Abstract
Identifying patients with mild cognitive impairment (MCI) who are at high risk of progressing to Alzheimer’s disease (AD) is crucial for early treatment of AD. However, it is difficult to predict the cognitive states of patients. This study developed an extreme learning machine (ELM)-based grading method to efficiently fuse multimodal data and predict MCI-to-AD conversion. First, features were extracted from magnetic resonance (MR) images, and useful features were selected using a feature selection method. Second, multiple modalities of MCI subjects, including MRI, positron emission tomography, cerebrospinal fluid biomarkers, and gene data, were individually graded using the ELM method. Finally, these grading scores calculated from different modalities were fed into a classifier to discriminate subjects with progressive MCI from those with stable MCI. The proposed approach has been validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, and an accuracy of 84.7% was achieved for an AD prediction within 3 years. Experiments on predicting AD conversion from MCI within different periods showed similar results with the 3-year prediction. The experimental results demonstrate that the proposed approach benefits from the efficient fusion of four modalities, resulting in an accurate prediction of MCI-to-AD conversion.
Highlights
Alzheimer’s disease (AD) is the most common cognitive impairment disease, which gradually impacts the activities of a patient’s daily life
Since the efficient multimodality fusion can improve the performance of an artificial intelligence system (Hu et al, 2018), in this work, we present a novel extreme learning machine (ELM)-based (Huang et al, 2012) grading method to combine four modalities (MRI, Fluorodeoxyglucose positron emission tomography (FDG-PET), cerebrospinal fluid (CSF), and apolipoprotein E (APOE) 4) that predict mild cognitive impairment (MCI)-to-AD conversion
There are three major steps in this framework: (i) MRI features are first preprocessed by feature selection with the least absolute shrinkage and selection operator (LASSO) algorithm; (ii) each modality (CSF and gene are combined as biological modality) of MCI is graded by ELM
Summary
Alzheimer’s disease (AD) is the most common cognitive impairment disease, which gradually impacts the activities of a patient’s daily life. 10–17% of those with MCI progress to AD over the course of a few years, yet some MCI patients remain stable after several years (Hamel et al, 2015). It is crucial to identify people who are at high risk of progressing from MCI to AD because it can help physicians treat these patients sooner and apply suitable therapies to slow down the progression or even improve a patient’s condition. The prediction of AD, discriminating progressive MCI (pMCI) from stable MCI (sMCI), is more challenging because the differences between these two groups are slight
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