bibtext: @InProceedings{ Das_SimBrainNet_MICCAI2024,
                   author = { Das Chakladar, Debashis and Simistira Liwicki, Foteini and Saini, Rajkumar },
                   title = { { SimBrainNet: Evaluating Brain Network Similarity for Attention Disorders } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15002 },
                   month = {October},
                   pages = { pending },
                }
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.
title: SimBrainNet: Evaluating Brain Network Similarity for Attention Disorders
authors: Das Chakladar, Debashis Simistira Liwicki, Foteini Saini, Rajkumar