application to cortical surface-based functional magnetic resonance imaging data from the human connectome project illustrates its effectiveness and scalability for analyzing large-scale imaging datasets.
