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.

翻译：近年来，多模态成像采集技术的进步使我们能够测量大脑结构和功能的不同方面。多模态融合方法，如关联独立成分分析（LICA），被广泛用于整合互补信息。然而，该方法一直受到缺失数据问题的困扰，这在神经影像数据中普遍存在。因此，本文提出一种全信息LICA算法（FI-LICA），以在LICA框架下处理多模态融合过程中的缺失数据问题。我们的方法基于完整案例，运用全信息原理，利用所有可用信息来恢复缺失的潜在信息。仿真实验表明，与现有方法相比，FI-LICA具有理想的性能。此外，我们将FI-LICA应用于阿尔茨海默病神经影像倡议（ADNI）研究的多模态数据，结果显示其在当前诊断分类以及对轻度认知障碍（MCI）参与者向阿尔茨海默病转化的预测方面表现更优，从而凸显了所提方法的实用价值。