Atypical functional connectivity during rest and task‐related dynamic alteration in young children with attention deficit hyperactivity disorder: An analysis using the phase‐locking value

Abstract

Aim The study investigated the electroencephalography (EEG) functional connectivity (FC) profiles during rest and tasks of young children with attention deficit hyperactivity disorder (ADHD) and typical development (TD). Methods In total, 78 children (aged 5–7 years) were enrolled in this study; 43 of them were diagnosed with ADHD and 35 exhibited TD. Four FC metrics, coherence, phase‐locking value (PLV), pairwise phase consistency, and phase lag index, were computed for feature selection to discriminate ADHD from TD. Results The support vector machine classifier trained by phase‐locking value (PLV) features yielded the best performance to differentiate the ADHD from the TD group and was used for further analysis. In comparing PLVs with the TD group at rest, the ADHD group exhibited significantly lower values on left intrahemispheric long interelectrode lower‐alpha and beta as well as frontal interhemispheric beta frequency bands. However, the ADHD group showed higher values of central interhemispheric PLVs on the theta, higher‐alpha, and beta bands. Regarding PLV alterations within resting and task conditions, left intrahemispheric long interelectrode beta PLVs declined from rest to task in the TD group, but the alterations did not differ in the ADHD group. Negative correlations were observed between frontal interhemispheric beta PLVs and the Disruptive Behavior Disorder Rating Scale as rated by teachers. Conclusions These results, which complement the findings of other sparse studies that have investigated task‐related brain FC dynamics, particularly in young children with ADHD, can provide clinicians with significant and interpretable neural biomarkers for facilitating the diagnosis of ADHD.

Developing a reliable and practical multi-domain model to facilitate the diagnosis of ADHD in older preschool children

“…Next, the assessor provided the test instructions and guided the children to practice before the required test. Finally, task EEG data was recorded during the formal K-CPT for 7.5 minutes after practice [23][24][25][26].…”

“…The impedance was set below 100 κOhms; the EEG data was recorded at a rate of 1000 samples per second and referenced to linked earlobes [25,27]. This comfortable and easy-to-use device has been shown to be highly applicable for clinical use in young children [23][24][25].…”

A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning

“…We have found articles with related objectives to ours such as [20] , [21] , [22] , [23] , that make a descriptive analysis of EEG signals from ADHD and TD, without applying more advanced methods of classification as ML or DL. We also found a set of studies [24] , [15] , [14] focused on the analysis of the connectivity among brain regions, applying only ML techniques.…”

The scalp prefrontal–limbic functional connectivity moderates stress‐related rumination effects on stress recovery

“…Previous EEG studies have shown that the alpha (8–12 Hz) and beta (13–30 Hz) bands are involved in rumination and stress (Chen et al., 2022). For example, Putnam and McSweeney (2008) showed that depressed patients have higher bilateral alpha power in the prefrontal cortex while resting.…”