Order Patterns Networks (orpan) – a method toestimate time-evolving functional connectivity frommultivariate time series
Complex networks provide an excellent framework for studying the functionof the human brain activity. Yet estimating functional networks from mea-sured signals is not trivial, especially if the data is non-stationary and noisyas it is often the case with physiological recordings. In this article we...
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2012-11-01
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Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00091/full |
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doaj-4fc24612327b419c857f56362a8888ed2020-11-24T23:24:05ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882012-11-01610.3389/fncom.2012.0009124874Order Patterns Networks (orpan) – a method toestimate time-evolving functional connectivity frommultivariate time seriesStefan eSchinkel0Stefan eSchinkel1Gorka eZamora-López2Gorka eZamora-López3Olaf eDimigen4Werner eSommer5Jürgen eKurths6Jürgen eKurths7Jürgen eKurths8Humboldt Universität zu BerlinHumboldt Universität zu BerlinBernstein Center for Computational Neuroscience BerlinHumboldt Universität zu BerlinHumboldt Universität zu BerlinHumboldt Universität zu BerlinHumboldt Universität zu BerlinPotsdam Institute for Climate Impact Research (PIK)University of AberdeenComplex networks provide an excellent framework for studying the functionof the human brain activity. Yet estimating functional networks from mea-sured signals is not trivial, especially if the data is non-stationary and noisyas it is often the case with physiological recordings. In this article we proposea method that uses the local rank structure of the data to define functionallinks in terms of identical rank structures. The method yields temporal se-quences of networks which permits to trace the evolution of the functionalconnectivity during the time course of the observation. We demonstrate thepotentials of this approach with model data as well as with experimentaldata from an electrophysiological study on language processing.http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00091/fullEEGERPsemantic primingtime series analysisFunctional Networksnetwork reconstruction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Stefan eSchinkel Stefan eSchinkel Gorka eZamora-López Gorka eZamora-López Olaf eDimigen Werner eSommer Jürgen eKurths Jürgen eKurths Jürgen eKurths |
spellingShingle |
Stefan eSchinkel Stefan eSchinkel Gorka eZamora-López Gorka eZamora-López Olaf eDimigen Werner eSommer Jürgen eKurths Jürgen eKurths Jürgen eKurths Order Patterns Networks (orpan) – a method toestimate time-evolving functional connectivity frommultivariate time series Frontiers in Computational Neuroscience EEG ERP semantic priming time series analysis Functional Networks network reconstruction |
author_facet |
Stefan eSchinkel Stefan eSchinkel Gorka eZamora-López Gorka eZamora-López Olaf eDimigen Werner eSommer Jürgen eKurths Jürgen eKurths Jürgen eKurths |
author_sort |
Stefan eSchinkel |
title |
Order Patterns Networks (orpan) – a method toestimate time-evolving functional connectivity frommultivariate time series |
title_short |
Order Patterns Networks (orpan) – a method toestimate time-evolving functional connectivity frommultivariate time series |
title_full |
Order Patterns Networks (orpan) – a method toestimate time-evolving functional connectivity frommultivariate time series |
title_fullStr |
Order Patterns Networks (orpan) – a method toestimate time-evolving functional connectivity frommultivariate time series |
title_full_unstemmed |
Order Patterns Networks (orpan) – a method toestimate time-evolving functional connectivity frommultivariate time series |
title_sort |
order patterns networks (orpan) – a method toestimate time-evolving functional connectivity frommultivariate time series |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Computational Neuroscience |
issn |
1662-5188 |
publishDate |
2012-11-01 |
description |
Complex networks provide an excellent framework for studying the functionof the human brain activity. Yet estimating functional networks from mea-sured signals is not trivial, especially if the data is non-stationary and noisyas it is often the case with physiological recordings. In this article we proposea method that uses the local rank structure of the data to define functionallinks in terms of identical rank structures. The method yields temporal se-quences of networks which permits to trace the evolution of the functionalconnectivity during the time course of the observation. We demonstrate thepotentials of this approach with model data as well as with experimentaldata from an electrophysiological study on language processing. |
topic |
EEG ERP semantic priming time series analysis Functional Networks network reconstruction |
url |
http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00091/full |
work_keys_str_mv |
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