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SimBrainNet: Evaluating Brain Network Similarity for Attention Disorders
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-6350-1019
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-6756-0147
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-8532-0895
2024 (English)In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 / [ed] Marius George Linguraru; Qi Dou; Aasa Feragen; Stamatia Giannarou; Ben Glocker; Karim Lekadir; Julia A. Schnabel, Springer Nature, 2024, p. 389-399Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Nature, 2024. p. 389-399
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 15002
Keywords [en]
Attention Disorder, Brain Connectivity Network, Similarity Score
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-110565DOI: 10.1007/978-3-031-72069-7_37ISI: 001342225800037Scopus ID: 2-s2.0-85206490467OAI: oai:DiVA.org:ltu-110565DiVA, id: diva2:1909539
Conference
27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024), Marrakesh, Morocco, October 6–10, 2024
Note

ISBN for host publication: 978-3-031-72068-0;

Available from: 2024-10-31 Created: 2024-10-31 Last updated: 2025-10-21Bibliographically approved

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Das Chakladar, DebashisSimistira Liwicki, FoteiniSaini, Rajkumar

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