Dimitriadis Stavros I, Drakesmith Mark, Bells Sonya, Parker Greg D, Linden David E, Jones Derek K
Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom.
Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.
Front Neurosci. 2017 Dec 19;11:694. doi: 10.3389/fnins.2017.00694. eCollection 2017.
Structural brain networks estimated from diffusion MRI (dMRI) via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS) can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI) from the Automated Anatomical Labeling (AAL) template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN). Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a) intra-class correlation coefficient (ICC) of well-known network metrics, both node-wise and per network level; and (b) the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node level. Importantly, both network and node-wise ICCs of network metrics derived from the topologically filtered ISBWN (ISWBN), were further improved compared to the non-filtered ISWBN. Finally, in the recognition accuracy tests, we assigned each single ISWBN to the correct subject. We also applied our methodology to a second dataset of diffusion-weighted MRI in healthy controls and individuals with psychotic experience. Following a binary classification scheme, the classification performance based on ISWBN outperformed the nine different weighting strategies and the ISWBN. Overall, these findings suggest that the proposed methodology results in improved characterization of genuine between-subject differences in connectivity leading to the possibility of network-based structural phenotyping.
通过纤维束成像从扩散磁共振成像（dMRI）估计的脑结构网络，已在健康对照者以及患有神经和精神疾病的患者中得到广泛研究。然而，很少有研究探讨特定节点和全网络衍生网络指标的可靠性。可以采用不同的网络加权策略（NWS）来权衡两个节点之间连接的强度，从而生成几乎完全加权的脑结构网络。在此，我们对五名健康参与者每人进行了五次扫描，使用扩散加权磁共振成像协议，并根据自动解剖标记（AAL）模板计算了90个感兴趣区域（ROI）之间的边。这些边根据九种不同方法进行加权。我们使用适当的扩散距离度量，将这九种NWS线性组合成一个单一图形。我们将得到的加权图形称为综合加权脑结构网络（ISWBN）。此外，我们考虑一种拓扑过滤方案，该方案在幸存连接的总成本约束下，使脑网络中的信息流最大化。我们基于以下方面的改进，比较了九种NWS中的每一种以及ISWBN：（a）著名网络指标的类内相关系数（ICC）（包括节点层面和全网络层面）；（b）在首先应用我们提出的拓扑过滤方案后，将每个受试者与队列中的其他受试者进行比较的识别准确率，以此来尝试获取每个受试者脑结构网络的独特性。基于网络层面ICC应>0.90的阈值，我们的研究结果表明，九种NWS中有六种在网络层面导致不可靠的结果，而所有九种NWS在节点层面都是不可靠的。相比之下，我们提出的ISWBN在网络层面的表现与表现最佳的单个NWS相当，并且在节点层面其ICC高于所有单个NWS。重要的是，与未过滤的ISWBN相比，从经过拓扑过滤的ISBWN（ISWBN）衍生的网络指标在网络和节点层面的ICC都进一步提高了。最后，在识别准确率测试中，我们将每个单独的ISWBN分配给了正确的受试者。我们还将我们的方法应用于健康对照者和有精神病体验者的第二个扩散加权磁共振成像数据集。按照二元分类方案，基于ISWBN的分类性能优于九种不同的加权策略和ISWBN。总体而言，这些发现表明，所提出的方法能够更好地表征个体间连接性的真实差异，从而实现基于网络的结构表型分析。
