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EEG biomarkers in major depressive disorder: Discriminative power and prediction of treatment response.

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Title: EEG biomarkers in major depressive disorder: Discriminative power and prediction of treatment response.
Authors: Olbrich, Sebastian (AUTHOR), Arns, Martijn (AUTHOR)
Source: International Review of Psychiatry. Oct2013, Vol. 25 Issue 5, p604-618. 15p.
Subjects: Antidepressants, Biomarkers, Mental depression, Electroencephalography, Evoked potentials (Electrophysiology), Health outcome assessment, Sleep, Treatment effectiveness
Abstract: Major depressive disorder (MDD) has high population prevalence and is associated with substantial impact on quality of life, not least due to an unsatisfactory time span of sometimes several weeks from initiation of treatment to clinical response. Therefore extensive research focused on the identification of cost-effective and widely available electroencephalogram (EEG)-based biomarkers that not only allow distinguishing between patients and healthy controls but also have predictive value for treatment response for a variety of treatments. In this comprehensive overview on EEG research on MDD, biomarkers that are either assessed at baseline or during the early course of treatment and are helpful in discriminating patients from healthy controls and assist in predicting treatment outcome are reviewed, covering recent decades up to now. Reviewed markers include quantitative EEG (QEEG) measures, connectivity measures, EEG vigilance-based measures, sleep-EEG-related measures and event-related potentials (ERPs). Further, the value and limitations of these different markers are discussed. Finally, the need for integrated models of brain function and the necessity for standardized procedures in EEG biomarker research are highlighted to enhance future research in this field. [ABSTRACT FROM AUTHOR]
Copyright of International Review of Psychiatry is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: EEG biomarkers in major depressive disorder: Discriminative power and prediction of treatment response.
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  Data: <searchLink fieldCode="AR" term="%22Olbrich%2C+Sebastian%22">Olbrich, Sebastian</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Arns%2C+Martijn%22">Arns, Martijn</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Review+of+Psychiatry%22">International Review of Psychiatry</searchLink>. Oct2013, Vol. 25 Issue 5, p604-618. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Antidepressants%22">Antidepressants</searchLink><br /><searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Evoked+potentials+%28Electrophysiology%29%22">Evoked potentials (Electrophysiology)</searchLink><br /><searchLink fieldCode="DE" term="%22Health+outcome+assessment%22">Health outcome assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Sleep%22">Sleep</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink>
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  Label: Abstract
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  Data: Major depressive disorder (MDD) has high population prevalence and is associated with substantial impact on quality of life, not least due to an unsatisfactory time span of sometimes several weeks from initiation of treatment to clinical response. Therefore extensive research focused on the identification of cost-effective and widely available electroencephalogram (EEG)-based biomarkers that not only allow distinguishing between patients and healthy controls but also have predictive value for treatment response for a variety of treatments. In this comprehensive overview on EEG research on MDD, biomarkers that are either assessed at baseline or during the early course of treatment and are helpful in discriminating patients from healthy controls and assist in predicting treatment outcome are reviewed, covering recent decades up to now. Reviewed markers include quantitative EEG (QEEG) measures, connectivity measures, EEG vigilance-based measures, sleep-EEG-related measures and event-related potentials (ERPs). Further, the value and limitations of these different markers are discussed. Finally, the need for integrated models of brain function and the necessity for standardized procedures in EEG biomarker research are highlighted to enhance future research in this field. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Review of Psychiatry is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3109/09540261.2013.816269
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      – Code: eng
        Text: English
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      – SubjectFull: Mental depression
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      – SubjectFull: Electroencephalography
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      – SubjectFull: Evoked potentials (Electrophysiology)
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      – SubjectFull: Health outcome assessment
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      – SubjectFull: Sleep
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      – SubjectFull: Treatment effectiveness
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              M: 10
              Text: Oct2013
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              Y: 2013
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