较短的TR与较精细的图谱相结合正调节脑网络的拓扑组织:静息状态fMRI研究。

IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Network-Computation in Neural Systems Pub Date : 2023-02-01 Epub Date: 2023-05-22 DOI:10.1080/0954898X.2023.2215860
Yan Zhang, Qili Hu, Jiali Liang, Zhenghui Hu, Tianyi Qian, Kuncheng Li, Xiaohu Zhao, Peipeng Liang
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引用次数: 0

摘要

背景:在rs功能磁共振成像中使用更短的TR和更精细的图谱可以提供更多关于大脑功能和解剖结构的细节。然而,人们对这种组合对大脑网络特性的影响了解有限。方法:对20名健康的年轻志愿者进行了一项研究,他们接受了短(0.5s)和长(2s)TR的rs-fMRI扫描。使用两个不同粒度(90和200个区域)的图谱来提取rs-fMR信号。计算了几个网络指标,包括小世界度、Cp、Lp、Eloc和Eg。对单谱和五个子频带进行了双因素方差分析和双样本t检验。结果:使用较短的TR和较精细的图谱组合构建的网络显示出Cp、Eloc和Eg的显著增强,以及单谱和子谱中Lp和γ的降低(p 结论:我们的研究结果表明,使用更短的TR和更精细的图谱可以积极影响脑网络的拓扑特征。这些见解可以为大脑网络构建方法的发展提供信息。
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Shorter TR combined with finer atlas positively modulate topological organization of brain network: A resting state fMRI study.

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 γ 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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来源期刊
Network-Computation in Neural Systems
Network-Computation in Neural Systems 工程技术-工程:电子与电气
CiteScore
3.70
自引率
1.30%
发文量
22
审稿时长
>12 weeks
期刊介绍: Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.
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