Latent Multimodal Functional Graphical Model Estimation

doi:10.1080/01621459.2023.2252142

Latent Multimodal Functional Graphical Model Estimation

2024-04-12

1

OA

AI

DOI：10.1080/01621459.2023.2252142

Original

Original request for help

Save

Abstract

En 中文

Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern approach enabled by recent engineering breakthroughs in the neurological and biological sciences. One prominent motivation to acquire such data is to enable new discoveries of the underlying connectivity by combining multimodal signals. Despite the scientific interest, there remains a gap in principled statistical methods for estimating the graph underlying multimodal functional data. To this end, we propose a new integrative framework that models the data generation process and identifies operators mapping from the observation space to the latent space. We then develop an estimator that simultaneously estimates the transformation operators and the latent graph. This estimator is based on the partial correlation operator, which we rigorously extend from the multivariate to the functional setting. Our procedure is provably efficient, with the estimator converging to a stationary point with quantifiable statistical error. Furthermore, we show recovery of the latent graph under mild conditions. Our work is applied to analyze simultaneously acquired multimodal brain imaging data where the graph indicates functional connectivity of the brain. We present simulation and empirical results that support the benefits of joint estimation. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.

Keywords:

Integrative analysis

Multimodal data

functional Gaussian graphical model

Neighborhood regression

Latent Multimodal Functional Graphical Model Estimation

Abstract

Journal

Organization