Neurofeedback

Sleep maintenance, spindling excessive beta and impulse control: an RDoC arousal and regulatory systems approach?

EEG

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“In this study we aimed to investigate the relation between arousal systems (EEG-beta phenotypes also referred to as spindling excessive beta (SEB), beta spindles or sub-vigil beta) and the behavioral dimensions: insomnia, impulsivity/hyperactivity and attention. This analysis is conducted within a large and heterogeneous outpatient psychiatric population, in order to verify if EEG-beta phenotypes are an objective neurophysiological marker for psychopathological properties shared across psychiatric disorders.”

EEG – Lessons From A Half-Million Brains

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Listen to the full podcast here Jay Gunkelman Podcast

This week we’re joined by QEEG Diplomate (yes, that’s a term!) and founder of Brain Science International Jay Gunkelman, to talk Electroencephalography.  After analyzing over a half-million EEG scans, Jay has a pretty robust set of human brain data at his disposal… And he uses that body of knowledge to guide the work of psychiatric and other health professionals who make proscriptive recommendations about patients’ brains.

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Differential effects of theta/beta and SMR neurofeedback in ADHD on sleep onset latency

Recent studies suggest a role for sleep and sleep problems in the etiology of attention deficit hyperactivity disorder (ADHD) and a recent model about the working mechanism of sensori-motor rhythm (SMR) neurofeedback, proposed that this intervention normalizes sleep and thus improves ADHD symptoms such as inattention and hyperactivity/impulsivity. In this study we compared adult ADHD patients (N = 19) to a control group (N = 28) and investigated if differences existed in sleep parameters such as Sleep Onset Latency (SOL), Sleep Duration (DUR) and overall reported sleep problems (PSQI) and if there is an association between sleep-parameters and ADHD symptoms. Secondly, in 37 ADHD patients we investigated the effects of SMR and Theta/Beta (TBR) neurofeedback on ADHD symptoms and sleep parameters and if these sleep parameters may mediate treatment outcome to SMR and TBR neurofeedback. In this study we found a clear continuous relationship between self-reported sleep problems (PSQI) and inattention in adults with- and without-ADHD. TBR neurofeedback resulted in a small reduction of SOL, this change in SOL did not correlate with the change in ADHD symptoms and the reduction in SOL only happened in the last half of treatment, suggesting this is an effect of symptom improvement not specifically related to TBR neurofeedback. SMR neurofeedback specifically reduced the SOL and PSQI score, and the change in SOL and change in PSQI correlated strongly with the change in inattention, and the reduction in SOL was achieved in the first half of treatment, suggesting the reduction in SOL mediated treatment response to SMR neurofeedback. Clinically, TBR and SMR neurofeedback had similar effects on symptom reduction in ADHD (inattention and hyperactivity/impulsivity). These results suggest differential effects and different working mechanisms for TBR and SMR neurofeedback in the treatment of ADHD.

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Pharmaco-EEG: A Study of Individualized Medicine in Clinical Practice

pharmacotherapy

Ronald J. Swatzyna, Gerald P. Kozlowski, and Jay D. Tarnow

Abstract

Pharmaco-electroencephalography (Pharmaco-EEG) studies using clinical EEG and quantitative EEG (qEEG) technologies have existed for more than 4 decades. This is a promising area that could improve psychotropic intervention using neurological data. One of the objectives in our clinical practice has been to collect EEG and quantitative EEG (qEEG) data. In the past 5 years, we have identified a subset of refractory cases (n = 386) found to contain commonalities of a small number of electrophysiological features in the following diagnostic categories: mood, anxiety, autistic spectrum, and attention deficit disorders, Four abnormalities were noted in the majority of medication failure cases and these abnormalities did not appear to significantly align with their diagnoses. Those were the following: encephalopathy, focal slowing, beta spindles, and transient discharges. To analyze the relationship noted, they were tested for association with the assigned diagnoses. Fisher’s exact test and binary logistics regression found very little (6%) association between particular EEG/qEEG abnormalities and diagnoses. Findings from studies of this type suggest that EEG/qEEG provides individualized understanding of pharmacotherapy failures and has the potential to improve medication selection.

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FDA Approval of EEG Aid for ADHD

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There has been a lot of discussion since the FDA announced approving a new medical device just approved to assist in the diagnosis of ADHD in children and adolescents. “The device, the Neuropsychiatric EEG-Based Assessment Aid (NEBA) System, is based on electroencephalogram technology, which records different kinds of electrical impulses given off by neurons in the brain and the number of times the impulses are given off each second. The NEBA System is a 15- to 20-minute noninvasive test that calculates the ratio of two standard brain-wave frequencies, known as theta and beta waves; the ratio has been shown to be higher in children and adolescents with ADHD than in those without it, according to FDA” (http://alert.psychiatricnews.org/2013/07/ fda-approves-device-to-help-diagnose.html). However, the use of this technology to assist in the diagnosis of ADHD is not new.

David Rabiner, Ph.D. (Senior Research Scientist, Duke University) published a report (Attention Research Update April 2001) titled “New Support for the Use of qEEG scanning in Diagnosing ADHD” (http://www.helpforadd.com/2001/april.htm). This report acknowledged utilizing the measure of the ratio of theta to beta waves in the prefrontal cortex as a marker for ADHD (ages 6-20). Therefore the technology is not new, and although the NEBA System is helping to bring scientific evidence into the realm of psychiatric diagnosis, there is more to it then has been discussed thus far.

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Technology Helps Explain Medication Failure

In almost every area of medicine, doctors can order tests to provide objective physical data to guide their medication selection. However, the practice of psychiatry is most often based on observation, self-report and psychological testing. It appears that we are better at measuring impairment than we are at identifying the source and prescribing an effective medication. Is there a way we can do better?

The director of the National Institute of Mental Health, Tomas Insel, suggests there are many medicines, but they are not working adequately. This is because the symptoms of mental illness are too illusive and are shared by many diagnoses. Insel (2012) says, “It’s much harder to fix something if you don’t know what is going wrong.” Medications are being prescribed to treat a set of symptoms suggestive of a specific disorder without any objective evidence of the cause. Additionally, the practice of polypharmacy has become way too common in children and adolescents.

Pharmaceutical industry advertising promotes adding a medication when the first medication fails to produce the desired results (i.e., adding Abilify to your antidepressant). The message is that when one medication fails, keep adding more in an effort to address the additional symptoms. Each additional medication increases the risk of side effects. It is not uncommon for children to come to us with several medications prescribed. Last month, for example, we saw a 9-year-old female with prescriptions for Olanzapine three times a day, Lithium Carbonate daily and Amphetamine Salts three times a day. Also, a 10-year-old male came to us on Focolin three times a day, Seroquel twice a day, Lexipro daily and Zyprexa daily. If there was a way to determine why a medication failed, would it not be prudent to investigate why? If current technology could help?

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National Institute for Health and Clinical Excellence (NHS NICE) has published final guidance recommending the use of brain monitoring technology

The healthcare guidance body NICE (National Institute for Health and Clinical Excellence (NHS NICE) has published final guidance recommending the use of brain monitoring technology such as the Bispectral Index (BIS, Covidien), E-Entropy (GE Healthcare) and Narcotrend-Compact M (MT MonitorTechnik GmbH & Co).  These EEG-based depth of anaesthesia monitors should be considered as positive options in patients receiving total intravenous anaesthesia (TIVA) and in patients who are considered at higher risk of adverse outcomes during any type of general anaesthesia, such as seniors, those with high body mass index, and those with cardiovascular and liver disease.

EEG based Brain Monitoring Systems helps clinicians assess patient consciousness levels through measuring the electrical activity in the brain. This includes patients who are at higher risk of unintended awareness (anesthesia too light) and also those patients who are at higher risk from excessively deep anaesthesia.

Superior surgical outcomes, the low cost of the testing and the ease of use of these technologies all contribute to the recommendations.

You can access these documents through ASET (www.ASET.org)