Knowledge Commons of Institute of Automation,CAS
Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study | |
Rixing Jing1; Pindong Chen2,3; Yongbin Wei4; Juanning Si1; Yuying Zhou5; Dawei Wang6; Chengyuan Song7; Hongwei Yang8; Zengqiang Zhang9; Hongxiang Yao10; Xiaopeng Kang2,3; Lingzhong Fan2; Tong Han11; Wen Qin12; Bo Zhou13; Tianzi Jiang2,3; Jie Lu8; Ying Han14,15,16; Xi Zhang13; Bing Liu17; Chunshui Yu12; Pan Wang5; Yong Liu2,3,4 | |
发表期刊 | Huamn Brian Mapping |
2023 | |
卷号 | 44期号:9页码:3467-3480 |
摘要 | Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD. |
七大方向——子方向分类 | 脑网络分析 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/58529 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
作者单位 | 1.School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China 2.Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 4.School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China 5.Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China 6.Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China 7.Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China 8.Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China 9.Branch of Chinese PLA General Hospital, Sanya, China 10.Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China 11.Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China 12.Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China 13.Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China 14.Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China 15.Beijing Institute of Geriatrics, Beijing, China 16.National Clinical Research Center for Geriatric Disorders, Beijing, China 17.State Key Laboratory of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, China |
推荐引用方式 GB/T 7714 | Rixing Jing,Pindong Chen,Yongbin Wei,et al. Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study[J]. Huamn Brian Mapping,2023,44(9):3467-3480. |
APA | Rixing Jing.,Pindong Chen.,Yongbin Wei.,Juanning Si.,Yuying Zhou.,...&Yong Liu.(2023).Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study.Huamn Brian Mapping,44(9),3467-3480. |
MLA | Rixing Jing,et al."Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study".Huamn Brian Mapping 44.9(2023):3467-3480. |
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