Investigating ADHD subtypes in children using temporal dynamics of the electroencephalogram (EEG) microstates

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children, usually categorized as three predominant subtypes, persistent inattention (ADHD-I), hyperactivity-impulsivity (ADHD-HI) and a combination of both (ADHD-C). Identifying reliable features to distinguish different subtypes is significant for clinical individualized treatment. In this work, we conducted a two-stage electroencephalogram (EEG) microstate analysis on 54 healthy controls and 107 ADHD children, including 54 ADHD-Is and 53 ADHD-Cs, aiming to examine the dynamic temporal alterations in ADHDs compared to healthy controls (HCs), as well as different EEG signatures between ADHD subtypes. Results demonstrated that the dynamics of resting-state EEG microstates, particularly centering on salience (state C) and frontal-parietal network (state D), were significantly aberrant in ADHDs. Specifically, the occurrence and coverage of state C were decreased in ADHDs (p=0.002; p=0.0015), while the duration and contribution of state D were observably increased (p=0.0016; p=0.0001) compared to HCs. Moreover, the transition probability between state A and C was significantly decreased (p=9.85e-7; p=2.33e-7) in ADHDs, but otherwise increased between state B and D (p=1.02e-7; p=1.07e-6). By contrast, remarkable subtype differences were found primarily on the visual network (state B) between ADHD-Is and ADHD-Cs. Specifically, ADHD-Cs have higher occurrence and coverage of state B than ADHD-Is (p=9.35e-5; p=1.51e-8), suggesting these patients more impulsively aimed to open their eyes when asked to keep eyes closed during the data collection. In summary, this work carefully leveraged EEG temporal dynamics to investigate the aberrant microstate features in ADHDs and provided a new window to look into the subtle differences between ADHD subtypes, which may help to assist precision diagnosis in future.Clinical Relevance- This work established the use of EEG microstate features to investigate ADHD dysfunction and its subtypes, providing a new window for better diagnosis of ADHD.