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

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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Main Authors: Pester, Britta, Ligges, Carolin, Leistritz, Lutz, Witte, Herbert, Schiecke, Karin
Format: Online
Language:English
Published: Public Library of Science 2015
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457931/
id pubmed-4457931
recordtype oai_dc
spelling pubmed-44579312015-06-09 Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence Pester, Britta Ligges, Carolin Leistritz, Lutz Witte, Herbert Schiecke, Karin Research Article 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. Public Library of Science 2015-06-05 /pmc/articles/PMC4457931/ /pubmed/26046537 http://dx.doi.org/10.1371/journal.pone.0129293 Text en © 2015 Pester et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Pester, Britta
Ligges, Carolin
Leistritz, Lutz
Witte, Herbert
Schiecke, Karin
spellingShingle Pester, Britta
Ligges, Carolin
Leistritz, Lutz
Witte, Herbert
Schiecke, Karin
Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence
author_facet Pester, Britta
Ligges, Carolin
Leistritz, Lutz
Witte, Herbert
Schiecke, Karin
author_sort Pester, Britta
title Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence
title_short Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence
title_full Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence
title_fullStr Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence
title_full_unstemmed Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence
title_sort advanced insights into functional brain connectivity by combining tensor decomposition and partial directed coherence
description 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.
publisher Public Library of Science
publishDate 2015
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457931/
_version_ 1613232484817829888

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