Abstract
BackgroundEarly diagnosis of Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis (MVPA) for the diagnosis of brain disorders are explicitly attracting attention in the neuroimaging community. In this paper, we propose a voxel-wise discriminative framework applied to multi-measure resting-state fMRI (rs-fMRI) that integrates hybrid MVPA and extreme learning machine (ELM) for the automated discrimination of AD and MCI from the cognitive normal (CN) state.Materials and methodsWe used two rs-fMRI cohorts: the public Alzheimer’s disease Neuroimaging Initiative database (ADNI2) and an in-house Alzheimer’s disease cohort from South Korea, both including individuals with AD, MCI, and normal controls. After extracting three-dimensional (3-D) patterns measuring regional coherence and functional connectivity during the resting state, we performed univariate statistical t-tests to generate a 3-D mask that retained only voxels showing significant changes. Given the initial univariate features, to enhance discriminative patterns, we implemented MVPA feature reduction using support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), in combination with the univariate t-test. Classifications were performed by an ELM, and its efficiency was compared to linear and nonlinear (radial basis function) SVMs.ResultsThe maximal accuracies achieved by the method in the ADNI2 cohort were 98.86% (p<0.001) and 98.57% (p<0.001) for AD and MCI vs. CN, respectively. In the in-house cohort, the same accuracies were 98.70% (p<0.001) and 94.16% (p<0.001).ConclusionFrom a clinical perspective, combining extreme learning machine and hybrid MVPA applied on concatenations of multiple rs-fMRI biomarkers can potentially assist the clinicians in AD and MCI diagnosis.
Highlights
Alzheimer’s disease (AD) is the most common neurodegenerative disease and is the main cause of 60% to 70% of dementia cases in aging societies
The maximal accuracies achieved by the method in the ADNI2 cohort were 98.86% (p
From a clinical perspective, combining extreme learning machine and hybrid multivariate pattern analysis (MVPA) applied on concatenations of multiple resting-state functional magnetic resonance imaging (rs-fMRI) biomarkers can potentially assist the clinicians in AD and MCI diagnosis
Summary
Alzheimer’s disease (AD) is the most common neurodegenerative disease and is the main cause of 60% to 70% of dementia cases in aging societies. Statistical analysis is performed on all voxels to show regions whose BOLD signal shows significant effects This approach is referred to as univariate t-test analysis, which is performed independently on each voxel, and has been used in neuroimaging research for decades [5, 6]. This approach can only show differences between group averages, and is not sufficient to diagnose individual subjects. Recently, a machine learning (ML) technique known as multivariate pattern analysis (MVPA) has been promisingly applied to classify individual subjects using neuroimaging scans [7, 8]. We propose a voxel-wise discriminative framework applied to multi-measure resting-state fMRI (rs-fMRI) that integrates hybrid MVPA and extreme learning machine (ELM) for the automated discrimination of AD and MCI from the cognitive normal (CN) state
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