Application of electroencephalogram spectrum analysis in early diagnosis and rehabilitation intervention evaluation of children with autism spectrum disorder

YI Aiwen; XIN Jing; BAI Yan; XU Jiaxin; LIANG Fujian; FENG Xiaojian

doi:10.11852/zgetbjzz2022-0504

PDF(1163 KB)

Chinese Journal of Child Health Care ›› 2023, Vol. 31 ›› Issue (3) : 311-315. DOI: 10.11852/zgetbjzz2022-0504

Application of electroencephalogram spectrum analysis in early diagnosis and rehabilitation intervention evaluation of children with autism spectrum disorder

YI Aiwen, XIN Jing, BAI Yan, XU Jiaxin, LIANG Fujian, FENG Xiaojian

Author information +

History +

Abstract

Objective To analyze the application value of electroencephalogram (EEG) characteristics in different frequency bands in early diagnosis and rehabilitation intervention evaluation of children with autism spectrum disorder (ASD) by using EEG spectrum analysis technology. Method From January to December 2021, 35 ASD children aged 2 to 4 years who met the inclusion criteria were recruited into the ASD group, and received comprehensive rehabilitation intervention for 3 months.The resting state EEG signals of ASD children were collected before and after treatment, and the symptom of ASD children were evaluated by Chinese version of Autism Treatment Evaluation Checklist (ATEC).Meanwhile, 30 normal children with similar age (normal children group) were selected to collect resting state EEG signals.The relative power changes of four frequency bands[delta (δ,1 - 4Hz), theta (θ,4 - 8Hz), alpha (α,8 - 13Hz) and beta (β,13 - 35Hz)]in each brain region were analyzed by EEG power spectrum analysis. Results ASD children had greater relative power in all five brain regions in the δ band than normal children before treatment (t=3.909, 2.941, 0.098, 0.097, 0.108), while the α band was significantly smaller than normal children (t=-3.919,-4.076,-4.924,-4.351,-4.453), and the differences were significant (P<0.05).For ASD children, the differences in both δ and α bands were statistically significant before and after intervention (P<0.05).For the θ and β bands, there was no statistically significant difference in the relative power in the five brain regions between ASD children before and after treatment compared to the normal group (P>0.05).The differences in the subscale scores and total scores on the ATEC scale for ASD children before and after treatment were statistically different (P<0.05). Conclusion The relative power changes of δ and α bands in five brain regions of ASD children are specific, which can provide a certain reference for the early diagnosis of ASD, and can be used as an objective evaluation indicator for the efficacy evaluation of rehabilitation intervention.

Key words

autism spectrum disorder / electroencephalogram spectrum / relative power

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

YI Aiwen, XIN Jing, BAI Yan, XU Jiaxin, LIANG Fujian, FENG Xiaojian. Application of electroencephalogram spectrum analysis in early diagnosis and rehabilitation intervention evaluation of children with autism spectrum disorder[J]. Chinese Journal of Child Health Care. 2023, 31(3): 311-315 https://doi.org/10.11852/zgetbjzz2022-0504

References

[1] 中华医学会儿科学分会发育行为学组,中国医师协会儿科分会儿童保健专业委员会,儿童孤独症诊断与防治技术和标准研究项目专家组.孤独症谱系障碍儿童早期识别筛查和早期干预专家共识[J].中华儿科杂志,2017,55(12):890-897.
The Subspecialty Group of Developmental and Behavioral Pediatrics, the Society of Pediatrics, Chinese Medical Association,The Subspecialty Group of Primary Health Care, the Society of Pediatrics, Chinese Medical Doctor Association,Project Expert Group of Diagnosis, Prevention and Therapy Techniques and Standards of Childhood Autism Spectrum Disorders.Consensus on early identification screening and early intervention for autism spectrum disorder[J].Chin J Pediatr, 2017,55(12): 890-897.
[2] Zhou H,Xu X,Yan W, et al.Prevalence of autism spectrum disorder in China: A nationwide multi-center population-based study among children aged 6 to 12 years[J].Neuroscience Bulletin, 2020, 36(9):12-22.
[3] 马冬梅,姜志梅,庞伟,等.孤独症谱系障碍儿童早期识别探析[J].中国听力语言康复科学杂志,2019,17(4):297-300.
Ma DM, Jiang ZM, Pang W, et al.Early identification of children with autism spectrum disorder[J].Chinese Scientific Journal of Hearing and Speech Rehabilitation,2019,17(4):297-300.
[4] 梁涛.儿童孤独症谱系障碍早期识别的研究进展[J].中国妇幼保健,2020,35(8):1554-1558.
Liang T.Research advances in early recognition of autism spectrum disorders in children[J].Maternal and Child Health Care of China, 2020,35(8):1554-1558.
[5] 盛倩倩,蔡春泉,赵澎.孤独症谱系障碍的病因研究进展[J].继续医学教育,2019,33(2):96-99.
Sheng QQ, Cai CQ, Zhao P.Recent advances of etiology of autism spectrum disorders[J].Continuing Medical Education, 2019,33(2):96-99.
[6] Ghanbari Y, Bloy L, Edgar JC, et al.Joint analysis of band-specific functional connectivity and signal complexityin autism[J].J Autism Dev Disord, 2013, 45(2):444-460.
[7] Distefano C, Dickinson A, Baker E, et al.EEG data collection in children with ASD: The role of state in data quality and spectral power[J].Res Autism Spectr Disord, 2019, 57:132-144.
[8] Murias M, Webb SJ, Greenson J, et al.Resting state cortical connectivity reflected in eeg coherence in individuals with autism[J].Biol Psychiatry, 2007, 62(3):270-273.
[9] American Psychiatric Association of Autism Spectrum Disorder.Diagnostic and statistical manual of mental disorders,5th edition(DSM-5)[M].Arlington, VA:American Psychiatric Publishing, 2013: 50-59.
[10] 靳亚娟.基于脑电和眼动多特征融合的孤独症与正常儿童分类研究[D].石家庄:河北大学,2021.
Jin YJ.Research on classification of autistic and normal children based on multi-feature fusion of EEG and eye tracking[D].Shijiazhuang: Hebei University, 2021.
[11] 赵杰,靳亚娟,张志明,等.融合多特征脑电评估孤独症儿童[J].中国生物医学工程学报,2021,40(5):550-558.
Zhao J, Jin YJ, Zhang ZM, et al.Study on multi-feature fusion of EEG to evaluate children with autism[J].Chinese Journal of Biomedical Engineering, 2021,40(5):550-558.
[12] 方慧,任艳玲,李春燕,等.孤独症治疗评定量表中文版的信度和效度检验[J].四川精神卫生,2019,32(6):518-522.
Fang H, Ren YL, Li CY, et al.Reliability and validity of the Chinese Version of Autism Treatment Evaluation Checklist[J].Sichuan Mental Health, 2019,32(6):518-522.
[13] 杨华.常规脑电图在儿童孤独症综合康复治疗中的临床价值[J].临床医药文献电子杂志,2019,6(16):52.
Yang H.Clinical value of routine electroencephalogram in comprehensive rehabilitation treatment of children with autism[J].Journal of Clinical Medical, 2019,6(16):52.
[14] 沈学丽.基于时频分析的头皮脑电特征提取及其模式识别应用研究[D].杭州:杭州电子科技大学,2012.
Shen XL.Scalp EEG feature extraction based on time-frequency analysis and its application in pattern recognition[D].Hangzhou: Hangzhou Dianzi University, 2012.
[15] Başar E.Gamma, alpha, delta, and theta oscillations govern cognitive processes[J].Int J Psychophysiol, 2001, 39(2/3): 241-248.
[16] Knyazev GG.EEG delta oscillations as a correlate of basic homeostatic and motivational processes[J].Neurosci Biobehav Rev, 2012, 36(1): 677-695.
[17] Stroganova TA, Nygren G, Tsetlin MM, et al.Abnormal EEG lateralization in boys with autism[J].Clin Neurophysiol, 2007, 118(8):1842-1854.
[18] Tierney-Adrienne L, Gabard-Durnam L, Vogel-Farley V, et al.Developmental trajectories of resting EEG power:An endophenotype of autism spectrum disorder[J].PloS One, 2012,7(6):e39127.
[19] Smith RX, Jann K, Dapretto M, et al.Imbalance of functional connectivity and temporal entropy in resting-state networks in autism spectrum disorder: A machine learning approach[J].Front Neurosci, 2018, 12: 869.
[20] Luschekina EA, Khaerdinova OY, Luschekin VS, et al.Interhemispheric differences in the spectral power and coherence of EEG rhythms in children with autism spectrum disorders[J].Hum Physiol, 2017, 43(3):265-273.
[21] Wang J.Resting state eeg abnormalities in autism spectrum disorders[J].J Neurodev Disord, 2013,5(1): 1-14.
[22] 李杰,许均瑜,罗建红.γ-氨基丁酸能中间神经元与自闭症谱系障碍的研究进展[J].浙江大学学报(医学版),2020,49(4):508-513.
Li J, Xu JY, Luo JH.Advances on GABAergic interneurons in autism spectrum disorders[J].Journal of Zhejiang University (Medical Sciences), 2020,49(4):508-513.
[23] 何长春.基于磁共振成像的孤独症谱系障碍脑连接异常发育模式研究[D].成都:电子科技大学,2021.
He CC.Abnormal developmental patterns of brain connectomics in autism spectrum disorder based on magnetic resonance imaging[D].Chengdu: University of Electronic Science and Technology of China, 2021.
[24] 李玉爽,敖亚雯,赵益林,等.基于图论分析对自闭症谱系障碍儿童脑功能网络异常拓扑学属性的研究[J].磁共振成像,2022,13(3):37-42.Li YS, Ao YW, Zhao YL, et al.The abnormal topological properties of brain functionalnetwork in children with autism spectrum disorders based on graph theory analysis[J].Chin J Magn Reson Imag, 2022,13(3):37-42.
[25] 随广红,孙凌.儿童孤独症谱系障碍治疗的研究进展[J].中国妇幼保健,2020,35(11):2148-2150.
Sui GH, Sun L.Research advances in the treatment of children with autism spectrum disorder[J].Maternal and Child Health Care of China, 2020,35(11):2148-2150.
[26] 周妍,张功,邓红珠,等.孤独症谱系障碍儿童的脑电特征与临床症状的相关性分析[J].新医学,2016,47(8):527-535.
Zhou Y, Zhang G, Deng HZ, et al.Correlation between electroencephalogram characteristics and clinical symptoms in children with autism spectrum disorder[J].Journal of New Medicine, 2016,47(8):527-535.