Attention Deficit Disorder

Epileptic Electroencephalography Profile Associates with Attention Problems in Children with Fragile X Syndrome: Review and Case Series

Fragile X syndrome (FXS) is the most common cause of inherited intellectual disability and a variant of autism spectrum disorder (ASD). The FXS population is quite heterogeneous with respect to comorbidities, which implies the need for a personalized medicine approach, relying on biomarkers or endophenotypes to guide treatment. There is evidence that quantitative electroencephalography (EEG) endophenotype-guided treatments can support increased clinical benefit by considering the patient’s neurophysiological profile. We describe a case series of 11 children diagnosed with FXS, aged one to 14 years, mean 4.6 years. Case data are based on longitudinal clinically-observed reports by attending physicians for comorbid symptoms including awake and asleep EEG profiles. We tabulate the comorbid EEG symptoms in this case series, and relate them to the literature on EEG endophenotypes and associated treatment options. The two most common endophenotypes in the data were diffuse slow oscillations and epileptiform EEG, which have been associated with attention and epilepsy respectively. This observation agrees with reported prevalence of comorbid behavioral symptoms for FXS. In this sample of FXS children, attention problems were found in 37% (4 of 11), and epileptic seizures in 45% (5 of 11). Attention problems were found to associate with the epilepsy endophenotype. From the synthesis of this case series and literature review, we argue that the evidence-based personalized treatment approach, exemplified by neurofeedback, could benefit FXS children by focusing on observable, specific characteristics of comorbid disease symptoms.

Ending the Evidentiary & Insurance Reimbursement Bias Against Neurofeedback to Treat ADHD: It will take Clinician Action in addition to the Compelling Science

Altered spontaneous low frequency brain activity in Attention Deficit/Hyperactivity Disorder

Background: Resting brain activity appears altered in Attention Deficit/Hyperactivity Disorder (ADHD). The default mode interference hypothesis (Sonuga-Barke and Castellanos, 2007) postulates that patterns of spontaneous very low frequency brain activity, typical of the resting brain, cause attention lapses in ADHD when they remain unattenuated following the transition from rest to active task performance. Here we test this hypothesis using DC-EEG. Methods: DC-EEG recordings of very low frequency brain activity (< 1.5 Hz) were compared for 16 male children with ADHD and 16 healthy controls during both rest and active task performance (two choice reaction time task). Results: A previously identified very low frequency resting network of electrodes was replicated. At rest ADHD children showed less EEG power in very low frequency bands (i.e., .02–.2 Hz). They also showed less attenuation of power at these frequency bands during rest-to-task transition. Reduced attenuation was associated with a number of measures of performance. Discussion: We confirmed the existence of altered very low frequency brain activity in ADHD. ADHD children may have deficits both in maintaining a resting brain when needed and ‘protecting’ an active brain from the intrusion of resting state brain activity.

Individual EEG Alpha Activity Analysis for Enhancement Neurofeedback Efficiency: Two Case Studies

The hypothesis was tested of whether neurofeedback training applied in order to increase or decrease power of individual EEG frequency ranges is more efficient than neurofeedback training of standard EEG frequency ranges. The sessions of decreasing the theta/beta ratio and reinforcing alpha neurofeedback training were carried out on two outpatients with attention deficit disorder (a schoolboy) and functional pain contraction (a professional musician). The neurofeedback utilizing standard EEG frequency ranges (theta 4-8, alpha 8-12, beta 13-18) was inefficient and even resulted in aggravation of symptoms in both cases. The individualized neurofeedback that utilized individual frequency ranges resulted in substantial clinical improvement.

Applicability of brain wave biofeedback to substance use disorder in adolescents

Neurofeedback treatment for addictions in adults is probably efficacious, and several reported approaches are described with their indications. Neurofeedback is promising as a treatment modality for adolescents, especially those with stimulant abuse and attention and conduct problems. It is attractive as a medication-free, neurophysiologic, and self-actualizing treatment for a substance-based, brain-impaired and self-defeating disorder. More research, beginning with case reporting, is needed to assess use and efficacy in adolescents.

A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities

Eighteen children with ADD/ADHD, some of whom were also LD, ranging in ages from 5 through 15 were randomly assigned to one of two conditions. The experimental condition consisted of 40 45-minute sessions of training in enhancing beta activity and suppressing theta activity, spaced over 6 months. The control condition, waiting list group, received no EEC biofeedback. No other psychological treatment or medication was administered to any subjects. All subjects were measured at pretreatment and at posttreatment on an IQ test and parent behavior rating scales for inattention, hyperactivity, and aggressive I defiant (oppositional) behaviors. At posttreatment the experimental group demonstrated a significant increase (mean of 9 points) on the K-Bit IQ Composite as compared to the control group (p < .05). The experimental group also significantly reduced inattentive behaviors as rated by parents (p < .05). The significant improvements in intellectual functioning and attentive behaviors might be explained as a result of the attentional enhancement affected by EEG biofeedback training. Further research utilizing improved data collection and analysis, more stringent control groups, and larger sample sizes are needed to support and replicate these findings