QEEG and VARETA based Neurophysiological Indices of Brain Dysfunction in Attention Deficit and Autistic Spectrum Disorder

Review Article

Austin J Autism & Relat Disabil. 2015;1(2): 1007.

QEEG and VARETA based Neurophysiological Indices of Brain Dysfunction in Attention Deficit and Autistic Spectrum Disorder

Robert J. Chabot¹, Robert Coben², Laurence Hirshberg³ and David S. Cantor4*

¹Department of Psychiatry, New York University School of Medicine, USA

²Department of Neuropsychology, Neurorehabilitation & Neuropsychological Services, USA

³Department of Psychiatry and Human Behavior, Brown University, USA

4Department of Mental Health Services, Mind & Motion Developmental Centers of Georgia, USA

*Corresponding author: David S. Cantor, Department of Mental Health Services, Mind & Motion Developmental Centers of Georgia, 5050 Research Ct, Ste 800, Suwanee, GA 30045, USA

Received: September 26, 2014; Accepted: May 20, 2015; Published: May 25, 2015


Quantitative Electroencephalogram (QEEG) and EEG source localization were used to describe the patho-physiological nature of brain dysfunction in children with Attention Deficit Hyperactivity Disorder (ADHD) or Autism Spectrum Disorder (ASD). QEEG frequency analyses revealed 4 subtypes that differed both in severity of abnormality and relative frequency of occurrence in both disorders but do not clarify distinctive neural networks associated within each of the disorders. Multivariate discriminant analyses proved to be effective in discriminating clinical groups from normal and from each other with high levels of sensitivity and specificity. EEG source localization indicted that ADHD was characterized by functional abnormality within the thalamus, hippocampus, caudate nucleus, and anterior cingulate, frontal/striatal, temporal, and parietal regions bilaterally and ASD by functional abnormality within the thalamus, hippocampus, caudate nucleus, and posterior cingulate, supramarginal gyrus, lateral and medial occipital/temporal, superior parietal, and occipital cortical regions bilaterally.

Keywords: Autism; ADHD; QEEG; VARETA; LORETA; Neurophysiology


Attention Deficit Hyperactivity Disorder (ADHD) and Autistic Spectrum Disorder (ASD) are two neurodevelopmental disorders which at various times in the past 40 years have been described as being epidemic in their scale amongst childhood psychiatric disorders. Both disorders occur early in childhood and can have extreme effects on the lives of these children. ADHD is characterized by symptoms of inattention, impulsive behavior, and varying degrees of hyperactivity which often result in problems in learning, cognition, and social interactions. Autistic spectrum disorder is characterized by deficits in social interaction and communication often accompanied by repetitive behavior and dysfunction in executive function, language, and emotional behavior. ASD individuals can also exhibit impaired attention regulation processes such as distractibility or at other times significant problems with hyper-focusing and difficulty in shifting their attention as needed. While several recent studies have documented the neuro-physiological and neuro-anatomical nature of these disorders [1,2], there are no published studies that examine the similarities and differences between the brain structures and neurological functions compromised within each if these disorders and a set of heuristics that can help provide the discriminability of these disorders

Quantitative EEG (QEEG) is a valuable technique used in the diagnosis and treatment of children and adults with psychiatric and neurological disorders [3,4]. The clinical utility of QEEG in child and adolescent psychiatric disorders including autism, specific developmental disorders, and attention deficit disorder has been documented [5]. QEEG is useful for aiding in the differential diagnosis of children with learning disorders and those with various subtypes of attention deficit disorder [1].

Variable Resolution Electromagnetic Tomography (VARETA) is a 3 dimensional source localization method that uses surface recorded EEG to analyze current density and to identify the most probable neuro-anatomical generators of each EEG frequency band. The results of these analyses can be used to generate maps based upon a probabilistic brain atlas resembling slices obtained from a Magnetic Resonance Image (MRI) [6]. When z-score transformed relative to a normal population these VARETA brain images can be used to depict the cortical and sub-cortical structures involved in the pathophysiology of various neuro-cognitive disorders. The VARETA technique has been shown to be useful in the identification of the neuro-anatomical structures involved in; (1) epileptic activity generation [7,8], (2) hypoperfused regions due to neurocysticercosis, reversible ischemic attacks, and cerebral artery disease [6,9,10], (3) space occupying lesions [11,12], (4) the localization of cognitive processes [13], obsessive-compulsive disorder [14], and attention deficit disorder [15].

The present study was designed to document QEEG differences between large samples of children diagnosed with ASD, ADHD, and a matched sample of children with no known neurological or psychiatric disorders. The goal was to document the specific types of QEEG profiles found within these populations and to develop QEEG feature based discriminant functions (possible biomarkers) to distinguish children with ASD and those with ADHD from the normal population of children as well as from each other. VARETA was utilized to identify the neuro-anatomical structures that underlie the pathophysiology of the childhood syndromes of ASD and ADHD and to identify any neurophysiological subtypes that exist within each disorder. We also identify the cortical and subcortical regions that generate abnormal activity within each disorder and then localize the anatomical differences between the two disorders. Collaborative evidence supporting our findings will be provided by reviewing the findings from brain structural imaging studies (MRI, fMRI, PET) of these two disorders.


Normal population

A sample of 92 normal children from the NYU database of normal children was selected to match the age and sex distributions of our sample of ASD and ADHD children. All normal subjects were free of neurological or medical disease, had no history of head injury, drug or alcohol abuse, were of normal IQ, showed evidence of adequate functioning at home/school for the past two years, and had not taken any prescription medication for at least 90 days prior to evaluation. Specific details of the procedures used to establish the normal data base have been previously published [16]. The reliability of this normal data base has been validated using independent samples of normal individuals [17-22]. This replication of the age-regression equations developed on the above data base justifies their generalized application [23].

Clinical populations

All Autistic Spectrum Disordered (ASD) children used in this study were referred to the Neurodevelopment Center in Providence Rhode Island or the Neurorehabilitation and Neuropsychological Center in Massapequa, New York. All ADHD children were referred to the Developmental Pediatrics and Learning Disorders Clinic in Sydney, Australia. Samples of 92 children were entered into this study from each of these clinical groups. All children were examined by a neuropsychologist and had a neuropsychological and QEEG evaluation. Children with histories of epilepsy, drug abuse, head injury, or psychotic disorders were excluded. The clinical and neuropsychological evaluations obtained on each child were those tests routinely administered at each outpatient clinic. None of children used in this sample, were on any medications.

The demographic information from these samples is shown in Table 1 indicating no significant differences between groups in terms of age or sex. None of the children used in this sample were on medication at the time of QEEG testing. An additional 14 ASD children had QEEG evaluations while on medication and these children were used to test for general medication effects on the QEEG.