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The purpose of this study is to evaluate the potential early EEG predictors of an individual's response to treatment with antidepressant medications.
- Prospectively confirm accuracy of current EEG biomarker algorithm
- Determine preferred clinical intervention for subjects with negative indicator
- Identify predictors of worsening suicide ideation
According to recent clinical studies sponsored by the NIH, fewer than half of subjects diagnosed with a major depressive episode respond to the first trial of an antidepressant medication. While the majority of subjects eventually respond to treatment with an antidepressant, failure with the first line medication puts subjects at increased risk for never receiving adequate treatment of their depression.
Several lines of reasoning support the rationale for further investigating EEG as a means of predicting response and resistance to antidepressants. Prior studies suggest that changes in neuronal activity in the anterior cingulate and prefrontal regions are related to depression and that changes in brain response to treatment may also produce alterations that can be detected by recoding frontal EEG activity.
In this protocol, we proposed to identify possible neurophysiologic indicators of treatment outcome in depression, particularly indicators of brain response that appear early (within 7 days) during treatment with antidepressants. We will test whether quantitative EEG (QEEG) biomarkers can be reliably associated with response or non-response to treatment with antidepressant medications, using both monotherapy and combination drug treatments.
Selecting the best treatment for subjects with resistance to an initial antidepressant poses a considerable challenge for clinicians. The most widely prescribed antidepressants usually require 4-6 weeks of therapeutic dosing before a marked clinical improvement in symptoms is observed. Therefore, determining the optimal regimen can take several weeks or months for subjects who are resistant to the first line antidepressant. A tool for predicting eventual clinical response to antidepressants could help inform and accelerate the process of identifying the most efficacious treatment option for a given subject.
Observational Model: Case Control, Time Perspective: Prospective
Major Depressive Disorder
Escitalopram, Bupropion XL
University of California, Los Angeles-Westwood
Aspect Medical Systems
Published on BioPortfolio: 2014-08-27T03:46:11-0400
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