Smooth Image-on-Scalar Regression for Brain Mapping

Brain mapping is an increasingly important tool in neurology and psychiatry researches for the realization of data-driven personalized medicine in the big data era, which learns the statistical links between brain images and subject level features. Taking images as responses, the task raises a lot of challenges due to the high dimensionality of the image with relatively small number of samples, as well as the noisiness of measurements in medical images. In this paper we propose a novel method {\it Smooth Image-on-scalar Regression} (SIR) for recovering the true association between an image outcome and scalar predictors. The estimator is achieved by minimizing a mean squared error with a total variation (TV) regularization term on the predicted mean image across all subjects. It denoises the images from all subjects and at the same time returns the coefficient maps estimation. We propose an algorithm to solve this optimization problem, which is efficient when combined with recent advances in graph fused lasso solvers. The statistical consistency of the estimator is shown via an oracle inequality. Simulation results demonstrate that the proposed method outperforms existing methods with separate denoising and regression steps. Especially, SIR shows an evident advantage in recovering signals in small regions. We apply SIR on Alzheimer's Disease Neuroimaging Initiative data and produce interpretable brain maps of the PET image to patient-level features include age, gender, genotype and disease groups.

Comments: 18 pages

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