Frontal alpha asymmetry as a diagnostic marker in depression: Fact or fiction? A meta-analysis

Abstract

BackgroundFrontal alpha asymmetry (FAA) has frequently been reported as potential discriminator between depressed and healthy individuals, although contradicting results have been published. The aim of the current study was to provide an up to date meta-analysis on the diagnostic value of FAA in major depressive disorder (MDD) and to further investigate discrepancies in a large cross-sectional dataset.MethodsSCOPUS database was searched through February 2017. Studies were included if the article reported on both MDD and controls, provided an FAA measure involving EEG electrodes F3/F4, and provided data regarding potential covariates. Hedges' d was calculated from FAA means and standard deviations (SDs). Potential covariates, such as age and gender, were explored. Post hoc analysis was performed to elucidate interindividual differences that could explain interstudy discrepancies.Results16 studies were included (MDD: n = 1883, controls: n = 2161). After resolving significant heterogeneity by excluding studies, a non-significant Grand Mean effect size (ES) was obtained (d = − 0.007;CI = [− 0.090]–[0.075]). Crosssectional analyses showed a significant three-way interaction for Gender × Age × Depression severity in the depressed group, which was prospectively replicated in an independent sample.ConclusionsThe main result was a non-significant, negligible ES, demonstrating limited diagnostic value of FAA in MDD. The high degree of heterogeneity across studies indicates covariate influence, as was confirmed by crosssectional analyses, suggesting future studies should address this Gender × Age × Depression severity interaction. Upcoming studies should focus more on prognostic and research domain usages of FAA rather than a pure diagnostic tool.

Assessing neurophysiological changes associated with combined transcranial direct current stimulation and cognitive emotional training for treatment-resistant depression

“…These measures were hypothesised a priori to change with treatment because they had been linked to antidepressant response (Spronk et al, 2011;Arns et al, 2015;Arns et al, 2016). Recently, there have been some criticisms of the viability of alpha asymmetry as a relevant factor for depression (Van Der Vinne et al, 2017). Debener et al (2000) challenge the notion that alpha asymmetry is a trait marker for depression, and report that increased variability, rather than increased alpha lateralisation, may be a more relevant measure.…”

“…Frontal alpha asymmetry was obtained under the eyes closed resting-state EEG condition (Van Der Vinne et al, 2017). To calculate this index, power in the alpha frequency band (8-13 Hz) was obtained at EEG channels F3 and F4.…”

“…Frontal asymmetry in upper alpha has also been shown to correlate with increased blood oxygenation level dependent (BOLD) activation in the amygdala during a task of emotion regulation (Zotev et al, 2016). Although recent research has questioned the diagnostic value of frontal alpha asymmetry as a biomarker for depression (Van Der Vinne et al, 2017), alpha asymmetry has been associated with response to antidepressant medications, and may therefore have prognostic value (Arns et al, 2016). However, this was only the case for females, in whom large alpha asymmetry predicted response to a course of selective serotonin reuptake inhibitors.…”

Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology

“…alpha asymmetry [11][12][13][14][15][16] ). While these important studies set the framework for understanding the neural basis for depression, no consensus on a biomarker has been reached 10,[17][18][19][20][21] . Recent computational advancements have introduced artificial intelligence in medical diagnostics with accuracies that rival physicians 22,23 .…”

“…Work over the past 50 years has highlighted the predictive potential of EEG to detect depression [68][69][70][71] . Disruption in frontal alpha power is one of the earliest and most-studied markers of depression [11][12][13][14][15][16] , but its predictive potential remains unclear due to inconsistent findings across studies [17][18][19] . We hypothesized that alpha power may be one part of a larger network story, and that by leveraging the high temporal resolution of iEEG, as well as the direct access to subcortical structures, we could perform a spectral analysis of brain activity across functional modules that would identify a network biomarker of depression.…”