A Generative Imputation Method for Multimodal Alzheimer's Disease Diagnosis

TL;DR

This paper introduces a generative adversarial network approach to impute missing neuroimaging modalities in multimodal Alzheimer's disease diagnosis, significantly improving classification accuracy.

Contribution

The study presents a novel GAN-based imputation method that effectively reconstructs missing modalities, enhancing diagnostic accuracy in neuroimaging data analysis.

Findings

9% improvement in classification accuracy

Effective reconstruction of missing modalities

Preservation of disease-specific patterns

Abstract

Multimodal data analysis can lead to more accurate diagnoses of brain disorders due to the complementary information that each modality adds. However, a major challenge of using multimodal datasets in the neuroimaging field is incomplete data, where some of the modalities are missing for certain subjects. Hence, effective strategies are needed for completing the data. Traditional methods, such as subsampling or zero-filling, may reduce the accuracy of predictions or introduce unintended biases. In contrast, advanced methods such as generative models have emerged as promising solutions without these limitations. In this study, we proposed a generative adversarial network method designed to reconstruct missing modalities from existing ones while preserving the disease patterns. We used T1-weighted structural magnetic resonance imaging and functional network connectivity as two modalities. Our findings showed a 9% improvement in the classification accuracy for Alzheimer's disease versus cognitive normal groups when using our generative imputation method compared to the traditional approaches.