Attentional processes in children with ADHD: an event-related potential study using the attention network test.Int J Psychophysiol. 2011 Aug; 81(2):82-90.IJ
A variety of event-related potential (ERP) based studies have shown differences in neuronal processes underlying attention, inhibition and error processing in children with attention-deficit/hyperactivity disorder (ADHD) compared to controls. However, so far there are no studies that have compared children with ADHD and typically developing (TD) children regarding effects in ERP components associated with the attention network test (ANT). The ANT allows to differentiate between three particular aspects of attention: alerting, orienting, conflict. Twenty-five children with ADHD and 19 TD children (comparable with respect to age, sex, and IQ) performed the ANT while ERPs were recorded. Based on DSM-IV, the group of children with ADHD was divided in an inattentive (ADHDin, n=10) and a combined (ADHDcom, n=15) subgroup. On the performance level, the ADHD group showed a significantly higher variability of reaction times. Concerning ERP measures, smaller cue-P3 amplitudes were found in the ADHD group indicating that children with ADHD allocate less attentional resources for cue processing. In addition, the target-P3 in ADHD showed smaller amplitudes. Subgroup analysis revealed reduced cue-P3 amplitudes in both subgroups and reduced target-P3 amplitudes in ADHDin compared to TD children. Except for a higher alerting score in ADHD after correction for cue-P3 group differences, performance data revealed no group differences specific for the three attention networks. No group differences related to the attention networks were observed at the ERP level. Our results suggest that deviant attentional processing in children with ADHD is only partly related to ANT-specific effects. Findings are compatible with the model of a suboptimal energetic state regulation in ADHD. Furthermore, our results suggest that deviant cue processing in ADHD and related differences in task modulations should be accounted for in data analysis.