Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques.
Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques.
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DOI：
10.1007/s10439-018-2022-x
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发表时间：
2018-07
影响因子：
3.8
通讯作者：
Winkelstein BA
中科院分区：
文献类型：
--
作者：
Sperry MM;Kartha S;Granquist EJ;Winkelstein BA
主体间网络用于模拟脑区域之间的相关性，对代谢成像技术特别有用，如18F-2-脱氧-2-(18F)氟-d -葡萄糖（FDG）正电子发射断层扫描（PET）。由于FDG PET通常产生单一图像，因此无法计算随时间的相关性。很少有人关注主体间网络的基本属性，以及它们是否受到群体大小和图像规范化的影响。从大鼠（n=18）获得FDG PET图像，通过全脑、视觉皮层或小脑FDG摄取进行归一化，并用于构建相关矩阵。通过系统地添加大鼠和评估局部网络连通性（节点强度和聚类系数）来研究群体大小对网络稳定性的影响。模块化和社区结构也在不同的标准化网络中进行评估，以评估中尺度网络关系。对于至少有10个组的组，无论归一化区域是什么，本地网络属性都是稳定的。全脑归一化网络比视觉皮层或小脑归一化网络更具模块化（p<0.00001）；然而，在大脑网络和随机网络的模块化差异最大的网络分辨率上，社区结构是相似的。层次分析揭示了不同尺度和空间邻近脑区聚类的一致模块。研究结果表明，受试者间FDG PET网络在合理的群体规模下是稳定的，并表现出多尺度模块化。
Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n=18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p<0.00001); however, community structure is similar at network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.
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影响因子：
5.7
作者：
Gonçalves, SI;de Munck, JC;da Silva, FHL
通讯作者：
da Silva, FHL
影响因子：
4.3
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4.2
作者：
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通讯作者：
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影响因子：
3.7
作者：
Chen, Zhang J.;He, Yong;Evans, Alan C.
通讯作者：
Evans, Alan C.