The effects of neurofeedback training on behavior and brain functional networks in children with autism spectrum disorder

被引:1
作者
Kang, Jiannan [1 ]
Lv, Shuaikang [1 ]
Li, Yuqi [1 ]
Hao, Pengfei [1 ]
Li, Xiaoli [2 ]
Gao, Chunxia [1 ]
机构
[1] Hebei Univ, Coll Elect & Informat Engn, Baoding, Peoples R China
[2] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Autism Spectrum Disorder; Neurofeedback Training; Electroencephalogram; Functional Connectivity; Static Functional Network; Dynamic Functional Network; CONNECTIVITY; VARIABILITY;
D O I
10.1016/j.bbr.2025.115425
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with an unclear pathogenesis to date. Neurofeedback (NFB) had shown therapeutic effects in patients with ASD. In this study,we analyzed the brain functional networks of children with ASD and investigated the impact of NFB targeting the beta rhythm training on these networks. The Autism Behavior Checklist (ABC) and Social Response Scale (SRS) were employed to evaluate the effects of NFB training on the behavioral abilities of children with ASD. We compared the differences in static and dynamic brain functional networks between ASD and Typically Developing (TD) children, also explored the changes in these networks in ASD children after 20 sessions of NFB training. The Weighted Phase Lag Index (wPLI) was used to construct static functional networks, and the Fuzzy Entropy (FuzzyEn) algorithm was further employed to measure the complexity of static functional connectivity and construct dynamic functional networks. This allowed the analysis of functional connectivity and fluctuations in the static functional networks of ASD and TD children, as well as the time variability of the dynamic functional networks. Additionally, the study explored the changes in brain functional networks and behavioral scales before and after NFB training. Results from behavioral scales indicated significant improvements in cognitive, communication, language, and social scores in ASD children following NFB intervention. EEG analysis revealed that static functional connectivity was lower, connectivity variability was higher, and temporal variability was greater in ASD children compared to TD children. Following NFB training, increased functional connectivity, reduced connectivity variability in the Delta frequency band, and decreased temporal variability were observed in ASD children. The results revealed abnormalities in both static and dynamic functional networks in children with ASD, with NFB training showed potential to modulate these networks. While our results showed that NFB training can assist participants in regulating connectivity and temporal variability in specific brain regions, robust evidence for its effectiveness in alleviating core symptoms of ASD remained limited.
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页数:11
相关论文
共 60 条
[51]   Assessing Consciousness through Neurofeedback and Neuromodulation: Possibilities and Challenges [J].
Vatrano, Martina ;
Nemirovsky, Idan Efim ;
Tonin, Paolo ;
Riganello, Francesco .
LIFE-BASEL, 2023, 13 (08)
[52]   Dynamic Reorganization of Functional Connectivity Unmasks Fatigue Related Performance Declines in Simulated Driving [J].
Wang, Hongtao ;
Liu, Xucheng ;
Hu, Hongying ;
Wan, Feng ;
Li, Ting ;
Gao, Lingyun ;
Bezerianos, Anastasios ;
Sun, Yu ;
Jung, Tzyy-Ping .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2020, 28 (08) :1790-1799
[53]   Increased EEG coherence in long-distance and short-distance connectivity in children with autism spectrum disorders [J].
Wang, Jia ;
Wang, Xiaomin ;
Wang, Xuelai ;
Zhang, Huiying ;
Zhou, Yong ;
Chen, Lei ;
Li, Yutong ;
Wu, Lijie .
BRAIN AND BEHAVIOR, 2020, 10 (10)
[54]   Relative Power of Specific EEG Bands and Their Ratios during Neurofeedback Training in Children with Autism Spectrum Disorder [J].
Wang, Yao ;
Sokhadze, Estate M. ;
El-Baz, Ayman S. ;
Li, Xiaoli ;
Sears, Lonnie ;
Casanova, Manuel F. ;
Tasman, Allan .
FRONTIERS IN HUMAN NEUROSCIENCE, 2016, 9
[55]   Altered brain structural and functional connectivity in schizotypy [J].
Wang, Yong-ming ;
Cai, Xin-lu ;
Zhang, Rui-ting ;
Zhang, Yi-jing ;
Zhou, Han-yu ;
Wang, Yi ;
Wang, Ya ;
Huang, Jia ;
Wang, Yan-yu ;
Cheung, Eric F. C. ;
Chan, Raymond C. K. .
PSYCHOLOGICAL MEDICINE, 2022, 52 (05) :834-843
[56]   Construction and Recognition of Functional Brain Network Model Based on Depression [J].
Wen, Lin ;
Liu, Shan ;
Cao, Yurong ;
Li, Guiling .
JOURNAL OF MEDICAL SYSTEMS, 2019, 43 (08)
[57]   An EEMD-ICA Approach to Enhancing Artifact Rejection for Noisy Multivariate Neural Data [J].
Zeng, Ke ;
Chen, Dan ;
Ouyang, Gaoxiang ;
Wang, Lizhe ;
Liu, Xianzeng ;
Li, Xiaoli .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2016, 24 (06) :630-638
[58]   Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders [J].
Zhang, Jie ;
Cheng, Wei ;
Liu, Zhaowen ;
Zhang, Kai ;
Lei, Xu ;
Yao, Ye ;
Becker, Benjamin ;
Liu, Yicen ;
Kendrick, Keith M. ;
Lu, Guangming ;
Feng, Jianfeng .
BRAIN, 2016, 139 :2307-2321
[59]   Abnormality of Functional Connections in the Resting State Brains of Schizophrenics [J].
Zhu, Yan ;
Zhu, Geng ;
Li, Bin ;
Yang, Yueqi ;
Zheng, Xiaohan ;
Xu, Qi ;
Li, Xiaoou .
FRONTIERS IN HUMAN NEUROSCIENCE, 2022, 16
[60]  
Zivoder I., 2015, Psychiatr. Danub., V27