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      Alpha Power Predicts Persistence of Bistable Perception

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          Abstract

          Perception is strongly affected by the intrinsic state of the brain, which controls the propensity to either maintain a particular perceptual interpretation or switch to another. To understand the mechanisms underlying the spontaneous drive of the brain to explore alternative interpretations of unchanging stimuli, we repeatedly recorded high-density EEG after normal sleep and after sleep deprivation while participants observed a Necker cube image and reported the durations of the alternating representations of their bistable perception. We found that local alpha power around the parieto-occipital sulcus within the first second after the emergence of a perceptual representation predicted the fate of its duration. An experimentally induced increase in alpha power by means of sleep deprivation increased the average duration of individual representations. Taken together, these findings show that high alpha power promotes the stability of a perceptual representation and suppresses switching to the alternative. The observations support the hypothesis that synchronization of alpha oscillations across a wide neuronal network promotes the maintenance and stabilization of its current perceptual representation. Elevated alpha power could also be key to the poorly understood cognitive deficits, that typically accompany sleep deprivation, such as the loss of mental flexibility and lapses of responsiveness.

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          Alpha-band oscillations, attention, and controlled access to stored information

          Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.
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            Removing electroencephalographic artifacts by blind source separation.

            Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
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              Dynamic imaging of coherent sources: Studying neural interactions in the human brain.

              Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept ("binding") is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto- or electroencephalography (MEG/EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.
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                Author and article information

                Contributors
                gio@gpiantoni.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 July 2017
                12 July 2017
                2017
                : 7
                : 5208
                Affiliations
                [1 ]Dept. Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
                [2 ]ISNI 0000 0001 2171 8263, GRID grid.419918.c, Dept. Sleep and Cognition, , Netherlands Institute for Neuroscience, ; Amsterdam, The Netherlands
                [3 ]ISNI 0000 0004 0435 165X, GRID grid.16872.3a, Dept. Anatomy and Neurosciences, , VU University Medical Center, ; Amsterdam, The Netherlands
                [4 ]ISNI 0000 0004 1754 9227, GRID grid.12380.38, Dept. Integrative Neurophysiology, , VU University, ; Amsterdam, The Netherlands
                [5 ]ISNI 0000 0004 0435 165X, GRID grid.16872.3a, Dept. Psychiatry, , VU University Medical Center, ; Amsterdam, The Netherlands
                Author information
                http://orcid.org/0000-0002-5308-926X
                Article
                5610
                10.1038/s41598-017-05610-8
                5507912
                28701732
                f013ab7e-0cf9-4e2d-b119-257f90eaa3ac
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 March 2017
                : 31 May 2017
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