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Electrophysiological and Clinical Predictors of Methylphenidate, Guanfacine, and Combined Treatment Outcomes in Children With Attention-Deficit/Hyperactivity Disorder.

Abstract

Objective

The combination of d-methylphenidate and guanfacine (an alpha-2A agonist) has emerged as a potential alternative to either monotherapy in children with attention deficit hyperactivity disorder (ADHD), but it is unclear what predicts response to these treatments. This study is the first to investigate pre-treatment clinical and electroencephalography (EEG) profiles as predictors of treatment outcome in children randomized to these different medications.

Method

181 children with ADHD (aged 7-14; 123 boys) completed an 8-week randomized, double-blind, comparative study with d-methylphenidate, guanfacine, or combined treatments. Pre-treatment assessments included ratings on ADHD, anxiety, and oppositional behavior. EEG activity from cortical sources localized within midfrontal and midoccipital regions was measured during a spatial working memory task with encoding, maintenance, and retrieval phases. Analyses tested whether pre-treatment clinical and EEG measures predicted treatment-related change in ADHD severity.

Results

Higher pre-treatment hyperactivity-impulsivity and oppositional symptoms and lower anxiety predicted greater ADHD improvements across all medication groups. Pre-treatment event-related midfrontal beta power predicted treatment outcome with combined and monotherapy treatments, albeit in different directions. Weaker beta modulations predicted improvements with combined treatment, whereas stronger modulation during encoding and retrieval predicted improvements with d-methylphenidate and guanfacine, respectively. A multivariate model including EEG and clinical measures explained twice as much variance in ADHD improvement with guanfacine and combined treatment (R2=.34-.41) as clinical measures alone (R2=.14-.21).

Conclusion

We identified treatment-specific and shared predictors of response to different pharmacotherapies in children with ADHD. If replicated, these findings would suggest that aggregating information from clinical and brain measures may aid personalized treatment decisions in ADHD.

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