Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Analysis of Eeg Brain Connectivity of Children With Adhd Using Graph Theory and Directional Information Transfer Publisher Pubmed



Ekhlasi A1 ; Nasrabadi AM2 ; Mohammadi M3
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
  2. 2. Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
  3. 3. Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran

Source: Biomedizinische Technik Published:2023


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. © 2022 Walter de Gruyter GmbH, Berlin/Boston.
Related Docs
View other Related Docs
1. Direction of Information Flow Between Brain Regions in Adhd and Healthy Children Based on Eeg by Using Directed Phase Transfer Entropy, Cognitive Neurodynamics (2021)
2. Analysis of Effective Connectivity Strength in Children With Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy, Iranian Journal of Psychiatry (2021)
3. Classification of the Children With Adhd and Healthy Children Based on the Directed Phase Transfer Entropy of Eeg Signals, Frontiers in Biomedical Technologies (2021)
4. Applicable Features of Electroencephalogram for Adhd Diagnosis, Research on Biomedical Engineering (2020)
5. Improving Transfer Entropy and Partial Transfer Entropy for Relative Detection of Effective Connectivity Strength Between Time Series, Communications in Nonlinear Science and Numerical Simulation (2023)
6. Effects of Transcranial Direct Current Stimulation on Attentional Bias to Methamphetamine Cues and Its Association With Eeg-Derived Functional Brain Network Topology, International Journal of Neuropsychopharmacology (2022)
7. Computer-Aided Tinnitus Detection Based on Brain Network Analysis of Eeg Functional Connectivity, Journal of Biomedical Physics and Engineering (2019)
8. Altered Effective Brain Network Topology in Tinnitus: An Eeg Source Connectivity Analysis, Biomedical Signal Processing and Control (2021)
Experts (# of related papers)
View all Related Experts
Mohammad Reza Mohammadi (6)
Bahador Makkiabadi (5)
Other Related Docs
9. Computer-Aided Classifying and Characterizing of Methamphetamine Use Disorder Using Resting-State Eeg, Cognitive Neurodynamics (2019)
10. Disrupted Resting-State Brain Functional Network in Methamphetamine Abusers: A Brain Source Space Study by Eeg, PLoS ONE (2019)
11. Differentiating Brain Connectivity Networks in Adhd and Normal Children Using Eeg, 2022 30th International Conference on Electrical Engineering# ICEE 2022 (2022)
12. Use of the Shearlet Transform and Transfer Learning in Offline Handwritten Signature Verification and Recognition, Sahand Communications in Mathematical Analysis (2020)
13. A Novel Feature Ranking Method for Prediction of Cancer Stages Using Proteomics Data, PLoS ONE (2017)
14. A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network, European Neurology (2020)
15. Eeg-Based Brain Connectivity Analysis in Autism Spectrum Disorder: Unraveling the Effects of Bumetanide Treatment, Biomedical Signal Processing and Control (2023)
16. Eeg-Based Functional Connectivity for Tactile Roughness Discrimination, Cognitive Neurodynamics (2023)
17. Classification of Drug-Naive Children With Attention-Deficit/Hyperactivity Disorder From Typical Development Controls Using Resting-State Fmri and Graph Theoretical Approach, Frontiers in Human Neuroscience (2022)
18. Visual and Auditory Steady-State Responses in Attention-Deficit/Hyperactivity Disorder, European Archives of Psychiatry and Clinical Neuroscience (2019)
19. Dynamic Functional Connectivity in Temporal Lobe Epilepsy: A Graph Theoretical and Machine Learning Approach, Neurological Sciences (2021)
20. Analysis of Resting-State Fmri Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension, Basic and Clinical Neuroscience (2017)
21. Evaluating Morphological Features of Electrocardiogram Signals for Diagnosing of Myocardial Infarction Using Classification-Based Feature Selection, Journal of Medical Signals and Sensors (2021)
22. Classification of Early-Mci Patients From Healthy Controls Using Evolutionary Optimization of Graph Measures of Resting-State Fmri, for the Alzheimer’S Disease Neuroimaging Initiative, PLoS ONE (2022)
23. Electroencephalogram Complexity Analysis in Children With Attention-Deficit/Hyperactivity Disorder During a Visual Cognitive Task, Journal of Clinical and Experimental Neuropsychology (2016)
24. Online Handwritten Signature Verification and Recognition Based on Dual-Tree Complex Wavelet Packet Transform, Journal of Medical Signals and Sensors (2020)