title: A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging.
abstract: Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer's disease (AD). Non-invasive diagnostic methods are desirable to identify MCI for early therapeutic interventions. In this study, we proposed a support vector machine (SVM)-based method to discriminate between MCI patients and normal controls (NCs) using multi-level characteristics of magnetic resonance imaging (MRI). This method adopted a radial basis function (RBF) as the kernel function, and a grid search method to optimize the two parameters of SVM. The calculated characteristics, i.e., the Hurst exponent (HE), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and gray matter density (GMD), were adopted as the classification features. A leave-one-out cross-validation (LOOCV) was used to evaluate the classification performance of the method. Applying the proposed method to the experimental data from 29 MCI patients and 33 healthy subjects, we achieved a classification accuracy of up to 96.77%, with a sensitivity of 93.10% and a specificity of 100%, and the area under the curve (AUC) yielded up to 0.97. Furthermore, the most discriminative features for classification were found to predominantly involve default-mode regions, such as hippocampus (HIP), parahippocampal gyrus (PHG), posterior cingulate gyrus (PCG) and middle frontal gyrus (MFG), and subcortical regions such as lentiform nucleus (LN) and amygdala (AMYG). Therefore, our method is promising in distinguishing MCI patients from NCs and may be useful for the diagnosis of MCI.
authors.affiliation: School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
authors.forename: Zhuqing
authors.initials: Z
authors.lastname: Long
authors.affiliation: School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Jing
authors.affiliation: School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
authors.forename: Huagang
authors.initials: H
authors.lastname: Yan
authors.affiliation: School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
authors.forename: Jianxin
authors.initials: J
authors.lastname: Dong
authors.affiliation: School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
authors.forename: Han
authors.initials: H
authors.lastname: Liu
authors.affiliation: School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
authors.forename: Xiao
authors.initials: X
authors.lastname: Mo
authors.affiliation: Department of Neurology, Xuan Wu Hospital of Capital Medical University, Beijing 100053, China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing 10053, China. Electronic address: 13621011941@163.com.
authors.forename: Ying
authors.initials: Y
authors.lastname: Han
authors.affiliation: School of Biomedical Engineering, Capital Medical University, Beijing 100069, China. Electronic address: haiyunli@ccmu.edu.cn.
authors.forename: Haiyun
authors.initials: H
authors.lastname: Li
mesh_terms: D000368:Aged; D019540:Area Under Curve; D001921:Brain / Q000000981:diagnostic imaging*; D060825:Cognitive Dysfunction / Q000145:classification* / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008609:Mental Status Schedule; D008875:Middle Aged; D012372:ROC Curve; D060388:Support Vector Machine
keywords: Hurst exponent; magnetic resonance imaging; mild cognitive impairment; support vector machine