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The way ahead for predictive EEG biomarkers in treatment of depression


Olbrich, Sebastian; Brunovsky, Martin (2021). The way ahead for predictive EEG biomarkers in treatment of depression. Clinical Neurophysiology, 132(2):616-617.

Abstract

The usage of EEG in the clinical diagnosis of psychiatric disorders has been a matter of debate for a long time (Williams, 1954). On one side, it has been argued that EEG recordings from psychiatric patients yield a relatively low abnormality detection rate, resulting in a neglectable value for diagnosis or impact on patient management (O’Sullivan et al., 2006). On the other side the psychiatric EEG has been described as a useful tool for monitoring psychopharmacological effects (Gallinat et al., 2016), and for the differential diagnosis of many disorders and syndromes, e.g., dementia, delirium, or sleep disorders. The EEG even has been approved by the U.S. Food and Drug Administration (FDA) as a biomarker testing device of e.g., attention deficit hyperactivity disorder (ADHD) (Gloss et al., 2016). Despite this dispute, it became clearer in recent years that the EEG could be of value not only for diagnostic purposes but might help in the decisions for finding the best treatment option for patients suffering from e.g., depressive disorders (Grzenda and Widge, 2020, Iosifescu, 2020). Not only since a meta-analysis on EEG biomarkers for prediction of treatment outcome in depression (Widge et al., 2019), many research groups focused on the identification of EEG signatures that could have a clinical value in the course of treatment (Olbrich and Arns, 2013). As a necessary new development and in response to critics that many studies were underpowered and markers were not replicated, data from several large studies (e.g. EMBARC (Trivedi et al., 2016) and i-SPOT-D (Williams et al., 2011)) has been used by different teams and in widespread international collaborations to look for new promising and reliable markers and to replicate other works, mainly in the field of antidepressants. Besides the search for predictors for psychopharmacological treatment outcome, markers for other interventions are needed to provide a benefit for marker-based therapy decisions. If biosignatures only yield information on e.g., a non-response to a single treatment option, then this renders useless for the patients since no alternatives for improved treatment outcomes are provided.

Abstract

The usage of EEG in the clinical diagnosis of psychiatric disorders has been a matter of debate for a long time (Williams, 1954). On one side, it has been argued that EEG recordings from psychiatric patients yield a relatively low abnormality detection rate, resulting in a neglectable value for diagnosis or impact on patient management (O’Sullivan et al., 2006). On the other side the psychiatric EEG has been described as a useful tool for monitoring psychopharmacological effects (Gallinat et al., 2016), and for the differential diagnosis of many disorders and syndromes, e.g., dementia, delirium, or sleep disorders. The EEG even has been approved by the U.S. Food and Drug Administration (FDA) as a biomarker testing device of e.g., attention deficit hyperactivity disorder (ADHD) (Gloss et al., 2016). Despite this dispute, it became clearer in recent years that the EEG could be of value not only for diagnostic purposes but might help in the decisions for finding the best treatment option for patients suffering from e.g., depressive disorders (Grzenda and Widge, 2020, Iosifescu, 2020). Not only since a meta-analysis on EEG biomarkers for prediction of treatment outcome in depression (Widge et al., 2019), many research groups focused on the identification of EEG signatures that could have a clinical value in the course of treatment (Olbrich and Arns, 2013). As a necessary new development and in response to critics that many studies were underpowered and markers were not replicated, data from several large studies (e.g. EMBARC (Trivedi et al., 2016) and i-SPOT-D (Williams et al., 2011)) has been used by different teams and in widespread international collaborations to look for new promising and reliable markers and to replicate other works, mainly in the field of antidepressants. Besides the search for predictors for psychopharmacological treatment outcome, markers for other interventions are needed to provide a benefit for marker-based therapy decisions. If biosignatures only yield information on e.g., a non-response to a single treatment option, then this renders useless for the patients since no alternatives for improved treatment outcomes are provided.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Psychiatric University Hospital Zurich > Clinic for Psychiatry, Psychotherapy, and Psychosomatics
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Life Sciences > Sensory Systems
Life Sciences > Neurology
Health Sciences > Neurology (clinical)
Health Sciences > Physiology (medical)
Uncontrolled Keywords:Physiology (medical), Clinical Neurology, Neurology, Sensory Systems
Language:English
Date:1 February 2021
Deposited On:07 Dec 2021 07:23
Last Modified:25 Feb 2024 02:50
Publisher:Elsevier
ISSN:1388-2457
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.clinph.2020.12.001
PubMed ID:33386211
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