BACKGROUND: @entity72 ( @entity72 ) are increasingly prevalent and have a significant impact on the lives of @entity1 and their families. Currently, the diagnosis is determined by clinical judgment and no definitive physiological biomarker for @entity72 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 @entity72 . 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 @entity1 with @entity72 to evaluate the rhythms and coupling patterns present in the EEG to develop and validate an electrophysiological biomarker of @entity72 . METHODS: EEG data were acquired from a population of @entity72 (n   =   27) and control (n   =   55) @entity1 4-8 years old. Data were divided into training (n   =   13 @entity72 , n   =   24 control) and validation (n   =   14 @entity72 , n   =   31 control) groups. Evaluation of spectral and functional network properties in the first group of @entity1 motivated three biomarkers that were computed in the second group of age-matched @entity1 for validation. RESULTS: Three biomarkers of @entity72 were identified in the first @entity1 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 @entity1 . Using the two validated biomarkers, we were able to classify @entity72 subjects with 83 % sensitivity and 68 % specificity in a post-hoc analysis. CONCLUSIONS: This study demonstrates that clinical EEG can provide quantitative biomarkers to assist diagnosis of @entity2438 . These results corroborate the general finding that @entity72 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.

Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in XXXX .

@entity1 :: ('9606', 'Species') :: ['children', 'patients', 'patient'] @entity72 :: ('MESH:D002659', 'Disease') :: ['ASD', 'Autism spectrum disorders'] @entity2438 :: ('MESH:D001321', 'Disease') :: ['autism']
@entity2438 :: (MESH:D001321,Disease) :: ['autism']
