Investigation of brain networks in children with attention deficit/hyperactivity disorder using a graph theoretical approach. (February 2018)

• Record Type: Journal Article
• Authors: Cao, Jianqin; Li, Yang; Yu, Hong; Zhao, Xiwu; Li, Yingli; Wang, Suhong
• Is Part Of: Biomedical signal processing and control. Volume 40(2018)
• Page Start: 351
• Page End: 358
• Publication Date: 2018-02
• Subjects: Graph theory -- Attention deficit hyperactivity disorder (ADHD) -- Brain network; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.10.005
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: When performing functional tasks, alpha bands can be used as an important parameter in ADHD research. The shortest path length can be used as a reference to assess ADHD. ADHD brains exhibit significant defects in left lateralization of neural networks. Abstract: In this study, brain networks in children with attention-deficit/hyperactivity disorder (ADHD) were investigated. Electroencephalogram (EEG) data were collected from 16 children with ADHD (ADHD group) and 16 healthy children (control group) while they performed an improved visual continuous performance test. A combination coherence and graph theory method was used to construct each subject's nerve conduction network using EEG signal data. Differences in brain network topology parameters between the two groups were then compared (two-sample t test). Results revealed the following: when performing functional tasks, alpha bands can be used as an important parameter in ADHD research; the shortest path length can be used as a reference to assess ADHD; and ADHD brains exhibit significant defects in left lateralization of neural networks. These results support the conclusions of the cognitive-energetic model.