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Current source density measures of electroencephalographic alpha predict antidepressant treatment response.

ABSTRACT: Despite recent success in pharmacologic treatment of depression, the inability to predict individual treatment response remains a liability. This study replicates and extends findings relating pretreatment electroencephalographic (EEG) alpha to treatment outcomes for serotonergic medications.Resting EEG (eyes-open and eyes-closed) was recorded from a 67-electrode montage in 41 unmedicated depressed patients and 41 healthy control subjects. Patients were tested before receiving antidepressants including a serotonergic mode of action (selective serotonin reuptake inhibitor [SSRI], serotonin and norepinephrine reuptake inhibitor, or SSRI plus norepinephrine and dopamine reuptake inhibitor). EEG was quantified by frequency principal components analysis of spectra derived from reference-free current source density (CSD) waveforms, which sharpens and simplifies EEG topographies, disentangles them from artifact, and yields measures that more closely represent underlying neuronal current generators.Patients who did not respond to treatment had significantly less alpha CSD compared with responders or healthy control subjects, localizable to well-defined posterior generators. The alpha difference between responders and nonresponders was greater for eyes-closed than eyes-open conditions and was present across alpha subbands. A classification criterion based on the median alpha for healthy control subjects showed good positive predictive value (93.3) and specificity (92.3). There was no evidence of differential value for predicting response to an SSRI alone or dual treatment targeting serotonergic plus other monoamine neurotransmitters.Findings confirm the value of EEG alpha amplitude as a viable predictor of antidepressant response and suggest that personalized treatments for depression may be identified using simple electrophysiologic CSD measures.

SUBMITTER: Tenke CE

PROVIDER: S-EPMC3142299 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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Current source density measures of electroencephalographic alpha predict antidepressant treatment response.

Tenke Craig E CE Kayser Jürgen J Manna Carlye G CG Fekri Shiva S Kroppmann Christopher J CJ Schaller Jennifer D JD Alschuler Daniel M DM Stewart Jonathan W JW McGrath Patrick J PJ Bruder Gerard E GE

Biological psychiatry 20110420 4

<h4>Background</h4>Despite recent success in pharmacologic treatment of depression, the inability to predict individual treatment response remains a liability. This study replicates and extends findings relating pretreatment electroencephalographic (EEG) alpha to treatment outcomes for serotonergic medications.<h4>Methods</h4>Resting EEG (eyes-open and eyes-closed) was recorded from a 67-electrode montage in 41 unmedicated depressed patients and 41 healthy control subjects. Patients were tested ...[more]

PMID: 21507383

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Project description:BackgroundSelective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are the most common form of medication treatment for major depression. However, approximately 50% of depressed patients fail to achieve an effective treatment response. Understanding how gene expression systems respond to treatments may be critical for understanding antidepressant resistance.MethodsWe take a novel approach to this problem by demonstrating that the gene expression system of the dentate gyrus responds to fluoxetine (FLX), a commonly used antidepressant medication, in a stereotyped-manner involving changes in the expression levels of thousands of genes. The aggregate behavior of this large-scale systemic response was quantified with principal components analysis (PCA) yielding a single quantitative measure of the global gene expression system state.ResultsQuantitative measures of system state were highly correlated with variability in levels of antidepressant-sensitive behaviors in a mouse model of depression treated with fluoxetine. Analysis of dorsal and ventral dentate samples in the same mice indicated that system state co-varied across these regions despite their reported functional differences. Aggregate measures of gene expression system state were very robust and remained unchanged when different microarray data processing algorithms were used and even when completely different sets of gene expression levels were used for their calculation.ConclusionsSystem state measures provide a robust method to quantify and relate global gene expression system state variability to behavior and treatment. State variability also suggests that the diversity of reported changes in gene expression levels in response to treatments such as fluoxetine may represent different perspectives on unified but noisy global gene expression system state level responses. Studying regulation of gene expression systems at the state level may be useful in guiding new approaches to augmentation of traditional antidepressant treatments.

Dataset Information

Current source density measures of electroencephalographic alpha predict antidepressant treatment response.

Publications

Current source density measures of electroencephalographic alpha predict antidepressant treatment response.

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