arXiv:2611.13187v1 Announce Type: cross Abstract: Large-scale neuroimaging studies provide high-dimensional imaging phenotypes for understanding brain-related diseases and their causal relations with biological risk factors. While instrumental variable (IV) methods can address unmeasured confounding in such observational data, existing IV methods face significant limitations when applied to high-dimensional imaging data, particularly in handling large-scale data and leveraging the intrinsic spatial structure of images. To address these challenges, we propose High-dimensional Functional-imaging Instrumental Variable Estimation (Hi-FIVE), a novel framework for estimating spatially varying causal effects when a majority of the candidate instruments may be locally invalid. We first establish identifiability conditions under which the spatial continuity of the causal effect surface propagates identification from regions where valid instruments form a plurality into regions where they do not, provided that invalid-IV effects switch on non-smoothly. Motivated by this result, Hi-FIVE combines penalized multivariate splines over triangulations for the causal surface and region-wise penalties for spatially localized invalid-IV effects, under an anchor constraint that keeps at least one relevant instrument valid on every simplex. A distributed algorithm parallelizes the computation over simplices for two- and three-dimensional images. We study the asymptotic properties of Hi-FIVE, establish consistency and pointwise asymptotic normality, and construct bootstrap-calibrated simultaneous confidence regions whose finite-sample coverage is evaluated through simulations. Hi-FIVE is illustrated with an analysis of the effect of phosphorylated tau on FDG-PET imaging in the Alzheimer's Disease Neuroimaging Initiative, interpreted under the stated IV assumptions.