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      The neural correlates of dreaming

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          Abstract

          Consciousness never fades during wake. However, if awakened from sleep, sometimes we report dreams and sometimes no experiences. Traditionally, dreaming has been identified with REM sleep, characterized by a wake-like, globally ‘activated’, high-frequency EEG. However, dreaming also occurs in NREM sleep, characterized by prominent low-frequency activity. This challenges our understanding of the neural correlates of conscious experiences in sleep. Using high-density EEG, we contrasted the presence and absence of dreaming within NREM and REM sleep. In both NREM and REM sleep, reports of dream experience were associated with a local decrease in low-frequency activity in posterior cortical regions. High-frequency activity within these regions correlated with specific dream contents. Monitoring this posterior ‘hot zone’ predicted whether an individual reported dreaming or the absence of experiences during NREM sleep in real time, suggesting that it may constitute a core correlate of conscious experiences in sleep.

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          Removing electroencephalographic artifacts by blind source separation.

          T P Jung, S Makeig, C. Humphries … (2000)
          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.
          Article 0 comments Cited 734 times – based on 0 reviews     Review now
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            Neural correlates of consciousness: progress and problems.

            Christof Koch, Marcello Massimini, Melanie Boly … (2016)
            There have been a number of advances in the search for the neural correlates of consciousness--the minimum neural mechanisms sufficient for any one specific conscious percept. In this Review, we describe recent findings showing that the anatomical neural correlates of consciousness are primarily localized to a posterior cortical hot zone that includes sensory areas, rather than to a fronto-parietal network involved in task monitoring and reporting. We also discuss some candidate neurophysiological markers of consciousness that have proved illusory, and measures of differentiation and integration of neural activity that offer more promising quantitative indices of consciousness.
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              Natural waking and sleep states: a view from inside neocortical neurons.

              M Steriade, I Timofeev, F Grenier (2001)
              In this first intracellular study of neocortical activities during waking and sleep states, we hypothesized that synaptic activities during natural states of vigilance have a decisive impact on the observed electrophysiological properties of neurons that were previously studied under anesthesia or in brain slices. We investigated the incidence of different firing patterns in neocortical neurons of awake cats, the relation between membrane potential fluctuations and firing rates, and the input resistance during all states of vigilance. In awake animals, the neurons displaying fast-spiking firing patterns were more numerous, whereas the incidence of neurons with intrinsically bursting patterns was much lower than in our previous experiments conducted on the intact-cortex or isolated cortical slabs of anesthetized cats. Although cortical neurons displayed prolonged hyperpolarizing phases during slow-wave sleep, the firing rates during the depolarizing phases of the slow sleep oscillation was as high during these epochs as during waking and rapid-eye-movement sleep. Maximum firing rates, exceeding those of regular-spiking neurons, were reached by conventional fast-spiking neurons during both waking and sleep states, and by fast-rhythmic-bursting neurons during waking. The input resistance was more stable and it increased during quiet wakefulness, compared with sleep states. As waking is associated with high synaptic activity, we explain this result by a higher release of activating neuromodulators, which produce an increase in the input resistance of cortical neurons. In view of the high firing rates in the functionally disconnected state of slow-wave sleep, we suggest that neocortical neurons are engaged in processing internally generated signals.
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9809671
                Journal ID (pubmed-jr-id): 21092
                Journal ID (nlm-ta): Nat Neurosci
                Journal ID (iso-abbrev): Nat. Neurosci.
                Title: Nature neuroscience
                ISSN (Print): 1097-6256
                ISSN (Electronic): 1546-1726
                Publication date Nihms-submitted: 1 April 2017
                Publication date (Electronic): 10 April 2017
                Publication date (Print): June 2017
                Publication date PMC-release: 10 October 2017
                Volume: 20
                Issue: 6
                Pages: 872-878
                Affiliations
                [1 ]Department of Psychiatry, University of Wisconsin - Madison, Madison, Wisconsin 53519, USA
                [2 ]Center for Research and Investigation in Sleep (CIRS), Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
                [3 ]Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
                [4 ]Department of Neuroscience, University of Geneva, Switzerland
                [5 ]IMT School for Advanced Studies, Lucca, Italy
                [6 ]Medical Scientist Training Program and Neuroscience Training Program, University of Wisconsin, Madison, Wisconsin 53519, USA
                [7 ]Department of Neurology, University of Wisconsin- Madison, Madison, Wisconsin 53519, USA
                [8 ]Department of Psychology, University of Wisconsin- Madison, Madison, Wisconsin 53519, USA
                Author notes
                Correspondence: Giulio Tononi, MD PhD, Department of Psychiatry, University of Wisconsin - Madison 6001 Research Park Blvd, 53719 Madison, Wisconsin, USA, gtononi@ 123456wisc.edu
                [†]

                These authors contributed equally to this work.

                Article
                Manuscript ID: NIHMS861021
                DOI: 10.1038/nn.4545
                PMC ID: 5462120
                PubMed ID: 28394322
                SO-VID: 8126682f-106a-4c5a-b662-6dd33e99bcc1
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                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                Subject: Article

                ScienceOpen disciplines: Neurosciences
                Keywords: consciousness,sleep,high-density eeg,source modeling,dreams
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: consciousness, sleep, high-density eeg, source modeling, dreams

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