Phase-coupling of theta–gamma EEG rhythms during short-term memory processing

Schack, Bärbel; Vath, Nuria; Petsche, H.; Geissler, Hans-Georg; Möller, Eva

doi:10.1016/s0167-8760(01)00199-4

Cited by 257 publications

(187 citation statements)

References 49 publications

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“…Although changes in oscillatory activity in the g-frequency band are most consistently observed, increases in the y-, a-, and b-frequency bands have also been associated with cognitive tasks (Gevins et al, 1997;Klimesch et al, 2001;Klimesch et al, 1997;Klimesch, 1999;Sarnthein et al, 1998;TallonBaudry et al, 1999;von Stein et al, 1999;Tesche and Karhu, 2000;Raghavachari et al, 2001;Jensen et al, 2002;Schack et al, 2002;Howard et al, 2003;Rizzuto et al, 2003;Gruber and Muller, 2005;Kaiser and Lutzenberger, 2005). In line with these studies, we found an increase in d-, y-, a-, and b-activity with WM load.…”

Section: Discussionsupporting

confidence: 90%

Potentiation of Gamma Oscillatory Activity through Repetitive Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex

Barr

Farzan

Rusjan

et al. 2009

Neuropsychopharmacol

105

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Neuronal oscillations in the gamma (g) frequency range (30-50 Hz) have been associated with cognition. Working memory (WM), a cognitive task involving the on-line maintenance and manipulation of information, elicits increases in g oscillations with greater cognitive demand, particularly in the dorsolateral prefrontal cortex (DLPFC). The generation and modulation of g oscillations have been attributed to inhibitory interneuron networks that use g -aminobutyric acid (GABA) as their principal neurotransmitter. Repetitive transcranial magnetic stimulation (rTMS) represents a non-invasive method to stimulate the cortex that has been shown to modify cognition and GABA inhibitory mechanisms, particularly with higher frequencies (ie, 10-20 Hz). We measured the effect of high-frequency rTMS applied to the DLPFC on g-oscillations elicited during the N-back WM task in healthy individuals. Active rTMS significantly increased g-oscillations generated during the N-back conditions with the greatest cognitive demand. Further, no significant changes were found in other frequency ranges, suggesting that rTMS selectively modulates g-oscillations in the frontal brain regions. These findings provide important insights into the neurophysiological mechanisms that underlie higher-order cognitive processes, and suggest that rTMS may be used as a cognitive enhancing strategy in neuropsychiatric disorders that suffer from cognitive deficits.

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Section: Discussionsupporting

confidence: 90%

Potentiation of Gamma Oscillatory Activity through Repetitive Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex

Barr

Farzan

Rusjan

et al. 2009

Neuropsychopharmacol

105

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“…A simple example of this is frequency doubling when one squares a sinusoid; i.e., exp(jωt) 2 = exp(j2ωt) (see Friston (2001)). Biological evidence speaks to the prevalence of non-linear interactions among cortical areas during cognitive tasks (e.g., Bullock et al, 1997;Schack et al, 2002). We have shown that second-order features of the data (i.e., the spectrum) can be modelled by DCM for induced responses in a way that can disambiguate between linear and non-linear coupling.…”

Section: Discussionmentioning

confidence: 95%

Dynamic causal modelling of induced responses

2008

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This paper describes a dynamic causal model (DCM) for induced or spectral responses as measured with the electroencephalogram (EEG) or the magnetoencephalogram (MEG). We model the time-varying power, over a range of frequencies, as the response of a distributed system of coupled electromagnetic sources to a spectral perturbation. The model parameters encode the frequency response to exogenous input and coupling among sources and different frequencies. The Bayesian inversion of this model, given data enables inferences about the parameters of a particular model and allows us to compare different models, or hypotheses. One key aspect of the model is that it differentiates between linear and non-linear coupling; which correspond to within and betweenfrequency coupling respectively. To establish the face validity of our approach, we generate synthetic data and test the identifiability of various parameters to ensure they can be estimated accurately, under different levels of noise. We then apply our model to EEG data from a faceperception experiment, to ask whether there is evidence for non-linear coupling between early visual cortex and fusiform areas.

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“…The increase of the synchronization of theta band during the math task as shown by the main task effect is explained by the increase in WM function during math [Canolty et al, 2006;Humphries et al, 2006;Schack et al, 2002;Stam et al, 2002]. The more prominent enhancement of theta band synchronization during WM in adults could be explained as the result of the differences in the WM capacity.…”

Section: Synchronization As Expressed By the Slmentioning

confidence: 86%

The influence of ageing on complex brain networks: A graph theoretical analysis

Micheloyannis

Vourkas

Tsirka

et al. 2007

Human Brain Mapping

136

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Objective: To determine the functional connectivity of different EEG bands at the ''baseline'' situation (rest) and during mathematical thinking in children and young adults to study the maturation effect on brain networks at rest and during a cognitive task. Methods: Twenty children (8-12 years) and twenty students (21-26 years) were studied. The synchronization likelihood was used to evaluate the interregional synchronization of different EEG frequency bands in children and adults, at rest and during math. Then, graphs were constructed and characterized in terms of local structure (clustering coefficient) and overall integration (path length) and the ''optimal'' organization of the connectivity i.e., the small world network (SWN). Results: The main findings were: (i) Enhanced synchronization for theta band during math more prominent in adults. (ii) Decrease of the optimal SWN organization of the alpha2 band during math. (iii) The beta and especially gamma bands showed lower synchronization and signs of lower SWN organization in both situations in adults. Conclusion: There are interesting findings related to the two age groups and the two situations. The theta band showed higher synchronization during math in adults as a result of higher capacity of the working memory in this age group. The alpha2 band showed some SWN disorganization during math, a process analog to the known desynchronization. In adults, a dramatic reduction of the connections in gray matter occurs. Although this maturation process is probably related to higher efficiency, reduced connectivity is expressed by lower synchronization and lower mean values of the graph parameters in adults.

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Phase-coupling of theta–gamma EEG rhythms during short-term memory processing

Cited by 257 publications

References 49 publications

Potentiation of Gamma Oscillatory Activity through Repetitive Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex

Potentiation of Gamma Oscillatory Activity through Repetitive Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex

Dynamic causal modelling of induced responses

The influence of ageing on complex brain networks: A graph theoretical analysis

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