机构：[1]Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China,神经内科[2]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China,[3]Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China,[4]Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China,[5]Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany,[6]Department of Psychology, University of Chinese Academy of Sciences, Beijing, China,[7]CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China,[8]Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States,[9]Department of Electronic and Information Engineering, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China,[10]Department of Radiology, XuanWu Hospital of Capital Medical University, Beijing, China,放射科[11]Beijing Institute of Geriatrics, XuanWu Hospital of Capital Medical University, Beijing, China,老年医学科[12]National Clinical Research Center for Geriatric Disorders, Beijing, China,[13]Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China
Background: Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention of Alzheimer's disease (AD). Previous brain network studies have suggested topological disruptions of the brain connectome in aMCI patients. However, whether brain connectome markers at baseline can predict longitudinal conversion from aMCI to AD remains largely unknown. Methods: In this study, 52 patients with aMCI and 26 demographically matched healthy controls from a longitudinal cohort were evaluated. During 2 years of follow-up, 26 patients with aMCI were retrospectively classified as aMCI converters and 26 patients remained stable as aMCI non-converters based on whether they were subsequently diagnosed with AD. For each participant, diffusion tensor imaging at baseline and deterministic tractography were used to map the whole-brain white matter structural connectome. Graph theoretical analysis was applied to investigate the convergent and divergent connectivity patterns of structural connectome between aMCI converters and non-converters. Results: Disrupted topological organization of the brain structural connectome were identified in both aMCI converters and non-converters. More severe disruptions of structural connectivity in aMCI converters compared with non-converters were found, especially in the default-mode network regions and connections. Finally, a support vector machine-based classification demonstrated the good discriminative ability of structural connectivity in differentiating aMCI patients from controls with an accuracy of 98%, and in discriminating converters from non-converters with an accuracy of 81%. Conclusion: Our study provides potential structural connectome/connectivity-based biomarkers for predicting disease progression in aMCI, which is important for the early diagnosis of AD.
第一作者机构：[1]Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China,
共同第一作者：
通讯作者：
通讯机构：[1]Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China,[2]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China,[3]Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China,[4]Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China,[11]Beijing Institute of Geriatrics, XuanWu Hospital of Capital Medical University, Beijing, China,[12]National Clinical Research Center for Geriatric Disorders, Beijing, China,[13]Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China
推荐引用方式(GB/T 7714)：
Yu Sun,Qiuhui Bi,Xiaoni Wang,et al.Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome[J].FRONTIERS IN NEUROLOGY.2019,9(JAN):1178.doi:10.3389/fneur.2018.01178.
APA：
Yu Sun,Qiuhui Bi,Xiaoni Wang,Xiaochen Hu,Huijie Li...&Ying Han.(2019).Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome.FRONTIERS IN NEUROLOGY,9,(JAN)
MLA：
Yu Sun,et al."Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome".FRONTIERS IN NEUROLOGY 9..JAN(2019):1178
