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A Comment on some Methodological Issues in EEG Connectivity Studies of Sensory Features in Youth with Autism

Abstract

Investigation of the neurological underpinnings of the diagnostic symptoms for Autism Spectrum Disorder (ASD) represents a potential pathway towards a biomarker for this disorder. One of the key symptoms of ASD is Sensory Features (SF), which refers to the difficulties that autistic people experience with particular kinds of environmental stimuli. Studies using eeg measures of neural connectivity across various regions of the brain hold promise in identifying how the autistic brain reacts to its environment. This commentary identifies several ‘participant’ and ‘measurement’ methodological issues that need to be adequately addressed in SF-eeg connectivity studies, and applies these comments to a sample of five previous studies. Recommendations are made for future research procedures.

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Notes

  1. 1.

    In deference to the preferences of autistic adults reported by Kenny et al., we use this term rather than “children with autism”.

References

  1. Altman, D., & Royston, P. (2006). The cost of dichotomising continuous variables. BMJ, 332(7549), 1080. https://doi.org/10.1136/bmj.332.7549.1080

    Article  PubMed  PubMed Central  Google Scholar 

  2. APA. (2013). Diagnostic and statistical manual of mental disorders-5th edition. American Psychiatric Association.

  3. Armstrong, R. (2014). When to use the Bonferroni correction. Ophthalmic & Physiological Optics, 34, 502–508

    Article  Google Scholar 

  4. Bae, G.-Y., & Luck, S. (2018). Dissociable decoding of spatial attention and working memory from EEG oscillations and sustained potentials. The Journal of Neuroscience, 38(2), 409–422. https://doi.org/10.1523/jneurosci.2860-17.2017

    Article  PubMed  PubMed Central  Google Scholar 

  5. Baranek, G. (1999). Autism during infancy: A retrospective video analysis of sensory-motor and social behaviors at 9–12 months of age. Journal of Autism & Developmental Disorders, 29, 213–224

    Article  Google Scholar 

  6. Baranek, G., David, F., Poe, M., Stone, W., & Watson, L. (2006). Sensory experiences questionnaire: Discriminating sensory features in young children with autism, developmental delays, and typical development. Journal of Child Psychology and Psychiatry, 47(6), 591–601. https://doi.org/10.1111/j.1469-7610.2005.01546.x

    Article  PubMed  Google Scholar 

  7. Ben-Sasson, A., Hen, L., Fluss, R., Cremak, S., Engel-Yeger, B., & Gal, E. (2009). A meta-analysis of sensory modulation symptoms in individuals with autism spectrum disorders. Journal of Autism and Developmental Disorders, 39, 1–11

    Article  Google Scholar 

  8. Bitsika, V., & Sharpley, C. (2019). The effects of ‘preferedness of task’ on stress, emotion, and behaviour responses to forced activity transitions in boys with ASD. International Journal of Developmental Neuroscience, 75, 36–43

    Article  Google Scholar 

  9. Bowyer, S. M. (2016). Coherence a measure of the brain networks: Past and present. Neuropsychiatric Electrophysiology, 2(1), 1–12

    Article  Google Scholar 

  10. Boyd, B., Baranek, G., Sideris, J., Poe, M., Watson, L., Patten, E., & Miller, H. (2010). Sensory features and repetitive behaviors in children with autism and developmental delays. Autism Research, 3(2), 78–87. https://doi.org/10.1002/aur.124

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bronzino, J. D. (2000). Principles of electroencephalography. In J. D. Bronzino (Ed.), The biomedical engineering handbook. (2nd ed.). CRC Press LLC.

  12. Brown, C., & Dunn, W. (2006). The adolesent/adult sensory profile. NCS Pearson.

  13. Cohen, J. (1988). Statistical power for the behavioural sciences. Erlbaun.

  14. Corbett, B., Schupp, C., Levine, S., & Mendoza, S. (2009). Comparing cortisol, stress, and sensory sensitivity in children with autism. Autism Research, 2, 39–49

    Article  Google Scholar 

  15. Di Martino, A., Yan, C., Li, Q., Denio, E., Castellanos, F., Alaerts, K., Anderson, J., Assaf, M., et al. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659–667. https://doi.org/10.1038/mp.2013.78

    Article  PubMed  Google Scholar 

  16. Dunn, W. (2001). The sensations of everyday life: Empirical, theoretical, and pragmatic considerations. American Journal of Occupational Therapy, 55, 608–620

    Article  Google Scholar 

  17. Ewen, J., Lakshmanan, B., Pillai, A., McAuliffe, D., Nettles, C., Hallett, M., Crone, N., & Mostofsky, S. (2016). Decreased modulation of EEG oscillations in high-functioning autism during a motor control task [original research]. Frontiers in Human Neuroscience, 10, 198. https://doi.org/10.3389/fnhum.2016.00198

    Article  PubMed  PubMed Central  Google Scholar 

  18. Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191

    Article  Google Scholar 

  19. Friston, K. J. (2011). Functional and effective connectivity: A review. Brain Connectivity, 1(1), 13–36

    Article  Google Scholar 

  20. Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10, 486–489

    Article  Google Scholar 

  21. Green, S., Hernandez, L., Bookheimer, S., & Dapretto, M. (2016). Salience network connectivity in autism is related to brain and behavioral markers of sensory overresponsivity. Journal of the American Academy of Child & Adolescent Psychiatry, 55(7), 618-626.e611. https://doi.org/10.1016/j.jaac.2016.04.013

    Article  Google Scholar 

  22. Hand, B., Lane, A., De Boeck, P., Basso, D., Nichols-Larsen, D., & Darrach, A. (2018). Caregiver burden varies by sensory subtypes and sensory dimension scores of children with autism. Journal of Autism and Developmental Disorders, 48, 1133–1146

    Article  Google Scholar 

  23. Hartley-McAndrew, M., & Weinstock, A. (2010). Autism spectrum disorder: Correlation between aberrant behaviors, EEG abnormalities and seizures. Neurology International, 2(1), e10. https://doi.org/10.4081/ni.2010.e10

    Article  PubMed  PubMed Central  Google Scholar 

  24. Heunis, T.-M., Aldrich, C., & de Vries, P. (2016). Recent advances in resting-state electroencephalography biomarkers for autism spectrum disorder—A review of methodological and clinical challenges. Pediatric Neurology, 61, 28–37

    Article  Google Scholar 

  25. Hochhauser, M., & Engel-Yeger, B. (2010). Sensory processing abilities and their relation to participation in leisure activities among children with high-functioning autism spectrum disorder (HFASD). Research in Autism Spectrum Disorders, 4, 746–754

    Article  Google Scholar 

  26. Hughes, J., & Melyn, M. (2005). EEG and seizures in autistic children and adolescents: Further findings with therapeutic implications. Clinical EEG Neuroscience, 36, 15–20

    Article  Google Scholar 

  27. Isler, J. R., Martien, K. M., Grieve, P. G., Stark, R. I., & Herbert, M. R. (2010). Reduced functional connectivity in visual evoked potentials in children with autism spectrum disorder. Clinical Neurophysiology, 121, 2035–2043.

  28. Jochaut, D., Lehongre, K., Saltovitch, A., Devauchelle, A. D., Olasagasti, I., Chabane, N., & Giraud, A. L. (2015). Aypical coordination of cortical oscillations in response to speech in autism. Frontiers in Human Neuroscience, 171(9), 1–12

    Google Scholar 

  29. Just, M., Cherkassky, V., Keller, T., & Minshew, N. (2004). Cortical activation and synchronization during sentence comprehension in high-functioning autism: Evidence of underconnectivity. Brain, 127, 1811–1821

    Article  Google Scholar 

  30. Kenny, L., Hattersley, C., Molins, B., Buckley, C., Povey, C., & Pellicano, E. (2016). Which terms should be used to describe autism? Perspectives from the UK autism community. Autism, 20(4), 442–462. https://doi.org/10.1177/1362361315588200

    Article  PubMed  Google Scholar 

  31. Kern, J., Trivedi, M., Garver, C., Grannemann, B., Andrews, A., & Salva, J. (2006). The pattern of sensory processing abnormalities in autism. Autism, 10, 480–494

    Article  Google Scholar 

  32. Lazarev, V. V., Pontes, A., Mitrofanov, A. A., & deAzevedo, L. C. (2010). Interhemispheric asymmetry in EEG photic driving coherence in childhood autism. Clinical Neurophysiology, 121(2), 145–152.

  33. Lazarev, V. V., Pontes, A., Mitrofanov, A. A., & deAzevedo, L. C. (2015). Reduced interhemispheric connectivity in childhood autism detected by electroencephalographic photic driving coherence. Journal of Autism and Developmental Disorders, 45(2), 537–547.

  34. Leekam, S. R., Nieto, C., Libby, S. J., Wing, L., & Gould, J. (2007). Describing the sensory abnormalities of children and adults with autism. Journal of Autism and Developmental Disorders, 37(5), 894–910. https://doi.org/10.1007/s10803-006-0218-7

    Article  PubMed  Google Scholar 

  35. Linden, W., & McEachern, H. (1985). A review of physiological prestress adaptation: Effects of duration and context. International Journal of Psychophysiology, 2, 239–245

    Article  Google Scholar 

  36. Losh, M., Adolphs, R., Poe, M., Couture, S., Penn, D., Baranek, G., & Piven, J. (2009). Neuropsychological profile of autism and the broad autism phenotype. Archives of General Psychiatry, 66, 518–526

    Article  Google Scholar 

  37. Machado, C., Estévez, M., Leisman, G., Melillo, R., Rodríguez, R., DeFina, P., ..., Beltran, C. (2015). QEEG spectral and coherence assessment of autistic children in three different experimental conditions. Journal of Autism and Developmental Disorders, 45, 406–424.

  38. Mazaheri, A., & Picton, T. (2005). EEG spectral dynamics during discrimination of auditory and visual targets. Cognitive Brain Research, 24, 81–96

    Article  Google Scholar 

  39. Milne, E. (2011). Increased intra-participant variability in children with autistic spectrum disorders: Evidence from single-trial analysis of evoked EEG. Frontiers in Psychology, 2(51), 1–12. https://doi.org/10.3389/fpsyg.2011.00051

    Article  Google Scholar 

  40. Misra, V. (2014). The social brain network and autism. Annals of Neuroscience, 21, 69

    Article  Google Scholar 

  41. Mohammad-Rezazadeh, I., Frohlich, J., Loo, S. K., & Jeste, S. S. (2016). Brain connectivity in autism spectrum disorder. Current Opinion in Neurology, 29(2), 137–147

    Article  Google Scholar 

  42. Nichols, T. E., & Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: A comparative review. Statistical Methods in Medical Research, 12, 419–446

    Article  Google Scholar 

  43. O’Donnell, S., Deitz, J., Kartin, D., Nalty, T., & Dawson, G. (2012). Sensory processing, problem behavior, adaptive behavior, and cognition in preschool children with autism spectrum disorders. American Journal of Occupational Therapy, 66, 586–594

    Article  Google Scholar 

  44. O’Reilly, C., Lewis, J. E., & Elsabbagh, M. (2017). Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies. PLOS One, 12(5), e0175870. https://doi.org/10.1371/journal.pone.0175870

    Article  PubMed  PubMed Central  Google Scholar 

  45. Orekhova, E. V., Elsabbagh, M., Jones, E. J., Dawson, G., Charman, T., Johnson, M. H., & Team, B. (2014). EEG hyper-connectivity in high-risk infants is associated with later autism. Journal of Neurodevelopmental Disorders, 6(40), 1866–1955. https://doi.org/10.1186/1866-1955-6-4025400705

    Article  Google Scholar 

  46. Pallant, J. (2016). SPSS survival manual: A step by step guide to data analysis using SPSS. (6th ed.). Allen & Unwin.

  47. Pivik, R., Broughton, R., Coppola, R., Davidson, R., Fox, N., & Nuwer, M. (1993). Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology, 30(6), 547–558. https://doi.org/10.1111/j.1469-8986.1993.tb02081.x

    Article  PubMed  Google Scholar 

  48. Reynolds, S., Lane, S., & Thacker, L. (2011). Sensory processing, physiological stress, and sleep behaviors in children with and without autism spectrum disorders. OTJR: Occupation, Participation and Health, 32, 246–257

    Google Scholar 

  49. Ronconi, L., Vitale, A., Federici, A., Pini, E., Molteni, M., & Casartelli, L. (2020). Altered beta-band oscillations and connectivity underlie detail-oriented visual processing in autism. bioRxiv, 2020.2001.2030.926261. https://doi.org/10.1101/2020.01.30.926261.

  50. Sarmukadam, K., Sharpley, C., Bitsika, V., McMillen, M., & Agnew, L. (2018). A review of the use of EEG connectivity to measure the neurological characteristics of sensory features in young people with autism. Reviews in the Neurosciences, 30, 497–501

    Article  Google Scholar 

  51. Sharpley, C., Bitsika, V., Andronicos, N., Agnew, L., & Mills, R. (2015). Which aspects of Sensory Features are associated with elevated cortisol concentrations in boys with an autism spectrum disorder? Journal of Developmental and Physical Disabilities, 27, 661–675

    Article  Google Scholar 

  52. Simon, D. M., Damiano, C. R., Woynaroski, T., Ibanez, L. V., Murias, M., Stone, W. L., Wallace, M. T., & Cascio, C. J. (2017). Neural correlates of sensory hyporesponsiveness in toddlers at high risk for autism spectrum disorder. Journal of Autism and Developmental Disorders, 47, 2710–2722. https://doi.org/10.1007/s10803-017-3191-4

    Article  PubMed  PubMed Central  Google Scholar 

  53. Sporns, O. (2014). Towards network substrates of brain disorders. Brain, 137(8), 2117–2118

    Article  Google Scholar 

  54. Streiner, D., & Norman, G. (2011). Correction for multiple testing. Chest, 140, 16–18

    Article  Google Scholar 

  55. Sullivan, G., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of Graduate Medical Education, 4, 279–282

    Article  Google Scholar 

  56. Tabachnick, B. G., & Fidell, L. S. (2014). Using multivariate statistics, sixth edition. (6th ed.). Pearson Education Limited.

  57. Tseng, M., Fu, C., Cermak, S., Lu, L., & Shieh, J. (2011). Emotional and behavioral problems in preschool children with autism: Relationship with sensory processing dysfunction. Research in Autism Spectrum Disorders, 5, 1441–1450

    Article  Google Scholar 

  58. van Eeghen, A., Pulsifer, M., Merker, V., Neumeyer, A., van Eeghen, E., Thibert, R., Cole, A., Leigh, F., Plotkin, S., & Theile, E. (2013). Understanding relationships between autism, intelligence, and epilepsy: A cross-disorder approach. Developmental Medicine & Child Neurology, 55(2), 146–153. https://doi.org/10.1111/dmcn.12044

    Article  Google Scholar 

  59. van Steensel, F., & Heeman, E. (2017). Anxiety levels in children with autism spectrum disorder: A meta-analysis. Journal of Child and Family Studies, 26, 1753–1767

    Article  Google Scholar 

  60. Vickers, A. (2005). Parametric versus non-parametric statistics in the analysis of randomized trials with non-normally distributed data. BMC Medical Research Methodology, 5(1), 35. https://doi.org/10.1186/1471-2288-5-35

    Article  PubMed  PubMed Central  Google Scholar 

  61. Vissers, M., Cohen, M., & Guerts, H. (2012). Brain connectivity andhigh functioning autism: A promising path of research that needs refined models, methodological convergence, and stronger behavioral links. Neuroscience and Biobehavioral Reviews, 36, 604–625

    Article  Google Scholar 

  62. Wang, J., Barstein, J., Ethridge, L., Mosconi, M., Takarae, Y., & Sweeney, J. (2013). Resting state EEG abnormalities in autism spectrum disorders. Journal of Neurodevelopmental Disorders, 5(1), 24. https://doi.org/10.1186/1866-1955-5-24

    Article  PubMed  PubMed Central  Google Scholar 

  63. Wasserstein, R., & Lazar, N. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70, 129–133

    Article  Google Scholar 

  64. Westhall, P., Johnson, W., & Utts, J. (1997). A Bayesian perspective on the Bonferroni adjustment. Biometrika, 84, 419–427

    Article  Google Scholar 

  65. White, S., Oswald, D., Ollendick, T., & Scahill, L. (2009). Anxiety in children and adolescents with autism spectrum disorders. Clinical Psychology Review, 29(3), 216–229. https://doi.org/10.1016/j.cpr.2009.01.003

    Article  PubMed  PubMed Central  Google Scholar 

  66. Williams, K., Kirby, A., Watson, L., Sideris, J., Bulluck, J., & Baranek, G. (2018). Sensory features as predictors of adaptive behaviors: A comparative longitudinal study of children with autism spectrum disorder and other developmental disabilities. Research in Developmental Disabilities, 81, 103–112

    Article  Google Scholar 

  67. Yasuhara, A. (2010). Correlation between EEG abnormalities and symptoms of autism spectrum disorder (ASD). Brain and Development, 32(10), 791–798. https://doi.org/10.1016/j.braindev.2010.08.010

    Article  PubMed  Google Scholar 

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Sharpley, C.F., Sarmukadam, K., Bitsika, V. et al. A Comment on some Methodological Issues in EEG Connectivity Studies of Sensory Features in Youth with Autism. J Dev Phys Disabil (2021). https://doi.org/10.1007/s10882-021-09799-5

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Keywords

  • Autism
  • Sensory features
  • Eeg
  • Connectivity