  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.
