Abstract
BackgroundThere are numerous event-related potential (ERP) studies in relation to attention-deficit hyperactivity disorder (ADHD), and a substantial number of ERP correlates of the disorder have been identified. However, most of the studies are limited to group differences in children. Independent component analysis (ICA) separates a set of mixed event-related potentials into a corresponding set of statistically independent source signals, which are likely to represent different functional processes. Using a support vector machine (SVM), a classification method originating from machine learning, this study aimed at investigating the use of such independent ERP components in differentiating adult ADHD patients from non-clinical controls by selecting a most informative feature set. A second aim was to validate the predictive power of the SVM classifier by means of an independent ADHD sample recruited at a different laboratory.MethodsTwo groups of age-matched adults (75 ADHD, 75 controls) performed a visual two stimulus go/no-go task. ERP responses were decomposed into independent components, and a selected set of independent ERP component features was used for SVM classification.ResultsUsing a 10-fold cross-validation approach, classification accuracy was 91%. Predictive power of the SVM classifier was verified on the basis of the independent ADHD sample (17 ADHD patients), resulting in a classification accuracy of 94%. The latency and amplitude measures which in combination differentiated best between ADHD patients and non-clinical subjects primarily originated from independent components associated with inhibitory and other executive operations.ConclusionsThis study shows that ERPs can substantially contribute to the diagnosis of ADHD when combined with up-to-date methods.
Highlights
There are numerous event-related potential (ERP) studies in relation to attention-deficit hyperactivity disorder (ADHD), and a substantial number of event-related potentials (ERPs) correlates of the disorder have been identified
Inclusion in the ADHD group was based on the DSM-IV criteria for ADHD [2], assessed in a diagnostic interview [30]. 24 subjects met the DSM-IV criteria for the ADHD combined type, 42 subjects met the criteria for the ADHD predominantly inattentive type, and 9 subjects met the criteria for the ADHD predominantly hyperactive-impulsive type
In this study, we investigated whether features of averaged ERPs, which were decomposed into independent components by means of Independent component analysis (ICA), can be used for an accurate classification of ADHD and control subjects
Summary
There are numerous event-related potential (ERP) studies in relation to attention-deficit hyperactivity disorder (ADHD), and a substantial number of ERP correlates of the disorder have been identified. One of the most influential theoretical models of ADHD postulates that a deficit in behavioral inhibition is the core of ADHD [7,8] According to this theory, behavioral inhibition is considered to be the foundation of the executive functions, which in turn influence the motor system. Kropotov [9] distinguishes four types of executive operations: engagement operations, disengagement operations, working memory, and monitoring operations These operations perform on representations of actions by initiating and suppressing actions, by storing plans of actions and by comparing ongoing actions and performance outcomes with internal goals and standards [10]. It is assumed that these operations are subserved by different neuronal mechanisms and - as well as sensory functions - are reflected in different components of scalp-recorded evoked potentials
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