Multivariate partial coherence analysis for identification of neuronal connectivity from multiple electrode array recordings

Makhtar, Siti Noormiza; Halliday, David M.; Senik, Mohd Harizal; Mason, Rob

doi:10.1109/iecbes.2014.7047613

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…This approach to determining a ij and w ij draws on the extensive theory for time series analysis and avoids the need for surrogate data. Similar approaches and further discussion are in [6,9,10,26,29].…”

Section: Edge Detection Using Statistical Filtering Of Coherence or P...mentioning

confidence: 91%

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

et al. 2020

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Objective. Graphical networks and network metrics are widely used to understand and characterise brain networks and brain function. These methods can be applied to a range of electrophysiological 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 graphical 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.

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Section: Edge Detection Using Statistical Filtering Of Coherence or P...mentioning

confidence: 91%

“…If the average exceeds the confidence limit then a ij = 1 and w ij is set equal to this average, otherwise a ij = w ij = 0. This approach to determining a ij and w ij is considered in [29].…”

Section: Network Metricsmentioning

confidence: 99%

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

et al. 2020

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“…Partial coherence has been defined in order to quantify the strength of a conditional relationship between two neurons and is suitable for multivariate analysis by using the predictor to distinguish direct connection from common inputs [ 75 ]. As an extension, multivariate partial coherence analysis has been introduced [ 76 ].…”

Section: Non-directional Connectivity Measuresmentioning

confidence: 99%

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Papana

2021

Entropy

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The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.

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Classification of functional interactions from multi-electrodes data using conditional modularity analysis

Makhtar

Senik

2018

AIP Conference Proceedings

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Multivariate partial coherence analysis for identification of neuronal connectivity from multiple electrode array recordings

Cited by 3 publications

References 15 publications

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

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

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Classification of functional interactions from multi-electrodes data using conditional modularity analysis

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