Effective Diagnosis of Alzheimer’s Disease via Multimodal Fusion Analysis Framework - Bi, Xia-an; Cai, Ruipeng; Wang, Yang; Liu, Yingchao (Frontiers in Genetics, 2019)
Alzheimer's disease (AD) is a complex neurodegenerative disease involving a variety of pathogenic factors, and the etiology detection of this disease has been a major concern of researchers. Neuroimaging is a basic and important means to explore the problem. It is currently the main scientific research direction for combining the neuroimaging with other modal data to dig deep into the potential information of AD by the complementarities between multiple data. Machine learning methods possess great potentiality and have made some achievements in this research. A few studies have proposed some solutions to the effects of multimodal data fusion, however, the overall analytical framework for data fusion and fusion results analysis is ignored. In this paper, we first put forward a novel multimodal data fusion method, and further presented a new machine learning framework of data fusion, classification, feature selection and disease-causing factor extraction. The real dataset of 37 AD patients and 35 control normal (CN) with functional Magnetic Resonance Imaging (fMRI) and genetic data was used to verify the effectiveness of the framework, which was more accurate in classification and optimal feature extraction than other methods. Furthermore, we found out disease-causing brain regions and genes, such as olfactory cortex, insula, posterior cingulate gyrus, lingual gyrus, CNTNAP2, LRP1B, FRMD4A and DAB1. The results show that the machine learning framework could effectively perform multimodal data fusion analysis, providing a new insight and perspective for the diagnosis of Alzheimer's disease.