Nodal centrality of functional network in the differentiation of schizophrenia.
Nodal centrality of functional network in the differentiation of schizophrenia.
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DOI：
10.1016/j.schres.2015.08.011
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发表时间：
2015-10
影响因子：
4.5
通讯作者：
Hetrick WP
中科院分区：
文献类型：
--
作者：
Cheng H;Newman S;Goñi J;Kent JS;Howell J;Bolbecker A;Puce A;O'Donnell BF;Hetrick WP
在精神处理过程中，信息整合的障碍与精神分裂症有关，可能是由于大脑区域内部和之间的错误沟通。图论测量允许量化功能性大脑网络。功能网络是从大脑区域的时间进程之间的相关性推导出来的。SZ和对照组之间的功能网络属性的组差异已被报道，但这种措施的潜力，以分类个别情况下，一直很少探索。我们测试了介数中心性的网络测量是否可以对精神分裂症患者和正常对照者进行分类。基于静息态功能MRI扫描，为19例精神分裂症患者和29例非精神病对照者构建了功能网络。计算每个节点的介数中心性，或通过它的最短路径的分数，以表征不同区域的中心性。具有高介数中心性的节点与以前结构和功能网络研究中报道的枢纽节点吻合得很好。使用线性支持向量机算法，精神分裂症组与非精神病对照组的10个节点，最高介数中心区分。分类准确率约为80%，并且对连接阈值稳定。当使用秩作为特征空间而不是介数中心的实际值时，实现了更好的性能。总的来说，我们的研究结果表明，功能枢纽的变化与精神分裂症有关，反映了潜在的功能网络和神经元通信的变化。此外，一个特定的网络属性，介数中心性，可以分类与SZ的人具有高水平的准确性。
A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy.