Abstract
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 the main current scientific research direction for combining neuroimaging with other modal data to dig deep into the potential information of AD through the complementarities among multiple data points. Machine learning methods possess great potentiality and have reached some achievements in this research area. A few studies have proposed some solutions to the effects of multimodal data fusion, however, the overall analytical framework for data fusion and fusion result analysis has thus far been ignored. In this paper, we first put forward a novel multimodal data fusion method, and further present 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 normal controls (NC) 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 revealed disease-causing brain regions and genes, such as the 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 new insights and perspectives for the diagnosis of Alzheimer’s disease.
Highlights
Alzheimer’s disease (AD) is an irreversible neuropsychiatric disorder, which often occurs in elderly and manifests clinically as memory deterioration, aphasia, social difficulties, and other symptoms (Morello et al, 2017; Tavana et al, 2018; Bregman et al, 2019)
FMRI images of each sample were segmented into 90 brain regions, and 82400 single nucleotide polymorphism (SNP) were preserved in the genetic data
We found that the quantity of decision trees had a significant impact on the performance of the multimodal random forest
Summary
Alzheimer’s disease (AD) is an irreversible neuropsychiatric disorder, which often occurs in elderly and manifests clinically as memory deterioration, aphasia, social difficulties, and other symptoms (Morello et al, 2017; Tavana et al, 2018; Bregman et al, 2019). The mortality rate of AD is high and is rising every year compared with other brain diseases (Association, 2017; Association, 2018). This disease affects approximately 36 million people throughout the world with the incidence anticipated to triple by 2050 (Neville et al, 2015). The complications caused by AD make the patient miserable (Association, 2015). In the later stages of the disease, the patient needs to carry the costs of expensive cost treatment, but can not take care of themselves and are completely dependent on caregivers, placing a heavy burden on their families and Effective Diagnosis AD via MFAF society (Association, 2016). Diagnosis of AD can delay the disease development and improve therapeutic effects, a diagnosis study of AD is urgent
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