Performance analysis of univariate and multivariate EEG measurements in the diagnosis of ADHD

González, José M.; Méndez, Leopoldo D.; Sangworasil, Manas; Duque, Ma Rosario; Pereda, Ernesto; Vera, Luis De

doi:10.1016/j.clinph.2012.12.006

Cited by 40 publications

(36 citation statements)

References 65 publications

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“…Interestingly, most studies find increased measures of coherence especially within the beta and theta bands during the resting state [139,140,141]. Also graph theory network parameters seem to support an increased functional connectivity in ADHD [142].…”

Section: Biomarkersmentioning

confidence: 99%

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

2015

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Personalized medicine in psychiatry is in need of biomarkers that resemble central nervous system function at the level of neuronal activity. Electroencephalography (EEG) during sleep or resting-state conditions and event-related potentials (ERPs) have not only been used to discriminate patients from healthy subjects, but also for the prediction of treatment outcome in various psychiatric diseases, yielding information about tailored therapy approaches for an individual. This review focuses on baseline EEG markers for two psychiatric conditions, namely major depressive disorder and attention deficit hyperactivity disorder. It covers potential biomarkers from EEG sleep research and vigilance regulation, paroxysmal EEG patterns and epileptiform discharges, quantitative EEG features within the EEG main frequency bands, connectivity markers and ERP components that might help to identify favourable treatment outcome. Further, the various markers are discussed in the context of their potential clinical value and as research domain criteria, before giving an outline for future studies that are needed to pave the way to an electrophysiological biomarker-based personalized medicine.

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Section: Biomarkersmentioning

confidence: 99%

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

2015

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“…First, we can explain increased intra-hemispheric coherence in various brain regions (frontal-temporal, temporal-occipital, and parietal-occipital) for alpha and beta frequency bands as a pre-existing vulnerability factor for IGD, that is, subclinical attention problems. Most previous studies conducting coherence analysis on ADHD reported increased coherence, mainly in the theta and beta bands (3334). Although none of the participants in this study satisfied the diagnostic criteria of ADHD, greater attentive problems in the MDD+IGD group than the MDD-only group may be associated with differences in intra-hemispheric coherence values between the 2 groups.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Electroencephalography (EEG) Coherence between Major Depressive Disorder (MDD) without Comorbidity and MDD Comorbid with Internet Gaming Disorder

et al. 2017

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Internet gaming disorder (IGD) has many comorbid psychiatric problems including major depressive disorder (MDD). In the present study, we compared the neurobiological differences between MDD without comorbidity (MDD-only) and MDD comorbid with IGD (MDD+IGD) by analyzing the quantitative electroencephalogram (QEEG) findings. We recruited 14 male MDD+IGD (mean age, 20.0 ± 5.9 years) and 15 male MDD-only (mean age, 20.3 ± 5.5 years) patients. The electroencephalography (EEG) coherences were measured using a 21-channel digital EEG system and computed to assess synchrony in the frequency ranges of alpha (7.5–12.5 Hz) and beta (12.5–35.0 Hz) between the following 12 electrode site pairs: inter-hemispheric (Fp1–Fp2, F7–F8, T3–T4, and P3–P4) and intra-hemispheric (F7–T3, F8–T4, C3–P3, C4–P4, T5–O1, T6–O2, P3–O1, and P4–O2) pairs. Differences in inter- and intra-hemispheric coherence values for the frequency bands between groups were analyzed using the independent t-test. Inter-hemispheric coherence value for the alpha band between Fp1–Fp2 electrodes was significantly lower in MDD+IGD than MDD-only patients. Intra-hemispheric coherence value for the alpha band between P3–O1 electrodes was higher in MDD+IGD than MDD-only patients. Intra-hemispheric coherence values for the beta band between F8–T4, T6–O2, and P4–O2 electrodes were higher in MDD+IGD than MDD-only patients. There appears to be an association between decreased inter-hemispheric connectivity in the frontal region and vulnerability to attention problems in the MDD+IGD group. Increased intra-hemisphere connectivity in the fronto-temporo-parieto-occipital areas may result from excessive online gaming.

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“…To date, two of these indices have been mainly applied to the EEG analysis of ADHD [4,6]. The first [6] is a modified fuzzy version of the well-known synchronization likelihood (SL) method developed by [38].…”

Section: The Concept Of Generalized Synchronizationmentioning

confidence: 99%

“…Over the years, other univariate measures coming from the analysis of nonlinear systems have been incorporated into the study of ADHD. These measures are dedicated, among other factors, to assessing the complexity of EEG channels [4,5]. Together with univariate measures, quantitative measures assess the linear or nonlinear interdependence between two EEG channels (bivariate measures) or a set of pairs of channels (multivariate measures) have been developed and used for the investigation of ADHD [4,[6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Together with univariate measures, quantitative measures assess the linear or nonlinear interdependence between two EEG channels (bivariate measures) or a set of pairs of channels (multivariate measures) have been developed and used for the investigation of ADHD [4,[6][7][8][9]. These measures represent an estimate of the functional connectivity (FC, to be defined below) between the signals recorded from different electrodes of the subject under study and appear to offer a better perspective with regard to the clinical diagnosis of ADHD than univariate measures [4,10]. This chapter is dedicated to describing and explaining the most commonly used multivariate EEG measures to date in the context of ADHD, as well as the most relevant results that have been derived from their implementation.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of ADHD Through Electroencephalographic Measures of Functional Connectivity

González¹,

Alba²,

Sangworasil³

et al. 2015

ADHD - New Directions in Diagnosis and Treatment

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Performance analysis of univariate and multivariate EEG measurements in the diagnosis of ADHD

Cited by 40 publications

References 65 publications

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

Comparison of Electroencephalography (EEG) Coherence between Major Depressive Disorder (MDD) without Comorbidity and MDD Comorbid with Internet Gaming Disorder

Assessment of ADHD Through Electroencephalographic Measures of Functional Connectivity

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