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SimBrainNet: Evaluating Brain Network Similarity for Attention Disorders

Debashis Das Chakladar, Foteini Simistira Liwicki, Rajkumar Saini

TL;DR

The changes in attention levels across various cognitive events are highlighted, offering insights into the underlying cognitive mechanisms, brain dynamics, and potential deficits in individuals with this disorder.

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.

SimBrainNet: Evaluating Brain Network Similarity for Attention Disorders

TL;DR

The changes in attention levels across various cognitive events are highlighted, offering insights into the underlying cognitive mechanisms, brain dynamics, and potential deficits in individuals with this disorder.

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.

Paper Structure

This paper contains 11 sections, 1 equation, 2 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Methods
  3. Dataset and Preprocessing
  4. Construction of Brain Network
  5. Finding Similarity in Brain Networks
  6. Results
  7. Brain Connectivity Network analysis
  8. Analysis of SimBrainNet
  9. Performance analysis
  10. Conclusion
  11. Disclosure of Interests.

Figures (2)

  • Figure 1: The proposed SimBrainNet model computes similarity scores between MTE-based brain connectivity networks (G1 and G2) using three algorithms: Substitution_node, Insertion_node, and Deletion_node. If the number of nodes of G1 and G2 is equal, it calculates the substitution cost (SC); otherwise, it incurs extra cost (EC) through Insertion_node, Deletion_node, or both. The final score combines SC and EC.
  • Figure 2: Age group-wise (a,b) common connectivity patterns of brain networks. Different colored nodes represent different brain regions whereas the average MTE value between the source and target node for all subjects represents the directed edge value. (c) The similarity score/Sim Score of all subjects based on event-wise MTE brain networks. (d) EEG-Theta/Beta Ratio (TBR), referred to as inattention indexmarkovska2017quantitative for age groups.