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      Narrative Review: Quantitative EEG in Disorders of Consciousness

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          Abstract

          In this narrative review, we focus on the role of quantitative EEG technology in the diagnosis and prognosis of patients with unresponsive wakefulness syndrome and minimally conscious state. This paper is divided into two main parts, i.e., diagnosis and prognosis, each consisting of three subsections, namely, (i) resting-state EEG, including spectral power, functional connectivity, dynamic functional connectivity, graph theory, microstates and nonlinear measurements, (ii) sleep patterns, including rapid eye movement (REM) sleep, slow-wave sleep and sleep spindles and (iii) evoked potentials, including the P300, mismatch negativity, the N100, the N400 late positive component and others. Finally, we summarize our findings and conclude that QEEG is a useful tool when it comes to defining the diagnosis and prognosis of DOC patients.

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          Matthew J L Page, Joanne E McKenzie, Patrick Bossuyt … (2024)
          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Assessment of coma and impaired consciousness. A practical scale.

            G Teasdale, B Jennett (1974)
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              Dynamic functional connectivity: promise, issues, and interpretations.

              R. Hutchison, Thilo Womelsdorf, Elena A. Allen … (2013)
              The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations. Copyright © 2013 Elsevier Inc. All rights reserved.
              Article 0 comments Cited 530 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Emilio Portaccio: Role: Academic Editor
                Journal
                Journal ID (nlm-ta): Brain Sci
                Journal ID (iso-abbrev): Brain Sci
                Journal ID (publisher-id): brainsci
                Title: Brain Sciences
                Publisher: MDPI
                ISSN (Electronic): 2076-3425
                Publication date (Electronic): 25 May 2021
                Publication date Collection: June 2021
                Volume: 11
                Issue: 6
                Electronic Location Identifier: 697
                Affiliations
                [1 ]Graduate School of Information Science and Technology, Osaka University, Suita 565-0871, Japan; b-wutzl@ 123456ist.osaka-u.ac.jp (B.W.); leibnitz@ 123456nict.go.jp (K.L.)
                [2 ]Symbiotic Intelligent Systems Research Center, Osaka University, Suita 565-0871, Japan
                [3 ]Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; s.golaszewski@ 123456salk.at (S.M.G.); patrickbenjamin.langthaler@ 123456stud.sbg.ac.at (P.B.L.); a.kunz@ 123456salk.at (A.B.K.); s.leis@ 123456salk.at (S.L.); k.schwenker@ 123456salk.at (K.S.); a.thomschewski@ 123456salk.at (A.T.); J.Bergmann@ 123456salk.at (J.B.)
                [4 ]Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, 5020 Salzburg, Austria
                [5 ]Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
                [6 ]Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita 565-0871, Japan
                [7 ]Department of Mathematics, Paris Lodron University of Salzburg, 5020 Salzburg, Austria
                [8 ]Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
                [9 ]Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
                Author notes
                [* ]Correspondence: e.trinka@ 123456salk.at ; Tel.: +43-5-7255-34600
                [†]

                Deceased 7 November 2020.

                Author information
                https://orcid.org/0000-0003-0183-7067
                https://orcid.org/0000-0002-3691-3675
                https://orcid.org/0000-0002-4386-8497
                https://orcid.org/0000-0002-1811-5469
                Article
                Publisher ID: brainsci-11-00697
                DOI: 10.3390/brainsci11060697
                PMC ID: 8228474
                PubMed ID: 34070647
                SO-VID: 0cf44bfe-ccb3-4a35-b6a4-5d3e05dd9ee9
                Copyright © © 2021 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 15 March 2021
                Date accepted : 21 May 2021
                Categories
                Subject: Review

                Keywords: unresponsive wakefulness syndrome,minimally conscious state,eeg,quantitative eeg,disorders of consciousness,diagnosis,prognosis
                Data availability:
                Keywords: unresponsive wakefulness syndrome, minimally conscious state, eeg, quantitative eeg, disorders of consciousness, diagnosis, prognosis

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