What future research should bring to help resolving the debate about the efficacy of EEG-neurofeedback in children with ADHD

In recent years a rising amount of randomized controlled trials, reviews, and meta-analyses relating to the efficacy of electroencephalographic-neurofeedback (EEG-NF) in children with attention-deficit/hyperactivity disorder (ADHD) have been published. Although clinical reports and open treatment studies suggest EEG-NF to be effective, double blind placebo-controlled studies as well as a rigorous meta-analysis failed to find support for the efficacy of EEG-NF. Since absence of evidence does not equate with evidence of absence, we will outline how future research might overcome the present methodological limitations. To provide conclusive evidence for the presence or absence of the efficacy of EEG-NF in the treatment of ADHD, there is a need to set up a well-designed study that ensures optimal implementation and embedding of the training, and possibly incorporates different forms of neurofeedback

Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity : Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses

Nonlinear analysis of EEG recordings allows detection of characteristics that would probably be neglected by linear methods. This study aimed to determine a suitable epoch length for nonlinear analysis of EEG data based on its recurrence rate in EEG alpha activity (electrodes Fz, Oz, and Pz) from 28 healthy and 64 major depressive disorder subjects. Two nonlinear metrics, Lempel-Ziv complexity and scaling index, were applied in sliding windows of 20 seconds shifted every 1 second and in nonoverlapping windows of 1 minute. In addition, linear spectral analysis was carried out for comparison with the nonlinear results. The analysis with sliding windows showed that the cortical dynamics underlying alpha activity had a recurrence period of around 40 seconds in both groups. In the analysis with nonoverlapping windows, long-term nonstationarities entailed changes over time in the nonlinear dynamics that became significantly different between epochs across time, which was not detected with the linear spectral analysis. Findings suggest that epoch lengths shorter than 40 seconds neglect information in EEG nonlinear studies. In turn, linear analysis did not detect characteristics from long-term nonstationarities in EEG alpha waves of control subjects and patients with major depressive disorder patients. We recommend that application of nonlinear metrics in EEG time series, particularly of alpha activity, should be carried out with epochs around 60 seconds. In addition, this study aimed to demonstrate that long-term nonlinearities are inherent to the cortical brain dynamics regardless of the presence or absence of a mental disorder

Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity : Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses

Nonlinear analysis of EEG recordings allows detection of characteristics that would probably be neglected by linear methods. This study aimed to determine a suitable epoch length for nonlinear analysis of EEG data based on its recurrence rate in EEG alpha activity (electrodes Fz, Oz, and Pz) from 28 healthy and 64 major depressive disorder subjects. Two nonlinear metrics, Lempel-Ziv complexity and scaling index, were applied in sliding windows of 20 seconds shifted every 1 second and in nonoverlapping windows of 1 minute. In addition, linear spectral analysis was carried out for comparison with the nonlinear results. The analysis with sliding windows showed that the cortical dynamics underlying alpha activity had a recurrence period of around 40 seconds in both groups. In the analysis with nonoverlapping windows, long-term nonstationarities entailed changes over time in the nonlinear dynamics that became significantly different between epochs across time, which was not detected with the linear spectral analysis. Findings suggest that epoch lengths shorter than 40 seconds neglect information in EEG nonlinear studies. In turn, linear analysis did not detect characteristics from long-term nonstationarities in EEG alpha waves of control subjects and patients with major depressive disorder patients. We recommend that application of nonlinear metrics in EEG time series, particularly of alpha activity, should be carried out with epochs around 60 seconds. In addition, this study aimed to demonstrate that long-term nonlinearities are inherent to the cortical brain dynamics regardless of the presence or absence of a mental disorder

The role of the circadian system in the etiology and pathophysiology of ADHD:time to redefine ADHD?

Attention-deficit/hyperactivity disorder (ADHD) is highly associated with the delayed sleep phase disorder, a circadian rhythm sleep–wake disorder, which is prevalent in 73–78% of children and adults with ADHD. Besides the delayed sleep phase disorder, various other sleep disorders accompany ADHD, both in children and in adults. ADHD is either the cause or the consequence of sleep disturbances, or they may have a shared etiological and genetic background. In this review, we present an overview of the current knowledge on the relationship between the circadian rhythm, sleep disorders, and ADHD. We also discuss the various pathways explaining the connection between ADHD symptoms and delayed sleep, ranging from genetics, behavioral aspects, daylight exposure, to the functioning of the eye. The treatment options discussed are focused on improvement of sleep quality, quantity, and phase-resetting, by means of improving sleep hygiene, chronotherapy, treatment of specific sleep disorders, and by strengthening certain neuronal networks involved in sleep, e.g., by sensorimotor rhythm neurofeedback. Ultimately, the main question is addressed: whether ADHD needs to be redefined. We propose a novel view on ADHD, where a part of the ADHD symptoms are the result of chronic sleep disorders, with most evidence for the delayed circadian rhythm as the underlying mechanism. This substantial subgroup should receive treatment of the sleep disorder in addition to ADHD symptom treatment

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

Background Frontal 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. Methods SCOPUS 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. Results 16 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. Conclusions The 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

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

Background: Frontal 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. Methods: SCOPUS 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. Results: 16 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. Conclusions: The 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. Keywords: Depression, MDD, Frontal alpha asymmetry, EEG, Electroencephalogram, Meta-analysi

Stability of frontal alpha asymmetry in depressed patients during antidepressant treatment

INTRODUCTION: Frontal alpha asymmetry (FAA) is a proposed prognostic biomarker in major depressive disorder (MDD), conventionally acquired with electroencephalography (EEG). Although small studies attributed trait-like properties to FAA, a larger sample is needed to reliably asses this characteristic. Furthermore, to use FAA to predict treatment response, determining its stability, including the potential dependency on depressive state or medication, is essential. METHODS: In the international Study to Predict Optimized Treatment in Depression (iSPOT-D), a multi-center, randomized, prospective open-label trial, 1008 MDD participants were randomized to treatment with escitalopram, sertraline or venlafaxine-extended release. Treatment response was established eight weeks after treatment initiation and resting state EEG was measured both at baseline and after eight weeks (n = 453). RESULTS: FAA did not change significantly after eight weeks of treatment (n = 453, p = .234), nor did we find associations with age, sex, depression severity, or change in depression severity. After randomizing females to escitalopram or sertraline, for whom treatment response could be predicted in an earlier study, FAA after eight weeks resulted in equivalent response prediction as baseline FAA (one tailed p = .028). CONCLUSION: We demonstrate that FAA is a stable trait, robust to time, state and pharmacological status. This confirms FAA stability. Furthermore, as prediction of treatment response is irrespective of moment of measurement and use of medication, FAA can be used as a state-invariant prognostic biomarker with promise to optimize MDD treatments