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Potential biomarker for early detection of ADHD using phase-based brain connectivity and graph theory

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Abstract

This research investigates an efficient strategy for early detection and intervention of attention-deficit hyperactivity disorder (ADHD) in children. ADHD is a neurodevelopmental condition characterized by inattention and hyperactivity/impulsivity symptoms, which can significantly impact a child’s daily life. This study employed two distinct brain functional connectivity measurements to assess our approach across various local graph features. Six common classifiers are employed to distinguish between children with ADHD and healthy control. Based on the phase-based analysis, the study proposes two biomarkers that differentiate children with ADHD from healthy control, with a remarkable accuracy of 99.174%. Our findings suggest that subgraph centrality of phase-lag index brain connectivity within the beta and delta frequency bands could be a promising biomarker for ADHD diagnosis. Additionally, we identify node betweenness centrality of inter-site phase clustering connectivity within the delta and theta bands as another potential biomarker that warrants further exploration. These biomarkers were validated using a t-statistical test and yielded a p-value of under 0.05, which approved their significant difference in these two groups. Suggested biomarkers have the potential to improve the accuracy of ADHD diagnosis and could help identify effective intervention strategies for children with the condition.

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Data availability

The utilized dataset can be accessed from the IEEE DataPort repository at the following address: https://doi.org/10.21227/rzfh-zn36.

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Acknowledgements

The EEG data employed in this research were obtained and archived under Prof. Ali Motie Nasrabadi and his colleagues. The authors hereby express their sincere appreciation to him and his esteemed research team for generously sharing their valuable dataset.

Funding

The authors declare that no funds, grants, or other forms of support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Farhad Abedinzadeh Torghabeh & Yeganeh Modaresnia

  2. Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Seyyed Abed Hosseini

Authors
  1. Farhad Abedinzadeh Torghabeh
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  2. Seyyed Abed Hosseini
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  3. Yeganeh Modaresnia
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Contributions

The idea presented in this research was conceived and developed by FAT and SAH. Additionally, FAT carried out the computations, material preparation, data collection, and analysis. He also drafted the initial version of the manuscript, which was subsequently reviewed and commented on by YM and SAH. All authors contributed to sha** the research and manuscript, providing constructive feedback. Furthermore, all authors actively participated in other aspects of the investigation, including visualization and manuscript review. Finally, all authors have read and approved the final manuscript. SAH supervised the project.

Corresponding author

Correspondence to Seyyed Abed Hosseini.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Consent to participate

According to the data description, informed consent was obtained from all individual participants included in the study.

Ethical approval

According to the data description, all ethical standards and guidelines were meticulously followed to ensure the protection of human subjects and the integrity of the research. The EEG recording procedure has been conducted in accordance with the ethical principles set forth by the Declaration of Helsinki and its subsequent amendments.

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Abedinzadeh Torghabeh, F., Hosseini, S.A. & Modaresnia, Y. Potential biomarker for early detection of ADHD using phase-based brain connectivity and graph theory. Phys Eng Sci Med 46, 1447–1465 (2023). https://doi.org/10.1007/s13246-023-01310-y

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  • DOI: https://doi.org/10.1007/s13246-023-01310-y

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