Aberrant brain dynamics and spectral power in children with ADHD and its subtypes
Na Luo1,2, Xiangsheng Luo3,4, Suli Zheng3,4
1Institute of Automation, Chinese Academy of Sciences, Brainnetome Center and National Laboratory of Pattern Recognition, Beijing, 100190, China.
Insights
Electroencephalogram (EEG) microstate dynamics and frequency patterns reveal distinct brain activity in children with attention-deficit/hyperactivity disorder (ADHD) and its subtypes. These findings offer potential for improved ADHD diagnosis and understanding of its variations.
Area of Science:
- Neuroscience
- Child Psychology
- Medical Imaging
Background:
- Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with poorly understood electroencephalogram (EEG) abnormalities across its subtypes.
- Existing research has not fully elucidated the unique and shared EEG characteristics of ADHD-I, ADHD-HI, and ADHD-C.
Purpose of the Study:
- To investigate temporal and frequency-based EEG abnormalities in children with ADHD and its subtypes using high-density EEG.
- To identify specific EEG microstate characteristics and power features that differentiate ADHD subtypes.
- To explore the potential of EEG markers for ADHD diagnosis.
Main Methods:
- Utilized high-density EEG data from 161 participants, including 54 ADHD-I, 53 ADHD-C, and 54 healthy controls (HC).
- Analyzed EEG data using microstate characteristics (e.g., coverage, duration) and frequency power features (e.g., delta, theta/beta ratio).
- Employed Support Vector Machine with Recursive Feature Elimination (SVM-RFE) for feature selection and classification accuracy assessment.
Main Results:
- ADHD diagnosis was associated with decreased salience network (state C) coverage and increased frontal-parietal network (state D) duration/contribution.
- Elevated fronto-central delta power and bilateral fronto-temporal theta/beta ratio were observed in ADHD.
- ADHD-C showed distinct differences in the visual network (state B) compared to ADHD-I, suggesting subtype-specific neural activity.
- SVM-RFE achieved 72.7% accuracy in classifying ADHD and 73.8% in classifying subtypes.
Conclusions:
- EEG microstate dynamics and frequency features are sensitive indicators for detecting differences in ADHD and its subtypes.
- These EEG markers hold promise for enhancing the diagnostic accuracy and understanding of ADHD.
- The study provides a novel perspective on neurophysiological differences underlying ADHD presentations.
Abstract:
Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children, usually categorized as three subtypes, predominant inattention (ADHD-I), predominant hyperactivity-impulsivity (ADHD-HI), and a combined subtype (ADHD-C). Yet, common and unique abnormalities of electroencephalogram (EEG) across different subtypes remain poorly understood. Here, we leveraged microstate characteristics and power features to investigate temporal and frequency abnormalities in ADHD and its subtypes using high-density EEG on 161 participants (54 ADHD-Is and 53 ADHD-Cs and 54 healthy controls). Four EEG microstates were identified. The coverage of salience network (state C) were decreased in ADHD compared to HC (p = 1.46e-3), while the duration and contribution of frontal-parietal network (state D) were increased (p = 1.57e-3; p = 1.26e-4). Frequency power analysis also indicated that higher delta power in the fronto-central area (p = 6.75e-4) and higher power of theta/beta ratio in the bilateral fronto-temporal area (p = 3.05e-3) were observed in ADHD. By contrast, remarkable subtype differences were found primarily on the visual network (state B), of which ADHD-C have higher occurrence and coverage than ADHD-I (p = 9.35e-5; p = 1.51e-8), suggesting that children with ADHD-C might exhibit impulsivity of opening their eyes in an eye-closed experiment, leading to hyper-activated visual network. Moreover, the top discriminative features selected from support vector machine model with recursive feature elimination (SVM-RFE) well replicated the above results, which achieved an accuracy of 72.7% and 73.8% separately in classifying ADHD and two subtypes. To conclude, this study highlights EEG microstate dynamics and frequency features may serve as sensitive measurements to detect the subtle differences in ADHD and its subtypes, providing a new window for better diagnosis of ADHD.
