EEG Biomarkers for Predicting Response to Antidepressant Therapy

Medtronic - MITG375 enrolled

Overview

The purpose of this study is to evaluate the potential early EEG predictors of an individual's response to treatment with antidepressant medications. Objectives: * 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. Comparison(s): 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.

Study Type

OBSERVATIONAL

Enrollment

375

Conditions

Major Depressive Disorder

Eligibility

Sex: ALLMin age: 21 YearsMax age: 75 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Subject has diagnosis of Major Depressive Disorder Exclusion Criteria: * Subject is suffering from cognitive, bipolar, or psychotic disorder * Subject has had a course of ECT within the past six months * Subject has any known contraindication for use of any of the study drugs * Subject has a known drug dependency or substance abuse within the past six mon ths * Subject is currently pregnant or not using a medically acceptable means of birth control

Locations (10)

University of California, Los Angeles-Westwood

Los Angeles, California, United States

Cedars-Sinai Medical Center

Los Angeles, California, United States

University of California, San Diego

San Diego, California, United States

Outcomes

Primary Outcomes

1. To confirm prospectively the accuracy of an EEG biomarker as a leading indicator of SSRI antidepressant treatment response;

2\. To identify the optimal positive and negative indicators of response to initial treatment with an SSRI; 3. To determine the preferred clinical intervention to perform following an initial negative treatment response indicator;

Time frame: 8 weeks

Secondary Outcomes

1. To confirm prospectively the accuracy of an EEG biomarker as a leading indicator of remission;

2\. To explore the relationship between EEG and genetic biomarkers as predictors of treatment response and remission; 3. To determine if certain baseline EEG values or changes early in the course of treatment may predict the emergence of worsening suicidal ideation.

Time frame: 8 weeks

Data from ClinicalTrials.gov

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