Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial.
Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial.
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DOI：
10.3389/fnhum.2013.00520
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发表时间：
2013
影响因子：
2.9
通讯作者：
Cohen MS
中科院分区：
文献类型：
--
作者：
Anderson A;Cohen MS
功能网络连接（FNC）是一种分析大脑解剖成分的时间关系、比较患者组或状况之间的同步性的方法。我们使用独立组件之间的功能连接测量来对静息状态下的精神分裂症患者和健康对照进行分类。连接性是使用各种图论连接性度量来测量的，例如图密度、平均路径长度和小世界性。与健康对照组相比，精神分裂症患者的组件之间的聚类（传递性）显着减少（p < 0.05，已校正），网络连接的可能性较小，并且与健康对照组相比，小世界连接性也较低。仅使用这些连通性测量，SVM 分类器（无需参数调整）就可以区分精神分裂症患者和健康对照，准确度为 65%，而几率为 51%。这意味着精神分裂症患者的静息态网络之间的整体功能连接发生了改变，患病患者的网络更有可能断开连接并且表现不同。我们使用 146 名精神分裂症患者和健康对照的公开 COBRE 数据集（作为 1000 个功能连接组项目的一部分提供）将这项研究结果作为教程呈现。我们演示了预处理，使用独立成分分析（ICA）来指定网络，计算图论连通性度量，最后使用这些连通性度量在患者组之间进行分类或使用正式假设检验评估组间差异。为运行命令行 FSL 预处理以及在 R 中计算所有统计测量和 SVM 分类提供了所有必要的代码。总的来说，这项工作不仅展示了精神分裂症静息态网络中 FNC 减少的发现，而且还提供了实用的连接教程。
Functional network connectivity (FNC) is a method of analyzing the temporal relationship of anatomical brain components, comparing the synchronicity between patient groups or conditions. We use functional-connectivity measures between independent components to classify between Schizophrenia patients and healthy controls during resting-state. Connectivity is measured using a variety of graph-theoretic connectivity measures such as graph density, average path length, and small-worldness. The Schizophrenia patients showed significantly less clustering (transitivity) among components than healthy controls (p < 0.05, corrected) with networks less likely to be connected, and also showed lower small-world connectivity than healthy controls. Using only these connectivity measures, an SVM classifier (without parameter tuning) could discriminate between Schizophrenia patients and healthy controls with 65% accuracy, compared to 51% chance. This implies that the global functional connectivity between resting-state networks is altered in Schizophrenia, with networks more likely to be disconnected and behave dissimilarly for diseased patients. We present this research finding as a tutorial using the publicly available COBRE dataset of 146 Schizophrenia patients and healthy controls, provided as part of the 1000 Functional Connectomes Project. We demonstrate preprocessing, using independent component analysis (ICA) to nominate networks, computing graph-theoretic connectivity measures, and finally using these connectivity measures to either classify between patient groups or assess between-group differences using formal hypothesis testing. All necessary code is provided for both running command-line FSL preprocessing, and for computing all statistical measures and SVM classification within R. Collectively, this work presents not just findings of diminished FNC among resting-state networks in Schizophrenia, but also a practical connectivity tutorial.
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DOI：
10.1523/jneurosci.1929-08.2008
发表时间：
2008-09-10
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Bassett DS;Bullmore E;Verchinski BA;Mattay VS;Weinberger DR;Meyer-Lindenberg A
通讯作者：
Meyer-Lindenberg A
影响因子：
5.3
作者：
Achard, S;Salvador, R;Bullmore, ET
通讯作者：
Bullmore, ET
影响因子：
4.7
作者：
Calhoun VD;Sui J;Kiehl K;Turner J;Allen E;Pearlson G
通讯作者：
Pearlson G
影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
影响因子：
3.7
作者：
Humphries MD;Gurney K
通讯作者：
Gurney K