Neural Oscillation Features of ADHD Symptoms in Children: EEG Evidence From Resting State and Oddball Task
Siyuan Zhang1,2, Shuting Yu1,2, Xiaobing Cui1,2
1State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Objective:
The aim of this study was to explore neural oscillation features (resting-state + oddball-EROs) of ADHD symptoms in children in a dimensional approach and to construct a multi-metric model combining objective EEG measures and subjective parental ratings to predict children's behavioral performance.
Method:
Seventy-seven children (age range: 6-12 years) participated in laboratory assessment. ADHD symptoms were first evaluated using the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), followed by EEG recordings during both resting-state and oddball task conditions. Three parent rating scales were also used to evaluate children's behavioral performance: the ADHD Rating Scale-IV (ADHD RS-IV): Home Version, the Child Behavior Checklist (CBCL), and the Conners' Parent Rating Scales (CPRS).
Results:
Seventy-one children with valid IVA-CPT results were included in data analysis. The main results revealed a relationship between poorer attention performance and decreased eye-open alpha1 power in the resting state, reduced N2 delta power in the oddball condition, and elevated non-delta band power in the standard condition of the oddball task. Poorer response control performance was associated with increased eye-closed alpha1 power, as well as increased eye-open alpha2 and beta2 power. Stepwise regression analysis showed that the inattention subscale from parental assessments on the RS-IV, combined with P3 alpha power in the standard condition of the oddball task, was the strongest predictor of children's attention performance.
Conclusion:
The current study identified important neural oscillation features of ADHD symptoms in both the resting state and during an oddball task and offers new insights into multi-metric prediction for ADHD assessment and diagnosis.
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