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Brain Complexity in Children with Mild and Severe Autism Spectrum Disorders: Analysis of Multiscale Entropy in EEG

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Abstract

Multiscale entropy (MSE) model quantifies the complexity of brain functions by measuring the entropy across multiple time-scales. Although MSE model has been applied in children with Autism spectrum disorders (ASD) in previous studies, they were limited to distinguish children with ASD from those normally developed without corresponding severity level of their autistic features. Therefore, we aims  to explore and to identify the MSE features and patterns in children with mild and severe ASD by using a high dense 64-channel array EEG system. This study is a cross-sectional study, where 36 children with ASD were recruited and classified into two groups: mild and severe ASD (18 children in each). Three calculated outcomes identified brain complexity of mild and severe ASD groups: averaged MSE values, MSE topographical cortical representation, and MSE curve plotting. Averaged MSE values of children with mild ASD were higher than averaged MSE value in children with severe ASD in right frontal (0.37 vs. 0.22, respectively, p = 0.022), right parietal (0.31 vs. 0.13, respectively, p = 0.017), left parietal (0.37 vs. 0.17, respectively, p = 0.018), and central cortical area (0.36 vs. 0.21, respectively, p = 0.026). In addition, children with mild ASD showed a clear and more increase in sample entropy values over increasing values of scale factors than children with severe ASD. Obtained data showed different brain complexity (MSE) features, values and topographical representations in children with mild ASD compared with those with severe ASD. As a result of this, MSE could serve as a sensitive method for identifying the severity level of ASD.

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References

  • Abarbanel HD, Rabinovich MI (2001) Neurodynamics: nonlinear dynamics and neurobiology. Curr Opin Neurobiol 11(4):423–430

    Article  CAS  Google Scholar 

  • Bosl W, Tierney A, Tager-Flusberg H, Nelson C (2011) EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med 9(1):18

    Article  Google Scholar 

  • Cardinale RC, Shih P, Fishman I, Ford LM, Müller RA (2013) Pervasive rightward asymmetry shifts of functional networks in autism spectrum disorder. JAMA Psychiatry 70(9):975–982

    Article  Google Scholar 

  • Catarino A, Churches O, Baron-Cohen S, Andrade A, Ring H (2011) Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clin Neurophysiol 122(12):2375–2383

    Article  Google Scholar 

  • Chan ZH, Sudirman R, Omar C (2017) Autistic spectrum disorder: EEG analysis and classification. J Telecommun Electron Comput Eng 9(3–9):53–57

    Google Scholar 

  • Coben R, Clarke AR, Hudspeth W, Barry RJ (2008) EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol 119(5):1002–1009

    Article  Google Scholar 

  • Costa M, Goldberger AL, Peng CK (2002) Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 89(6):068102

    Article  Google Scholar 

  • Costa M, Goldberger AL, Peng CK (2005) Multiscale entropy analysis of biological signals. Phys Rev E 71(2):021906

    Article  Google Scholar 

  • Escudero J, Abásolo D, Hornero R, Espino P, López M (2006) Analysis of electroencephalograms in Alzheimer’s disease patients with multiscale entropy. Physiol Meas 27(11):1091

    Article  CAS  Google Scholar 

  • Fox MD, Greicius M (2010) Clinical applications of resting state functional connectivity. Front Syst Neurosci 4:19

    PubMed  PubMed Central  Google Scholar 

  • Ghanbari Y, Bloy L, Edgar JC, Blaskey L, Verma R, Roberts TP (2015) Joint analysis of band-specific functional connectivity and signal complexity in autism. J Autism Dev Disord 45(2):444–460

    Article  Google Scholar 

  • Iacoboni M, Dapretto M (2006) The mirror neuron system and the consequences of its dysfunction. Nat Rev Neurosci 7(12):942

    Article  CAS  Google Scholar 

  • Jamal W, Das S, Oprescu IA, Maharatna K, Apicella F, Sicca F (2014) Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. J Neural Eng 11(4):046019

    Article  Google Scholar 

  • Johnson CP, Myers SM (2007) Identification and evaluation of children with autism spectrum disorders. Pediatrics 120(5):1183–1215

    Article  Google Scholar 

  • Kulisek R, Hrncir Z, Hrdlicka M, Faladova L, Sterbova K, Krsek P et al (2008) Nonlinear analysis of the sleep EEG in children with pervasive developmental disorder. Neuro Endocrinol Lett 29(4):512–517

    PubMed  Google Scholar 

  • Liu T, Chen Y, Chen D, Li C, Qiu Y, Wang J (2017) Altered electroencephalogram complexity in autistic children shown by the multiscale entropy approach. NeuroReport 28(3):169

    Article  Google Scholar 

  • Magiati I, Moss J, Yates R, Charman T, Howlin P (2011) Is the Autism Treatment Evaluation Checklist a useful tool for monitoring progress in children with autism spectrum disorders? J Intellect Disabil Res 55(3):302–312

    Article  CAS  Google Scholar 

  • Mayes SD, Calhoun SL, Murray MJ, Pearl A, Black A, Tierney CD (2014) Final DSM-5 under-identifies mild autism spectrum disorder: agreement between the DSM-5, CARS, CASD, and clinical diagnoses. Res Autism Spectr Disord 8(2):68–73

    Article  Google Scholar 

  • Oberman LM, Ramachandran VS (2007) The simulating social mind: the role of the mirror neuron system and simulation in the social and communicative deficits of autism spectrum disorders. Psychol Bull 133(2):310

    Article  Google Scholar 

  • Oberman LM, Hubbard EM, McCleery JP, Altschuler EL, Ramachandran VS, Pineda JA (2005) EEG evidence for mirror neuron dysfunction in autism spectrum disorders. Cogn Brain Res 24(2):190–198

    Article  Google Scholar 

  • Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049

    Article  CAS  Google Scholar 

  • Richman JS, Lake DE, Moorman JR (2004) Sample entropy. In: Methods in enzymology, vol 384. Academic Press, San Diego, pp. 172–184

  • Rizzolatti G, Fabbri-Destro M (2010) Mirror neurons: from discovery to autism. Exp Brain Res 200(3–4):223–237

    Article  Google Scholar 

  • Sporns O, Tononi G, Edelman GM (2000) Connectivity and complexity: the relationship between neuroanatomy and brain dynamics. Neural Netw 13(8–9):909–922

    Article  CAS  Google Scholar 

  • Takahashi T (2013) Complexity of spontaneous brain activity in mental disorders. Prog Neuropsychopharmacol Biol Psychiatry 45:258–266

    Article  Google Scholar 

  • Takahashi T, Cho RY, Mizuno T, Kikuchi M, Murata T, Takahashi K, Wada Y (2010) Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. Neuroimage 51(1):173–182

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Wass S (2011) Distortions and disconnections: disrupted brain connectivity in autism. Brain Cogn 75(1):18–28

    Article  Google Scholar 

  • Yousef A, Youssef U, El-Shabrawy A, Fattah NA, Khedr H (2017) EEG abnormalities and severity of symptoms in non-epileptic autistic children. Egypt J Psychiatry 38(2):59–59

    Article  Google Scholar 

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Acknowledgements

This work is supported by the grant from Scientific Research Support Fund – Jordanian Ministry of Higher Education (MPH/1/20/2014) under number of 275/2015 at deanship of research of Jordan University of Science &Technology. In addition, thanks go to Dr. Muhamed Nazzal, Dr. Hanan Khalil, and Dr. Nihad Almasri for their logistics help of this work.

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Correspondence to Hikmat Hadoush.

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Hadoush, H., Alafeef, M. & Abdulhay, E. Brain Complexity in Children with Mild and Severe Autism Spectrum Disorders: Analysis of Multiscale Entropy in EEG. Brain Topogr 32, 914–921 (2019). https://doi.org/10.1007/s10548-019-00711-1

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