Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities
- Autores
- von Wegner, F.; Tagliazucchi, Enzo Rodolfo; Laufs, H.
- Año de publicación
- 2017
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- We present an information-theoretical analysis of temporal dependencies in EEG microstate sequences during wakeful rest. We interpret microstate sequences as discrete stochastic processes where each state corresponds to a representative scalp potential topography. Testing low-order Markovianity of these discrete sequences directly, we find that none of the recordings fulfils the Markov property of order 0, 1 or 2. Further analyses show that the microstate transition matrix is non-stationary over time in 80% (window size 10 s), 60% (window size 20 s) and 44% (window size 40 s) of the subjects, and that transition matrices are asymmetric in 14/20 (70%) subjects. To assess temporal dependencies globally, the time-lagged mutual information function (autoinformation function) of each sequence is compared to the first-order Markov model defined by the classical transition matrix approach. The autoinformation function for the Markovian case is derived analytically and numerically. For experimental data, we find non-Markovian behaviour in the range of the main EEG frequency bands where distinct periodicities related to the subject's EEG frequency spectrum appear. In particular, the microstate clustering algorithm induces frequency doubling with respect to the EEG power spectral density while the tail of the autoinformation function asymptotically reaches the first-order Markov confidence interval for time lags above 1000 ms. In summary, our results show that resting state microstate sequences are non-Markovian processes which inherit periodicities from the underlying EEG dynamics. Our results interpolate between two diverging models of microstate dynamics, memoryless Markov models on one side, and long-range correlated models on the other: microstate sequences display more complex temporal dependencies than captured by the transition matrix approach in the range of the main EEG frequency bands, but show finite memory content in the long run.
Fil: von Wegner, F.. Goethe Universitat Frankfurt; Alemania
Fil: Tagliazucchi, Enzo Rodolfo. Goethe Universitat Frankfurt; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Laufs, H.. Goethe Universitat Frankfurt; Alemania - Materia
-
Eeg
Information Theory
Microstates
Non-Markov Process
Periodicity
Resting State
Stationarity - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/56063
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Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicitiesvon Wegner, F.Tagliazucchi, Enzo RodolfoLaufs, H.EegInformation TheoryMicrostatesNon-Markov ProcessPeriodicityResting StateStationarityhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1We present an information-theoretical analysis of temporal dependencies in EEG microstate sequences during wakeful rest. We interpret microstate sequences as discrete stochastic processes where each state corresponds to a representative scalp potential topography. Testing low-order Markovianity of these discrete sequences directly, we find that none of the recordings fulfils the Markov property of order 0, 1 or 2. Further analyses show that the microstate transition matrix is non-stationary over time in 80% (window size 10 s), 60% (window size 20 s) and 44% (window size 40 s) of the subjects, and that transition matrices are asymmetric in 14/20 (70%) subjects. To assess temporal dependencies globally, the time-lagged mutual information function (autoinformation function) of each sequence is compared to the first-order Markov model defined by the classical transition matrix approach. The autoinformation function for the Markovian case is derived analytically and numerically. For experimental data, we find non-Markovian behaviour in the range of the main EEG frequency bands where distinct periodicities related to the subject's EEG frequency spectrum appear. In particular, the microstate clustering algorithm induces frequency doubling with respect to the EEG power spectral density while the tail of the autoinformation function asymptotically reaches the first-order Markov confidence interval for time lags above 1000 ms. In summary, our results show that resting state microstate sequences are non-Markovian processes which inherit periodicities from the underlying EEG dynamics. Our results interpolate between two diverging models of microstate dynamics, memoryless Markov models on one side, and long-range correlated models on the other: microstate sequences display more complex temporal dependencies than captured by the transition matrix approach in the range of the main EEG frequency bands, but show finite memory content in the long run.Fil: von Wegner, F.. Goethe Universitat Frankfurt; AlemaniaFil: Tagliazucchi, Enzo Rodolfo. Goethe Universitat Frankfurt; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Laufs, H.. Goethe Universitat Frankfurt; AlemaniaElsevier2017-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/56063von Wegner, F.; Tagliazucchi, Enzo Rodolfo; Laufs, H.; Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities; Elsevier; Journal Neuroimag; 158; 9-2017; 99-1111053-8119CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2017.06.062info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:56:02Zoai:ri.conicet.gov.ar:11336/56063instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:56:02.504CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities |
title |
Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities |
spellingShingle |
Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities von Wegner, F. Eeg Information Theory Microstates Non-Markov Process Periodicity Resting State Stationarity |
title_short |
Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities |
title_full |
Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities |
title_fullStr |
Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities |
title_full_unstemmed |
Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities |
title_sort |
Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities |
dc.creator.none.fl_str_mv |
von Wegner, F. Tagliazucchi, Enzo Rodolfo Laufs, H. |
author |
von Wegner, F. |
author_facet |
von Wegner, F. Tagliazucchi, Enzo Rodolfo Laufs, H. |
author_role |
author |
author2 |
Tagliazucchi, Enzo Rodolfo Laufs, H. |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Eeg Information Theory Microstates Non-Markov Process Periodicity Resting State Stationarity |
topic |
Eeg Information Theory Microstates Non-Markov Process Periodicity Resting State Stationarity |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
We present an information-theoretical analysis of temporal dependencies in EEG microstate sequences during wakeful rest. We interpret microstate sequences as discrete stochastic processes where each state corresponds to a representative scalp potential topography. Testing low-order Markovianity of these discrete sequences directly, we find that none of the recordings fulfils the Markov property of order 0, 1 or 2. Further analyses show that the microstate transition matrix is non-stationary over time in 80% (window size 10 s), 60% (window size 20 s) and 44% (window size 40 s) of the subjects, and that transition matrices are asymmetric in 14/20 (70%) subjects. To assess temporal dependencies globally, the time-lagged mutual information function (autoinformation function) of each sequence is compared to the first-order Markov model defined by the classical transition matrix approach. The autoinformation function for the Markovian case is derived analytically and numerically. For experimental data, we find non-Markovian behaviour in the range of the main EEG frequency bands where distinct periodicities related to the subject's EEG frequency spectrum appear. In particular, the microstate clustering algorithm induces frequency doubling with respect to the EEG power spectral density while the tail of the autoinformation function asymptotically reaches the first-order Markov confidence interval for time lags above 1000 ms. In summary, our results show that resting state microstate sequences are non-Markovian processes which inherit periodicities from the underlying EEG dynamics. Our results interpolate between two diverging models of microstate dynamics, memoryless Markov models on one side, and long-range correlated models on the other: microstate sequences display more complex temporal dependencies than captured by the transition matrix approach in the range of the main EEG frequency bands, but show finite memory content in the long run. Fil: von Wegner, F.. Goethe Universitat Frankfurt; Alemania Fil: Tagliazucchi, Enzo Rodolfo. Goethe Universitat Frankfurt; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina Fil: Laufs, H.. Goethe Universitat Frankfurt; Alemania |
description |
We present an information-theoretical analysis of temporal dependencies in EEG microstate sequences during wakeful rest. We interpret microstate sequences as discrete stochastic processes where each state corresponds to a representative scalp potential topography. Testing low-order Markovianity of these discrete sequences directly, we find that none of the recordings fulfils the Markov property of order 0, 1 or 2. Further analyses show that the microstate transition matrix is non-stationary over time in 80% (window size 10 s), 60% (window size 20 s) and 44% (window size 40 s) of the subjects, and that transition matrices are asymmetric in 14/20 (70%) subjects. To assess temporal dependencies globally, the time-lagged mutual information function (autoinformation function) of each sequence is compared to the first-order Markov model defined by the classical transition matrix approach. The autoinformation function for the Markovian case is derived analytically and numerically. For experimental data, we find non-Markovian behaviour in the range of the main EEG frequency bands where distinct periodicities related to the subject's EEG frequency spectrum appear. In particular, the microstate clustering algorithm induces frequency doubling with respect to the EEG power spectral density while the tail of the autoinformation function asymptotically reaches the first-order Markov confidence interval for time lags above 1000 ms. In summary, our results show that resting state microstate sequences are non-Markovian processes which inherit periodicities from the underlying EEG dynamics. Our results interpolate between two diverging models of microstate dynamics, memoryless Markov models on one side, and long-range correlated models on the other: microstate sequences display more complex temporal dependencies than captured by the transition matrix approach in the range of the main EEG frequency bands, but show finite memory content in the long run. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-09 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/56063 von Wegner, F.; Tagliazucchi, Enzo Rodolfo; Laufs, H.; Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities; Elsevier; Journal Neuroimag; 158; 9-2017; 99-111 1053-8119 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/56063 |
identifier_str_mv |
von Wegner, F.; Tagliazucchi, Enzo Rodolfo; Laufs, H.; Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities; Elsevier; Journal Neuroimag; 158; 9-2017; 99-111 1053-8119 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2017.06.062 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.13397 |