Functional Brain Network Measures for Alzheimer’s Disease Classification

Abstract

Background: Alzheimer’s disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. The therapy of AD depends heavily on an early diagnosis. In this study, our primary objective is to evaluate the classification framework, which combines graph theory and machine learning techniques for functional magnetic resonance imaging (fMRI), to distinguish AD, early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and healthy control (HC). Methods: A novel multi-feature selection method, incorporating the dual graph theoretical approach, is proposed for classification. This method utilizes three different feature selection methods after brain areas selection through graph-theory analyses in 96 subjects with brain parcellation by using the joint human connectome project multimodal parcellation (J-HCPMMP) of 180 areas per hemisphere. Results: The classification results show that the optimal features selected by the minimal redundancy maximal relevance (MRMR) based on support vector machine linear (SVM-linear) from graph measures for 36 areas of 360 areas. The classification accuracies for identifying HC vs. EMCI, HC vs. LMCI, HC vs. AD, EMCI vs. LMCI, LMCI vs. AD, and EMCI vs. AD, are 85.60%, 92.90%, 96.80%, 83.30%, 84.90% and 89.50%, respectively. Conclusion: The results indicate that the combination of graph measures and machine learning in fMRI connectivity analysis might be helpful for the diagnosis of AD, especially the use of local measures, which may better reflect functional changes in local brain regions because of cognitive impairment.

Keywords

• Alzheimer’s disease
• functional brain network measures
• feature selection
• classification
• fMRI