Application of EEG Multiscale Entropy Analysis in Attention-Deficit/Hyperactivity Disorder

Abstract

Analyzing electroencephalography (EEG) complexity could provide insight into neural connectivity and its relationships with attention-deficit/hyperactivity disorder (ADHD) symptoms. I calculated multiscale entropy (MSE) within the EEG signal to evaluate the variability in brain activity and tested for differences between adults with ADHD and their peers during resting and go/nogo task states. MSE transitions (MSE-Δ) from the resting state to the active task state also examine the brain’s ability to flexibly change from a resting state to an active state. Thirty unmedicated adults with ADHD were compared to thirty match-paired healthy peers on the MSE in the resting and active task states as well as the MSE-Δ. The MSE from Individuals with ADHD was smaller than their peers’ in the resting state. Significant differences of the MSE-Δ were also observed between individuals with ADHD and their peers, specifically at frontal sites. Interestingly, individuals without ADHD performed better with decreasing MSE, showing higher accuracy, shorter reaction time, and a smaller standard deviation of reaction time. Significant correlations between MSE-Δ and task measurements were also confirmed in adults without ADHD, while greater MSE-Δ corresponded to better task performance. These findings suggest MSE could not only provide insight into brain activity complexity differences between adults with ADHD and their peers but also allow us to gain a better understanding of the relationship between brain activity complexity and behavioral performance.