2019/03/31 by Wei Hu, Tianyu Pan, Hu, Wei +5 · 1 citation
Medicine · Neuroscience · #Advanced MRI Techniques and Applications #Advanced Neuroimaging Techniques and Applications #Applications (stat.AP) #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1904.00495
openalex publication_date 2019/03/31 · openalex created_date 2019/04/11 · openalex updated_date 2026/07/28
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.