U-shaped Relation between Prestimulus Alpha-band and Poststimulus Gamma-band Power in Temporal Tactile Perception in the Human Somatosensory Cortex

Neuronal oscillations are a ubiquitous phenomenon in the human nervous system. Alpha-band oscillations (8–12 Hz) have been shown to correlate negatively with attention and performance, whereas gamma-band oscillations (40–150 Hz) correlate positively. Here, we studied the relation between prestimulus alpha-band power and poststimulus gamma-band power in a suprathreshold tactile discrimination task. Participants received two electrical stimuli to their left index finger with different SOAs (0 msec, 100 msec, intermediate SOA, intermediate SOA ± 10 msec). The intermediate SOA was individually determined so that stimulation was bistable, and participants perceived one stimulus in half of the trials and two stimuli in the other half. We measured neuronal activity with magnetoencephalography (MEG). In trials with intermediate SOAs, behavioral performance correlated inversely with prestimulus alpha-band power but did not correlate with poststimulus gamma-band power. Poststimulus gamma-band power was high in trials with low and high prestimulus alpha-band power and low for intermediate prestimulus alpha-band power (i.e., U-shaped). We suggest that prestimulus alpha activity modulates poststimulus gamma activity and subsequent perception: (1) low prestimulus alpha-band power leads to high poststimulus gamma-band power, biasing perception such that two stimuli were perceived; (2) intermediate prestimulus alpha-band power leads to low gamma-band power (interpreted as inefficient stimulus processing), consequently, perception was not biased in either direction; and (3) high prestimulus alpha-band power leads to high poststimulus gamma-band power, biasing perception such that only one stimulus was perceived.

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Decrease in early right alpha band phase synchronization and late gamma band oscillations in processing syntax in music

Human Brain Mapping ◽

10.1002/hbm.20584 ◽

2009 ◽

Vol 30 (4) ◽

pp. 1207-1225 ◽

Cited By ~ 22

Author(s):

María Herrojo Ruiz ◽

Stefan Koelsch ◽

Joydeep Bhattacharya

Keyword(s):

Phase Synchronization ◽

Gamma Band ◽

Alpha Band ◽

Gamma Band Oscillations

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Comparison of evoked potentials and high-frequency (gamma-band) oscillating potentials in rat auditory cortex

Journal of Neurophysiology ◽

10.1152/jn.1995.74.1.96 ◽

1995 ◽

Vol 74 (1) ◽

pp. 96-112 ◽

Cited By ~ 61

Author(s):

M. N. Franowicz ◽

D. S. Barth

Keyword(s):

Steady State ◽

Auditory Cortex ◽

Evoked Potentials ◽

Slow Wave ◽

Gamma Oscillations ◽

Gamma Band ◽

Gamma Activity ◽

Sharp Wave ◽

Gamma Band Oscillations ◽

40 Hz

1. Transient and steady-state (40 Hz) evoked potentials, as well as spontaneous and click-evoked gamma-band oscillations, were recorded from 15 lightly anesthetized rats using an 8 x 8 electrode epipial array covering auditory cortex and adjacent areas to determine and compare the spatiotemporal distributions of these four phenomena. 2. The transient evoked response replicated earlier findings in our laboratory, consisting of an initial biphasic sharp wave in area 41, a similar but delayed biphasic sharp wave in area 36, and more widely distributed slow-wave components. Spatiotemporal analysis supported a model of parallel and asynchronous activation of distinct groups of thalamocortical projections underlying the neurogenesis of these temporal components of the middle-latency auditory evoked potential (MAEP) complex. 3. The 40-Hz response to click trains was superimposed on a steady potential shift (SP), both of which were localized within primary auditory cortex. Epipial distributions of the SP were similar to those of the shortest-latency negative peak in area 41 recorded in the same animals, suggesting similar neural generators. The 40-Hz response was more focal and dissimilar from the SP and any other temporal components of the MAEP complex, suggesting that a unique subpopulation of cells underlies its neurogenesis. 4. Spontaneous gamma-band activity, as assessed by power spectrum analysis, was localized to primary and secondary auditory cortex but had a variable distribution between rats that did not conform to the cytoarchitectonic boundaries within subdivisions of this region. Digital movies computed for individual bursts of gamma-activity indicated a high degree of spatiotemporal variability within and between bursts. 5. Single-trial spectral analysis of click responses indicated an inhibition of gamma-band oscillations during most of the MAEP complex, with subsequent enhanced gamma-activity during the 300- to 350-ms slow-wave component that outlasted the MAEP by approximately 500 ms. The epipial distributions of prestimulus and enhanced poststimulus gamma-oscillations were the same. In contrast to the 40-Hz response to click trains, phase-locking of gamma-oscillations by the single click stimulus was not observed. 6. These results suggest that both the MAEP complex and the steady-state 40-Hz response with its associated SP are highly stereotyped in lightly anesthetized rodent cortex. Their spatiotemporal distributions are probably determined in large part by asynchronous activation of parallel thalamocortical projection systems. Our data suggest no direct link between either the MAEP or the steady-state 40-Hz response to spontaneous or evoked gamma-band oscillations in auditory cortex.(ABSTRACT TRUNCATED AT 400 WORDS)

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Differing event-related patterns of gamma-band power in brain waves of fast- and slow-reacting subjects.

PsycEXTRA Dataset ◽

10.1037/e476592004-001 ◽

1994 ◽

Author(s):

H. Jokeit ◽

S. Makeig

Keyword(s):

Gamma Band ◽

Brain Waves ◽

Band Power

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Pre‐stimulus alpha‐band power and phase fluctuations originate from different neural sources and exert distinct impact on stimulus‐evoked responses

European Journal of Neuroscience ◽

10.1111/ejn.15138 ◽

2021 ◽

Author(s):

Agnese Zazio ◽

Philipp Ruhnau ◽

Nathan Weisz ◽

Andreas Wutz

Keyword(s):

Evoked Responses ◽

Alpha Band ◽

Phase Fluctuations ◽

Band Power

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SC/FDE combined with MIMO: An improved out of band power and performance via tamed frequency modulation

2007 IEEE Sarnoff Symposium ◽

10.1109/sarnof.2007.4567313 ◽

2007 ◽

Cited By ~ 2

Author(s):

T. Buzid ◽

M. Huemer ◽

S. Reinhardt

Keyword(s):

Frequency Modulation ◽

Band Power ◽

And Performance ◽

Out Of Band Power

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A Novel Tool for the Removal of Muscle Artefacts from EEG: Improving Data Quality in the Gamma Frequency Range

10.1101/2020.11.23.393702 ◽

2020 ◽

Author(s):

Alina Pauline Liebisch ◽

Thomas Eggert ◽

Alina Shindy ◽

Elia Valentini ◽

Stephanie Irving ◽

...

Keyword(s):

Data Quality ◽

Time Course ◽

Muscular Activity ◽

Gamma Oscillations ◽

Gamma Band ◽

Gamma Activity ◽

Single Muscle ◽

Frequency Range ◽

Continuous Eeg ◽

Artefact Removal

AbstractBackgroundThe past two decades have seen a particular focus towards high-frequency neural activity in the gamma band (>30Hz). However, gamma band activity shares frequency range with unwanted artefacts from muscular activity.New MethodWe developed a novel approach to remove muscle artefacts from neurophysiological data. We re-analysed existing EEG data that were decomposed by a blind source separation method (independent component analysis, ICA), which helped to better spatially and temporally separate single muscle spikes. We then applied an adapting algorithm that detects these singled-out muscle spikes.ResultsWe obtained data almost free from muscle artefacts; we needed to remove significantly fewer artefact components from the ICA and we included more trials for the statistical analysis compared to standard ICA artefact removal. All pain-related cortical effects in the gamma band have been preserved, which underlines the high efficacy and precision of this algorithm.ConclusionsOur results show a significant improvement of data quality by preserving task-relevant gamma oscillations of cortical origin. We were able to precisely detect, gauge, and carve out single muscle spikes from the time course of neurophysiological measures. We advocate the application of the tool for studies investigating gamma activity that contain a rather low number of trials, as well as for data that are highly contaminated with muscle artefacts. This validation of our tool allows for the application on event-free continuous EEG, for which the artefact removal is more challenging.

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