Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity

doi:10.1016/j.gpb.2019.09.002

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity
	Liu, Zhenyu 2,10; Liu, Jiangang 3,11; Yuan, Huijuan 4,5; Liu, Taiyuan 5,6; Cui, Xingwei 7,8; Tang, Zhenchao 2,9; Du, Yang 2; Wang, Meiyun 5,6; Lin, Yusong 7,8; Tian, Jie 1,2,10,11
刊名	GENOMICS PROTEOMICS & BIOINFORMATICS
	2019-08-01
卷号	17 期号:4 页码:441-452
关键词	Type 2 diabetes mellitus Resting state functional connectivity Elastic net Support vector machines MoCA
ISSN号	1672-0229
DOI	10.1016/j.gpb.2019.09.002
通讯作者	Wang, Meiyun(mywang@ha.edu.cn) ; Lin, Yusong(yslin@ha.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Majority of type 2 diabetes mellitus (T2DM) patients are highly susceptible to several forms of cognitive impairments, particularly dementia. However, the underlying neural mechanism of these cognitive impairments remains unclear. We aimed to investigate the correlation between whole brain resting state functional connections (RSFCs) and the cognitive status in 95 patients with T2DM. We constructed an elastic net model to estimate the Montreal Cognitive Assessment (MoCA) scores, which served as an index of the cognitive status of the patients, and to select the RSFCs for further prediction. Subsequently, we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected RSFCs. The estimated and chronological MoCA scores were significantly correlated with R = 0.81 and the mean absolute error (MAE) = 1.20. Additionally, cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54% and the area under the receiver operating characteristic (ROC) curve (AUC) of 0.9737. This connectivity pattern not only included the connections between regions within the default mode network (DMN), but also the functional connectivity between the task-positive networks and the DMN, as well as those within the task-positive networks. The results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.
资助项目	National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[61673051] ; National Natural Science Foundation of China[81641168] ; National Natural Science Foundation of China[31470047] ; National Natural Science Foundation of China[81271565] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61231004] ; National Key R&D Program of China[2017YFA0205200] ; Youth Innovation Promotion Association, Chinese Academy of Sciences[2019136]
WOS关键词	ALZHEIMERS-DISEASE ; INSULIN-RESISTANCE ; NETWORK ; REGULARIZATION ; IMPAIRMENT ; DISORDERS ; PATTERNS ; MRI
WOS研究方向	Genetics & Heredity
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000505053300010
资助机构	National Natural Science Foundation of China ; National Key R&D Program of China ; Youth Innovation Promotion Association, Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/29445]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Wang, Meiyun; Lin, Yusong; Tian, Jie
作者单位	1.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian 710126, Shaanxi, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 3.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China 4.Zhengzhou Univ, Dept Endocrinol & Metab, Henan Prov Peoples Hosp, Zhengzhou 450003, Henan, Peoples R China 5.Zhengzhou Univ, Peoples Hosp, Zhengzhou 450003, Henan, Peoples R China 6.Zhengzhou Univ, Dept Radiol, Henan Prov Peoples Hosp, Zhengzhou 450003, Henan, Peoples R China 7.Zhengzhou Univ, Cooperat Innovat Ctr Internet Healthcare, Zhengzhou 450003, Henan, Peoples R China 8.Zhengzhou Univ, Sch Software, Zhengzhou 450003, Henan, Peoples R China 9.Shandong Univ Weihai, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China 10.Univ Chinese Acad Sci, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Liu, Zhenyu,Liu, Jiangang,Yuan, Huijuan,et al. Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity[J]. GENOMICS PROTEOMICS & BIOINFORMATICS,2019,17(4):441-452.
APA	Liu, Zhenyu.,Liu, Jiangang.,Yuan, Huijuan.,Liu, Taiyuan.,Cui, Xingwei.,...&Tian, Jie.(2019).Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity.GENOMICS PROTEOMICS & BIOINFORMATICS,17(4),441-452.
MLA	Liu, Zhenyu,et al."Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity".GENOMICS PROTEOMICS & BIOINFORMATICS 17.4(2019):441-452.