Comparative research on brain networks of children with attention deficit hyperactivity disorder and normal children based on visual cognitive tasks_Journal of Biomedical Engineering

Authors：

SONG Zhiwei ^1,2 , LI Wenjie ^1,2 , BI Hui ^1,2 , WANG Suhong ³ ,  ZOU Ling ^1,2

1. School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R China;
2. Key Laboratory of Biomedical Information Technology, Changzhou University, Changzhou, Jiangsu 213164, P.R China;
3. Brain Science Research Center, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, P.R China;

Corresponding author：

ZOU Ling, Email: zouling@cczu.edu.cn

Keywords：

attention deficit hyperactivity disorder; brain network; visual brain region; sub-network; classification

DOI：

10.7507/1001-5515.201912058

Video：

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Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder (ADHD) and normal children in the task-executing state, this paper conducted a comparative study using the network features of the visual function area. Functional magnetic resonance imaging (fMRI) data of 23 children with ADHD ［age: (8.27 ± 2.77) years］ and 23 normal children ［age: (8.70 ± 2.58) years］ were obtained by the visual capture paradigm when the subjects were performing the guessing task. First, fMRI data were used to build a visual area brain function network. Then, the visual area brain function network characteristic indicators including degree distribution, average shortest path, network density, aggregation coefficient, intermediary, etc. were obtained and compared with the traditional whole brain network. Finally, support vector machines (SVM) and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children. In this study, visual brain function network features were used for classification, with a classification accuracy of up to 96%. Compared with the traditional method of constructing a whole brain network, the accuracy was improved by about 10%. The test results show that the use of visual area brain function network analysis can better distinguish ADHD children from normal children. This method has certain help to distinguish the brain network between ADHD children and normal children, and is helpful for the auxiliary diagnosis of ADHD children.

Citation： SONG Zhiwei, LI Wenjie, BI Hui, WANG Suhong, ZOU Ling. Comparative research on brain networks of children with attention deficit hyperactivity disorder and normal children based on visual cognitive tasks. Journal of Biomedical Engineering, 2020, 37(5): 749-755, 764. doi: 10.7507/1001-5515.201912058 Copy

1.	Salari R, Bohlin G, Rydell A M, et al. Neuropsychological functioning and attachment rep-resentations in early school age as predictors of ADHD symptoms in late adolescence. Child Psychiatry Hum Dev, 2017, 48(3): 370-384.
2.	Roberto L. Current trends in medicalisation: Universalising ADHD diagnosis and treatments. Sociol Compass, 2019, 13(6): e12697.
3.	Alegria A A, Wulff M, Brinson H, et al. Real-time fMRI neurofeedback in adolescents with attention deficit hyperactivity disorder. Hum Brain Mapp, 2017, 38(6): 3190-3209.
4.	Kimmig H, Greenlee M W, Huethe F, et al. MR-eyetracker: a new method for eye movement recording in functional magnetic resonance imaging. Exp Brain Res, 1999, 126(3): 443-449.
5.	Sporns O, Chialvo D R, Kaiser M, et al. Organization, development and function of complex brain networks. Trends Cogn Sci, 2004, 8(9): 418-425.
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7.	Fair D A, Posner J, Nagel B J, et al. Atypical default network connectivity in youth with attention-deficit/hyperactivity disorder. Biol Psychiatry, 2010, 68(12): 1084-1091.
8.	Sanefuji M, Craig M, Parlatini V, et al. Double-dissociation between the mechanism leading to impulsivity and inattention in attention deficit hyperactivity disorder. Cortex, 2016, 8(6): 290-302.
9.	Xu T, Cullen K R, Mueller B, et al. Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI. Neuroimage Clin, 2016, 11: 302-315.
10.	邹凌, 徐逸, 周仁来. 密度K-means算法在认知重评脑功能连接中的应用. 计算机辅助设计与图形学学报, 2015, 27(5): 841-846.
11.	Li Wenjie, Zhou Tiantong, Ling Zou, et al. Identification of attention deficit/hyperactivity disorder in children using multiple ERP features. Curr Bioinform, 2018, 13(5): 501-507.
12.	Francx W, Oldehinkel M, Oosterlaan J, et al. The executive control network and symptomatic improvement in attention-deficit/hyperactivity disorder. Cortex, 2015, 73: 62-72.
13.	Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 2002, 15(1): 273-289.
14.	Çelik Z Ç, Çolak Ç, Di Biase M A, et al. Structural connectivity in adolescent synthetic cannabinoid users with and without ADHD. Brain Imaging Behav, 2020, 14(2): 505-514.
15.	解, 汪小帆. 复杂网络中的社团结构分析算法研究综述. 复杂系统与复杂性科学, 2005, 2(3): 1-12.
16.	Jiao Zhuqing, Xia Zhengwang, Min Cai, et al. Hub recognition for brain functional networks by using multiple-feature combination. Computers & Electrical Engineering, 2018, 69: 740-752.
17.	Xia Mingrui, Wang Jinhui, He Yong. BrainNet viewer: a network visualization tool for human brain connectomics. PLoS One, 2013, 8(7): e68910.
18.	Fulford J, Milton F, Salas D, et al. The neural correlates of visual imagery vividness-an fMRI study and literature review. Cortex, 2018, 105: 26-40.
19.	杨姣姣, 郭倩, 李文杰, 等. 基于脑电主成分分析和k-最近邻的多动症儿童与正常儿童分类研究. 生物医学工程学杂志, 2016, 33(2): 232-238.

1. Salari R, Bohlin G, Rydell A M, et al. Neuropsychological functioning and attachment rep-resentations in early school age as predictors of ADHD symptoms in late adolescence. Child Psychiatry Hum Dev, 2017, 48(3): 370-384.
2. Roberto L. Current trends in medicalisation: Universalising ADHD diagnosis and treatments. Sociol Compass, 2019, 13(6): e12697.
3. Alegria A A, Wulff M, Brinson H, et al. Real-time fMRI neurofeedback in adolescents with attention deficit hyperactivity disorder. Hum Brain Mapp, 2017, 38(6): 3190-3209.
4. Kimmig H, Greenlee M W, Huethe F, et al. MR-eyetracker: a new method for eye movement recording in functional magnetic resonance imaging. Exp Brain Res, 1999, 126(3): 443-449.
5. Sporns O, Chialvo D R, Kaiser M, et al. Organization, development and function of complex brain networks. Trends Cogn Sci, 2004, 8(9): 418-425.
6. Bullmore E T, Sporns O. The economy of brain network organization. Nat Rev Neurosci, 2012, 13(5): 336-349.
7. Fair D A, Posner J, Nagel B J, et al. Atypical default network connectivity in youth with attention-deficit/hyperactivity disorder. Biol Psychiatry, 2010, 68(12): 1084-1091.
8. Sanefuji M, Craig M, Parlatini V, et al. Double-dissociation between the mechanism leading to impulsivity and inattention in attention deficit hyperactivity disorder. Cortex, 2016, 8(6): 290-302.
9. Xu T, Cullen K R, Mueller B, et al. Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI. Neuroimage Clin, 2016, 11: 302-315.
10. 邹凌, 徐逸, 周仁来. 密度K-means算法在认知重评脑功能连接中的应用. 计算机辅助设计与图形学学报, 2015, 27(5): 841-846.
11. Li Wenjie, Zhou Tiantong, Ling Zou, et al. Identification of attention deficit/hyperactivity disorder in children using multiple ERP features. Curr Bioinform, 2018, 13(5): 501-507.
12. Francx W, Oldehinkel M, Oosterlaan J, et al. The executive control network and symptomatic improvement in attention-deficit/hyperactivity disorder. Cortex, 2015, 73: 62-72.
13. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 2002, 15(1): 273-289.
14. Çelik Z Ç, Çolak Ç, Di Biase M A, et al. Structural connectivity in adolescent synthetic cannabinoid users with and without ADHD. Brain Imaging Behav, 2020, 14(2): 505-514.
15. 解, 汪小帆. 复杂网络中的社团结构分析算法研究综述. 复杂系统与复杂性科学, 2005, 2(3): 1-12.
16. Jiao Zhuqing, Xia Zhengwang, Min Cai, et al. Hub recognition for brain functional networks by using multiple-feature combination. Computers & Electrical Engineering, 2018, 69: 740-752.
17. Xia Mingrui, Wang Jinhui, He Yong. BrainNet viewer: a network visualization tool for human brain connectomics. PLoS One, 2013, 8(7): e68910.
18. Fulford J, Milton F, Salas D, et al. The neural correlates of visual imagery vividness-an fMRI study and literature review. Cortex, 2018, 105: 26-40.
19. 杨姣姣, 郭倩, 李文杰, 等. 基于脑电主成分分析和k-最近邻的多动症儿童与正常儿童分类研究. 生物医学工程学杂志, 2016, 33(2): 232-238.

Journal of Biomedical Engineering

Comparative research on brain networks of children with attention deficit hyperactivity disorder and normal children based on visual cognitive tasks

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