title: Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism.
abstract: BACKGROUND
  Autism 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.
  
  
  METHODS
  EEG 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.
  
  
  RESULTS
  Three 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.
  
  
  CONCLUSIONS
  This 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.
authors.affiliation: Graduate Program in Neuroscience, Boston University, 677 Beacon st., Boston, MA, 02215, USA. smatlis@bu.edu.
authors.forename: Sean
authors.initials: S
authors.lastname: Matlis
authors.affiliation: Department of Neurology, Massachusetts General Hospital, 175 Cambridge St., Ste 340, Boston, MA, 02114, USA. kboric@mgh.harvard.edu.|Harvard Medical School, Boston, MA, 02115, USA. kboric@mgh.harvard.edu.
authors.forename: Katica
authors.initials: K
authors.lastname: Boric
authors.affiliation: Department of Neurology, Massachusetts General Hospital, 175 Cambridge St., Ste 340, Boston, MA, 02114, USA. cjchu@mgh.harvard.edu.|Harvard Medical School, Boston, MA, 02115, USA. cjchu@mgh.harvard.edu.
authors.forename: Catherine J
authors.initials: CJ
authors.lastname: Chu
authors.affiliation: Department of Mathematics and Statistics, Boston University, 111 Cummington Mall, Boston, MA, 02215, USA. mak@bu.edu.
authors.forename: Mark A
authors.initials: MA
authors.lastname: Kramer
mesh_terms: D000513:Alpha Rhythm / Q000502:physiology*; D000067877:Autism Spectrum Disorder / Q000175:diagnosis / Q000503:physiopathology; D001321:Autistic Disorder / Q000175:diagnosis / Q000503:physiopathology*; D001921:Brain / Q000503:physiopathology; D058256:Brain Waves; D016022:Case-Control Studies; D002540:Cerebral Cortex / Q000503:physiopathology*; D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D009434:Neural Pathways / Q000503:physiopathology*; D059906:Neuroimaging; D015995:Prevalence; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity