[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit ::
:: Volume 9, Issue 2 (Spring 2021) ::
Shefaye Khatam 2021, 9(2): 10-21 Back to browse issues page
Analysis of Electroencephalogram of Autism Spectrum Disorder Using Correlation Dimension Changes in brain Map
Nahid Ghoreishi, Samane Zare Molkabad, Somayeh Baratzade, Ateke Goshvarpoor, Ghasem Sadeghi Bajestani *
Department of Medical Engineering, Imam Reza University, Mashhad, Iran , g.sadeghi@imamreza.ac.ir
Abstract:   (2256 Views)
Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects information processing in the nervous system and the procedure of natural brain evolution. Therefore, the processing and analysis of the brain function of these patients have captivated the attention of many researchers. Qualitative electroencephalography (EEG) is an evaluation method for determining functional brain abnormalities that is different from quantitative EEG. Chaotic tools are used in qualitative EEG whereas linear and nonlinear methods are applied in quantitative EEG. The purpose of the present study is to compare qualitative EEG findings of healthy and autistic subjects. Materials and Methods: In this study, 19 channels of brain signals (Cz, C4, F4, Fz, F3, C3, P3, Pz, P4, T4, F8, Fp2, Fp1, F7, T3, T5, O1, O2, T6) of 6 healthy and 5 autistic subjects were evaluated in two phases. At first 5-minutes with closed eyes and then 5-minutes with opened eyes, the subject's EEG was recorded. After removing the artifacts, the correlation dimension of the signals was calculated, and brain maps were plotted to analyze the changes of correlation dimension on the scalp surface. Results: By comparison of the brain maps of the healthy and autistic groups between the opened and closed eyes periods, we found there was a difference between the brain function of the groups, especially in the T3 and T4 regions of the temporal regions as well as frontal and posterior areas. Conclusion: Using brain maps, correlation dimension mapping on the brain surface provides a better understanding of brain dynamics in autistic subjects.
Keywords: Brain Mapping, Electroencephalography, Autism Spectrum Disorder
Full-Text [PDF 1415 kb]   (769 Downloads)    
Type of Study: Research --- Open Access, CC-BY-NC | Subject: Cognitive Neuroscience
References
1. Sparks BF, Friedman SD, Shaw DW, Aylward EH, Echelard D, Artru AA, et al. Brain structural abnormalities in young children with autism spectrum disorder. Neurology. 2002; 59(2): 184-92. [DOI:10.1212/WNL.59.2.184]
2. Coben R. Connectivity-guided neurofeedback for autistic spectrum disorder. Biofeedback. 2007; 35(4): 131-5.
3. Wöhr M, Scattoni ML. Neurobiology of autism. Behavioural brain research. 2013; 251. [DOI:10.1016/j.bbr.2013.06.014]
4. Landa RJ. Diagnosis of autism spectrum disorders in the first 3 years of life. Nature Clinical Practice Neurology. 2008; 4(3): 138-47. [DOI:10.1038/ncpneuro0731]
5. Christensen DL, Braun KVN, Baio J, Bilder D, Charles J, Constantino JN, et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, United States, 2012. MMWR Surveillance Summaries. 2018; 65(13):1. [DOI:10.15585/mmwr.ss6513a1]
6. Gray DE. Lay conceptions of autism: Parents' explanatory models. Medical Anthropology. 1994; 16(1-4): 99-118. [DOI:10.1080/01459740.1994.9966111]
7. Hadjikhani N, Joseph RM, Snyder J, Chabris CF, Clark J, Steele S, et al. Activation of the fusiform gyrus when individuals with autism spectrum disorder view faces. Neuroimage. 2004; 22(3): 1141-50. [DOI:10.1016/j.neuroimage.2004.03.025]
8. Park HR, Lee JM, Moon HE, Lee DS, Kim B-N, Kim J, et al. A short review on the current understanding of autism spectrum disorders. Experimental neurobiology. 2016; 25(1): 1-13. [DOI:10.5607/en.2016.25.1.1]
9. Divanbeigi A, Divanbeigi A. A brief review on the causes of autism spectrum disorder. Shefaye Khatam. 2015; 3(1): 157-16. [DOI:10.18869/acadpub.shefa.3.1.157]
10. Golinska AK. Detrended fluctuation analysis (DFA) in biomedical signal processing: selected examples. Stud Logic Grammar Rhetoric. 2012; 29: 107-15.
11. Liston C, Cohen MM, Teslovich T, Levenson D, Casey B. Atypical prefrontal connectivity in attention-deficit/hyperactivity disorder: pathway to disease or pathological end point? Biological psychiatry. 2011; 69(12): 1168-77. [DOI:10.1016/j.biopsych.2011.03.022]
12. Schumann CM, Barnes CC, Lord C, Courchesne E. Amygdala enlargement in toddlers with autism related to severity of social and communication impairments. Biological psychiatry. 2009; 66(10): 942-9. [DOI:10.1016/j.biopsych.2009.07.007]
13. Pelphrey KA, Morris JP, McCarthy G. Neural basis of eye gaze processing deficits in autism. Brain. 2005; 128(5): 1038-48. [DOI:10.1093/brain/awh404]
14. Dziobek I, Bahnemann M, Convit A, Heekeren HR. The role of the fusiform-amygdala system in the pathophysiology of autism. Archives of general psychiatry. 2010; 67(4): 397-405. [DOI:10.1001/archgenpsychiatry.2010.31]
15. Elhabashy H, Raafat O, Afifi L, Raafat H, Abdullah K. Quantitative EEG in autistic children. The Egyptian Journal of Neurology, Psychiatry and Neurosurgery. 2015; 52(3): 176. [DOI:10.4103/1110-1083.162031]
16. Dimitrov PD, Petrov P, Aleksandrov I, Dimitrov I, Mihailova M, Radkova G, et al. Quantitative EEG Comparative Analysis between Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). Journal of IMAB-Annual Proceeding Scientific Papers. 2017; 23(1): 1441-3. [DOI:10.5272/jimab.2017231.1441]
17. Askari E, Setarehdan SK, Sheikhani A, Mohammadi MR, Teshnehlab M. Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis. Artificial intelligence in medicine. 2018; 89: 40-50. [DOI:10.1016/j.artmed.2018.05.003]
18. Ma Y, Shi W, Peng C-K, Yang AC. Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches. Sleep medicine reviews. 2018; 37: 85-93. [DOI:10.1016/j.smrv.2017.01.003]
19. Rodriguez-Bermudez G, Garcia-Laencina PJ. Analysis of EEG signals using nonlinear dynamics and chaos: a review. Applied mathematics & information sciences. 2015; 9(5): 2309.
20. Abdolzadegan D, Moattar MH, Ghoshuni M. A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method. Biocybernetics and Biomedical Engineering. 2020; 40(1): 482-93. [DOI:10.1016/j.bbe.2020.01.008]
21. Bosl W, Tierney A, Tager-Flusberg H, Nelson C. EEG complexity as a biomarker for autism spectrum disorder risk. BMC medicine. 2011; 9(1): 18. [DOI:10.1186/1741-7015-9-18]
22. Sharma M, Pachori RB, Acharya UR. A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognition Letters. 2017; 94: 172-9. [DOI:10.1016/j.patrec.2017.03.023]
23. Natarajan K, Acharya R, Alias F, Tiboleng T, Puthusserypady SK. Nonlinear analysis of EEG signals at different mental states. Biomedical engineering online. 2004; 3(1): 7. [DOI:10.1186/1475-925X-3-7]
24. DeCoster GP, Mitchell DW. The efficacy of the correlation dimension technique in detecting determinism in small samples. Journal of Statistical Computation and Simulation. 1991; 39(4): 221-9. [DOI:10.1080/00949659108811357]
25. Khalili Z, Moradi MH, editors. Emotion recognition system using brain and peripheral signals: using correlation dimension to improve the results of EEG. 2009 International Joint Conference on Neural Networks; 2009: IEEE. [DOI:10.1109/IJCNN.2009.5178854]
26. Roy R, Sikdar D, Mahadevappa M. Chaotic behaviour of EEG responses with an identical grasp posture. Computers in Biology and Medicine. 2020: 103822. [DOI:10.1016/j.compbiomed.2020.103822]
27. Hasanzadeh F, Mohebbi M, Rostami R. Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal. Journal of affective disorders. 2019; 256: 132-42. [DOI:10.1016/j.jad.2019.05.070]
28. Röschke J, Aldenhoff J. A nonlinear approach to brain function: deterministic chaos and sleep EEG. Sleep. 1992; 15(2): 95-101. [DOI:10.1093/sleep/15.2.95]
29. Hilborn RC. Chaos and nonlinear dynamics: an introduction for scientists and engineers: Oxford University Press on Demand; 2000.
30. Bosl WJ, Tager-Flusberg H, Nelson CA. EEG analytics for early detection of autism spectrum disorder: a data-driven approach. Scientific reports. 2018; 8(1): 1-20. [DOI:10.1038/s41598-018-24318-x]
31. Zilbovicius M, Meresse I, Chabane N, Brunelle F, Samson Y, Boddaert N. Autism, the superior temporal sulcus and social perception. Trends in neurosciences. 2006; 29(7): 359-66. [DOI:10.1016/j.tins.2006.06.004]
32. Jambaque I, Mottron L, Ponsot G, Chiron C. Autism and visual agnosia in a child with right occipital lobectomy. Journal of Neurology, Neurosurgery & Psychiatry. 1998; 65(4): 555-60. [DOI:10.1136/jnnp.65.4.555]



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ghoreishi N, Zare Molkabad S, Baratzade S, Goshvarpoor A, Sadeghi Bajestani G. Analysis of Electroencephalogram of Autism Spectrum Disorder Using Correlation Dimension Changes in brain Map. Shefaye Khatam. 2021; 9 (2) :10-21
URL: http://shefayekhatam.ir/article-1-2094-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 9, Issue 2 (Spring 2021) Back to browse issues page
مجله علوم اعصاب شفای خاتم The Neuroscience Journal of Shefaye Khatam
Persian site map - English site map - Created in 0.05 seconds with 30 queries by YEKTAWEB 4463