HOUSTON – The pandemic is taking a toll on our mental health.
Healthcare workers and first responders are also learning to cope as well, which is why a Houston neuro therapist and clinical researcher is stepping in to alleviate some of that stress.
“I mean every time we go on a run, we know it has (the) potential to take us out of our ability to provide for our families. I mean that’s traumatizing in itself, especially that we know there’s a lot of people out there that’s out of work,” said Dr. Ronald Swatzyna, director and chief scientist at Houston Neuroscience Brain Center.
Swatzyna runs the Houston Neuroscience Brain Center. He’s also a retired firefighter who has served time in Vietnam and with Operation Desert Storm. He knows first-hand the stress frontline workers are currently under.
“I think it’s a perfect opportunity for me to say I’ve been where you are and I understand these challenges you’re having,” Swatzyna said.
The neuro therapist and clinical researcher is offering his services free of charge to first responders.
Swatzyna is doing 30-minute Zoom sessions with any frontline worker who wants to talk and mentally decompress. They just need to reach out to his office and identify themselves as a nurse, doctor, firefighter, EMT, police officer, etc. by clicking the link here.
“We know that a good number of first responders that are getting the coronavirus and so that stress in itself,” he said.
Swatzyna said you don’t have to be a frontline worker to take charge of your mental health.
He suggests starting with these three things: Sleeping, exercising and eating healthy meals.
“We’re going to get through this and be even stronger on the other side,” he said. “I just want to make it as easy as possible for the ones out there busting their butts every day because they’re doing a great job for us.” he said.
Texans can also call the COVID-19 Mental Health Hotline if they need help with anxiety or stress at 833-986-1919.
Background and Objective. In a previous study, we showed a new EEG processing methodology called Multi-Scale Ranked Organizing Map/Implicit Function As Squashing Time (MS-ROM/IFAST) performing an almost perfect distinction between computerized EEG of Italian children with autism spectrum disorder (ASD) and typically developing children. In this study, we assessed this system in distinguishing ASD subjects from children affected with other neuropsychiatric disorders (NPD). Methods. At a psychiatric practice in Texas, 20 children diagnosed with ASD and 20 children diagnosed with NPD were entered into the study. Continuous segments of artifact-free EEG data lasting 10 minutes were entered in MS-ROM/IFAST. From the new variables created by MS-ROM/IFAST, only 12 has been selected according to a correlation criterion. The selected features represent the input on which supervised machine learning systems (MLS) acted as blind classifiers. Results. The overall predictive capability in distinguishing ASD from other NPD cases ranged from 93% to 97.5%. The results were confirmed in further experiments in which Italian and US data have been combined. In this analysis, the best MLS reached 95.0% global accuracy in 1 out of 3 classes distinction (ASD, NPD, controls). This study demonstrates the value of EEG processing with advanced MLS in the differential diagnosis between ASD and NPD cases. The results were not affected by age, ethnicity and technicalities of EEG acquisition, confirming the existence of a specific EEG signature in ASD cases. To further support these findings, it was decided to test the behavior of already trained neural networks on 10 Italian very young ASD children (25-37 months). In this test, 9 out of 10 cases have been correctly recognized as ASD subjects in the best case. Conclusions. These results confirm the possibility of an early automatic autism detection based on standard EEG.
You approach your child’s bedroom door feeling anxious. When you knock on his door, there is no answer, so you let yourself in. He sits in front of his television, Xbox controller tightly in hand, eyes virtually unblinking, staring at the screen. You say his name a couple of times and get no response. When he finally seems to hear you, the irritation at being interrupted seems like a drastic overreaction to such a minor inconvenience. Many parents experience different versions of the same story and feel helpless and even slightly afraid. What you are observing is a real phenomenon – a video game addiction.
Routine electroencephalograms (EEG) are not recommended as a screen for epileptic discharges (EDs) in current practice guidelines for children with autism spectrum disorder (ASD). However, a review of the research from the last three decades suggests that this practice should be reevaluated. The significant comorbidity between epilepsy and ASD, its shared biologi- cal pathways, risk for developmental regression, and cognitive challenges demand increased clinical investigation requiring a proactive approach. This review highlights and explains the need for screening EEGs for children with ASD. EEG would assist in differentiating EDs from core features of ASD and could be included in a comprehensive assessment. EEG also meets the demand for evidence-based precision medicine and focused care for the individual, especially when overlapping processes of development are present.
The routine use of stimulants in pediatrics has increased dramatically over the past 3 decades and the long-term consequences have yet to be fully studied. Since 1978 there have been 7 articles identifying electroencephalogram (EEG) abnormalities, particularly epileptiform discharges in children with attention deficit hyperactivity disorder (ADHD). Many have studied the prevalence of these discharges in this population with varying results. An article published in 2011 suggests that EEG technology should be considered prior to prescribing stimulants to children diagnosed with ADHD due to a high prevalence of epileptiform discharges. The 2011 study found a higher prevalence (26%) of epileptiform discharges when using 23-hour and sleep-deprived EEGs in comparison with other methods of activation (hyperventilation or photostimulation) and conventional EEG. We sought to replicate the 2011 results using conventional EEG with the added qEEG technologies of automatic spike detection and low- resolution electromagnetic tomography analysis (LORETA) brain mapping. Our results showed 32% prevalence of epileptiform discharges, which suggests that an EEG should be considered prior to prescribing stimulant medications.
Many atiepileptic drugs (AEDs) have been tested on nonepileptic patients with a variety of patients with a variety of diagnoses. The Food and Drug Administration has only approved certain AEDs for a small number of psychiatric conditions. There a few studies of nonepileptic patients that recommended an empirical trial of AEDs when isolated epileptiform discharges (IEDs) are identified in the electroencephalogram (EEG). However, no trials have been published. The purpose of this study is to evaluate the outcome of treating nonepileptic patients with AEDs when IEDs are present.
Data from an EEG is not commonly used by psychiatrists to plan treatment and medication. However, EEG abnormalities such as isolated epileptiform discharges are found to be more prevalent in psychiatric patients, particularly those diagnosed with autism spectrum disorder (ASD). Most medications prescribed for ASD lower seizure threshold and increase side effects. Therefore, it may be prudent to order an EEG for ASD cases, especially those categorized as refractory. The data set was obtained from a multidisciplinary practice that treats a wide variety of neuroatypical children and adolescent refractory patients. This study investigated 140 nonepileptic subjects diagnosed with ASD, aged 4 to 25 years. Visual inspection of the EEG was performed to search for paroxysmal, focal, or lateralizing patterns.
In 2009 the United States National Institute of Mental Health (NIMH) introduced the Research Domain Criteria (RDoC) project, which intends to explicate fundamental bio-behavioral dimensions that cut across heterogeneous disorder categories in psychiatry. One major research domain is defined by arousal and regulatory systems. 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.
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.
The author discloses a personal history of undiagnosed mile traumatic brain injury (MBTI) and identifies a typical course of progression of this condition. He advocates a careful inquiry for possibly head injury whenever the clinical history shows an original period of normal functioning, a progression of disturbance over time, multiple diagnoses, and poor response to treatment with medication. He discusses the use of quantitative electroencephalography (QEEG) in assessing possible mild traumatic brain injury, describes typical features of quantitative electroencephalography in mild traumatic brain injury, and cautions ab out the frequency false negatives. He provides two case histories showing the progression of disturbing cognitive, personality, and impulse control problems following early head injuries.
In psychiatry, the reliability of diagnosis (a symptom-based approach) is instrumental in the management of medications and treatments. The National Institute of Mental Health (NIMH) is now searching for neurobiological measures that account for observed and reported symptoms. The results of our study of 386 refractory clinical cases suggest there are four neurobiological measures that account for medication failure: encephalopathy, focal slowing, beta spindles and transient discharges. One or more of these neurobiological measures (neurobiomarkers) explained why medication failed in each of these refractory cases. Our investigation found positive correlations between numbers of medications being prescribed and numbers of neurobiomarkers being identified in children and adolescents. In adults we also we found a positive correlation between numbers of medications being prescribed and number of diagnoses. This study represents a model that could improve the efficacy of the psychotropic intervention and treatment selection in refractory psychiatric cases.
In early 2013, the National Institute of Mental Health (NIMH) launched the Research Domain Criteria (RDoC), in an effort to evolve the diagnostic process by incorporating a multidisciplinary ap- proach that relies not only on symptoms, but also on genetics, neuroimaging, and cognitive science. This movement away from the traditional categorization of the Diagnostic and Statistical Manual (DSM) towards a science-based classification highlights the importance of psychiatry fully exploring the potential of avail- able electrophysiological testing. There are previous classifications of ADHD by Joel Lubar and Daniel Amen. However, our five year research (N=386 pending publication) led to the development of a neurobiomarker profiling model which we use in our clinic. Based on clinically correlated electroencephalogram (EEG) and quantitative EEG (qEEG) findings, our model is both concise and suitable to application by neurofeedback practi- tioners. There is not a layman’s equiva-lent to the names used in this suggested classification. To date, the application of clinical EEG and qEEG have been very limited in psychiatry, although studies suggest effective application in diagnosis, medication response, and treatment selection (Coburn, Lauterbach, Boutros, Black, Arciniegas, & Coffey, 2006). Neuro- biomarkers specific to ADHD symptom presentation are numerous and account for the variance in treatment response (Johnstone, Gunkelman, & Lunt, 2005). are identified through testing, behav- ioral observation, and self-report; how- ever, the diagnostic specificity of these approaches is limited by the fact that many similar issues can cause identical symptoms.
Previous research into the benefits of children eating breakfast has focused on educational and cognitive performance, and behavior. This single case study used qEEG in order to assess how different breakfast choices affect a 12-year-old female’s brainwave activity. The three breakfast conditions included no-breakfast, a high sugar/high carbohydrate breakfast, and a nutritionally balanced breakfast. The results show that skipping breakfast or eating a high sugar/high carbohydrate breakfast increased high beta activity in the brain, which is associated with anxiety and focus issues. These findings suggest that eating a nutritionally balanced breakfast may reduce anxiety and increased focus as compared to other breakfast options.
When I was invited to write an article on my professional and personal experience with traumatic brain injury, I had no idea what I was getting into. Regardless, the purpose of this article is to convey to my colleagues not only what I experienced, but also what many of my patients experience following a traumatic brain injury. First, I want to make it clear that it is estimated that 80% of all individuals who have a mild traumatic brain injury (MTBI) or concussion will completely recover. Second, the 20% who do not often are misdiagnosed or ‘‘miss diagnosis.’’ Third, in those cases where a brain injury was serious enough to warrant a trip to the emergency room and a concussion was diagnosed, the symptoms of postconcussive syndrome can take from months to years to fully present. Therefore, the delayed presentation of postconcussive symptoms contributes to the failure of a MTBI diagnosis and efficacious treatment. However, there is an obvious pattern of pathology in MTBI that medical and mental health professionals should be prepared to recognize. To best demonstrate this pattern, I will tell you the story of my concussion and present two recent cases that eluded detection for years.
To date, the application of clinical EEG and qEEG have been very limited in psychiatry, although studies suggest effective application in diagnosis, medication response, and treatment selection. ADD symptoms are common to many diagnoses and can often elude detection – however there are for subtypes of ADHD that can be identified by four distinctly different neurobiomarkers on a EEG/qEEG. The following newsletter article will explain which neurobiomarkers for ADHD respond well to medication, which neurobiomarkers are commonly associated with medication failure, and why physicians should incorporate this valuable information into treatment plans.
In psychiatry, diagnosis reliability is instrumental for proper management of medications and treatments. Going along with this, The National Institute of Mental Health (NIMH) is searching for neurobiological measures that account for observed and reported symptoms. The purpose of this study was to find neurobiomarkers that account for medication failure in 386 refractory clinical cases. The results found 4 such measures that could explain why medication failed in each of the refractory cases: encephalopathy, focal slowing, beta spindles, and transient discharges. The study found positive correlations between number of mediations prescribed and the number of neurobiomarkers identified in children and adolescents. A positive correlation between number of medications prescribed and number of diagnosis was found in adults.
Pharmaco-electroencephalography research using neurological data from clinical EEG and qEEG technologies could improve medication selection and treatment planning in psychiatry. Over the past 5 years in a multidisciplinary practice, 386 refractory cases have been found to contain a small number of brainwave abnormalities related to the following diagnostic categories: mood, anxiety, Autism Spectrum Disorder, and Attention Deficit Hyperactivity Disorder. The brainwave abnormalities were the following: encephalopathy, focal slowing, beta spindles, and transient discharges. However, results found that although these abnormalities were noted in the majority of cases with medication failure they did not align directly with specific diagnoses. These findings suggest that data from the EEG and qEEG can help guide individualized medicine selection and treatment planning, especially when previous medications have failed.
In 2009 the National Institute of Mental Health (NIMH) introduced the Research Domain Criteria project (RDoC) which sought to analyze basic dimensions of functioning that underlie human behavior to find new ways of studying mental disorders. The purpose of this study was to investigate the relationship between arousal systems and behavioral dimensions. Specifically, this study analyzed spindling excessive beta (SEB) and its relation to insomnia, impulsivity/hyperactivity and attention. The results found an SEB occurrence in patients between 0-10.8%. It was found that patients with frontal SEBs only had significantly higher impulsivity/hyperactivity and insomnia complaints. To conclude, this data reveals that frontal SEB may be an electroencephalographic marker caused by sleep maintenance problems with concurrent impulse control problems.
Over the last 30 years, the use of stimulants in pediatrics has increased, yet long-term consequences have yet to be fully explored. Past studies have identified isolated epileptiform discharges (IEDs), a brainwave abnormality, in children with attention deficit hyperactivity disorder (ADHD). An article published in 2011 suggested that EEG technology should be considered prior to prescribing stimulants to children with ADHD in order to screen for epileptiform discharges. The 2011 study found 26% prevalence of IEDs when using sleep-deprived EEGs. This study sought to replicate the 2011 results using conventional EEG and qEEG technologies. Our results showed 32% of patients with ADHD had IEDs, which further supports that an EEG screening should be considered before prescribing stimulant medications.
Autism Spectrum Disorder (ASD) often presents a treatment challenge due to the variety of symptoms that make each case unique. Medication prescribed to manage ASD associated symptoms such as anxiety, depression, attention issues, and behavioral problems often fail to alleviate symptoms and can produce undesirable side effects. This medication failure could be related to the increased prevalence of isolated epileptiform discharges (IEDs) in psychiatric patients, that go undetected without the use of an electroencephalogram (EEG). The purpose of this study was to reveal the prevalence of IEDs in the ASD population, and to demonstrate the usefulness of the EEG for providing data to treating physicians. The study was comprised of 140 nonepileptic patients with ASD under the age of 25. Of the 140 patients, 36.4% were found to have IEDs after an EEG screening. The results show that compared to a healthy population, many patients with ASD have IEDs despite never having a seizure. These findings support the use of EEG in patients with ASD, to allow for more individualized and precise medication selection.
Although antiepileptic drugs (AEDs) are prescribed to nonepileptic patients with a variety of diagnoses, the FDA has only approved their use for a small number of psychiatric conditions. Previous research recommends an empirical trial of AEDs when isolated epileptiform discharges (IEDs) are identified in the electroencephalogram (EEG). The purpose of this study was to evaluate the outcome of treating nonepileptic patients with AEDs when IEDs are present. The sample was comprised of 76 refractory cases from a multidisciplinary practice whose EEG readings contained IEDs. The psychiatrist’s progress notes were assessed to determine the impact of adding anticonvulsants based on parent, teacher reports and clinical observation. The findings found that the majority of the patients improved (85.53%) when AEDs were prescribed. These results suggest that IEDs may predict positive treatment outcome to anticonvulsant medication regardless of age, gender, or diagnosis.
Research into the benefits of children eating breakfast has previously focused on educational and cognitive performance as well as behavior. Few nutritional investigations have utilized brain imaging technology in order to examine how breakfast influences brain function. This single case study used quantitative electroencephalography (qEEG) in order to assess how three different breakfast choices affected a 12-year-old female’s brainwave activity. The three different breakfast conditions included no breakfast, a high-sugar/high- carbohydrate breakfast, and a nutritionally balanced breakfast. The findings indicated that skipping breakfast significantly increased high beta activity associated with anxiety and focus issues. Eating a high-sugar/high- carbohydrate breakfast was also associated with increased high beta activity, but less significant than the no- breakfast option. Most importantly, eating a nutritionally balanced breakfast was found to normalize the qEEG. The variation in high beta activity in the different breakfast options suggested that eating a nutritionally balanced breakfast may reduce anxiety and increase focus compared to skipping breakfast. These results may help explain why previous research has found cognitive, academic, and behavioral improvements when children consume breakfast. Furthermore, the qEEG should be considered in future nutritional studies as a measurement of brain function.