Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence

Quantification of functional connectivity in physiological networks is frequently performed by means of time-variant partial directed coherence (tvPDC), based on time-variant multivariate autoregressive models. The principle advantage of tvPDC lies in the combination of directionality, time variance...

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Bibliographic Details
Published in:PloS one 2015-06, Vol.10 (6), p.e0129293-e0129293
Main Authors: Pester, Britta, Ligges, Carolin, Leistritz, Lutz, Witte, Herbert, Schiecke, Karin
Format: Article
Language:English
Subjects:
Algorithms
Autoregressive models
Autoregressive processes
Brain
Brain - physiology
Child & adolescent psychiatry
Coherence
Computer Simulation
Condensates
Connectome - methods
Decomposition
Documentation
EEG
Electroencephalography
Evoked Potentials - physiology
Fourier transforms
Frequency dependence
Humans
Medical statistics
Models, Neurological
Nerve Net - physiology
Neural networks
Neurosciences
Principal components analysis
Schiller, Johann Christoph Friedrich von (1759-1805)
Visual evoked potentials
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Summary:Quantification of functional connectivity in physiological networks is frequently performed by means of time-variant partial directed coherence (tvPDC), based on time-variant multivariate autoregressive models. The principle advantage of tvPDC lies in the combination of directionality, time variance and frequency selectivity simultaneously, offering a more differentiated view into complex brain networks. Yet the advantages specific to tvPDC also cause a large number of results, leading to serious problems in interpretability. To counter this issue, we propose the decomposition of multi-dimensional tvPDC results into a sum of rank-1 outer products. This leads to a data condensation which enables an advanced interpretation of results. Furthermore it is thereby possible to uncover inherent interaction patterns of induced neuronal subsystems by limiting the decomposition to several relevant channels, while retaining the global influence determined by the preceding multivariate AR estimation and tvPDC calculation of the entire scalp. Finally a comparison between several subjects is considerably easier, as individual tvPDC results are summarized within a comprehensive model equipped with subject-specific loading coefficients. A proof-of-principle of the approach is provided by means of simulated data; EEG data of an experiment concerning visual evoked potentials are used to demonstrate the applicability to real data.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0129293