Volume 9, Issue 3 (volume9, Issue 3 2021)
CPJ 2021, 9(3): 27-40 |
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Azhdarloo A, Tabiee M, Azhdarloo M. The Comparison of Quantitative Electroencephalography of Neural Connections between Children aged 6 to 13 years with Autism Spectrum Disorder and Typically Developing Children. CPJ 2021; 9 (3) :27-40
URL: http://jcp.khu.ac.ir/article-1-3426-en.html
URL: http://jcp.khu.ac.ir/article-1-3426-en.html
Shiraz University , m.tabiee.m@gmail.com
Abstract: (5399 Views)
Recent neuroimaging studies have shown that main symptoms of Autism Spectrum Disorder (ASD) such as deficits in social communication, speech and repetitive behaviors are associated with abnormalities in neural connectivity. The abnormalities in neural connectivity have been studied by several methods. Among these methods, electroencephalography is an efficient and a non-invasive tool that records brain electrical activity and helps us to gain information about brain neural connectivity and cognitive characters. Therefore, the purpose of this study was to analyze electroencephalogram resting state data to compare brain connectivity (coherence) patterns between children with ASD and typically developing children. The method of this study was descriptive-analytical. The population of the study consisted of all children with ASD (aged 6-13) referred to psychologists in Mehraz Andisheh Clinic in Shiraz. Fifteen children with ASD (boys = 11 and girls = 4) were selected via purposeful sampling method. Moreover, a group of fifteen typically developing children who were matched based on chronological age and gender were recruited. Quantitative Electroencephalography data analyses showed a significant difference between the two groups and indicating hyper connectivity in most frequency bands among children with ASD. Therefore, quantitative electroencephalography patterns of children with ASD indicated an increase in the levels of coherence in delta (p < .05) and theta (p < .05) powers in the prefrontal region, theta (p < .05) and alpha (p < .05) waves in the central region, in theta (p < .001), alpha (p < .001) and beta (p < .001) waves in the occipital region, in addition to delta (p < .001), theta (p < .001) and alpha (p < .001) waves in the temporal region. The findings demonstrated abnormalities in brain connectivity (coherence) patterns of children with ASD which is supported by cortical connectivity theory. Consequently, these findings (hyper connectivity patterns) can be considered as a useful marker to better diagnose ASD. Moreover, changing these patterns may have a positive impact on the treatment of individuals with ASD.
Keywords: Autism Spectrum Disorder, Coherence, Quantitative Electroencephalography, Disrupted Cortical Connectivity, Typically Developing Children
Type of Study: Research |
Subject:
Cognitive Sciences - Cognitive Psychology
Received: 2021/05/13 | Accepted: 2021/09/26 | Published: 2021/10/2
Received: 2021/05/13 | Accepted: 2021/09/26 | Published: 2021/10/2
References
1. Akshoomoff, N., Christina, C., & Heather, St. (2006). The Role of the Autism Diagnostic Observation Schedule in the Assessment of Autism Spectrum Disorders in School and Community Setting. The California School Psychologist 11, 7-19. [DOI:10.1007/BF03341111]
2. Baxter, A. J., Brugha, T., Erskine, H., Scheurer, R., Vos, T., & Scott, J. (2015). The epidemiology and global burden of autism spectrum disorders. Psychological Medicine, 45(3), 601-613. [DOI:10.1017/S003329171400172X]
3. Borup, J. & Kolgaard, C. (2014). Disrupted cortical connectivity as an explanatory model of autism spectrum disorder. Journal of European Psychology Students, 5(1), 19-24. [DOI:10.5334/jeps.bn]
4. Carson, A.M., Salowitz, N.M.G., Scheidt, R. A., Dolan, B.K. & Van Hecke, A.V. (2014). Electroencephalogram Coherence in Children with and Without Autism Spectrum Disorders: Decreased Interhemispheric Connectivity in Autism. Autism Research, 7, 334-43. [DOI:10.1002/aur.1367]
5. Coben, R., Chabot, R. J., & Hirshberg, L. (2013). "EEG analyses in the assessment of autistic Disorders," in imaging the Brain in Autism, Eds M. F. Casanova, A. S. El-Baz, and J. S. Suri. New York, NY: Springer, 349-370. [DOI:10.1007/978-1-4614-6843-1_12]
6. Courchesne, E., Campbell, K., & Solso, S. (2011). Brain growth across the life span in autism: age-specific changes in anatomical pathology. Brain Research, 1380.138-145 [DOI:10.1016/j.brainres.2010.09.101]
7. Dawson, G., Webb, S.J. & McPartland, J. (2005). Understanding the nature of face processing impairment in autism: Insights from behavioral and electrophysiological studies. Developmental Neuropsychology, 27, 403-424. [DOI:10.1207/s15326942dn2703_6]
8. Dickinson, A., Distefano, Ch., Lin, Y.Y., Scheffler, A. W., Senturk. D. & Jeste, Sh. S. (2018). Interhemispheric alpha-band hypoconnectivity in children with spectrum disorder. Behavioral Brain Research, 348. 227-234. [DOI:10.1016/j.bbr.2018.04.026]
9. Di Martino, A., Yan, C.G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K. et al. (2014). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19, 659-667. [DOI:10.1038/mp.2013.78]
10. Dumas, G., Soussignan, R., Hugueville, L., Martinerie, J., and Nadel, J. (2014). Revisiting mu suppression in autism spectrum disorder. Brain Research. 1585, 108-119. [DOI:10.1016/j.brainres.2014.08.035]
11. Duffy, F.H, Shankardass, A., McAnulty, G.B. & ALS, H. (2013). The relationship of Asperger's syndrome to autism: a preliminary EEG coherence study. BMC Medicine, 11,175. [DOI:10.1186/1741-7015-11-175]
12. Fedor, J. Lynn, A. Foran, W. & Dicicco-Bloom, J. (2018). Patterns of fixation during face recognition: Differences in autism across age. Autism, 22 (4). [DOI:10.1177/1362361317714989]
13. Fishman, I., Linke, A. C., Hau, J., Carper, R.A. & Muller, R. A. (2018). Atypical functional connectivity of amygdale related to reduced symptom severity in children with Autism. Journal of the American Academy of Child & Adolescent Psychiatry, 57 (10). 764-774. [DOI:10.1016/j.jaac.2018.06.015]
14. Gabard-Durnam, L.J., Wilkinson, C., Kapur, K., Tager-Flusberg, H., Levin, A.R. & Nelson C.A. (2019). Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nature Communications, 10 (1), 4188. [DOI:10.1038/s41467-019-12202-9]
15. Giona, F., Pagano, J., Verpelli, Ch. & Sala, C. (2021). Another step toward understanding brain functional connectivity alterations in autism. Journal of Neurochemistry. [DOI:10.1111/jnc.15452]
16. Just, M. A., Keller, T. A., Malave, V. L., Kana, R. K. & Varma, S. (2012). Autism as a neural systems disorder. A theory of frontal-posterior underconnectivity. Neuroscience and Biobehavioral Reviews 36(4). 1292-1313. [DOI:10.1016/j.neubiorev.2012.02.007]
17. Kana, R. K., Libero, L. E. & Moore, M. S. (2011). Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders. Physics of Life Reviews 8(4): 410-437. [DOI:10.1016/j.plrev.2011.10.001]
18. Keehn, B., Westerfield, M., Müller, R.-A., and Townsend, J. (2017). Autism, attention, and alpha oscillations: an electrophysiological study of attentional capture. Bioogical Psychiatry: Cognitive Neuroscience Neuroimaging 2 (6), 528-536. [DOI:10.1016/j.bpsc.2017.06.006]
19. Loomes, R., Hull, L., & Mandy, W. P. L. (2017). What Is the Male-to-Female Ratio in Autism Spectrum Disorder? A Systematic Review and Meta-Analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 56(6), 466-474. [DOI:10.1016/j.jaac.2017.03.013]
20. Lord, V., & Opacka‐Juffry, J. (2016). Electroencephalography (EEG) measures of neural connectivity in the assessment of brain responses to salient auditory stimuli in patients with disorders of consciousness. Frontiers in Psychology, 7, 397 10.3389. [DOI:10.3389/fpsyg.2016.00397]
21. Lushchekina, E.A., Khaerdinova, O.Y., Novototskii-Vlasov, V.Y., Lushchekin, V.S. & Strelets, V.B. (2016). Synchronization of EEG Rhythms in Baseline Conditions and during Counting in Children with Autism Spectrum Disorders. Neuroscience and Behavioral Physiology, 46, 382-9. [DOI:10.1007/s11055-016-0246-5]
22. Lynch, C. J., Uddin, L. Q., Supekar, K., Khouzam, A., Phillips, J., & Menon, V. (2013). Default mode network in childhood autism: Posteromedial cortex heterogeneity and relationship with social deficits. Biological Psychiatry, 74 (3), 212-219. [DOI:10.1016/j.biopsych.2012.12.013]
23. Madipakkam, A.R., Rothkirch, M., Dziobek, I. & Sterzer, Ph. (2017). Unconscious avoidance of eye contact in autism spectrum disorder. Scientific Reports, 7 (1). [DOI:10.1038/s41598-017-13945-5]
24. Morrison, K.E., Pinkham, A.E., Penn, D.L., Kelsven, S., Ludwig, K. & Sasson, N.J. (2017). Distinct profiles of social skill in adults with autism spectrum disorder and schizophrenia. Autism Research, 10 (5). 878-887. [DOI:10.1002/aur.1734]
25. Pascual-Belda, A., Diaz-Parra, A. & Moratal, D. (2018). Evaluating functional connectivity alterations in autism spectrum disorder using network-based statistics. Diagnostics (Basel), 8 (3). [DOI:10.3390/diagnostics8030051]
26. Rinaldi, T., Perrodin, C. & Markram, H. (2008). Hyper-connectivity and hyper-plasticity in the medial prefrontal cortex in the valproic acid animal model of autism. Frontiers in Psychology, 2,1-7. [DOI:10.3389/neuro.04.004.2008]
27. 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. Neuroimage Clinical, 28. 102484. [DOI:10.1016/j.nicl.2020.102484]
28. Sandin S, Lichtenstein P, Kuja-Halkola R, Larsson H, Hultman CM, Reichenberg A. (2014). The familial risk of autism. JAMA, 311, 1770-7. [DOI:10.1001/jama.2014.4144]
29. Shephard, E., Tye, C., Ashwood, K.L., Azadi. B., Asherson, P., Bolton, P.F. & McLoughlin, G. (2018). Resting-state neurophysiological activity patterns in young people with ASD, ADHD, and ASD + ADHD. Journal of Autism and Developmental Disorders, 48 (1), 110-122. [DOI:10.1007/s10803-017-3300-4]
30. Supekar, S., Uddin, L.Q., Khouzam, A., Philips, J., Gaillard, W.D., Kenworthy, L.E. et al. (2013). Brain hyper-connectivity in children with autism and its links to social deficits. Cell Reports, 5 (3). 738-747. [DOI:10.1016/j.celrep.2013.10.001]
31. Tsapkini, K., Frangakis, C.E. & Hillis, A.E. (2011). The function of the left anterior temporal pole: Evidence from acute stroke and infarct volume. Brain, 134, 3094-3105. [DOI:10.1093/brain/awr050]
32. Uccelli, N. A., Codagnone, M. G., Traetta, M. E., Levanovich, N., Rosato Siri, M. V., Urrutia, L. et al. (2021). Neurobiological substrates underlying corpus callosum hypoconnectivity and brain metabolic patterns in the valproic acid rat model of autism spectrum disorder. Journal of Neurochemistry. [DOI:10.1111/jnc.15444]
33. Uddin, L.Q., Supekar, K., Menon, V. (2013). Reconceptualizing functional brain connectivity in autism from a developmental perspective. Frontiers in Human Neuroscience, 7 (458). [DOI:10.3389/fnhum.2013.00458]
34. Urbain, Ch., Vogan, V.M., Ye, A.X., Pang, E.W., Doesburg, S.M. & Taylor, M.J. (2016). Desynchronization of fronto-temporal networks during working memory processing in autism. Human Brain Mapping, 37. 153-164. [DOI:10.1002/hbm.23021]
35. Vissers, M. E., Cohen, M. X. & Geurts, H. M. (2012). Brain connectivity and high functioning autism. A promising path of research that needs refined models, methodological convergence and stronger Behavioural links. Neuroscience and Biobehavioral Reviews 36(1): 604-625. [DOI:10.1016/j.neubiorev.2011.09.003]
36. Wang, J., Barstein. J., Ethridge, L.E., Mosconi, M.W., Takarae, Y. & Sweeney, J.A. (2013). Resting state EEG abnormalities in autism spectrum disorders. Journal of Neurodevelopmental Disorder, 5 (1), 1-14. [DOI:10.1186/1866-1955-5-24]
37. Wang, J., Wang, X., Wang, X., Zhang, H., Zhou, Y., Chen, L., Li, Y., et al. (2020). Increased EEG coherence and short-distance connectivity in children with autism spectrum disorders. Brain and Behavior, 10, 1-10. [DOI:10.1002/brb3.1796]
38. Yuk, V., Urbain, Ch., Anagnostou, E. & Taylor, M. J. (2020). Frontoparietal network connectivity during an N-Back task in adults with Autism Spectrum Disorder. Front Psychiatry, 11: 551808. [DOI:10.3389/fpsyt.2020.551808]
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