A tensor based varying-coefficient model for multi-modal neuroimaging data analysis.

ABSTRACT: All neuroimaging modalities have their own strengths and limitations. A current trend is toward interdisciplinary approaches that use multiple imaging methods to overcome limitations of each method in isolation. At the same time neuroimaging data is increasingly being combined with other non-imaging modalities, such as behavioral and genetic data. The data structure of many of these modalities can be expressed as time-varying multidimensional arrays (tensors), collected at different time-points on multiple subjects. Here, we consider a new approach for the study of neural correlates in the presence of tensor-valued brain images and tensor-valued covariates, where both data types are collected over the same set of time points. We propose a time-varying tensor regression model with an inhere

SUBMITTER: Niyogi PG 

PROVIDER: S-EPMC11521373 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature