Integrating temporal and spatial scales: human structural network motifs across age and region of interest size.
Integrating temporal and spatial scales: human structural network motifs across age and region of interest size.
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DOI：
10.3389/fninf.2011.00010
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发表时间：
2011
影响因子：
3.5
通讯作者：
Kaiser M
中科院分区：
文献类型：
--
作者：
Echtermeyer C;Han CE;Rotarska-Jagiela A;Mohr H;Uhlhaas PJ;Kaiser M
人脑网络可以在不同的时间或空间尺度上表征，这是由受试者的年龄或神经成像方法的空间分辨率决定的。只有当合并后的网络显示出相似的架构时，跨尺度的数据集成才能成功。比较网络的一种方法是查看空间特征（基于光纤长度）和单个节点的拓扑特征，其中异常节点形成单个节点图案，其频率产生网络的指纹。在这里，我们观察了健康人类扩散张量成像结构连接的特征单节点基序是如何随年龄（12-23岁）和网络大小（414、813和1615个节点）而变化的。首先，我们发现网络中基序的数量和多样性是强相关的。第二，对比不同尺度，母题的数量和多样性在时间尺度（被试年龄）和空间尺度（网络分辨率）上存在差异：某些母题可能只在一个空间尺度或特定年龄范围内出现。第三，以较低分辨率显示一个基序的感兴趣区域可能以较高分辨率显示一系列基序，这些基序可能包括或可能不包括较低分辨率的原始基序。因此，在不同的空间分辨率下，母题的类型和定位都是不同的。我们的研究结果还表明，空间分辨率对拓扑测量有更高的影响，而基于光纤长度的空间测量在分辨率之间仍然更具可比性。因此，在比较拓扑和空间网络特征给出的特征节点指纹时，空间分辨率至关重要。由于节点基序是基于大脑连接网络的拓扑和空间特性，这些结论也与其他使用连接组分析的研究相关。
Human brain networks can be characterized at different temporal or spatial scales given by the age of the subject or the spatial resolution of the neuroimaging method. Integration of data across scales can only be successful if the combined networks show a similar architecture. One way to compare networks is to look at spatial features, based on fiber length, and topological features of individual nodes where outlier nodes form single node motifs whose frequency yields a fingerprint of the network. Here, we observe how characteristic single node motifs change over age (12–23 years) and network size (414, 813, and 1615 nodes) for diffusion tensor imaging structural connectivity in healthy human subjects. First, we find the number and diversity of motifs in a network to be strongly correlated. Second, comparing different scales, the number and diversity of motifs varied across the temporal (subject age) and spatial (network resolution) scale: certain motifs might only occur at one spatial scale or for a certain age range. Third, regions of interest which show one motif at a lower resolution may show a range of motifs at a higher resolution which may or may not include the original motif at the lower resolution. Therefore, both the type and localization of motifs differ for different spatial resolutions. Our results also indicate that spatial resolution has a higher effect on topological measures whereas spatial measures, based on fiber lengths, remain more comparable between resolutions. Therefore, spatial resolution is crucial when comparing characteristic node fingerprints given by topological and spatial network features. As node motifs are based on topological and spatial properties of brain connectivity networks, these conclusions are also relevant to other studies using connectome analysis.