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BMC Neurology

, 15:97

First Online: 27 June 2015Received: 03 June 2015Accepted: 15 June 2015


BackgroundAutism spectrum disorders ASD are increasingly prevalent and have a significant impact on the lives of patients and their families. Currently, the diagnosis is determined by clinical judgment and no definitive physiological biomarker for ASD exists. Quantitative biomarkers obtainable from clinical neuroimaging data – such as the scalp electroencephalogram EEG - would provide an important aid to clinicians in the diagnosis of ASD. The interpretation of prior studies in this area has been limited by mixed results and the lack of validation procedures. Here we use retrospective clinical data from a well-characterized population of children with ASD to evaluate the rhythms and coupling patterns present in the EEG to develop and validate an electrophysiological biomarker of ASD.

MethodsEEG data were acquired from a population of ASD n = 27 and control n = 55 children 4–8 years old. Data were divided into training n = 13 ASD, n = 24 control and validation n = 14 ASD, n = 31 control groups. Evaluation of spectral and functional network properties in the first group of patients motivated three biomarkers that were computed in the second group of age-matched patients for validation.

ResultsThree biomarkers of ASD were identified in the first patient group: 1 reduced posterior-anterior power ratio in the alpha frequency range 8–14 Hz, which we label the -peak alpha ratio-, 2 reduced global density in functional networks, and 3 a reduction in the mean connectivity strength of a subset of functional network edges. Of these three biomarkers, the first and third were validated in a second group of patients. Using the two validated biomarkers, we were able to classify ASD subjects with 83 % sensitivity and 68 % specificity in a post-hoc analysis.

ConclusionsThis study demonstrates that clinical EEG can provide quantitative biomarkers to assist diagnosis of autism. These results corroborate the general finding that ASD subjects have decreased alpha power gradients and network connectivities compared to control subjects. In addition, this study demonstrates the necessity of using statistical techniques to validate EEG biomarkers identified using exploratory methods.

KeywordsASD EEG Functional networks Biomarker Classification Autism Power spectra Validation AbbreviationsASDAutism spectrum disorders

EEGScalp electroencephalogram

FDRFalse detection rate

fMRIfunctional Magnetic resonance imaging

NREMNonrapid eye movement

QDAQuadratic discriminant analysis

Catherine J. Chu and Mark A. Kramer contributed equally to this work.

Electronic supplementary materialThe online version of this article doi:10.1186-s12883-015-0355-8 contains supplementary material, which is available to authorized users.

An erratum to this article can be found at

An erratum to this article is available at

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Autor: Sean Matlis - Katica Boric - Catherine J. Chu - Mark A. Kramer


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