Nonparametric Matrix Response Regression with Application to Brain Imaging Data Analysis
Abstract
With the rapid growth of neuroimaging technologies, a great effort has been dedicated recently to investigate the dynamic changes in brain activity. Examples include time course calcium imaging and dynamic brain functional connectivity. In this paper, we propose a novel nonparametric matrix response regression model to characterize the nonlinear association between 2D image outcomes and predictors such as time and patient information. Our estimation procedure can be formulated as a nuclear norm regularization problem, which can capture the underlying low-rank structure of the dynamic 2D images. We present a computationally efficient algorithm, derive the asymptotic theory and show that the method outperforms other existing approaches in simulations. We then apply the proposed method to a calcium imaging study for estimating the change of fluorescent intensities of neurons, and an electroencephalography study for a comparison in the dynamic connectivity covariance matrices between alcoholic and control individuals. For both studies, the method leads to a substantial improvement in prediction error.
Cite
@article{arxiv.1904.00495,
title = {Nonparametric Matrix Response Regression with Application to Brain Imaging Data Analysis},
author = {Wei Hu and Tianyu Pan and Dehan Kong and Weining Shen},
journal= {arXiv preprint arXiv:1904.00495},
year = {2020}
}