EEG and ERP characteristics of Attention Deficit Hyperactivity Disorder in children and adolescents

Лазарев, В. В.; Pontes, Monique Castro; Pontes, Adailton T.; Vieira, Juliana; Quero-Cunha, Carla; Tamborino, Tiago; Galhanone, Paulo Ricardo; Azevedo, Leonardo C. de; Abramov, Dimitri Marques

doi:10.1016/j.ijpsycho.2016.07.243

Cited by 4 publications

(3 citation statements)

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“…Several studies have shown brain asymmetries related to ADHD phenomenology, especially in the frontal-striatal network (Barkley, 1997; Barkley, 2006; Langleben et al, 2001; Dang et al, 2016). Asymmetrical topographic patterns were shown in our previous studies by spectral and coherence analyses of the resting EEG, with signs of relative inactivation of the frontal and left temporal areas, in accordance with their importance for voluntary attention, which is impaired in ADHD subjects (Lazarev et al, 2016).…”

Section: Discussionsupporting

confidence: 67%

Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples

Abramov

Лазарев

Gomes

et al. 2019

PeerJ

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Objective To estimate whether the “Diagnostic and Statistical Manual of Mental Disorders” (DSM) is biologically accurate for the diagnosis of Attention Deficit/ Hyperactivity Disorder (ADHD) using a biological-based classifier built by a special method of multivariate analysis of a large dataset of a small sample (much more variables than subjects), holding neurophysiological, behavioral, and psychological variables. Methods Twenty typically developing boys and 19 boys diagnosed with ADHD, aged 10–13 years, were examined using the Attentional Network Test (ANT) with recordings of event-related potentials (ERPs). From 774 variables, a reduced number of latent variables (LVs) were extracted with a clustering of variables method (CLV), for further reclassification of subjects using the k-means method. This approach allowed a multivariate analysis to be applied to a significantly larger number of variables than the number of cases. Results From datasets including ERPs from the mid-frontal, mid-parietal, right frontal, and central scalp areas, we found 82% of agreement between DSM and biological-based classifications. The kappa index between DSM and behavioral/psychological/neurophysiological data was 0.75, which is regarded as a “substantial level of agreement”. Discussion The CLV is a useful method for multivariate analysis of datasets with much less subjects than variables. In this study, a correlation is found between the biological-based classifier and the DSM outputs for the classification of subjects as either ADHD or not. This result suggests that DSM clinically describes a biological condition, supporting its validity for ADHD diagnostics.

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Section: Discussionsupporting

confidence: 67%

Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples

Abramov

Лазарев

Gomes

et al. 2019

PeerJ

Self Cite

View full text Add to dashboard Cite

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“…The different behavior of neurophysiological correlates of the efficiency of the alerting network in the groups reinforced the idea that changes in this network, which controls and modulates sustained attention [20], would play an essential role in ADHD physiology. Moreover, our results seem to corroborate the topography of this effect, pinpointing it in the right temporal lobe [55].…”

Section: Discussionsupporting

confidence: 80%

“…In ANT responses, these right-hemisphere mechanisms were apparently not involved since ANT is a set of non-verbal tasks, explaining the rightward asymmetry found in the controls. In another study by our laboratory (with the same subjects), analyzing the ongoing EEG, ADHD subjects showed certain signs of reduced activation of the left temporal and frontal areas during relaxed wakefulness and intermittent photic stimulation, without verbal tasks or any other tasks demanding voluntary attention [55].…”

Section: Discussionmentioning

confidence: 99%

Neurophysiological and behavioral correlates of alertness impairment and compensatory processes in ADHD evidenced by the Attention Network Test

et al. 2019

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In Attention Deficit Hyperactivity disorder (ADHD), fMRI studies show asymmetric alterations: widespread hypoactivation in anterior cortical areas and hyperactivation in some posterior regions, and the latter is considered to be related to compensatory processes. In Posner’s attentional networks, an important role is attributed to functional interhemispheric asymmetries. The psychophysiological Attention Network Test (ANT), which measures the efficiency of the alerting, orienting, and executive networks, seems particularly informative for ADHD. Potentials related to ANT stimuli (ANT-RPs) have revealed reduced cognitive potential P3 in ADHD. However, there are no studies associated with asymmetry of ANT-RPs. In the present study, conducted with 20 typically developing boys and 19 boys with ADHD, aged 11–13 years, the efficiency of the three Posner’s networks regarding performance and amplitude asymmetries in ANT-RPs was evaluated according to the arithmetic difference of these parameters between different cue and target presentation conditions. The results were correlated to Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) scores. Regarding accuracy and intraindividual variation in reaction time, ADHD subjects showed lower efficiency of executive and alerting network, and this effect was correlated with DSM. Regarding alerting network, ANT-RPs in ADHD did not have the right-side amplitude prevalence in the temporal regions, which was observed in controls. In all ANT conditions, significantly higher asymmetries were observed in ADHD than in controls in the occipital regions 40–200 ms after target onset. Their amplitude in ADHD subjects was inversely proportional to DSM scores of inattentiveness and directly proportional to accuracy and efficiency of the executive network. The results suggest impaired alerting and executive networks in ADHD and compensatory occipital mechanisms.

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Identifying attention-deficit/hyperactivity disorder through the electroencephalogram complexity

Abramov,

Lima,

Lazarev

et al. 2024

Physica A: Statistical Mechanics and its Applications

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EEG and ERP characteristics of Attention Deficit Hyperactivity Disorder in children and adolescents

Cited by 4 publications

References 0 publications

Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples

Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples

Neurophysiological and behavioral correlates of alertness impairment and compensatory processes in ADHD evidenced by the Attention Network Test

Identifying attention-deficit/hyperactivity disorder through the electroencephalogram complexity

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