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.

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

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.

Insomnia is a sleeping disorder, usually studied from a behavioural perspective, with a focus on somatic and cognitive arousal. Recent studies have suggested that an impairment of information processes due to the presence of cortical hyperarousal might interfere with normal sleep onset and/or consolidation. As such, a treatment modality focussing on CNS arousal, and thus influencing information processing, might be of interest. Seventien insomnia patients were randomly assigned to either a tele-neurofeedback (n = 9) or an electromyography tele-biofeedback (n = 8) protocol. Twelve healthy controls were used to compare baseline sleep measures. A polysomnography was performed pre and post treatment. Total Sleep Time (TST), was considered as our primary outcome variable. Sleep latency decreased pre to post treatment in both groups, but a significant improvement in TST was found only after the neurofeedback (NFB) protocol. Furthermore, sleep logs at home showed an overall improvement only in the neurofeedback group, whereas the sleep logs in the lab remained the same pre to post training. Only NFB training resulted in an increase in TST. The mixed results concerning perception of sleep might be related to methodological issues, such as the different locations of the training and sleep measurements.

BackgroundBenzodiazepine withdrawal programmes have never been experimentally compared with a nonintervention control condition.AimsTo evaluate the efficacy and feasibility of tapering off long-term benzodiazepine use in general practice, and to evaluate the value of additional group cognitive–behavioural therapy (CBT).MethodA 3-month randomised, controlled trial was conducted in which 180 people attempting to discontinue long-term benzodiazepine use were assigned to tapering off plus group CBT, tapering off alone or usual care.ResultsTapering off led to a significantly higher proportion of successful discontinuations than usual care (62% v. 21%). Adding group CBT did not increase the success rate (58% v. 62%). Neither successful discontinuation nor intervention type affected psychological functioning. Both tapering strategies showed good feasibility in general practice.ConclusionsTapering off is a feasible and effective way of discontinuing long-term benzodiazepine use in general practice. The addition of group CBT is of limited value.