Modeling brain resonance phenomena using a neural mass model.

Stimulation with rhythmic light flicker (photic driving) plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment) is used to assess the functional flexibility o...

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Autores principales:	Andreas Spiegler, Thomas R Knösche, Karin Schwab, Jens Haueisen, Fatihcan M Atay
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/11d32d7122574de1b9cf0e966e7957c8
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spelling	oai:doaj.org-article:11d32d7122574de1b9cf0e966e7957c82021-11-18T05:51:43ZModeling brain resonance phenomena using a neural mass model.1553-734X1553-735810.1371/journal.pcbi.1002298https://doaj.org/article/11d32d7122574de1b9cf0e966e7957c82011-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22215992/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Stimulation with rhythmic light flicker (photic driving) plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment) is used to assess the functional flexibility of the brain. We aim to gain deeper understanding of the mechanisms underlying this technique and to predict the effects of stimulus frequency and intensity. For this purpose, a modified Jansen and Rit neural mass model (NMM) of a cortical circuit is used. This mean field model has been designed to strike a balance between mathematical simplicity and biological plausibility. We reproduced the entrainment phenomenon observed in EEG during a photic driving experiment. More generally, we demonstrate that such a single area model can already yield very complex dynamics, including chaos, for biologically plausible parameter ranges. We chart the entire parameter space by means of characteristic Lyapunov spectra and Kaplan-Yorke dimension as well as time series and power spectra. Rhythmic and chaotic brain states were found virtually next to each other, such that small parameter changes can give rise to switching from one to another. Strikingly, this characteristic pattern of unpredictability generated by the model was matched to the experimental data with reasonable accuracy. These findings confirm that the NMM is a useful model of brain dynamics during photic driving. In this context, it can be used to study the mechanisms of, for example, perception and epileptic seizure generation. In particular, it enabled us to make predictions regarding the stimulus amplitude in further experiments for improving the entrainment effect.Andreas SpieglerThomas R KnöscheKarin SchwabJens HaueisenFatihcan M AtayPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 12, p e1002298 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Andreas Spiegler Thomas R Knösche Karin Schwab Jens Haueisen Fatihcan M Atay Modeling brain resonance phenomena using a neural mass model.
description	Stimulation with rhythmic light flicker (photic driving) plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment) is used to assess the functional flexibility of the brain. We aim to gain deeper understanding of the mechanisms underlying this technique and to predict the effects of stimulus frequency and intensity. For this purpose, a modified Jansen and Rit neural mass model (NMM) of a cortical circuit is used. This mean field model has been designed to strike a balance between mathematical simplicity and biological plausibility. We reproduced the entrainment phenomenon observed in EEG during a photic driving experiment. More generally, we demonstrate that such a single area model can already yield very complex dynamics, including chaos, for biologically plausible parameter ranges. We chart the entire parameter space by means of characteristic Lyapunov spectra and Kaplan-Yorke dimension as well as time series and power spectra. Rhythmic and chaotic brain states were found virtually next to each other, such that small parameter changes can give rise to switching from one to another. Strikingly, this characteristic pattern of unpredictability generated by the model was matched to the experimental data with reasonable accuracy. These findings confirm that the NMM is a useful model of brain dynamics during photic driving. In this context, it can be used to study the mechanisms of, for example, perception and epileptic seizure generation. In particular, it enabled us to make predictions regarding the stimulus amplitude in further experiments for improving the entrainment effect.
format	article
author	Andreas Spiegler Thomas R Knösche Karin Schwab Jens Haueisen Fatihcan M Atay
author_facet	Andreas Spiegler Thomas R Knösche Karin Schwab Jens Haueisen Fatihcan M Atay
author_sort	Andreas Spiegler
title	Modeling brain resonance phenomena using a neural mass model.
title_short	Modeling brain resonance phenomena using a neural mass model.
title_full	Modeling brain resonance phenomena using a neural mass model.
title_fullStr	Modeling brain resonance phenomena using a neural mass model.
title_full_unstemmed	Modeling brain resonance phenomena using a neural mass model.
title_sort	modeling brain resonance phenomena using a neural mass model.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/11d32d7122574de1b9cf0e966e7957c8
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Modeling brain resonance phenomena using a neural mass model.

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