Attention changes the peak latency of the visual gamma-band oscillation of the EEG

Shibata, Tadahiko; Shimoyama, Ichiro; Iwamoto, Toshihiko; Abla, Dilshat; Iwasa, Hiroto; Koseki, Keijiro; Yamanouchi, Naoto; Sato, Toshio; Nakajima, Yoshio

doi:10.1097/00001756-199904260-00002

Cited by 38 publications

(24 citation statements)

References 11 publications

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“…The ERS values obtained using the original method agree with the results obtained in previous works: ERS ≈ 10–20% [54,55]. More recent studies [56] observed an increase in gamma-band activity in different tasks (emotional stimuli, face recognition, and motor control) using invasive techniques, such as electrocorticography.…”

Section: Discussionsupporting

confidence: 88%

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition

Amo

Santiago

Barea

et al. 2017

Sensors

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The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD decomposes the original signal into a finite number of intrinsic mode functions (IMFs). EEGs from 25 control subjects were registered in basal and motor activity (hand movements) using only one EEG channel. Over the basic signal, IMF signals are computed. Gamma-band activity is computed using power spectrum density in the 30–60 Hz range. Event-related synchronization (ERS) was defined as the ratio of motor and basal activity. To evaluate the performance of the new EMD based method, ERS was computed from the basic and IMF signals. The ERS obtained using IMFs improves, from 31.00% to 73.86%, on the original ERS for the right hand, and from 22.17% to 47.69% for the left hand. As EEG processing is improved, the clinical applications of gamma-band activity will expand.

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Section: Discussionsupporting

confidence: 88%

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition

Amo

Santiago

Barea

et al. 2017

Sensors

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“…For example, cats (Gray et al, 1989; Lakatos et al, 2004), rats (Sukov and Barth, 2001) and mice (Ehrlichman et al, 2009; Lazarewicz et al, 2010; Nase et al, 2003) show a peak in phase-locked gamma activity (∼40 Hz) within the first 100 ms of auditory or visual stimulation, following a time course similar to that observed in humans (Pantev et al, 1991; Shibata et al, 1999). Indeed, similar evoked GBRs have been observed in insects during sensory stimulation, suggesting that gamma synchrony is a phylogenetically conserved neural coding mechanism (MacLeod and Laurent, 1996).…”

Section: 0 Gamma Oscillations In Preclinical Studies Of Schizophreniamentioning

confidence: 76%

Gamma synchrony: Towards a translational biomarker for the treatment-resistant symptoms of schizophrenia

et al. 2012

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The lack of efficacy for antipsychotics with respect to negative symptoms and cognitive deficits is a significant obstacle for the treatment of schizophrenia. Developing new drugs to target these symptoms requires appropriate neural biomarkers that can be investigated in model organisms, be used to track treatment response, and provide insight into pathophysiological disease mechanisms. A growing body of evidence indicates that neural oscillations in the gamma frequency range (30–80 Hz) are disturbed in schizophrenia. Gamma synchrony has been shown to mediate a host of sensory and cognitive functions, including perceptual encoding, selective attention, salience, and working memory – neurocognitive processes that are dysfunctional in schizophrenia and largely refractory to treatment. This review summarizes the current state of clinical literature with respect to gamma band responses (GBRs) in schizophrenia, focusing on resting and auditory paradigms. Next, preclinical studies of schizophrenia that have investigated gamma band activity are reviewed to gain insight into neural mechanisms associated with these deficits. We conclude that abnormalities in gamma synchrony are ubiquitous in schizophrenia and likely reflect an elevation in baseline cortical gamma synchrony (‘noise’) coupled with reduced stimulus-evoked GBRs (‘signal’). Such a model likely reflects hippocampal and cortical dysfunction, as well as reduced glutamatergic signaling with downstream GABAergic deficits, but is probably less influenced by dopaminergic abnormalities implicated in schizophrenia. Finally, we propose that analogous signal-to-noise deficits in the flow of cortical information in preclinical models are useful targets for the development of new drugs that target the treatment-resistant symptoms of schizophrenia.

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“…Advances in neuroimaging technologies such as the hemodynamic functional imaging methodologies (fMRI and PET) and the ability to conduct high-density multi-channel electro- and magneto- encephalographic recordings (EEG and MEG) have allowed for unprecedented advances in our understanding of the physiology of human selective attention. This report specifically addresses evidence for the role of oscillatory brain mechanisms in selective attention (e.g., Vanni et al, 1997; Foxe et al, 1998; Gruber et al, 1999; Shibata et al, 1999; Sokolov et al, 1999; Worden et al, 2000; Sauseng et al, 2005; Yamagishi et al, 2005; Kelly et al, 2006; Thut et al, 2006; Rihs et al, 2007; Snyder and Foxe, 2010). …”

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confidence: 86%

The Role of Alpha-Band Brain Oscillations as a Sensory Suppression Mechanism during Selective Attention

Foxe¹,

Snyder²

2011

Front. Psychology

1,115

938

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Evidence has amassed from both animal intracranial recordings and human electrophysiology that neural oscillatory mechanisms play a critical role in a number of cognitive functions such as learning, memory, feature binding and sensory gating. The wide availability of high-density electrical and magnetic recordings (64–256 channels) over the past two decades has allowed for renewed efforts in the characterization and localization of these rhythms. A variety of cognitive effects that are associated with specific brain oscillations have been reported, which range in spectral, temporal, and spatial characteristics depending on the context. Our laboratory has focused on investigating the role of alpha-band oscillatory activity (8–14 Hz) as a potential attentional suppression mechanism, and this particular oscillatory attention mechanism will be the focus of the current review. We discuss findings in the context of intersensory selective attention as well as intrasensory spatial and feature-based attention in the visual, auditory, and tactile domains. The weight of evidence suggests that alpha-band oscillations can be actively invoked within cortical regions across multiple sensory systems, particularly when these regions are involved in processing irrelevant or distracting information. That is, a central role for alpha seems to be as an attentional suppression mechanism when objects or features need to be specifically ignored or selected against.

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Attention changes the peak latency of the visual gamma-band oscillation of the EEG

Cited by 38 publications

References 11 publications

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition

Gamma synchrony: Towards a translational biomarker for the treatment-resistant symptoms of schizophrenia

The Role of Alpha-Band Brain Oscillations as a Sensory Suppression Mechanism during Selective Attention

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