Masud Mohammad Shahed, Borisyuk Roman, Stuart Liz
Institute of Statistical Research and Training (ISRT), University of Dhaka, Dhaka-1000, Bangladesh.
School of Computing, Electronics and Mathematics, Centre for Robotics and Neural Systems, Plymouth University, Plymouth, UK; Institute of Mathematical Problems of Biology, The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, Pushchino, Russia.
J Neurosci Methods. 2017 Jul 15;286:78-101. doi: 10.1016/j.jneumeth.2017.05.016. Epub 2017 May 12.
This study analyses multiple spike trains (MST) data, defines its functional connectivity and subsequently visualises an accurate diagram of connections. This is a challenging problem. For example, it is difficult to distinguish the common input and the direct functional connection of two spike trains.
The new method presented in this paper is based on the traditional pairwise cross-correlation function (CCF) and a new combination of statistical techniques. First, the CCF is used to create the Advanced Correlation Grid (ACG) correlation where both the significant peak of the CCF and the corresponding time delay are used for detailed analysis of connectivity. Second, these two features of functional connectivity are used to classify connections. Finally, the visualization technique is used to represent the topology of functional connections.
Examples are presented in the paper to demonstrate the new Advanced Correlation Grid method and to show how it enables discrimination between (i) influence from one spike train to another through an intermediate spike train and (ii) influence from one common spike train to another pair of analysed spike trains.
The ACG method enables scientists to automatically distinguish between direct connections from spurious connections such as common source connection and indirect connection whereas existing methods require in-depth analysis to identify such connections.
The ACG is a new and effective method for studying functional connectivity of multiple spike trains. This method can identify accurately all the direct connections and can distinguish common source and indirect connections automatically.
本研究分析多脉冲序列（MST）数据，定义其功能连接性，并随后可视化连接的精确图表。这是一个具有挑战性的问题。例如，很难区分两个脉冲序列的共同输入和直接功能连接。
本文提出的新方法基于传统的成对互相关函数（CCF）和一种新的统计技术组合。首先，使用CCF创建高级相关网格（ACG）相关性，其中CCF的显著峰值和相应的时间延迟都用于详细分析连接性。其次，功能连接的这两个特征用于对连接进行分类。最后，使用可视化技术来表示功能连接的拓扑结构。
本文给出了示例，以演示新的高级相关网格方法，并展示它如何能够区分（i）通过中间脉冲序列从一个脉冲序列到另一个脉冲序列的影响，以及（ii）从一个共同脉冲序列到另一对被分析脉冲序列的影响。
ACG方法使科学家能够自动区分直接连接与诸如共同源连接和间接连接等虚假连接，而现有方法需要深入分析才能识别此类连接。
ACG是一种研究多脉冲序列功能连接性的新的有效方法。该方法能够准确识别所有直接连接，并能自动区分共同源连接和间接连接。
