Full Information Linked ICA: addressing missing data problem in multimodal fusion

TL;DR

This paper introduces FI-LICA, a novel algorithm that effectively handles missing data in multimodal neuroimaging fusion, improving classification and prediction in Alzheimer's disease research.

Contribution

We propose a Full Information LICA algorithm that utilizes all available data to address missing data issues in multimodal neuroimaging fusion under the LICA framework.

Findings

FI-LICA outperforms existing methods in simulations.

FI-LICA improves classification accuracy in AD diagnosis.

FI-LICA enhances prediction of AD transition in MCI patients.

Abstract

Recent advances in multimodal imaging acquisition techniques have allowed us to measure different aspects of brain structure and function. Multimodal fusion, such as linked independent component analysis (LICA), is popularly used to integrate complementary information. However, it has suffered from missing data, commonly occurring in neuroimaging data. Therefore, in this paper, we propose a Full Information LICA algorithm (FI-LICA) to handle the missing data problem during multimodal fusion under the LICA framework. Built upon complete cases, our method employs the principle of full information and utilizes all available information to recover the missing latent information. Our simulation experiments showed the ideal performance of FI-LICA compared to current practices. Further, we applied FI-LICA to multimodal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, showcasing better performance in classifying current diagnosis and in predicting the AD transition of participants with mild cognitive impairment (MCI), thereby highlighting the practical utility of our proposed method.