A High-Dimensional Latent Regression Item Response Model for Psychometric-Neuroimaging Association Studies

Oregon Convention Center 

Neuroimaging data are increasingly used in evaluating cognitive patterns underlying psychometric measurements. Challenges in such analysis include: 1. Latent cognitive traits are not directly observable. 2. Neuroimaging data are high dimensional. To address these issues, we propose a novel high-dimensional latent regression item response model. Unlike conventional item response models that model the latent trait solely based on the observed psychometric measurements, our model associates latent traits to high-dimensional neuroimaging features and filters the important features predictive of the latent traits and outcome measurements. We implemented a high-dimensional EM algorithm, it employs a Metropolis-Hasting resampling mechanism in E-steps to evaluate the latent traits and a regularization regression in M-steps to select predictive features. Through extensive simulations, our approach showed great performance in feature selection and outcome prediction. The method is applied to the National Alzheimer's Coordinating Center psychometric and neuroimaging datasets to learn the association patterns between cognitive abilities and brain regions.

High-dimensional data

Item response model

Metropolis-Hasting algorithm

Neuroimaging

Psychometrics measurements

Variable selection 

Section on Statistics in Imaging