Improved functional connectivity network estimation for brain networks using multivariate partial coherence

Halliday, David M.; Senik, Mohd H.; Makhtar, Siti N.; Stevenson, Carl W.; Mason, Rob

Abstract

Objective: Graphical networks and network metrics are widely used to understand and char-acterise brain networks and brain function. These methods can be applied to a range of electro-physiological data including electroencephalography, local field potential and single unit recordings. Functional networks are often constructed using pair-wise correlation between variables. The objective of this study is to demonstrate that functional networks can be more accurately estimated using partial correlation than with pair-wise correlation.
Approach: We compared network metrics derived from unconditional and conditional graphical networks, obtained using coherence and multivariate partial coherence (MVPC), respectively. Graphical networks were constructed using coherence and MVPC estimates, and binary and weighted network metrics derived from these: node degree, path length, clustering coefficients and small-world index.
Main Results: Network metrics were applied to simulated and experimental single unit spike train data. Simulated data used a 10x10 grid of simulated cortical neurons with centre-surround connectivity. Conditional network metrics gave a more accurate representation of the known connectivity: Numbers of excitatory connections had range 3-11, unconditional binary node degree had range 6-80, conditional node degree had range 2-13. Experimental data used multi-electrode array recording with 19 single-units from left and right hippocampal brain areas in a rat model for epilepsy. Conditional network analysis showed similar trends to simulated data, with lower binary node degree and longer binary path lengths compared to unconditional networks. Significance: We conclude that conditional networks, where common dependencies are removed through partial coherence analysis, give a more accurate representation of the interactions in a graphical network model. These results have important implications for graphi-cal network analyses of brain networks and suggest that functional networks should be derived using partial correlation, based on MVPC estimates, as opposed to the common approach of pair-wise correlation.

Citation

Halliday, D. M., Senik, M. H., Makhtar, S. N., Stevenson, C. W., & Mason, R. (2020). Improved functional connectivity network estimation for brain networks using multivariate partial coherence. Journal of Neural Engineering, 17(2), Article 026013. https://doi.org/10.1088/1741-2552/ab7a50

• Journal Article Type: Article
• Acceptance Date: Feb 26, 2020
• Online Publication Date: Feb 26, 2020
• Publication Date: Mar 26, 2020
• Deposit Date: Mar 7, 2020
• Publicly Available Date: Mar 28, 2020
• Journal: Journal of neural engineering
• Print ISSN: 1741-2560
• Electronic ISSN: 1741-2552
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 2
• Article Number: 026013
• DOI: https://doi.org/10.1088/1741-2552/ab7a50
• Keywords: Coherence, Partial coherence, Network theory, Network metrics, Small world network
• Public URL: https://nottingham-repository.worktribe.com/output/4051979
• Publisher URL: https://iopscience.iop.org/article/10.1088/1741-2552/ab7a50