Use of EEG to diagnose ADHD

Abstract

Electroencephalography (EEG) has, historically, played a focal role in the assessment of neural function in children with attention deficit hyperactivity disorder (ADHD). We review here the most recent developments in the utility of EEG in the diagnosis of ADHD, with emphasis on the most commonly used and emerging EEG metrics and their reliability in diagnostic classification. Considering the clinical heterogeneity of ADHD and the complexity of information available from the EEG signals, we suggest that considerable benefits are to be gained from multivariate analyses and a focus towards understanding of the neural generators of EEG. We conclude that while EEG cannot currently be used as a diagnostic tool, vast developments in analytical and technological tools in its domain anticipate future progress in its utility in the clinical setting.

Figure 1

Diagnosis of ADHD can be based on temporal (a), spectral (a) and spatial (b) features of EEG, either alone or in combination (c). Raw EEG (a, top left), can be decomposed into spectral components that are quantified by power, which represents the amplitude of oscillations of varying frequencies that are present in the continuous signal. These measures capture the background “state” of brain activity. Alternatively, the data can be segmented (or epoched) around an event of interest (x). The epochs are averaged and normalized by pre-stimulus activity, to produce the event-related potential. These measures quantify temporal dynamics of information processing. By combining spectral analysis with event-related averaging, one can analyze event-related spectral power, changes in synchronization that may represent changes in the brain state during information processing. Spatial features (b) of EEG are scalp topography maps (spectral and time-domain values across electrodes) and their estimated cortical sources. Any of the spatial, temporal and spectral features of the EEG signal may be used to distinguish between patients with and without ADHD. Diagnosis based on EEG features therefore benefits from multivariate approaches that use patterns across features to classify patients (c). The lower panel shows an extreme example of the benefit of multivariate classification. Whereas each measure alone shows only weak trend differences between the two populations (e.g., red = ADHD, blue = Control), the combination of the two metrics (middle scatter plot) produces a linear function that dissociates between the two groups (e.g., ADHD fall above and Control fall below the line). Such an approach is likely to be of value in ADHD, known to exhibit significant variability in EEG measures.