Advanced correlation grid: Analysis and visualisation of functional connectivity among multiple spike trains
| Autor: | Mohammad Shahed Masud, Roman Borisyuk, L.J. Stuart |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Time Factors Theoretical computer science Computer science Spike train Models Neurological Connection (vector bundle) Action Potentials 03 medical and health sciences 0302 clinical medicine Computer Systems Animals Humans Neurons business.industry General Neuroscience Pattern recognition Function (mathematics) Grid Visualization 030104 developmental biology Train Spike (software development) Pairwise comparison Artificial intelligence Nerve Net business 030217 neurology & neurosurgery |
| Zdroj: | Journal of Neuroscience Methods. 286:78-101 |
| ISSN: | 0165-0270 |
| DOI: | 10.1016/j.jneumeth.2017.05.016 |
| Popis: | Background 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. New method 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. Results 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. Comparison with existing methods 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. Conclusions 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. |
| Databáze: | OpenAIRE |
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