Brain connectome differences between attention deficit hyperactivity disorder (ADHD) and neurotypical children during
Afshin Fayyazi1,2, Samaneh Safari3,4, Sajjad Farashi5
1Department of Pediatrics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
Brain connectivity differences in attention deficit hyperactivity disorder (ADHD) were identified using electroencephalogram (EEG) and graph theory. Minimum spanning tree (MST) graph features perfectly distinguished ADHD from neurotypical individuals, highlighting potential diagnostic markers.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Medical Informatics
Background:
- Attention deficit hyperactivity disorder (ADHD) is associated with distinct visual attention task performance compared to neurotypical individuals.
- Brain connectome differences during visual attention tasks between ADHD and neurotypical groups were investigated using multichannel EEG and graph theory.
Purpose of the Study:
- To compare brain connectome differences during visual attention between ADHD and neurotypical individuals.
- To identify discriminative features of brain connectivity for ADHD using graph theory applied to EEG data.
Main Methods:
- Constructed a minimum spanning tree (MST) graph based on EEG data similarities for both ADHD and neurotypical groups.
- Extracted MST graph features across different EEG frequency sub-bands.
- Assessed the discriminative capability of MST features using a classification approach.
Main Results:
- MST graph features achieved perfect discrimination (100% accuracy, AUC=1) between ADHD and neurotypical individuals.
- The alpha frequency band yielded the most discriminative MST features.
- ADHD group exhibited reduced leaf number, mean eccentricity, radius, and diameter in the high alpha band, alongside fewer frontal processing hubs and weaker frontoparietal connections.
Conclusions:
- MST graph features are highly effective for discriminating between ADHD and neurotypical individuals.
- These findings suggest MST graph features are promising for investigating the underlying neural mechanisms of ADHD.
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