Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia
Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia
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DOI：
10.3389/fnsys.2010.00147
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发表时间：
2010-01-01
影响因子：
3
通讯作者：
Bullmore, Edward T.
中科院分区：
文献类型：
--
作者：
Alexander-Bloch, Aaron F.;Gogtay, Nitin;Bullmore, Edward T.
模块性是系统神经科学中的一个基本概念，指的是密集内部连接的节点与其他模块中的节点稀疏互连的局部集团或模块的形成。脑功能网络的拓扑模块性可以量化理论上预期的异常的脑网络社区结构-所谓的模块性障碍-在发育障碍，如儿童期发作的精神分裂症（COS）。我们使用图论来研究来自13名COS患者和19名健康志愿者的静息状态fMRI数据的网络拓扑结构。我们测量了每对100个区域节点之间的功能连接性，重点是频率间隔0.05-0.1 Hz的小波相关性，然后应用全局和局部阈值规则从每个个体关联矩阵中构建图表。我们展示了如何本地阈值的最小生成树的基础上促进组比较网络的稀疏图的连通性。阈值相关的图理论结果与k均值无监督学习算法和模块化多分辨率（旋转玻璃）方法的结果兼容，这两种方法也可以找到社区结构，但不需要对关联矩阵进行阈值处理。在一般的模块化的大脑功能网络显着减少COS，由于相对减少的密度相邻区域之间的模块内连接。在COS组中，本地组织的其他网络措施（如集群）也有所减少，而全球效率和鲁棒性的补充措施则有所增加。复杂网络特性的组差异反映在数据的简单统计特性的差异上，例如全局时间序列的变异性和感兴趣解剖区域内时间序列的内部同质性。
Modularity is a fundamental concept in systems neuroscience, referring to the formation of local cliques or modules of densely intra-connected nodes that are sparsely inter-connected with nodes in other modules. Topological modularity of brain functional networks can quantify theoretically anticipated abnormality of brain network community structure - so-called dysmodularity - in developmental disorders such as childhood-onset schizophrenia (COS). We used graph theory to investigate topology of networks derived from resting-state fMRI data on 13 COS patients and 19 healthy volunteers. We measured functional connectivity between each pair of 100 regional nodes, focusing on wavelet correlation in the frequency interval 0.05-0.1 Hz, then applied global and local thresholding rules to construct graphs from each individual association matrix over the full range of possible connection densities. We show how local thresholding based on the minimum spanning tree facilitates group comparisons of networks by forcing the connectedness of sparse graphs. Threshold-dependent graph theoretical results are compatible with the results of a k-means unsupervised learning algorithm and a multi-resolution (spin glass) approach to modularity, both of which also find community structure but do not require thresholding of the association matrix. In general modularity of brain functional networks was significantly reduced in COS, due to a relatively reduced density of intra-modular connections between neighboring regions. Other network measures of local organization such as clustering were also decreased, while complementary measures of global efficiency and robustness were increased, in the COS group. The group differences in complex network properties were mirrored by differences in simpler statistical properties of the data, such as the variability of the global time series and the internal homogeneity of the time series within anatomical regions of interest.