Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands.
Neuroimage. 2011 Mar 15;55(2):557-65. doi: 10.1016/j.neuroimage.2010.12.012. Epub 2010 Dec 13.
Diffusion MRI can be used to study the structural connectivity within the brain. Brain connectivity is often represented by a binary network whose topology can be studied using graph theory. We present a framework for the construction of weighted structural brain networks, containing information about connectivity, which can be effectively analyzed using statistical methods. Network nodes are defined by segmentation of subcortical structures and by cortical parcellation. Connectivity is established using a minimum cost path (mcp) method with an anisotropic local cost function based directly on diffusion weighted images. We refer to this framework as Statistical Analysis of Minimum cost path based Structural Connectivity (SAMSCo) and the weighted structural connectivity networks as mcp-networks. In a proof of principle study we investigated the information contained in mcp-networks by predicting subject age based on the mcp-networks of a group of 974 middle-aged and elderly subjects. Using SAMSCo, age was predicted with an average error of 3.7 years. This was significantly better than predictions based on fractional anisotropy or mean diffusivity averaged over the whole white matter or over the corpus callosum, which showed average prediction errors of at least 4.8 years. Additionally, we classified subjects, based on the mcp-networks, into groups with low and high white matter lesion load, while correcting for age, sex and white matter atrophy. The SAMSCo classification outperformed the classification based on the diffusion measures with a classification accuracy of 76.0% versus 63.2%. We also performed a classification in groups with mild and severe atrophy, correcting for age, sex and white matter lesion load. In this case, mcp-networks and diffusion measures yielded similar classification accuracies of 68.3% and 67.8% respectively. The SAMSCo prediction and classification experiments indicate that the mcp-networks contain information regarding age, white matter lesion load and white matter atrophy, and that in case of age and white matter lesion load the mcp-network based models outperformed the predictions based on diffusion measures.
扩散磁共振成像可用于研究大脑内的结构连通性。脑连通性通常通过二值网络表示，其拓扑结构可以使用图论进行研究。我们提出了一种构建包含连通性信息的加权结构脑网络的框架，该框架可以使用统计方法进行有效分析。网络节点由亚皮质结构的分割和皮质分区定义。使用基于扩散加权图像的各向异性局部成本函数的最小成本路径 (mcp) 方法建立连接。我们将此框架称为基于最小成本路径的统计分析结构连通性 (SAMSCo)，并将加权结构连通性网络称为 mcp 网络。在一项原理验证研究中，我们通过基于一组 974 名中老年人的 mcp 网络预测受试者年龄，研究了 mcp 网络中包含的信息。使用 SAMSCo，年龄的平均预测误差为 3.7 年。这明显优于基于整个白质或胼胝体的平均各向异性分数或平均弥散度的预测，其平均预测误差至少为 4.8 年。此外，我们基于 mcp 网络，在考虑年龄、性别和白质萎缩的情况下，将受试者分类为低白质病变负荷和高白质病变负荷组。SAMSCo 分类的准确率为 76.0%，优于基于扩散测量的分类，后者的准确率为 63.2%。我们还在考虑年龄、性别和白质病变负荷的情况下，在轻度和重度萎缩的组中进行了分类。在这种情况下，mcp 网络和扩散测量的分类准确率分别为 68.3%和 67.8%。SAMSCo 的预测和分类实验表明，mcp 网络包含与年龄、白质病变负荷和白质萎缩有关的信息，并且在年龄和白质病变负荷的情况下，基于 mcp 网络的模型优于基于扩散测量的预测。
