Robert Coben

EEG power and coherence in autistic spectrum disorder

OBJECTIVEnAutistic spectrum disorder (ASD) has been defined as a neurodevelopmental disorder with associated deficits in executive function, language, emotional, and social function. ASD has been associated with pathophysiology in cerebral organization. The current study investigated quantitative EEG findings in twenty children diagnosed with autistic disorders as compared to 20 controls matched for gender, age and IQ.nnnMETHODSnThe EEG was recorded during an eyes-closed resting condition and topographical differences in cerebral functioning were examined using estimates of absolute, relative, and total power, as well as intrahemispheric and interhemispheric coherences.nnnRESULTSnThere were group differences in power, intrahemispheric and interhemispheric coherences. Findings included excessive theta, primarily in right posterior regions, in autistics. There was also a pattern of deficient delta over the frontal cortex and excessive midline beta. More significantly, there was a pattern of underconnectivity in autistics compared to controls. This included decreased intrahemispheric delta and theta coherences across short to medium and long inter-electrode distances. Interhemispherically, delta and theta coherences were low across the frontal region. Delta, theta and alpha hypocoherence was also evident over the temporal regions. Lastly, there were low delta, theta and beta coherence measurements across posterior regions.nnnCONCLUSIONSnThese results suggest dysfunctional integration of frontal and posterior brain regions in autistics along with a pattern of neural underconnectivity. This is consistent with other EEG, MRI and fMRI research suggesting that neural connectivity anomalies are a major deficit leading to autistic symptomatology.nnnSIGNIFICANCEnThis paper reports the largest integrated study of EEG power and coherence during a resting state in children suffering autism spectrum disorder.

Neurofeedback for Autistic Spectrum Disorder: A Review of the Literature

There is a need for effective interventions to address the core symptoms and problems associated with autistic spectrum disorder (ASD). Behavior therapy improves communication and behavioral functioning. Additional treatment options include psychopharmacological and biomedical interventions. Although these approaches help children with autistic problems, they may be associated with side effects, risks or require ongoing or long-term treatment. Neurofeedback is a noninvasive approach shown to enhance neuroregulation and metabolic function in ASD. We present a review of the literature on the application of Neurofeedback to the multiple problems associated with ASD. Directions for future research are discussed.

The Relative Efficacy of Connectivity Guided and Symptom Based EEG Biofeedback for Autistic Disorders

Autism is a neurodevelopmental disorder characterized by deficits in communication, social interaction, and a limited range of interests with repetitive stereotypical behavior. Various abnormalities have been documented in the brains of individuals with autism, both anatomically and functionally. The connectivity theory of autism is a recently developed theory of the neurobiological cause of autisic symptoms. Different patterns of hyper- and hypo-connectivity have been identified with the use of quantitative electroencephalogray (QEEG), which may be amenable to neurofeedback. In this study, we compared the results of two published controlled studies examining the efficacy of neurofeedback in the treatment of autism. Specifically, we examined whether a symptom based approach or an assessment/connectivity guided based approach was more effective. Although both methods demonstrated significant improvement in symptoms of autism, connectivity guided neurofeedback demonstrated greater reduction on various subscales of the Autism Treatment Evaluation Checklist (ATEC). Furthermore, when individuals were matched for severity of symptoms, the amount of change per session was significantly higher in the Coben and Padolsky (J Neurother 11:5–23, 2007) study for all five measures of the ATEC. Our findings suggest that an approach guided by QEEG based connectivity assessment may be more efficacious in the treatment of autism. This permits the targeting and amelioration of abnormal connectivity patterns in the brains of people who are autistic.

A review of traditional and novel treatments for seizures in autism spectrum disorder: findings from a systematic review and expert panel

Despite the fact that seizures are commonly associated with autism spectrum disorder (ASD), the effectiveness of treatments for seizures has not been well studied in individuals with ASD. This manuscript reviews both traditional and novel treatments for seizures associated with ASD. Studies were selected by systematically searching major electronic databases and by a panel of experts that treat ASD individuals. Only a few anti-epileptic drugs (AEDs) have undergone carefully controlled trials in ASD, but these trials examined outcomes other than seizures. Several lines of evidence point to valproate, lamotrigine, and levetiracetam as the most effective and tolerable AEDs for individuals with ASD. Limited evidence supports the use of traditional non-AED treatments, such as the ketogenic and modified Atkins diet, multiple subpial transections, immunomodulation, and neurofeedback treatments. Although specific treatments may be more appropriate for specific genetic and metabolic syndromes associated with ASD and seizures, there are few studies which have documented the effectiveness of treatments for seizures for specific syndromes. Limited evidence supports l-carnitine, multivitamins, and N-acetyl-l-cysteine in mitochondrial disease and dysfunction, folinic acid in cerebral folate abnormalities and early treatment with vigabatrin in tuberous sclerosis complex. Finally, there is limited evidence for a number of novel treatments, particularly magnesium with pyridoxine, omega-3 fatty acids, the gluten-free casein-free diet, and low-frequency repetitive transcranial magnetic simulation. Zinc and l-carnosine are potential novel treatments supported by basic research but not clinical studies. This review demonstrates the wide variety of treatments used to treat seizures in individuals with ASD as well as the striking lack of clinical trials performed to support the use of these treatments. Additional studies concerning these treatments for controlling seizures in individuals with ASD are warranted.

Using quantitative and analytic EEG methods in the understanding of connectivity in autism spectrum disorders: a theory of mixed over- and under-connectivity

Neuroimaging technologies and research has shown that autism is largely a disorder of neuronal connectivity. While advanced work is being done with fMRI, MRI-DTI, SPECT and other forms of structural and functional connectivity analyses, the use of EEG for these purposes is of additional great utility. Cantor et al. (1986) were the first to examine the utility of pairwise coherence measures for depicting connectivity impairments in autism. Since that time research has shown a combination of mixed over and under-connectivity that is at the heart of the primary symptoms of this multifaceted disorder. Nevertheless, there is reason to believe that these simplistic pairwise measurements under represent the true and quite complicated picture of connectivity anomalies in these persons. We have presented three different forms of multivariate connectivity analysis with increasing levels of sophistication (including one based on principle components analysis, sLORETA source coherence, and Granger causality) to present a hypothesis that more advanced statistical approaches to EEG coherence analysis may provide more detailed and accurate information than pairwise measurements. A single case study is examined with findings from MR-DTI, pairwise and coherence and these three forms of multivariate coherence analysis. In this case pairwise coherences did not resemble structural connectivity, whereas multivariate measures did. The possible advantages and disadvantages of different techniques are discussed. Future work in this area will be important to determine the validity and utility of these techniques.

Connectivity Theory of Autism: Use of Connectivity Measures in Assessing and Treating Autistic Disorders

ABSTRACT Background. Autism is a disorder characterized by deficits in communication, social interaction, a limited range of interests, and repetitive stereotypical behavior. Although it is believed that changes in the brain leading to Autism occur early on in prenatal and early postnatal development, there is no definitive test for a diagnosis of Autism. The diagnosis is made on the basis of behavioral signs and symptoms alone and is usually not made until age 2 or later. There have been numerous neuroanatomical abnormalities noted in Autism, some of which can be linked to neuropsychological dysfunction. Recently a new theory has become prominent which suggests the disorder may be due to aberrant neural connectivity patterns. Evidence in support of this theory has come from anatomical studies of white matter as well as functional neuroimaging studies. Methods. Most studies have employed functional magnetic resonance imaging to investigate connectivity, or electroencephalography (EEG) coherence studies. T...

Neural Connectivity in Epilepsy as Measured by Granger Causality.

Epilepsy is a chronic neurological disorder characterized by repeated seizures or excessive electrical discharges in a group of brain cells. Prevalence rates include about 50 million people worldwide and 10% of all people have at least one seizure at one time in their lives. Connectivity models of epilepsy serve to provide a deeper understanding of the processes that control and regulate seizure activity. These models have received initial support and have included measures of EEG, MEG, and MRI connectivity. Preliminary findings have shown regions of increased connectivity in the immediate regions surrounding the seizure foci and associated low connectivity in nearby regions and pathways. There is also early evidence to suggest that these patterns change during ictal events and that these changes may even by related to the occurrence or triggering of seizure events. We present data showing how Granger causality can be used with EEG data to measure connectivity across brain regions involved in ictal events and their resolution. We have provided two case examples as a demonstration of how to obtain and interpret such data. EEG data of ictal events are processed, converted to independent components and their dipole localizations, and these are used to measure causality and connectivity between these locations. Both examples have shown hypercoupling near the seizure foci and low causality across nearby and associated neuronal pathways. This technique also allows us to track how these measures change over time and during the ictal and post-ictal periods. Areas for further research into this technique, its application to epilepsy, and the formation of more effective therapeutic interventions are recommended.

Infrared Imaging and Neurofeedback: Initial Reliability and Validity

ABSTRACT Introduction. The neurological correlates underlying positive treatment outcomes for neurofeedback have been either unavailable or difficult to demonstrate. Assessment of brain-related changes associated with neurofeedback is needed to further establish its empirical basis. Infrared (IR) imaging is a noninvasive assessment of brain activity with high spatial and temporal resolution. Method. Study 1, a reliability study, assessed the test-retest stability of IR imaging. In Validity Study 2 and 3, IR imaging assessed brain-related changes prior to and following neurofeedback and passive infrared hemoencephalography (pir HEG) training, respectively. Results. In Study 1, high correlations occurred in pre-post comparisons for IR measures unrelated to treatment. Lower correlation between measures of IR imaging indicated changes in brain activation associated with thermoregulation following neurofeedback training. In Study 2, changes in thermal regulation occurred both within and across sessions. The ch...

EEG Analyses in the Assessment of Autistic Disorders

Autistic spectrum disorders (ASD) are a heterogeneous group of pervasive developmental disorders including autistic disorder, Rett’s disorder, childhood disintegrative disorder, pervasive developmental disorder-not otherwise specified (PDD-NOS), and Asperger’s disorder. Children with ASD demonstrate impairment in social interaction, verbal and nonverbal communication, and behaviors or interests (DSM-IV-TR; APA 2000). ASD may be comorbid with sensory integration difficulties, mental retardation, or seizure disorders. Children with ASD may have severe sensitivity to sounds, textures, tastes, and smells. Cognitive deficits are often associated with impaired communication skills (National Institute of Mental Health; NIMH 2006). Repetitive stereotyped behaviors, perseveration, and obsessionality, common in ASD, are associated with executive deficits. Executive dysfunction in inhibitory control and set shifting have been attributed to ASD (Schmitz et al. 2006). Seizure disorders may occur in one out of four children with ASD, frequently beginning in early childhood or adolescence (National Institute of Mental Health; NIMH 2006).

Neurofeedback for Autistic Disorders: Emerging Empirical Evidence

Autism spectrum disorders (ASD) are a group of pervasive developmental disorders impacting communication, social skills, behavioral interests, and daily functioning. With rates rising to as high as 1 in 80 in the Unites States alone, their impact on children, families, and our society is immense. Despite this, treatment for these conditions is poorly understood, and most have limited empirical support. While ASD can be conceptualized as having system-wide effects in the human body, many of the primary symptoms we associate with these children are clearly related to dysfunction of the central nervous system. While certain brain regions have been shown susceptibility, connectivity across regions of the brain appears to be the primary dysfunction leading to symptoms and developmental delays in these children. Any successful treatment should be able to demonstrate the ability to change and improve these primary effects. Neurofeedback is currently being studied as a noninvasive intervention with the potential to do just that. Empirical evidence is emerging, demonstrating this as a potentially effective and safe form of intervention for ASD. There is also preliminary data suggesting that this intervention may facilitate therapeutic enhancements in brain functioning and connectivity and that the results of treatment may endure even after the therapy has ended. Clearly, more research is needed to demonstrate the efficacy of this intervention, mechanisms that underlie these changes, and studies looking at the duration of enduring effects.