Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer

Ali Ekhlasi; Ali Motie Nasrabadi; Mohammadreza Mohammadi

doi:10.1515/bmt-2022-0100

Article

Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer

Ali Ekhlasi , Ali Motie Nasrabadi and Mohammadreza Mohammadi

Published/Copyright: October 6, 2022

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From the journal Biomedical Engineering / Biomedizinische Technik Volume 68 Issue 2

Abstract

Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed. The edges between two nodes (electrodes) were calculated by Phase Transfer Entropy (PTE). PTE is calculated for five frequency bands: delta, theta, alpha, beta, and gamma. The graph theory measures were divided into two categories: global and local. Statistical analysis with global measures indicates that in children with ADHD, the segregation of brain connectivity increases while the integration of the brain connectivity decreases compared to healthy children. These brain network differences were identified in the delta and theta frequency bands. The classification accuracy of 89.4% is obtained for both in-degree and strength measures in the theta band. Our result indicated local graph measures classified ADHD and healthy subjects with accuracy of 91.2 and 90% in theta and delta bands, respectively. Our analysis may provide a new understanding of the differences in the EEG brain network of children with ADHD and healthy children.

Keywords: attention deficit hyperactivity disorder (ADHD); classification; effective connectivity; electroencephalography (EEG); graph analysis; phase transfer entropy (PTE)

Corresponding author: Ali Motie Nasrabadi, Department of Biomedical Engineering, Faculty of Engineering, Shahed University, P.O Box: 3319118651, Tehran, Iran, E-mail: nasrabadi@shahed.ac.ir

Research funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not for profit sectors.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Informed consent: Not applicable.
Ethical approval: This research was approved by the Institutional Review Board (IRB) and Ethical Committee of Tehran University of Medical Sciences (TUMS) (In accordance with the ethical guidelines of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards).

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/bmt-2022-0100).

Received: 2022-03-07

Accepted: 2022-09-13

Published Online: 2022-10-06

Published in Print: 2023-04-25

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Keywords for this article

attention deficit hyperactivity disorder (ADHD); classification; effective connectivity; electroencephalography (EEG); graph analysis; phase transfer entropy (PTE)