DTI脑连接组分析中分割模板的选择

由于脑网络性质会因网络构建方法不同(如脑区划分模板、弥散磁共振成像(magnetic resonance imaging,MRI)模型、纤维素追踪算法和网络加权方案等)而出现较大的差异,针对弥散张量成像(diffusion tensor imaging,DTI),基于临床研究中的典型设置,研究了不同分割模板对脑网络拓扑参数的影响.参与本研究的75例健康老年人均接受了相同的认知功能综合评估和同一3T磁共振采集系统的全脑扫描.结果显示全局拓扑属性对脑区划分的空间尺度非常敏感,而对脑区划分原则并不是特别敏感.脑网络的模块化结构相对较稳定,不易受到节点划分尺度和节点划分原则的影响.中等分辨率的模板在大脑老化分析中具有更高的敏感性,可能更适合于常规DTI脑网络分析.

Selection of Parcellation Atlas in DTI Brain Connectome Analysis

Analysis of the structural connectomes based on diffusion magnetic resonance imaging (MRI) can show network attributes for segregation and integration in the human brain. However, it is reported that there are large discrepancies from those network measures, which may result from the network construction methodology (such as brain parcellations, DWI acquisition model, tractography algorithms and network weighting schemes). The influence of parcellation atlas on the topological network measures of diffusion tensor imaging(DTI) network with the typical setting used in many clinical applications were evaluated in this paper. The data of research were acquired in 75 healthy older adults who underwent several neuropsychological tests and structural MRI. It shows that those network measures are very sensitive to the spatial scales of brain parcellation. When the scale is close,the principle of parcellation doesn't change those measures that much. The modularity of brain network is relatively stable on different kinds of brain parcellation. The mesoscale atlas,which shows higher sensitivity in ageing study,may be an optimal choice for typical DTI network analysis.