Causal influence of gamma oscillations on the sensorimotor rhythm

Grosse-Wentrup, Moritz; Schölkopf, Bernhard; Hill, N. Jeremy

doi:10.1016/j.neuroimage.2010.04.265

Cited by 106 publications

(99 citation statements)

References 21 publications

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“…Thus, we compared the difference scores at 12-15 Hz EEG relative power among Fz, Cz, Pz, and Oz between pre-and posttest sessions. Previous work has shown that the SMR originated in the centro-parietal region (Grosse-Wentrup, Schölkopf, & Hill, 2011). Thus, we hypothesized that the difference score of 12-15 Hz at Cz would be greater than that of the frontal and occipital regions for SMR group participants after training.…”

Section: Within-session Learningmentioning

confidence: 87%

Sensorimotor Rhythm Neurofeedback Enhances Golf Putting Performance

Cheng

Huang

Chang

et al. 2015

Journal of Sport and Exercise Psychology

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Sensorimotor rhythm (SMR) activity has been related to automaticity during skilled action execution. However, few studies have bridged the causal link between SMR activity and sports performance. This study investigated the effect of SMR neurofeedback training (SMR NFT) on golf putting performance. We hypothesized that preelite golfers would exhibit enhanced putting performance after SMR NFT. Sixteen preelite golfers were recruited and randomly assigned into either an SMR or a control group. Participants were asked to perform putting while electroencephalogram (EEG) was recorded, both before and after intervention. Our results showed that the SMR group performed more accurately when putting and exhibited greater SMR power than the control group after 8 intervention sessions. This study concludes that SMR NFT is effective for increasing SMR during action preparation and for enhancing golf putting performance. Moreover, greater SMR activity might be an EEG signature of improved attention processing, which induces superior putting performance.

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Section: Within-session Learningmentioning

confidence: 87%

Sensorimotor Rhythm Neurofeedback Enhances Golf Putting Performance

Cheng

Huang

Chang

et al. 2015

Journal of Sport and Exercise Psychology

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“…Most of these studies focus on inter-subject variability from a physiological [2][3][4][5][6], anatomical [7,8], or psychological [9,10] perspectives. Although precise distinction between user-related and system-related causes of performance variations may not be simple [11], these studies provide a better understanding of these causes.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of these studies are limited to the analysis of a single session using offline recordings (where no feedback was provided to the subject). For instance, it is suggested that classification certainty for each subject is positively correlated with the power of EEG oscillations in the gamma band (55 − 85 Hz) [3,6]. Similarly, trial-by-trial classification performance was found to correlate with high-frequency gamma oscillations (70 − 80 Hz) prior to the beginning of each trial [15], while others found correlations with a weighted combination of the theta (3 − 8 Hz), alpha (8 − 13 Hz), and beta (16 − 24 Hz) oscillations in frontal, pariatel and central areas of the brain, respectively [16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

Saeedi

Chavarriaga

Leeb

et al. 2016

IEEE Comput. Intell. Mag.

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Abstract. One of the challenges of using brain-computer interfaces (BCIs) over extended periods of time is the variation of the users' performance from one experimental day to another. The goal of the current study is to propose a performance estimator for an electroencephalography-based motor imagery BCI by assessing the reliability of a command (i.e., predicting a 'short' or 'long' command delivery time, CDT). Using a short time window (< 1.5 s, shorter than the delivery time) of the mental task execution and a linear discriminant analysis classifier, we could reliably differentiate between short and long CDT (Area under the sensitivity-specificity curve, AUC ≈ 0.8) for 9 healthy subjects. Moreover, we assessed the feasibility of providing online adaptive assistance using the performance estimator in a BCI game by comparing two conditions: (i) allowing a 'fixed timeout' to deliver each command or (ii) providing 'adaptive assistance' by giving more time if the performance estimator detects a long CDT. The results revealed that providing adaptive assistance increases the ratio of correct commands significantly (p < 0.01). Moreover, the task load index (measured via the NASA TLX questionnaire) shows a significantly higher user acceptance in case of providing adaptive assistance (p < 0.01). Furthermore, the results obtained in this study were used to simulate a robotic navigation scenario, which showed how adaptive assistance improved performance.

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“…Of particular interest is rhythmic network activity in the gamma-frequency band (30-80 Hz, gamma oscillations) (15)(16)(17). Gamma oscillations are a result of the synchronized electrical activity of the neurons within a network and are thought to be important for temporal encoding, binding of sensory features, and memory storage and retrieval (18)(19)(20)(21)(22).…”

mentioning

confidence: 99%

TrkB signaling in parvalbumin-positive interneurons is critical for gamma-band network synchronization in hippocampus

Zheng

An²,

Yang

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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Although brain-derived neurotrophic factor (BDNF) is known to regulate circuit development and synaptic plasticity, its exact role in neuronal network activity remains elusive. Using mutant mice (TrkB-PV −/− ) in which the gene for the BDNF receptor, tyrosine kinase B receptor (trkB), has been specifically deleted in parvalbuminexpressing, fast-spiking GABAergic (PV+) interneurons, we show that TrkB is structurally and functionally important for the integrity of the hippocampal network. The amplitude of glutamatergic inputs to PV+ interneurons and the frequency of GABAergic inputs to excitatory pyramidal cells were reduced in the TrkB-PV −/− mice. Functionally, rhythmic network activity in the gamma-frequency band (30-80 Hz) was significantly decreased in hippocampal area CA1. This decrease was caused by a desynchronization and overall reduction in frequency of action potentials generated in PV+ interneurons of TrkB-PV −/− mice. Our results show that the integration of PV+ interneurons into the hippocampal microcircuit is impaired in TrkB-PV −/− mice, resulting in decreased rhythmic network activity in the gamma-frequency band.gamma oscillations | synaptic transmission | Cre recombinase | dendrite | slice T yrosine kinase B receptor (TrkB), the cognate receptor for brain-derived neurotrophic factor (BDNF) and neurotrophin-4, mediates key signaling events that control many aspects of neuronal development and function (1-4), including the maturation of parvalbumin-positive (PV+) interneurons in the hippocampal microcircuit. BDNF is preferentially synthesized in, and secreted from glutamatergic neurons, whereas trkB is expressed in both glutamatergic and γ-aminobutyric acid (GABA)-ergic neurons in hippocampus (5). Among cortical interneurons, PV+ interneurons express trkB abundantly (6). This anatomical organization of the BDNF signaling components and the known importance of feedback and feedforward communication between principal cells and interneurons (7-11) suggest a potential role for TrkB signaling in modulating neuronal network function.Rhythmic activity in cortical networks is important for the formation of neuronal assemblies (12)(13)(14). Of particular interest is rhythmic network activity in the gamma-frequency band (30-80 Hz, gamma oscillations) (15-17). Gamma oscillations are a result of the synchronized electrical activity of the neurons within a network and are thought to be important for temporal encoding, binding of sensory features, and memory storage and retrieval (18)(19)(20)(21)(22). Moreover, gamma oscillations are altered in several brain disorders, such as Alzheimer's disease (23-25), schizophrenia (24, 26-31), and epilepsy (24,32,33). Gamma oscillations are exquisitely susceptible to modulation of the cellular and synaptic mechanisms underlying the rhythmic activity. Fast-spiking PV+ interneurons are the main recipient of recurrent glutamatergic innervations in the hippocampal circuitry, and their role in gamma-frequency synchronization in cortical and hippocampal networks is well-establish...

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Causal influence of gamma oscillations on the sensorimotor rhythm

Cited by 106 publications

References 21 publications

Sensorimotor Rhythm Neurofeedback Enhances Golf Putting Performance

Sensorimotor Rhythm Neurofeedback Enhances Golf Putting Performance

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

TrkB signaling in parvalbumin-positive interneurons is critical for gamma-band network synchronization in hippocampus

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