论文标题
人类结构连接组的网络分析，包括脑干
Network analysis of the human structural connectome including the brainstem
论文作者
论文摘要
潜在的解剖结构是对脑网络研究的基础，但是从结构的角度来看，脑干的作用不是很好。我们对人类结构连接组进行了计算和图理论研究，其中包含包括脑干在内的各种皮层结构。我们的计算方案涉及使用Python Dipy和Nibabel库使用100名健康的成人受试者开发结构连接组。然后，我们计算学位，特征向量和中心中心识别几个高度连接的结构，发现脑干在所有检查的指标中排名最高，即使连通性矩阵通过体积归一化，这一结果也会成立。我们还研究了连接组中的一些全球拓扑特征，例如整合和隔离的平衡，发现脑干的统治通常会导致网络变得较少集成和隔离。我们的结果强调了将脑干纳入结构网络分析的重要性。
The underlying anatomical structure is fundamental to the study of brain networks, but the role of brainstem from a structural perspective is not very well understood. We conduct a computational and graph-theoretical study of the human structural connectome incorporating a variety of subcortical structures including the brainstem. Our computational scheme involves the use of Python DIPY and Nibabel libraries to develop structural connectomes using 100 healthy adult subjects. We then compute degree, eigenvector, and betweenness centralities to identify several highly connected structures and find that the brainstem ranks highest across all examined metrics, a result that holds even when the connectivity matrix is normalized by volume. We also investigated some global topological features in the connectomes, such as the balance of integration and segregation, and found that the domination of the brainstem generally causes networks to become less integrated and segregated. Our results highlight the importance of including the brainstem in structural network analyses.
