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EEG – Lessons From A Half-Million Brains

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

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

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

Ronald J. Swatzyna, Ph.D. – BCN (Associate Fellow), Director of Electrophysiological Research, The Tarnow Center for Self-Management, Houston, TX

Jay D. Tarnow, M.D. – Director for The Tarnow Center for Self-Management, Houston, TX

Jonika D. Tannous, B.S. – Statistical Research Intern, Rice University, The Tarnow Center for Self-Management, Houston, TX

Vijayan Pillai, Ph.D. – Professor, The University of Texas at Arlington Graduate School of Social Work, Arlington, TX

Christina Schieszler, B.S. – Research Intern, The University of Houston Graduate College of Social Work, The Tarnow Center for Self-Management, Houston, TX

Gerald P. Kozlowski, Ph.D. – BCN (Certified Senior Fellow), Faculty Member Department of Clinical Psychology, Saybrook University, San Francisco, CA

Abstract

Pharmaco-EEG studies using clinical electroencephalograms (EEG) and quantitative EEG (qEEG) technologies have existed for over four decades. This is a promising area which could improve psychotropic intervention using neurological data. One of the objectives in our clinical practice has been to collect electroencephalography (EEG) and quantitative EEG (qEEG) data. In the past five 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: 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.

Psychiatry does well in many instances: however there are perplexing cases that do not respond to traditional psychotropic intervention. Many abnormalities seen in the EEG are considered normal variants in the general population; however, minor abnormalities are important when clinical correlation exists. The utilization of EEG and qEEG, together with clinical presentation to identify neurobiomarkers has the potential to link neuronal irregularities with presenting symptom separately for each age group.

Results

  • N = 386 clinical cases: Ages 5-11 (n = 119), 12 – 17 (n = 105), and 18 – 69 (n = 162).
  • All cases were refractory, failing on at least two attempts of psychotropicintervention.

There were four emerging neurobiomarkers in these cases:

  • Encephalopathy (EN) can be defined as an organic diffuse disturbance in brain function producing neurological and psychological manifestations.
  • Focal Slowing (FS) is characterized by a predominance of slower electrical activity in a particular area of the brain.
  • Beta Spindles (BS) are identified as synchronous activity in the beta range around  a specific frequency and are indicative of hyper-arousal and most often seen fronto-centrally.
  • Transient Discharges (TD) are defined as EEG cerebral dysrhythmias identified by isolated episodic paroxysmal bursts of slow activity, controversial/anomalous spikey waveforms and/or true non-controversial epileptiform discharges.

There were two diagnoses and two diagnostic categories analyzed in this study:

  • Attention Deficit Hyperactivity Disorder (ADD)
  • Autistic Spectrum Disorder (ASD)
  • Depressive Disorders (DEP)
  • Anxiety Disorders (ANX)

The relationship between the diagnoses and the neurobiomarkers identified was examined separately for each age group.

Findings: Equally across all three age groups, there is very poor association (6.25%) between the patient’s diagnosis and the neurobiomarker responsible for their symptoms.

Table 1: Fisher’s Exact Test Signifigant P Values **

ADD ASD DEP ANX
Children

5 – 11

N=119

EN

FS

BS

TD

.209

.811

.093

1.00

.325

.538

.203

1.00

.788

.724

.588

.819

.003**

.620

.619

.836

Adolescents

12-17

N=105

EN

FS

BS

TD

.612

.757

.619

1.00

.070

.470

.560

.789

.732

.384

.019**

.067

.262

.619

.420

.207

Adults

18-69

N=162

EN

FS

BS

TD

.371

.621

.480

.108

.788

.436

.209

.536

.341

.384

.008**

.610

.063

.865

.865

.618

Table 2: Binary Logistics Regression Signifigant P Values **

ADD ASD DEP ANX
Children

5 – 11

N=119

EN

FS

BS

TD

.138

.457

.050

.984

.370

.741

.224

.860

.778

.131

.483

.550

.019**

.654

.826

.909

Adolescents

12-17

N=105

EN

FS

BS

TD

.445

.752

.521

.988

.036

.279

.486

.460

.282

.709

.010**

.062

.210

.420

.374

.123

Adults

18-69

N=162

EN

FS

BS

TD

.394

.298

.273

.080

.740

.201

.110

.339

.159

.765

.006**

.394

.067

.916

.892

.708

Discussion

  • The Fisher’s Exact Test showed only 3 significant findings out of 48 possible combinations.
  • The same three combinations identified by Fisher’s Exact Test were found to be significant using binary regression analysis. Children with EN were 11 times less likely to have ANX. In adolescents, those with BS were over five times more likely to be diagnosed with MDD. Likewise, adults with BS were three times more likely to be diagnosed with MDD. No other significant relationships were identified.

Conclusions

  • When used separately, an EEG, a qEEG, and clinical presentation lacks synergy.
  • However, when all three are combined in the hands of an experienced psychiatrist, the shortcomings of each are minimized.
  • Therefore, EEG and qEEG technologies can identify and quantify neuronal irregularities that reflect brain dysfunction causing clinically correlated psychiatric pathologies.
  • Using this neurobiomarker model across all age groups, we found evidence explaining why the medications tried previously had failed.
  • If left unidentified, any substantial improvement in psychiatric medication management and efficacious treatment planning would be thwarted.
  • Experienced psychiatrists who use this technology to assist in medication  selection have a great advantage.

Prior Meeting Presentations

  • International Society for Neurofeedback & Research 21st Annual Conference, September 2013, Dallas, Texas, USA
  • Biofeedback Federation of Europe 17th Annual Meeting, February, 2014, Venice, Italy
  • Joint Meeting of the EEG & Clinical Neuroscience Society (ECNS), the Society for Neuroimaging in Psychiatry (ISNIP), and the International Society for Brain Electromagnetic Topography (ISBET) September, 2014, Halifax, Nova Scotia, Canada
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Techniques Effecting the LORETA Source Solution

LORETA can be done in a few ways… as a source analysis for the entire EEG or segment being analyzed… or after a decomposition into the ICA components… The later will provide a better estimate of generators of the
individual components which are all blended for the overall LORETA, distorting the sources with other sources. Continue reading →

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Prof. Juri D. Kropotov Interview 2013

Juri speaks about the recent ERP meeting in St Petersburg Russia, held during the summer’s “white nights” when the night sky does not darken fully and the night is very short. Juri is interviewed in his offices, only 200 meters from Pavlov’s famous laboratory.

The discussion of the new methodology of ICA decomposition of the ERP, as well as the benefit of ERP added to the EEG/qEEG is discussed. There is discussion of the diagnostic specificity of the ERP methods, and the concept of biomarkers.

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Medication Failure: EEG/qEEG Findings Provide Evidence

This is the PowerPoint “Medication Failure: EEG/qEEG Findings Provide Evidence” as presented at ISNR Conference Workshop 20 September 21, 2013

Presented by:
Ronald J. Swatzyna, Ph.D., L.C.S.W.
The Tarnow Center for Self-Management
drron@tarnowcenter.com

Vijayan K. Pillai, Ph.D.
The University of Texas at Arlington
pillai@uta.edu

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FDA Approval of EEG Aid for ADHD

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. Continue reading →

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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? Continue reading →

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Electroencephalography (EEG) Underused Investigative Tool in Hospitals, Study Finds

child eeg Electroencephalography (EEG) Underused Investigative Tool in Hospitals, Study Finds

A retrospective study of patients who had in-hospital electroencephalography (EEG) has established that EEG is a valuable tool that could be deployed more widely to identify treatable causes of impaired consciousness in the hospital setting.

The study is published in the April issue of the Mayo Clinic Proceedings.

Altered mental status (AMS) and paroxysmal spells of uncertain origin are common among hospitalized patients. Impaired consciousness can sometimes be linked to metabolic or cardiac causes, but some of these spells may represent seizures or non-convulsive epilepsy, which can be detected only by electroencephalography (EEG). Although EEG is the key test in making these diagnoses, it is relatively underused in the inpatient setting owing to lack of availability and neurologic consultation at many hospitals in the United States. Continue reading →

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