Electroencephalography signatures of attention-deficit/hyperactivity disorder: clinical utility

Abstract

The techniques and the most important results on the use of electroencephalography (EEG) to extract different measures are reviewed in this work, which can be clinically useful to study subjects with attention-deficit/hyperactivity disorder (ADHD). First, we discuss briefly and in simple terms the EEG analysis and processing techniques most used in the context of ADHD. We review techniques that both analyze individual EEG channels (univariate measures) and study the statistical interdependence between different EEG channels (multivariate measures), the so-called functional brain connectivity. Among the former ones, we review the classical indices of absolute and relative spectral power and estimations of the complexity of the channels, such as the approximate entropy and the Lempel-Ziv complexity. Among the latter ones, we focus on the magnitude square coherence and on different measures based on the concept of generalized synchronization and its estimation in the state space. Second, from a historical point of view, we present the most important results achieved with these techniques and their clinical utility (sensitivity, specificity, and accuracy) to diagnose ADHD. Finally, we propose future research lines based on these results.

The blessing of Dimensionality: Feature Selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation

“…Thus, low frequency activity in δ band is modified in the right hemisphere during CE condition, whereas higher frequency α , β and γ band FC changes mainly in the OE condition for interhemispheric connections. This is consistent with what is known about EEG activity in CE/OE conditions, where low frequency activity is enhanced in the former one, and also with the EEG changes associated to ADHD (see, e.g., [ 2 , 25 ] and references therein). Note, however, that, as commented before, PLV and PLI measured different things [ 38 ], which justifies the use of both of them in the feature vectors.…”

“…In this work we apply a well-known machine learning algorithm, the Bayesian Network Classifiers [ 24 ] (BNC) to classify 33 children into two different groups (healthy controls and ADHD) from their functional brain connectivity EEG patterns, obtained by using two indices of phase synchronisation (PS). ADHD is a well-known disorder, which has received a lot of attention recently in this framework [ 13 , 25 , 26 ]. Normally, the theta/beta power spectral ratio is used as the (already FDA supported) biomarker of reference to be used as adjunct to clinical assessment of such disease, although the latest literature [ 13 ] indicates that things may not be so clear-cut.…”

“…Normally, the theta/beta power spectral ratio is used as the (already FDA supported) biomarker of reference to be used as adjunct to clinical assessment of such disease, although the latest literature [ 13 ] indicates that things may not be so clear-cut. Besides, little is known [ 2 , 13 , 25 ] about the possibility of using EEG-based FC methods for this purpose (and the best strategy thereof).…”

Comparison of QEEG Findings between Adolescents with Attention Deficit Hyperactivity Disorder (ADHD) without Comorbidity and ADHD Comorbid with Internet Gaming Disorder

“…In previous ADHD studies, an increase in relative theta and delta power as well as theta/beta ratio has been commonly reported ( 9 ). In addition, a decrease in relative alpha and beta power has been also reported ( 11 24 25 ).…”

“…This provides information regarding amount of brain activity in different frequency bands, including delta (0.5–3.5 Hz), theta (3.5–7.5 Hz), alpha (7.5–12.5 Hz), and beta (12.5–35 Hz). The other is coherence analysis, which quantifies the inter-dependence or statistical correlation between different EEG channels to estimate functional connectivity between different cortical areas in the time domain ( 9 ).…”