Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease

Abstract

Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers

“…Huang et al used longitudinal changes over 4 years of T1-weighted MRI scans to predict AD conversion in MCI subjects. Their results showed that the model with longitudinal data consistently outperformed the model with baseline data, especially achieved 17% higher sensitivity than the model with baseline data (Huang et al, 2017 ). In our study, the results showed that the longitudinal features failed to provide additional information for identifying aMCI and naMCI compared with cross-sectional features.…”

“…Huang et al presented a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Their method using longitudinal data consistently outperformed the method using baseline data only (Huang et al, 2017 ). Despite these efforts, employing machine learning technique with longitudinal MRI features for MCI subtypes classification is rarely studied.…”

“…Longitudinal measurements of Magnetic Resonance Imaging (MRI) in MCI and dementia may provide crucial predictors for tracking the disease progression of dementia (Misra et al, 2009 ; Risacher et al, 2010 ; Liu et al, 2013 ; Mayo et al, 2017 ). However, only a few studies used longitudinal data for automated classification of MCI and dementia (McEvoy et al, 2011 ; Li et al, 2012 ; Zhang et al, 2012a ; Ardekani et al, 2017 ; Huang et al, 2017 ). Zhang et al proposed an AD prediction method using longitudinal data which achieved greater classification results than using baseline visit data (Zhang et al, 2012a ).…”

Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data

“…follow-up patient visits), in an effort to improve long-term prognosis. However, this is a challenging task requiring longitudinally consistent feature selection and mitigation of missing timepoints [ 6 , 10 ].…”

Longitudinal structural MRI analysis and classification in Alzheimer's disease and mild cognitive impairment

“…Table shows that the nested LOOCV method can select the optimal feature dimension for different between‐group comparisons and receives the best classification performance in this dimension. The accuracy rates obtained by the classification method and the feature combination strategy are all higher than those of previous studies . The accuracies, sensitivities, specificities, PPVs, NPVs and AUCs have been greatly improved after the combination of longitudinal time points (ie, bl + 12 m and bl + 12 m + 24 m).…”