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 Effective Connectivity Strength in Children With Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy Publisher



Ekhlasi A1 ; Nasrabadi AM2 ; Mohammadi MR3
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, School of Engineering, Shahed University, Tehran, Iran
  3. 3. Psychiatry and Psychology Research Center, Tehran University of Medical Sciences, Tehran, Iran

Source: Iranian Journal of Psychiatry Published:2021


Abstract

Objective: This study aimed to investigate differences in brain networks between healthy children and children with attention deficit hyperactivity disorder (ADHD) during an attention test. Method: To fulfill this, we constructed weighted directed graphs based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children with the same age. Nodes of graphs were 19 EEG electrodes, and the edges were phase transfer entropy (PTE) between each pair of electrodes. PTE is a measure for directed connectivity that determines the effective relationship between signals in linear and nonlinear coupling. Connectivity graphs of each sample were constructed using PTE in the five frequency bands as follows: delta, theta, alpha, beta, and gamma. To investigate the differences in connectivity strength of each node after the sparsification process with two values (0.5 and 0.25), the permutation statistical test was used with the statistical significance level of p<0.01. Results: The results indicate stronger inter-regional connectivity in the prefrontal brain regions of the control group compared to the ADHD group. However, the strength of inter-regional connectivity in the central regions of the ADHD group was higher. A comparison of the prefrontal regions between the two groups revealed that the areas of the Fp1 electrode (left prefrontal) in healthy individuals play stronger transmission roles. Conclusion: Our research can provide new insights into the strength and direction of connectivity in ADHD and healthy individuals during an attention task. Copyright © 2021 Tehran University of Medical Sciences.
Related Docs
View other Related Docs
1. Analysis of Eeg Brain Connectivity of Children With Adhd Using Graph Theory and Directional Information Transfer, Biomedizinische Technik (2023)
2. Direction of Information Flow Between Brain Regions in Adhd and Healthy Children Based on Eeg by Using Directed Phase Transfer Entropy, Cognitive Neurodynamics (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. Differentiating Brain Connectivity Networks in Adhd and Normal Children Using Eeg, 2022 30th International Conference on Electrical Engineering# ICEE 2022 (2022)
5. Eeg-Based Brain Connectivity Analysis in Autism Spectrum Disorder: Unraveling the Effects of Bumetanide Treatment, Biomedical Signal Processing and Control (2023)
6. 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)
7. Altered Effective Brain Network Topology in Tinnitus: An Eeg Source Connectivity Analysis, Biomedical Signal Processing and Control (2021)
8. Computer-Aided Classifying and Characterizing of Methamphetamine Use Disorder Using Resting-State Eeg, Cognitive Neurodynamics (2019)
Experts (# of related papers)
View all Related Experts
Bahador Makkiabadi (5)
Mohammad Reza Mohammadi (5)
Other Related Docs
9. Disrupted Resting-State Brain Functional Network in Methamphetamine Abusers: A Brain Source Space Study by Eeg, PLoS ONE (2019)
10. Eeg Classification of Adhd and Normal Children Using Non-Linear Features and Neural Network, Biomedical Engineering Letters (2016)
11. Analysis of Resting-State Fmri Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension, Basic and Clinical Neuroscience (2017)
12. Attention Deficit Hyperactivity Disorder: Behavioral or Neurodevelopmental Disorder? Testing the Hitop Framework Using Machine Learning Methods, JCI Insight (2023)
13. Altered Topological Properties of Brain Networks in the Early Ms Patients Revealed by Cognitive Task-Related Fmri and Graph Theory, Biomedical Signal Processing and Control (2018)
14. Computer-Aided Tinnitus Detection Based on Brain Network Analysis of Eeg Functional Connectivity, Journal of Biomedical Physics and Engineering (2019)
15. Visual and Auditory Steady-State Responses in Attention-Deficit/Hyperactivity Disorder, European Archives of Psychiatry and Clinical Neuroscience (2019)
16. Eeg-Based Functional Connectivity for Tactile Roughness Discrimination, Cognitive Neurodynamics (2023)
17. 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)
18. Applicable Features of Electroencephalogram for Adhd Diagnosis, Research on Biomedical Engineering (2020)