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A generalized framework for quantifying the dynamics of EEG event-related desynchronization.

Lemm S, Müller KR, Curio G - PLoS Comput. Biol. (2009)

Bottom Line: Here, we establish a novel generalized concept to measure event-related desynchronization (ERD), which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states.Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization), which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics.Moreover, this computational approach also encompasses the more general notion of a "conditional ERD," through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Data Analysis Group, Fraunhofer Institute FIRST, Berlin, Germany. steven.lemm@first.fraunhofer.de

ABSTRACT
Brains were built by evolution to react swiftly to environmental challenges. Thus, sensory stimuli must be processed ad hoc, i.e., independent--to a large extent--from the momentary brain state incidentally prevailing during stimulus occurrence. Accordingly, computational neuroscience strives to model the robust processing of stimuli in the presence of dynamical cortical states. A pivotal feature of ongoing brain activity is the regional predominance of EEG eigenrhythms, such as the occipital alpha or the pericentral mu rhythm, both peaking spectrally at 10 Hz. Here, we establish a novel generalized concept to measure event-related desynchronization (ERD), which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states. Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization), which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics. Moreover, this computational approach also encompasses the more general notion of a "conditional ERD," through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study. Thus, the generalized ERD represents a powerful novel analysis tool for extending our understanding of inter-trial variability of evoked responses and therefore the robust processing of environmental stimuli.

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Estimated functional relationship between the ERD characteristics and the explanatory factors.Panel A shows the estimated dependency of ERD magnitude on the three explanatory factors, i.e., on the local pre-stimulus  (solid), on the pre-stimulus activity of occipital  (dash-dotted) and on the strength of the preceding ERS response (dashed). The estimated relationship between ERD latency and the three factors is depicted in panel B.
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pcbi-1000453-g007: Estimated functional relationship between the ERD characteristics and the explanatory factors.Panel A shows the estimated dependency of ERD magnitude on the three explanatory factors, i.e., on the local pre-stimulus (solid), on the pre-stimulus activity of occipital (dash-dotted) and on the strength of the preceding ERS response (dashed). The estimated relationship between ERD latency and the three factors is depicted in panel B.

Mentions: For further investigations we restrict ourselves solely to the generalized state conditional ERD. Using the proposed state conditional measure, we test the hypothesis of a monotonic interrelationship between the three explanatory variables and the ERD magnitude on the one hand and the ERD latency on the other. To this end, we define the latency and the magnitude of the ERD as a function of the explanatory variable, based on the minimum in the interval :(21)(22)In Fig. 7 the corresponding functions are illustrated for the three different explanatory variables. Note, the different domains of the state variables and . The step function like appearance in case of the ERD latency is due to sub-sampling the data to 100 Hz.


A generalized framework for quantifying the dynamics of EEG event-related desynchronization.

Lemm S, Müller KR, Curio G - PLoS Comput. Biol. (2009)

Estimated functional relationship between the ERD characteristics and the explanatory factors.Panel A shows the estimated dependency of ERD magnitude on the three explanatory factors, i.e., on the local pre-stimulus  (solid), on the pre-stimulus activity of occipital  (dash-dotted) and on the strength of the preceding ERS response (dashed). The estimated relationship between ERD latency and the three factors is depicted in panel B.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2713829&req=5

pcbi-1000453-g007: Estimated functional relationship between the ERD characteristics and the explanatory factors.Panel A shows the estimated dependency of ERD magnitude on the three explanatory factors, i.e., on the local pre-stimulus (solid), on the pre-stimulus activity of occipital (dash-dotted) and on the strength of the preceding ERS response (dashed). The estimated relationship between ERD latency and the three factors is depicted in panel B.
Mentions: For further investigations we restrict ourselves solely to the generalized state conditional ERD. Using the proposed state conditional measure, we test the hypothesis of a monotonic interrelationship between the three explanatory variables and the ERD magnitude on the one hand and the ERD latency on the other. To this end, we define the latency and the magnitude of the ERD as a function of the explanatory variable, based on the minimum in the interval :(21)(22)In Fig. 7 the corresponding functions are illustrated for the three different explanatory variables. Note, the different domains of the state variables and . The step function like appearance in case of the ERD latency is due to sub-sampling the data to 100 Hz.

Bottom Line: Here, we establish a novel generalized concept to measure event-related desynchronization (ERD), which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states.Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization), which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics.Moreover, this computational approach also encompasses the more general notion of a "conditional ERD," through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Data Analysis Group, Fraunhofer Institute FIRST, Berlin, Germany. steven.lemm@first.fraunhofer.de

ABSTRACT
Brains were built by evolution to react swiftly to environmental challenges. Thus, sensory stimuli must be processed ad hoc, i.e., independent--to a large extent--from the momentary brain state incidentally prevailing during stimulus occurrence. Accordingly, computational neuroscience strives to model the robust processing of stimuli in the presence of dynamical cortical states. A pivotal feature of ongoing brain activity is the regional predominance of EEG eigenrhythms, such as the occipital alpha or the pericentral mu rhythm, both peaking spectrally at 10 Hz. Here, we establish a novel generalized concept to measure event-related desynchronization (ERD), which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states. Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization), which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics. Moreover, this computational approach also encompasses the more general notion of a "conditional ERD," through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study. Thus, the generalized ERD represents a powerful novel analysis tool for extending our understanding of inter-trial variability of evoked responses and therefore the robust processing of environmental stimuli.

Show MeSH