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首页> 美国卫生研究院文献>Frontiers in Human Neuroscience >Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial
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Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial

机译：精神分裂症中休息状态网络之间的小世界功能网络连通性和群集减少：fMRI分类教程

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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.
机译：功能网络连接（FNC）是一种分析大脑解剖部位的时间关系，比较患者组或病情之间的同步性的方法。我们使用独立组件之间的功能连接性度量对精神分裂症患者和健康对照在休息状态之间进行分类。连通性是使用各种图形理论上的连通性度量来衡量的，例如图形密度，平均路径长度和小世界。精神分裂症患者的各个组件之间的聚集性（传递性）显着低于健康对照组（p <0.05，已校正），并且网络连接的可能性较小，并且与健康对照组相比，小世界连接性较低。仅使用这些连通性措施，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％，而机会为51％。这意味着在精神分裂症中，静息状态网络之间的全局功能连通性发生了变化，对于患病患者，网络更可能断开连接并表现出不同的行为。我们将这项研究发现作为指南，使用了1000个功能连接套项目的一部分提供的146个精神分裂症患者和健康对照者的公开可用COBRE数据集。我们演示了预处理，使用独立成分分析（ICA）提名网络，计算图论连接性，最后使用这些连接性对患者组进行分类或使用正式的假设检验评估组间差异。为运行命令行FSL预处理，计算R中的所有统计量度和SVM分类提供了所有必需的代码。总的来说，这项工作不仅提供了精神分裂症静息状态网络中FNC减少的发现，而且还提供了实用的连接性。教程。

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