Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis

Maenner MJ, Shaw KA, Baio J, Washington A, Patrick M, DiRienzo M, et al. Prevalence of Autism spectrum disorder among children aged 8 years—Autism and developmental disabilities monitoring network, 11 sites, United States, 2016. MMWR Surveill Summ. 2020;69(4):1–12.

PubMed  PubMed Central  Article  Google Scholar 

Lord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T, et al. Autism spectrum disorder. Nat Rev Dis Primer. 2020;6(1):1–23.

Article  Google Scholar 

Masi A, DeMayo MM, Glozier N, Guastella AJ. An overview of Autism spectrum disorder, heterogeneity and treatment options. Neurosci Bull. 2017;33(2):183–93.

PubMed  PubMed Central  Article  Google Scholar 

Kang E, Keifer CM, Levy EJ, Foss-Feig JH, McPartland JC, Lerner MD. Atypicality of the N170 event-related potential in Autism spectrum disorder: a meta-analysis. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(8):657–66.

PubMed  Google Scholar 

Cooke E. Letter of support for N170 ERP as a prognostic biomarker for adaptive social functioning and its potential to stratify study populations in people with Autism spectrum disorders (ASD) without intellectual disability. 2020. Available from: https://www.ema.europa.eu/en/documents/other/letter-support-n170-erp-prognostic-biomarker-adaptive-social-functioning-its-potential-stratify_en.pdf

Pereda E, Quiroga RQ, Bhattacharya J. Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol. 2005;77(1):1–37.

PubMed  Article  Google Scholar 

Schnitzler A, Gross J. Normal and pathological oscillatory communication in the brain. Nat Rev Neurosci. 2005;6(4):285–96.

CAS  PubMed  Article  Google Scholar 

Wang J, Barstein J, Ethridge LE, Mosconi MW, Takarae Y, Sweeney JA. Resting state EEG abnormalities in Autism spectrum disorders. J Neurodev Disord. 2013;5(1):24.

PubMed  PubMed Central  Article  Google Scholar 

Chan AS, Leung WWM. Differentiating autistic children with quantitative encephalography: a 3-month longitudinal study. J Child Neurol. 2006;21(5):391–9.

PubMed  Article  Google Scholar 

Cornew L, Roberts TPL, Blaskey L, Edgar JC. Resting-state oscillatory activity in Autism spectrum disorders. J Autism Dev Disord. 2012;42(9):1884–94.

PubMed  PubMed Central  Article  Google Scholar 

Sutton SK, Burnette CP, Mundy PC, Meyer J, Vaughan A, Sanders C, et al. Resting cortical brain activity and social behavior in higher functioning children with Autism. J Child Psychol Psychiatry. 2005;46(2):211–22.

PubMed  Article  Google Scholar 

Elhabashy H, Raafat O, Afifi L, Raafat H, Abdullah K. Quantitative EEG in autistic children. Egypt J Neurol Psychiatry Neurosurg. 2015;52(3):176.

Article  Google Scholar 

Sheikhani A, Behnam H, Mohammadi MR, Noroozian M, Mohammadi M. Detection of abnormalities for diagnosing of children with Autism disorders using of quantitative electroencephalography analysis. J Med Syst. 2012;36(2):957–63.

PubMed  Article  Google Scholar 

Takagaki K, Russell J, Lippert MT, Motamedi GK. Development of the posterior basic rhythm in children with autism. Clin Neurophysiol. 2015;126(2):297–303.

PubMed  Article  Google Scholar 

Coben R, Clarke AR, Hudspeth W, Barry RJ. EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol. 2008;119(5):1002–9.

PubMed  Article  Google Scholar 

Orekhova EV, Elsabbagh M, Jones EJ, Dawson G, Charman T, Johnson MH, et al. EEG hyper-connectivity in high-risk infants is associated with later autism. J Neurodev Disord. 2014;6(1):40.

PubMed  PubMed Central  Article  Google Scholar 

O’Reilly C, Lewis JD, Elsabbagh M. Is functional brain connectivity atypical in autism. A systematic review of EEG and MEG studies. PLoS ONE. 2017;12(5):e0175870.

PubMed  PubMed Central  Article  CAS  Google Scholar 

Takahashi T, Yamanishi T, Nobukawa S, Kasakawa S, Yoshimura Y, Hiraishi H, et al. Band-specific atypical functional connectivity pattern in childhood autism spectrum disorder. Clin Neurophysiol. 2017;128(8):1457–65.

PubMed  Article  Google Scholar 

Vakorin VA, Doesburg SM, Leung RC, Vogan VM, Anagnostou E, Taylor MJ. Developmental changes in neuromagnetic rhythms and network synchrony in autism. Ann Neurol. 2017;81(2):199–211.

PubMed  Article  Google Scholar 

Wang J, Wang X, Wang X, Zhang H, Zhou Y, Chen L, et al. Increased EEG coherence in long-distance and short-distance connectivity in children with autism spectrum disorders. Brain Behav. 2020;10(10):e01796.

PubMed  PubMed Central  Google Scholar 

Charman T, Loth E, Tillmann J, Crawley D, Wooldridge C, Goyard D, et al. The EU-AIMS longitudinal European Autism Project (LEAP): clinical characterisation. Mol Autism. 2017;23(8):27.

Article  Google Scholar 

Loth E, Charman T, Mason L, Tillmann J, Jones EJH, Wooldridge C, et al. The EU-AIMS longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders. Mol Autism. 2017;23(8):24.

Article  Google Scholar 

Muthukumaraswamy S. High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations. Front Hum Neurosci. 2013. https://doi.org/10.3389/fnhum.2013.00138/full.

Article  PubMed  PubMed Central  Google Scholar 

Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural Netw. 2000;13(4):411–30.

PubMed  Article  Google Scholar 

Lodder SS, van Putten MJAM. Automated EEG analysis: characterizing the posterior dominant rhythm. J Neurosci Methods. 2011;200(1):86–93.

PubMed  Article  Google Scholar 

Holiga Š, Hipp JF, Chatham CH, Garces P, Spooren W, D’Ardhuy XL, et al. Patients with autism spectrum disorders display reproducible functional connectivity alterations. Sci Transl Med. 2019;11(481):eaat9223.

PubMed  Article  Google Scholar 

Penny WD, Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE. Statistical parametric mapping: the analysis of functional brain images. Elsevier; 2011.

Google Scholar 

Oostenveld R, Fries P, Maris E, Schoffelen J-M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. 2011;2011(1):9.

Google Scholar 

Gabriel S, Lau RW, Gabriel C. The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Phys Med Biol. 1996;41(11):2251.

CAS  PubMed  Article  Google Scholar 

McCann H, Pisano G, Beltrachini L. Variation in reported human head tissue electrical conductivity values. Brain Topogr. 2019;32(5):825–58.

PubMed  PubMed Central  Article  Google Scholar 

Birot G, Spinelli L, Vulliémoz S, Mégevand P, Brunet D, Seeck M, et al. Head model and electrical source imaging: a study of 38 epileptic patients. NeuroImage Clin. 2014;1(5):77–83.

Article  Google Scholar 

Vorwerk J, Oostenveld R, Piastra MC, Magyari L, Wolters CH. The FieldTrip-SimBio pipeline for EEG forward solutions. Biomed Eng OnLine. 2018;17(1):37.

PubMed  PubMed Central  Article  Google Scholar 

Veen BDV, Drongelen WV, Yuchtman M, Suzuki A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng. 1997;44(9):867–80.

PubMed  Article  Google Scholar 

Sekihara K, Sahani M, Nagarajan SS. Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction. Neuroimage. 2005;25(4):1056–67.

PubMed  Article  Google Scholar 

Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat Neurosci. 2012;15(6):884–90.

CAS  PubMed  Article  Google Scholar 

Vinck M, Oostenveld R, van Wingerden M, Battaglia F, Pennartz CMA. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage. 2011;55(4):1548–65.

PubMed  Article  Google Scholar 

Nolte G, Bai O, Wheaton L, Mari Z, Vorbach S, Hallett M. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol. 2004;115(10):2292–307.

PubMed  Article  Google Scholar 

Spence JR, Stanley DJ. Prediction Interval: what to expect when you’re expecting … a replication. PLoS ONE. 2016;11(9):e0162874.

PubMed  PubMed Central  Article  CAS  Google Scholar 

Pinheiro JC, Bates DM. Mixed-effects models in S and S-PLUS. New York: Springer; 2000.

Book  Google Scholar 

Luke SG. Evaluating significance in linear mixed-effects models in R. Behav Res Methods. 2017;49(4):1494–502.

PubMed  Article  Google Scholar 

Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15(1):1–25.

PubMed  Article  Google Scholar 

Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer; 2009.

Book  Google Scholar 

Abraham A, Pedregosa F, Eickenberg M, Gervais P, Mueller A, Kossaifi J, et al. Machine learning for neuroimaging with scikit-learn. Front Neuroinformatics. 2014. https://doi.org/10.3389/fninf.2014.00014/full.

Article  Google Scholar 

Abraham A, Milham MP, Di Martino A, Craddock RC, Samaras D, Thirion B, et al. Deriving reproducible biomarkers from multi-site resting-state data: an Autism-based example. Neuroimage. 2017;15(147):736–45.

Article  Google Scholar 

Plitt M, Barnes KA, Wallace GL, Kenworthy L, Martin A. Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism. Proc Natl Acad Sci U S A. 2015;112(48):E6699-6706.

CAS  PubMed  PubMed Central  Article 

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