Identifying the default mode network structure using dynamic causal modeling on resting-state fMRI
Identifying the default mode network structure using dynamic causal modeling on resting-state fMRI
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DOI：
10.7490/f1000research.1093584.1
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
X. Di;B. Biswal
中科院分区：
文献类型：
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作者：
X. Di;B. Biswal
•分析了来自1000个功能连接体项目的64名受试者的静息状态数据（http://fcon_1000.projects.nitrc.org/）（21名男性，平均年龄21.2岁）5。•每个受试者获得230张图像，TR为2s。扫描参数可以在项目网站上找到。•使用SPM8进行预处理，包括运动校正，通过对解剖图像进行归一化对功能图像进行空间归一化，以及使用8mm高斯核进行空间平滑。•使用GIFT6进行空间独立成分分析来定义DMN。利用傅立叶级数建模低频波动
• Resting-state data of 64 subjects from the 1000 Functional Connectomes Project was analyzed (http://fcon_1000.projects.nitrc.org/) (21 male, mean age 21.2 years)5. • 230 images were acquired for each subject using a TR of 2s. Scanning parameters can be found in the project website. • Preprocessing using SPM8 included motion correction, spatial normalization of the functional images via normalizing the anatomical image, and spatial smoothing using an 8mm Gaussian kernel. • Spatial independent component analysis was conducted to define the DMN using GIFT6. Modeling low-frequency fluctuations using Fourier Series