Test-Retest Reliability of Resting Brain Small-World Network Properties across Different Data Processing and Modeling Strategies

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Wu, Qianying;Lei, Hui;Mao, Tianxin;Deng, Yao;Zhang, Xiaocui;...

通讯作者：

Rao, HY;Liu, JH

作者机构：

[Mao, Tianxin; Rao, Hengyi; Deng, Yao; Wu, Qianying] Shanghai Int Studies Univ, Sch Business & Management, Key Lab Brain Machine Intelligence Informat Behav, Minist Educ & Shanghai, Shanghai 201613, Peoples R China.

[Zhang, Xiaocui; Mao, Tianxin; Rao, Hengyi; Zhong, Xue; Deng, Yao; Jiang, Yali; Wu, Qianying; Lei, Hui; Detre, John A.] Univ Penn, Dept Neurol, Philadelphia, PA 19104 USA.

[Wu, Qianying] Univ Sci & Technol China, Sch Life Sci, Hefei 230026, Peoples R China.

[Lei, Hui] Hunan Agr Univ, Coll Educ, Changsha 410127, Peoples R China.

[Zhang, Xiaocui; Zhong, Xue; Jiang, Yali] Cent South Univ, Xiangya Hosp 2, Med Psychol Ctr, Changsha 410017, Peoples R China.

通讯机构：

[Rao, HY ] S

[Liu, JH ] U

Shanghai Int Studies Univ, Sch Business & Management, Key Lab Brain Machine Intelligence Informat Behav, Minist Educ & Shanghai, Shanghai 201613, Peoples R China.

Univ Penn, Dept Neurol, Philadelphia, PA 19104 USA.

Univ Penn, Sch Nursing, Dept Family & Community Hlth, Philadelphia, PA 19104 USA.

语种：

英文

关键词：

resting-state fMRI;test-retest reliability;graph theoretical modeling;small-world network (SWN);intra-class correlation coefficient (ICC)

期刊：

Brain Sciences

ISSN：

2076-3425

年：

2023

卷：

期：

页码：

825-

DOI：

10.3390/brainsci13050825

基金类别：

This research was supported in part by the National Natural Science Foundation of China (71942003), National Institutes of Health grants (R01-HL102119, R21-AG051981), and Shanghai International Studies University Research Projects (20171140020). The funders had no role in the study design, data collection and analysis, data interpretation, writing of the manuscript, or the decision to submit the article for publication.

机构署名：

本校为其他机构

院系归属：

教育学院

摘要：

Resting-state functional magnetic resonance imaging (fMRI) with graph theoretical modeling has been increasingly applied for assessing whole brain network topological organization, yet its reproducibility remains controversial. In this study, we acquired three repeated resting-state fMRI scans from 16 healthy controls during a strictly controlled in-laboratory study and examined the test-retest reliability of seven global and three nodal brain network metrics using different data processing and modeling strategies. Among the global network metrics, the characteristic path length exhibited the ...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

Test-Retest Reliability of Resting Brain Small-World Network Properties across Different Data Processing and Modeling Strategies

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问