EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD. (22nd January 2020) - Kiiski, Hanni; Bennett, Marc; Rueda‐Delgado, Laura M.; Farina, Francesca R.; Knight, Rachel; Boyle, Rory; Roddy, Darren; Grogan, Katie; Bramham, Jessica; Kelly, Clare; Whelan, Robert
Abstract: Adults with attention‐deficit/hyperactivity disorder (ADHD) have been described as having altered resting‐state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR). However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behaviour (mediational endophenotypes). Resting‐state eyes‐open and eyes‐closed EEG was measured from 38 adults with ADHD, 45 first‐degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1–45 Hz) and TBR could classify participants into ADHD, first‐degree relatives and/or control groups. Random‐label permutation was used to quantify any bias in the analysis. Eyes‐open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic = 0.71–0.77). The best predictors of ADHD status were increased power in delta, theta and low‐alpha over centro‐parietal regions, and in frontal low‐beta and parietal mid‐beta. TBR did not successfully classify ADHD status. Elevated eyes‐open power in delta, theta, low‐alpha and low‐beta distinguished first‐degree relatives from controls (area under receiver operating characteristic = 0.68–0.72), suggesting that these features may be a mediational endophenotype for adult ADHD. Resting‐state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD. Abstract : Machine learning classification analysis with penalized logistic regression revealed specific eyes‐open resting‐state EEG spectral power neuromarkers for adults with attention‐deficit/hyperactivity disorder (ADHD). These can be developed and integrated into complementary assessment/prognostic tools for adult ADHD diagnostics. We also found specific EEG characteristics that mediate between genes and behaviour. Our results did not support theta/beta ratio as a diagnostic neuromarker for adult ADHD.