Martijn Arns

Efficacy of Neurofeedback Treatment in ADHD: the Effects on Inattention, Impulsivity and Hyperactivity: a Meta-Analysis

Since the first reports of neurofeedback treatment in Attention Deficit Hyperactivity Disorder (ADHD) in 1976, many studies have investigated the effects of neurofeedback on different symptoms of ADHD such as inattention, impulsivity and hyperactivity. This technique is also used by many practitioners, but the question as to the evidence-based level of this treatment is still unclear. In this study selected research on neurofeedback treatment for ADHD was collected and a meta-analysis was performed. Both prospective controlled studies and studies employing a pre-and post-design found large effect sizes (ES) for neurofeedback on impulsivity and inattention and a medium ES for hyperactivity. Randomized studies demonstrated a lower ES for hyperactivity suggesting that hyperactivity is probably most sensitive to nonspecific treatment factors. Due to the inclusion of some very recent and sound methodological studies in this meta-analysis, potential confounding factors such as small studies, lack of randomization in previous studies and a lack of adequate control groups have been addressed, and the clinical effects of neurofeedback in the treatment of ADHD can be regarded as clinically meaningful. Three randomized studies have employed a semi-active control group which can be regarded as a credible sham control providing an equal level of cognitive training and client-therapist interaction. Therefore, in line with the AAPB and ISNR guidelines for rating clinical efficacy, we conclude that neurofeedback treatment for ADHD can be considered “Efficacious and Specific” (Level 5) with a large ES for inattention and impulsivity and a medium ES for hyperactivity.

A decade of EEG Theta/Beta Ratio Research in ADHD: a meta-analysis.

Objective: Many EEG studies have reported that ADHD is characterized by elevated Theta/Beta ratio (TBR). In this study we conducted a meta-analysis on the TBR in ADHD. Method: TBR data during Eyes Open from location Cz were analyzed from children/adolescents 6-18 years of age with and without ADHD. Results: Nine studies were identified with a total of 1253 children/adolescents with and 517 without ADHD. The grand-mean effect size (ES) for the 6-13 year-olds was 0.75 and for the 6-18 year-olds was 0.62. However the test for heterogeneity remained significant; therefore these ESs are misleading and considered an overestimation. Post-hoc analysis found a decreasing difference in TBR across years, explained by an increasing TBR for the non-ADHD groups. Conclusion: Excessive TBR cannot be considered a reliable diagnostic measure of ADHD, however a substantial sub-group of ADHD patients do deviate on this measure and TBR has prognostic value in this sub-group, warranting its use as a prognostic measure rather than a diagnostic measure.

Evaluation of neurofeedback in ADHD: the long and winding road

Among the clinical applications of neurofeedback, most research has been conducted in ADHD. As an introduction a short overview of the general history of neurofeedback will be given, while the main part of the paper deals with a review of the current state of neurofeedback in ADHD. A meta-analysis on neurofeedback from 2009 found large effect sizes for inattention and impulsivity and medium effects sizes for hyperactivity. Since 2009 several new studies, including 4 placebo-controlled studies, have been published. These latest studies are reviewed and discussed in more detail. The review focuses on studies employing (1) semi-active, (2) active, and (3) placebo-control groups. The assessment of specificity of neurofeedback treatment in ADHD is discussed and it is concluded that standard protocols such as theta/beta, SMR and slow cortical potentials neurofeedback are well investigated and have demonstrated specificity. The paper ends with an outlook on future questions and tasks. It is concluded that future controlled clinical trials should, in a next step, focus on such known protocols, and be designed along the lines of learning theory.

Neurofeedback and Basic Learning Theory: Implications for Research and Practice

Brain activity assessed by electroencephalography (EEG) has been demonstrated to respond to conditioning techniques. The concept of modulating this activity has been called EEG biofeedback, more recently neurofeedback, and is based on operant learning principles. Technological advancements have significantly enhanced the ease and affordability of recording and analyzing brain activity. Thus, properly trained practitioners can implement these conditioning strategies in their practice. Recent research indicating evidenced-based efficacy has made this technique a more viable option for clinical intervention. The objective of this article is to highlight the learning principles that have provided the fundamentals of this neuromodulatory approach. In addition, it is recommended that future applications in clinical work, research, and development adhere to these principles.

The increase in theta/beta ratio on resting-state EEG in boys with attention-deficit/hyperactivity disorder is mediated by slow alpha peak frequency.

Attention-deficit/hyperactivity disorder (ADHD) was found to be characterized by a deviant pattern of electrocortical activity during resting state, particularly increased theta and decreased beta activity. The first objective of the present study is to confirm whether individuals with slow alpha peak frequency contribute to the finding of increased theta activity in ADHD. The second objective is to explore the relation between resting-state brain oscillations and specific cognitive functions. From 49 boys with ADHD and 49 healthy control boys, resting-state EEG during eyes open and eyes closed was recorded, and a variety of cognitive tasks were administered. Theta and beta power and theta/beta ratio were calculated using both fixed frequency bands and individualized frequency bands. As expected, theta/beta ratio, calculated using fixed frequency bands, was significantly higher in ADHD children than control children. However, this group effect was not significant when theta/beta ratio was assessed using individualized frequency bands. No consistent relation was found between resting-state brain oscillations and cognition. The present results suggest that previous findings of increased theta/beta ratio in ADHD may reflect individuals with slow alpha peak frequencies in addition to individuals with true increased theta activity. Therefore, the often reported theta/beta ratio in ADHD can be considered a non-specific measure combining several distinct neurophysiological subgroups such as frontal theta and slowed alpha peak frequencies. Future research should elucidate the functional role of resting-state brain oscillations by investigating neurophysiological subgroups, which may have a clearer relation to cognitive functions than single frequency bands.

EEG PHENOTYPES PREDICT TREATMENT OUTCOME TO STIMULANTS IN CHILDREN WITH ADHD

This study demonstrates that the EEG phenotypes as described by Johnstone, Gunkelman & Lunt are identifiable EEG patterns with good inter-rater reliability. Furthermore, it was also demonstrated that these EEG phenotypes occurred in both ADHD subjects as well as healthy control subjects. The Frontal Slow and Slowed Alpha Peak Frequency and the Low Voltage EEG phenotype discriminated ADHD subjects best from controls (however the difference was not significant). The Frontal Slow group responded to a stimulant with a clinically relevant decreased number of false negative errors on the CPT. The Frontal Slow and Slowed Alpha Peak Frequency phenotypes have different etiologies as evidenced by the treatment response to stimulants. In previous research Slowed Alpha Peak Frequency has most likely erroneously shown up as a frontal theta sub-group. This implies that future research employing EEG measures in ADHD should avoid using traditional frequency bands, but dissociate Slowed Alpha Peak Frequency from frontal theta by taking the individual alpha peak frequency into account. Furthermore, the divergence from normal of the frequency bands pertaining to the various phenotypes is greater in the clinical group than in the controls. Investigating EEG phenotypes provides a promising new way to approach EEG data, explaining much of the variance in EEGs and thereby potentially leading to more specific prospective treatment outcomes.

P300 Development across the Lifespan: A Systematic Review and Meta-Analysis

Background The P300 component of the event-related potential is a large positive waveform that can be extracted from the ongoing electroencephalogram using a two-stimuli oddball paradigm, and has been associated with cognitive information processing (e.g. memory, attention, executive function). This paper reviews the development of the auditory P300 across the lifespan. Methodology/Principal Findings A systematic review and meta-analysis on the P300 was performed including 75 studies (n = 2,811). Scopus was searched for studies using healthy subjects and that reported means of P300 latency and amplitude measured at Pz and mean age. These findings were validated in an independent, existing cross-sectional dataset including 1,572 participants from ages 6–87. Curve-fitting procedures were applied to obtain a model of P300 development across the lifespan. In both studies logarithmic Gaussian models fitted the latency and amplitude data best. The P300 latency and amplitude follow a maturational path from childhood to adolescence, resulting in a period that marks a plateau, after which degenerative effects begin. We were able to determine ages that mark a maximum (in P300 amplitude) or trough (in P300 latency) segregating maturational from degenerative stages. We found these points of deflection occurred at different ages. Conclusions/Significance It is hypothesized that latency and amplitude index different aspects of brain maturation. The P300 latency possibly indexes neural speed or brain efficiency. The P300 amplitude might index neural power or cognitive resources, which increase with maturation.

Disorder specificity despite comorbidity: Resting EEG alpha asymmetry in major depressive disorder and post-traumatic stress disorder

The approach-withdrawal and valence-arousal models highlight that specific brain laterality profiles may distinguish depression and anxiety. However, studies remain to be conducted in multiple clinical populations that directly test the diagnostic specificity of these hypotheses. The current study compared electroencephalographic data under resting state, eyes closed conditions in patients with major depressive disorder (MDD) (N=15) and post-traumatic stress disorder (PTSD) (N=14) relative to healthy controls (N=15) to examine the specificity of brain laterality in these disorders. Key findings included (1) reduced left-frontal activity in MDD, (2) a positive correlation between PTSD severity and right-frontal lateralisation, (3) greater activity in PTSD patients relative to MDD within the right-parietotemporal region, and (4) globally increased alpha power in MDD. Findings partially support the diagnostic applicability of the theoretical frameworks. Future studies may benefit from examining task-driven differences between groups.

EEG biomarkers in major depressive disorder: Discriminative power and prediction of treatment response

Abstract Major depressive disorder (MDD) has high population prevalence and is associated with substantial impact on quality of life, not least due to an unsatisfactory time span of sometimes several weeks from initiation of treatment to clinical response. Therefore extensive research focused on the identification of cost-effective and widely available electroencephalogram (EEG)-based biomarkers that not only allow distinguishing between patients and healthy controls but also have predictive value for treatment response for a variety of treatments. In this comprehensive overview on EEG research on MDD, biomarkers that are either assessed at baseline or during the early course of treatment and are helpful in discriminating patients from healthy controls and assist in predicting treatment outcome are reviewed, covering recent decades up to now. Reviewed markers include quantitative EEG (QEEG) measures, connectivity measures, EEG vigilance-based measures, sleep–EEG-related measures and event-related potentials (ERPs). Further, the value and limitations of these different markers are discussed. Finally, the need for integrated models of brain function and the necessity for standardized procedures in EEG biomarker research are highlighted to enhance future research in this field.

Neurophysiological predictors of non-response to rTMS in depression.

BACKGROUND The application of rTMS in Depression has been very well investigated over the last few years. However, little is known about predictors of non-response associated with rTMS treatment. OBJECTIVE This study examined neurophysiological parameters (EEG and ERP) in 90 depressed patients treated with rTMS and psychotherapy and sought to identify predictors of non-response. METHODS This study is a multi-site open-label study assessing pre-treatment EEG and ERP measures associated with non-response to rTMS treatment. RESULTS Non-responders were characterized by 1) Increased fronto-central theta EEG power, 2) a slower anterior individual alpha peak frequency, 3) a larger P300 amplitude, and 4) decreased pre-frontal delta and beta cordance. A discriminant analysis yielded a significant model, and subsequent ROC curve demonstrated an area under the curve of 0.814. CONCLUSIONS Several EEG variables demonstrated clear differences between R and NR such as the anterior iAPF, fronto-central Theta and pre-frontal cordance in the Delta and Beta band (representative of increased relative pre-frontal perfusion). The increased P300 amplitude as a predictor for non-response requires further study, since this was the opposite as hypothesized and there were no correlations of this measure with clinical improvement for the whole sample. Combining these biomarkers in a discriminant analysis resulted in a reliable identification of non-responders with low false positive rates. Future studies should prospectively replicate these findings and also further investigate appropriate treatments for the sub-groups of non-responders identified in this study, given that most of these biomarkers have also been found in antidepressant medication studies.