Literature DB >> 24151805

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

Sebastian Olbrich1, Martijn Arns.   

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.

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Year:  2013        PMID: 24151805     DOI: 10.3109/09540261.2013.816269

Source DB:  PubMed          Journal:  Int Rev Psychiatry        ISSN: 0954-0261


  57 in total

Review 1.  Moving pharmacoepigenetics tools for depression toward clinical use.

Authors:  Laura M Hack; Gabriel R Fries; Harris A Eyre; Chad A Bousman; Ajeet B Singh; Joao Quevedo; Vineeth P John; Bernhard T Baune; Boadie W Dunlop
Journal:  J Affect Disord       Date:  2019-02-06       Impact factor: 4.839

2.  A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

Authors:  Wajid Mumtaz; Syed Saad Azhar Ali; Mohd Azhar Mohd Yasin; Aamir Saeed Malik
Journal:  Med Biol Eng Comput       Date:  2017-07-13       Impact factor: 2.602

3.  The effectiveness of prefrontal theta cordance and early reduction of depressive symptoms in the prediction of antidepressant treatment outcome in patients with resistant depression: analysis of naturalistic data.

Authors:  Martin Bares; Tomas Novak; Miloslav Kopecek; Martin Brunovsky; Pavla Stopkova; Cyril Höschl
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2014-05-22       Impact factor: 5.270

4.  Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

Authors:  Amin Zandvakili; Noah S Philip; Stephanie R Jones; Audrey R Tyrka; Benjamin D Greenberg; Linda L Carpenter
Journal:  J Affect Disord       Date:  2019-03-30       Impact factor: 4.839

Review 5.  Computational psychiatry as a bridge from neuroscience to clinical applications.

Authors:  Quentin J M Huys; Tiago V Maia; Michael J Frank
Journal:  Nat Neurosci       Date:  2016-03       Impact factor: 24.884

Review 6.  Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years.

Authors:  G Voegeli; M L Cléry-Melin; N Ramoz; P Gorwood
Journal:  Drugs       Date:  2017-12       Impact factor: 9.546

7.  Resting-State Quantitative Electroencephalography Demonstrates Differential Connectivity in Adolescents with Major Depressive Disorder.

Authors:  Molly McVoy; Michelle E Aebi; Kenneth Loparo; Sarah Lytle; Alla Morris; Nicole Woods; Elizabeth Deyling; Curtis Tatsuoka; Farhad Kaffashi; Samden Lhatoo; Martha Sajatovic
Journal:  J Child Adolesc Psychopharmacol       Date:  2019-05-09       Impact factor: 2.576

8.  Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis.

Authors:  Alik S Widge; M Taha Bilge; Rebecca Montana; Weilynn Chang; Carolyn I Rodriguez; Thilo Deckersbach; Linda L Carpenter; Ned H Kalin; Charles B Nemeroff
Journal:  Am J Psychiatry       Date:  2018-10-03       Impact factor: 18.112

9.  Modulation of motor cortex excitability predicts antidepressant response to prefrontal cortex repetitive transcranial magnetic stimulation.

Authors:  Albino J Oliveira-Maia; Daniel Press; Alvaro Pascual-Leone
Journal:  Brain Stimul       Date:  2017-03-31       Impact factor: 8.955

10.  EEG marker of inhibitory brain activity correlates with resting-state cerebral blood flow in the reward system in major depression.

Authors:  A Cantisani; T Koenig; K Stegmayer; A Federspiel; H Horn; T J Müller; R Wiest; W Strik; S Walther
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2015-11-21       Impact factor: 5.270

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