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Gamma oscillations as a biomarker for major depression: an emerging topic
Identifying biomarkers for major depression is of high importance for improving diagnosis and treatment of this common and debilitating neuropsychiatric disorder, as the field seeks to move toward both personalized and more effective treatments. Here we focus on electroencephalography (EEG) or direc...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123432/ https://www.ncbi.nlm.nih.gov/pubmed/30181587 http://dx.doi.org/10.1038/s41398-018-0239-y |
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author | Fitzgerald, Paul J. Watson, Brendon O. |
author_facet | Fitzgerald, Paul J. Watson, Brendon O. |
author_sort | Fitzgerald, Paul J. |
collection | PubMed |
description | Identifying biomarkers for major depression is of high importance for improving diagnosis and treatment of this common and debilitating neuropsychiatric disorder, as the field seeks to move toward both personalized and more effective treatments. Here we focus on electroencephalography (EEG) or direct scalp voltage recordings as such a biomarker, with an emphasis on gamma and high gamma oscillations (or “rhythms”). In the last several decades, alpha and theta band rhythms have been found to provide information on depressive state as well as recovery, but the gamma band is less well characterized with respect to depression. We summarize some key findings on gamma rhythms (especially their amplitude) as a biomarker or endophenotype for major depression. These studies suggest: (1) under certain conditions gamma rhythms can distinguish subjects with major depression from healthy controls, (2) gamma may distinguish bipolar disorder from unipolar depression, (3) various pharmacological and non-pharmacological treatments that counteract depression also alter gamma, (4) animal models of depression-like behavior show gamma abnormalities, with changes in gamma associated with therapeutic recovery. The most informative approaches in the future may combine profiles of gamma band power across the brain to assess ratios of activity across regions. Overall we have good evidence to suggest that gamma rhythms may provide objective information on major depressive disease status, but we will need further work to better define the precise measures to follow. |
format | Online Article Text |
id | pubmed-6123432 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61234322018-09-05 Gamma oscillations as a biomarker for major depression: an emerging topic Fitzgerald, Paul J. Watson, Brendon O. Transl Psychiatry Perspective Identifying biomarkers for major depression is of high importance for improving diagnosis and treatment of this common and debilitating neuropsychiatric disorder, as the field seeks to move toward both personalized and more effective treatments. Here we focus on electroencephalography (EEG) or direct scalp voltage recordings as such a biomarker, with an emphasis on gamma and high gamma oscillations (or “rhythms”). In the last several decades, alpha and theta band rhythms have been found to provide information on depressive state as well as recovery, but the gamma band is less well characterized with respect to depression. We summarize some key findings on gamma rhythms (especially their amplitude) as a biomarker or endophenotype for major depression. These studies suggest: (1) under certain conditions gamma rhythms can distinguish subjects with major depression from healthy controls, (2) gamma may distinguish bipolar disorder from unipolar depression, (3) various pharmacological and non-pharmacological treatments that counteract depression also alter gamma, (4) animal models of depression-like behavior show gamma abnormalities, with changes in gamma associated with therapeutic recovery. The most informative approaches in the future may combine profiles of gamma band power across the brain to assess ratios of activity across regions. Overall we have good evidence to suggest that gamma rhythms may provide objective information on major depressive disease status, but we will need further work to better define the precise measures to follow. Nature Publishing Group UK 2018-09-04 /pmc/articles/PMC6123432/ /pubmed/30181587 http://dx.doi.org/10.1038/s41398-018-0239-y Text en © The Author(s) 2018 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/. |
spellingShingle | Perspective Fitzgerald, Paul J. Watson, Brendon O. Gamma oscillations as a biomarker for major depression: an emerging topic |
title | Gamma oscillations as a biomarker for major depression: an emerging topic |
title_full | Gamma oscillations as a biomarker for major depression: an emerging topic |
title_fullStr | Gamma oscillations as a biomarker for major depression: an emerging topic |
title_full_unstemmed | Gamma oscillations as a biomarker for major depression: an emerging topic |
title_short | Gamma oscillations as a biomarker for major depression: an emerging topic |
title_sort | gamma oscillations as a biomarker for major depression: an emerging topic |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123432/ https://www.ncbi.nlm.nih.gov/pubmed/30181587 http://dx.doi.org/10.1038/s41398-018-0239-y |
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