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Shorter TR combined with finer atlas positively modulate topological organization of brain network: A resting state fMRI study

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机构: [1]China Jiliang Univ, Coll Opt & Elect Technol, Hangzhou, Peoples R China [2]Fudan Univ, Peoples Hosp Shanghai 5, Dept Imaging, Shanghai, Peoples R China [3]Zhejiang Univ Sch Med, Affiliated Hosp 2, MR Dept, Hangzhou, Peoples R China [4]Zhejiang Univ Technol, Coll Sci, Ctr Opt & Optoelect Res, Hangzhou, Peoples R China [5]Siemens Healthcare China, MR Collaborat, Beijing, Peoples R China [6]Capital Med Univ, Xuanwu Hosp, Dept Radiol, Beijing, Peoples R China [7]Beijing Key Lab MRI & Brain Informat, Beijing, Peoples R China [8]Capital Normal Univ, Sch Psychol, Beijing, Peoples R China
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关键词: fMRI brain network construction finer atlas shorter TR multi-spectrum

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Background: The use of shorter TR and finer atlases in rs-fMRI can provide greater detail on brain function and anatomy. However, there is limited understanding of the effect of this combination on brain network properties. Methods: A study was conducted with 20 healthy young volunteers who underwent rs-fMRI scans with both shorter (0.5s) and long (2s) TR. Two atlases with different degrees of granularity (90 vs 200 regions) were used to extract rs-fMRI signals. Several network metrics, including small-worldness, Cp, Lp, Eloc, and Eg, were calculated. Two-factor ANOVA and two-sample t-tests were conducted for both the single spectrum and five sub-frequency bands. Results: The network constructed using the combination of shorter TR and finer atlas showed significant enhancements in Cp, Eloc, and Eg, as well as reductions in Lp and gamma in both the single spectrum and subspectrum (p < 0.05, Bonferroni correction). Network properties in the 0.082-0.1 Hz frequency range were weaker than those in the 0.01-0.082 Hz range. Conclusion: Our findings suggest that the use of shorter TR and finer atlas can positively affect the topological characteristics of brain networks. These insights can inform the development of brain network construction methods.

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出版当年[2022]版:
大类 | 4 区 计算机科学
小类 | 4 区 神经科学 4 区 工程:电子与电气 4 区 计算机:人工智能
最新[2023]版:
大类 | 3 区 计算机科学
小类 | 3 区 工程:电子与电气 4 区 计算机:人工智能 4 区 神经科学
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出版当年[2021]版:
Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Q4 NEUROSCIENCES
最新[2023]版:
Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Q4 NEUROSCIENCES

影响因子: 最新[2023版] 最新五年平均 出版当年[2021版] 出版当年五年平均 出版前一年[2020版] 出版后一年[2022版]

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第一作者机构: [1]China Jiliang Univ, Coll Opt & Elect Technol, Hangzhou, Peoples R China
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通讯机构: [2]Fudan Univ, Peoples Hosp Shanghai 5, Dept Imaging, Shanghai, Peoples R China [8]Capital Normal Univ, Sch Psychol, Beijing, Peoples R China [*1]Fudan Univ, Peoples Hosp Shanghai 5, Dept Imaging, 128 Ruili Rd, Shanghai, Peoples R China [*2]School of Psychology, Capital Normal University, Beijing, China
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