Abstract
Functional brain connectivity networks obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been extensively utilized for the diagnosis of Alzheimer’s disease (AD). However, the traditional correlation analysis technique only explores the pairwise relation, which may not be suitable for revealing sufficient and proper functional connectivity links among brain regions. Additionally, previous literature typically focuses on only lower-order dynamics, without considering higher-order dynamic networks properties, and they particularly focus on single frequency range time series of rs-fMRI. To solve these problems, in this article, a new diagnosis scheme is proposed by constructing a high-order dynamic functional network at different frequency level time series (full-band (0.01–0.08 Hz); slow-4 (0.027–0.08 Hz); and slow-5 (0.01–0.027 Hz)) data obtained from rs-fMRI to build the functional brain network for all brain regions. Especially, to tune the precise analysis of the regularized parameters in the Support Vector Machine (SVM), a nested leave-one-out cross-validation (LOOCV) technique is adopted. Finally, the SVM classifier is trained to classify AD from HC based on these higher-order dynamic functional brain networks at different frequency ranges. The experiment results illustrate that for all bands with a LOOCV classification accuracy of 94.10% with a 90.95% of sensitivity, and a 96.75% of specificity outperforms the individual networks. Utilization of the given technique for the identification of AD from HC compete for the most state-of-the-art technology in terms of the diagnosis accuracy. Additionally, results obtained for the all-band shows performance further suggest that our proposed scheme has a high-rate accuracy. These results have validated the effectiveness of the proposed methods for clinical value to the identification of AD.
Highlights
Alzheimer’s disease (AD) is an inevitable, neuronal disorder progressively appearing in older age and slowly altering the brain tissue that is subjected to memory, thinking, learning, and behavioral pattern
We referred to HC as negative samples, patients with AD as positive samples, TN represents the number of negative sample sets that are correctly classified, total positive (TP) denotes the number of positive samples correctly categorized, false positive (FP) denotes the portion of the negative dataset classified as positive, and false-negative (FN) denotes the number of positive datasets classified as negative samples
First, we examined high-order dynamic functional networks measure at different frequency band using resting-state functional magnetic resonance imaging (rs-functional MRI (fMRI)) obtained by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) core laboratory biomarkers
Summary
Alzheimer’s disease (AD) is an inevitable, neuronal disorder progressively appearing in older age and slowly altering the brain tissue that is subjected to memory, thinking, learning, and behavioral pattern. The functional MRI (fMRI) [4] has been extensively used in the brain research area. FMRI is useful to generate and analyze hemodynamic alterations and is useful to record the real-time brain functions employing blood oxygenation level dependence (BOLD) [5]. Network analysis techniques are largely utilized in the early detection of brain diseases [7–9]. Several studies have demonstrated that the brain network features and machine learning technique with fMRI yield useful information for the accurate diagnosis of Alzheimer’s. Khazaee et al [10] utilized time series to obtained brain functional connectivity, and linear SVM classifiers to diagnose AD and achieved 100% diagnosis accuracy; this may be caused by the limited number of experimental data. Functional response of AD individuals shows a noticeable difference in the hippocampus, medial prefrontal, and posterior cingulate regions in the slow-4 and slow-5 frequency bands, and the better diagnostic accuracy was obtained through the division of the BOLD frequency [17]
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