Quantitative EEG (QEEG) as a Predictor of Treatment Outcome in Depression
|Major Depressive Disorder||Drug: Selective serotonin reuptake inhibitors (SSRI)||Phase 4|
|Study Design:||Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Treatment
|Official Title:||The Use of Quantitative EEG (QEEG) as a Predictor of Treatment Outcome in Major Depressive Disorder|
- Ham-D-17 [ Time Frame: 8 weeks ]
|Study Start Date:||April 2003|
|Study Completion Date:||August 2006|
|Primary Completion Date:||August 2006 (Final data collection date for primary outcome measure)|
Drug: Selective serotonin reuptake inhibitors (SSRI)
Despite the availability of effective clinical treatments for major depressive disorder (MDD), 30-40% of subjects with MDD still fail to respond significantly to antidepressant treatment (Fava and Davidson, 1996). In the absence of biological predictors of treatment outcome in MDD, clinicians face a difficult dilemma in selecting an antidepressant treatment.
Currently we have only preliminary knowledge on the mechanisms and the biological correlates of treatment response in MDD (Mayberg et al, 1997 and 1999). Functional neuroimaging studies have demonstrated decreased metabolism and glucose consumption in specific limbic and paralimbic brain areas which are related to affective regulation. Quantitative EEG (QEEG) studies in subjects with MDD have revealed other functional brain abnormalities, such as decreased power in the EEG theta wave band. Studies with auditory evoked potentials have shown P300 latency in subjects with MDD. Moreover, some quantitative EEG parameters (e.g., cordance), appear to predict clinical response to antidepressants.
The principal aim of this study is to identify, by measuring QEEG, predictors and correlates of treatment response in a group of patients with MDD. We will also carry out exploratory analyses to identify correlations between QEEG metrics and multiple clinical parameters of depressed subjects: gender, age, chronicity of depression, age of onset, comorbid anxiety, atypical and melancholic depressive subtypes.
Our understanding of the relationship between treatment outcome in MDD and EEG measurements promises to provide clinically useful information for selecting antidepressant treatments; it can also provide important information useful in the early testing of new compounds with putative antidepressant efficacy. Furthermore, the knowledge gained and techniques used may help shed light on the pathophysiology of major depression and perhaps other neuropsychiatric disorders associated with depressed mood.
Please refer to this study by its ClinicalTrials.gov identifier: NCT00157547
|Principal Investigator:||Dan Iosifescu, MD||Massachusetts General Hospital|