Combined Support Vector Machine Classifier and Brain Structural Network Features for the Individual Classification of Amnestic Mild Cognitive Impairment and Subjective Cognitive Decline Patients
机构：[1]State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China,[2]Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China,[3]Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China,[4]Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China,神经科系统神经内科首都医科大学宣武医院[5]Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China,首都医科大学附属北京友谊医院[6]School of Electrical Engineering, Yanshan University, Qinhuangdao, China,[7]Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, China,[8]Beijing University of Posts and Telecommunications, Beijing, China,[9]Biomedical Engineering Institute, Hainan University, Haikou, China,[10]Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China,[11]National Clinical Research Center for Geriatric Disorders, Beijing, China
Objective Individuals with subjective cognitive decline (SCD) or amnestic mild cognitive impairment (aMCI) represent important targets for the early detection and intervention of Alzheimer's disease (AD). In this study, we employed a multi-kernel support vector machine (SVM) to examine whether white matter (WM) structural networks can be used for screening SCD and aMCI. Methods A total of 138 right-handed participants [51 normal controls (NC), 36 SCD, 51 aMCI] underwent MRI brain scans. For each participant, three types of WM networks with different edge weights were constructed with diffusion MRI data: fiber number-weighted networks, mean fractional anisotropy-weighted networks, and mean diffusivity (MD)-weighted networks. By employing a multiple-kernel SVM, we seek to integrate information from three weighted networks to improve classification performance. The accuracy of classification between each pair of groups was evaluated via leave-one-out cross-validation. Results For the discrimination between SCD and NC, an area under the curve (AUC) value of 0.89 was obtained, with an accuracy of 83.9%. Further analysis revealed that the methods using three types of WM networks outperformed other methods using single WM network. Moreover, we found that most of discriminative features were from MD-weighted networks, which distributed among frontal lobes. Similar classification performance was also reported in the differentiation between subjects with aMCI and NCs (accuracy = 83.3%). Between SCD and aMCI, an AUC value of 0.72 was obtained, with an accuracy of 72.4%, sensitivity of 74.5% and specificity of 69.4%. The highest accuracy was achieved with features only selected from MD-weighted networks. Conclusion White matter structural network features help machine learning algorithms accurately identify individuals with SCD and aMCI from NCs. Our findings have significant implications for the development of potential brain imaging markers for the early detection of AD.
基金：
This work was supported by the National Natural Science
Foundation of China (Grant Numbers 81671761, 81871425,
61633018, and 82020108031) and Hebei Provincial Natural
Science Foundation, China (Grant Number F2019203515). The
preliminary results were published in the conference abstract
of Alzheimer’s Association International Conferences (AAIC)
(Li et al., 2018).
第一作者机构：[1]State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China,[2]Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China,[3]Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China,
共同第一作者：
通讯作者：
通讯机构：[1]State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China,[2]Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China,[3]Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China,[4]Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China,[9]Biomedical Engineering Institute, Hainan University, Haikou, China,[10]Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China,[11]National Clinical Research Center for Geriatric Disorders, Beijing, China
推荐引用方式(GB/T 7714)：
Weijie Huang,Xuanyu Li,Xin Li,et al.Combined Support Vector Machine Classifier and Brain Structural Network Features for the Individual Classification of Amnestic Mild Cognitive Impairment and Subjective Cognitive Decline Patients[J].FRONTIERS IN AGING NEUROSCIENCE.2021,13:doi:10.3389/fnagi.2021.687927.
APA：
Weijie Huang,Xuanyu Li,Xin Li,Guixia Kang,Ying Han&Ni Shu.(2021).Combined Support Vector Machine Classifier and Brain Structural Network Features for the Individual Classification of Amnestic Mild Cognitive Impairment and Subjective Cognitive Decline Patients.FRONTIERS IN AGING NEUROSCIENCE,13,
MLA：
Weijie Huang,et al."Combined Support Vector Machine Classifier and Brain Structural Network Features for the Individual Classification of Amnestic Mild Cognitive Impairment and Subjective Cognitive Decline Patients".FRONTIERS IN AGING NEUROSCIENCE 13.(2021)
