Antidepressant response trajectories and quantitative electroencephalography (QEEG) biomarkers in major depressive disorder.

TitleAntidepressant response trajectories and quantitative electroencephalography (QEEG) biomarkers in major depressive disorder.
Publication TypeJournal Article
Year of Publication2010
AuthorsHunter, AM, Muthén BO, Cook IA, Leuchter AF
JournalJournal of psychiatric research
Date Published2010 Jan
KeywordsAntidepressive Agents, Brain Mapping, Cyclohexanols, Depressive Disorder, Major, Double-Blind Method, Fluoxetine, Humans, Numerical Analysis, Computer-Assisted, Psychiatric Status Rating Scales, Treatment Outcome

Individuals with Major Depressive Disorder (MDD) vary regarding the rate, magnitude and stability of symptom changes during antidepressant treatment. Growth mixture modeling (GMM) can be used to identify patterns of change in symptom severity over time. Quantitative electroencephalographic (QEEG) cordance within the first week of treatment has been associated with endpoint clinical outcomes but has not been examined in relation to patterns of symptom change. Ninety-four adults with MDD were randomized to eight weeks of double-blinded treatment with fluoxetine 20mg or venlafaxine 150mg (n=49) or placebo (n=45). An exploratory random effect GMM was applied to Hamilton Depression Rating Scale (Ham-D(17)) scores over 11 timepoints. Linear mixed models examined 48-h, and 1-week changes in QEEG midline-and-right-frontal (MRF) cordance for subjects in the GMM trajectory classes. Among medication subjects an estimated 62% of subjects were classified as responders, 21% as non-responders, and 17% as symptomatically volatile-i.e., showing a course of alternating improvement and worsening. MRF cordance showed a significant class-by-time interaction (F((2,41))=6.82, p=.003); as hypothesized, the responders showed a significantly greater 1-week decrease in cordance as compared to non-responders (mean difference=-.76, Std. Error=.34, df=73, p=.03) but not volatile subjects. Subjects with a volatile course of symptom change may merit special clinical consideration and, from a research perspective, may confound the interpretation of typical binary endpoint outcomes. Statistical methods such as GMM are needed to identify clinically relevant symptom response trajectories.

Alternate JournalJ Psychiatr Res