BOLD-fMRI technology provides a good foundation for the research of human brain dynamic functional connectivity and brain state analysis. However, due to the complexity of brain function connectivity and the high dimensionality expression of brain dynamic attributions, more research studies are focusing on tracking the time-varying characteristics through the transition between different brain states. The transition process is considered to occur instantaneously at some special time point in the above research studies, whereas our work found the brain state transition may be completed in a time section gradually rather than instantaneously. In this paper, a brain state conversion rate model is constructed to observe the procedure of brain state transition trend at each time point, and the state change can be observed by the values of conversion rate. According to the results, the transition of status always lasts for a few time points, and a brain state network model with both steady state and transition state is presented. Network topological overlap coefficient is built to analyze the features of time-varying networks. With this method, some common regular patterns of time-varying characteristics can be observed strongly in healthy children but not in the autism children. This distinct can help us to distinguish children with autism from healthy children.

中文翻译：

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基于静止状态功能磁共振成像跟踪动态功能连接的脑状态转换过程。

BOLD-fMRI技术为人脑动态功能连接性和脑状态分析的研究提供了良好的基础。但是，由于大脑功能连接的复杂性和大脑动态归因的高维表达，更多的研究集中在通过不同大脑状态之间的转换来跟踪时变特征。在上述研究中，过渡过程被认为是在某个特定的时间点瞬间发生的，而我们的工作发现，大脑状态的过渡可能是在某个时间段内逐渐而不是瞬间完成的。本文建立了一个大脑状态转换率模型，以观察每个时间点的大脑状态转换趋势的过程，通过转换率的值可以观察到状态变化。根据结果​​，状态的过渡总是持续几个时间点，并提出了一个同时具有稳态和过渡状态的大脑状态网络模型。建立网络拓扑重叠系数以分析时变网络的特征。使用这种方法，可以在健康儿童中强烈观察到一些常见的时变特征规律，而在自闭症儿童中则观察不到。这种独特之处可以帮助我们将自闭症儿童与健康儿童区分开。建立网络拓扑重叠系数以分析时变网络的特征。使用此方法，可以在健康儿童中强烈观察到一些常见的时变特征规律，而在自闭症儿童中则观察不到。这种独特之处可以帮助我们将自闭症儿童与健康儿童区分开。建立网络拓扑重叠系数以分析时变网络的特征。使用这种方法，可以在健康儿童中强烈观察到一些常见的时变特征规律，而在自闭症儿童中则观察不到。这种独特之处可以帮助我们将自闭症儿童与健康儿童区分开。