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SimBrainNet: Evaluating Brain Network Similarity for Attention Disorders

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 (MICCAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15002))

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Abstract

Electroencephalography (EEG)-based attention disorder research seeks to understand brain activity patterns associated with attention. Previous studies have mainly focused on identifying brain regions involved in cognitive processes or classifying Attention-Deficit Hyperactivity Disorder (ADHD) and control subjects. However, analyzing effective brain connectivity networks for specific attentional processes and comparing them has not been explored. Therefore, in this study, we propose multivariate transfer entropy-based connectivity networks for cognitive events and introduce a new similarity measure, “SimBrainNet”, to assess these networks. A high similarity score suggests similar brain dynamics during cognitive events, indicating less attention variability. Our experiment involves 12 individuals with attention disorders (7 children and 5 adolescents). Noteworthy that child participants exhibit lower similarity scores compared to adolescents, indicating greater changes in attention. We found strong connectivity patterns in the left pre-frontal cortex for adolescent individuals compared to the child. Our study highlights the changes in attention levels across various cognitive events, offering insights into the underlying cognitive mechanisms, brain dynamics, and potential deficits in individuals with this disorder.

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Notes

  1. 1.

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Authors and Affiliations

  1. Luleå University of Technology, 97187, Luleå, Sweden

    Debashis Das Chakladar, Foteini Simistira Liwicki & Rajkumar Saini

Authors
  1. Debashis Das Chakladar
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  2. Foteini Simistira Liwicki
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  3. Rajkumar Saini
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Corresponding author

Correspondence to Debashis Das Chakladar .

Editor information

Editors and Affiliations

  1. Children’s National Hospital/George Washington University, Washington, DC, USA

    Marius George Linguraru

  2. The Chinese University of Hong Kong, Hong Kong, China

    Qi Dou

  3. Technical University of Denmark, Kgs Lyngby, Denmark

    Aasa Feragen

  4. Imperial College London, London, UK

    Stamatia Giannarou

  5. Imperial College London, London, UK

    Ben Glocker

  6. Universitat de Barcelona, Barcelona, Spain

    Karim Lekadir

  7. Helmholtz Munich, Technical University of Munich and King’s College London, Munich, Germany

    Julia A. Schnabel

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Das Chakladar, D., Simistira Liwicki, F., Saini, R. (2024). SimBrainNet: Evaluating Brain Network Similarity for Attention Disorders. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15002. Springer, Cham. https://doi.org/10.1007/978-3-031-72069-7_37

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  • DOI: https://doi.org/10.1007/978-3-031-72069-7_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72068-0

  • Online ISBN: 978-3-031-72069-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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