SimBrainNet: Evaluating Brain Network Similarity for Attention Disorders

TL;DR

This study introduces SimBrainNet, a novel method for comparing brain connectivity networks derived from EEG data to better understand attention variability in individuals with attention disorders.

Contribution

We developed a new similarity measure for brain networks and applied it to EEG data, revealing developmental differences and connectivity patterns in attention disorders.

Findings

Children show lower similarity scores than adolescents.

Strong connectivity observed in the left pre-frontal cortex of adolescents.

Attention variability correlates with changes in brain network similarity.

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.