This study investigated quantitative electroencephalography (QEEG) subtypes as auxiliary tools to assess Attention Deficit Hyperactivity Disorder (ADHD). A total of 74 subjects (58 male and 16 female) were assessed using the Korean version of the Diagnostic Interview Schedule for Children Version IV and were assigned to one of three groups: ADHD, ADHD-Not Otherwise specified (NOS), and Neurotypical (NT). We measured absolute and relative EEG power in 19 channels and conducted an auditory continuous performance test. We analyzed QEEG according to the frequency range: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), and beta (13.5–30 Hz). The subjects were then grouped by Ward’s method of cluster analysis using the squared Euclidian distance to measure dissimilarities. We discovered four QEEG clusters, which were characterized by: (a) elevated delta power with less theta activity, (b) elevated slow alpha relative power, (c) elevated theta with deficiencies of alpha and beta relative power, and (d) elevated fast alpha and beta absolute power. The largest proportion of participants in clusters (a) and (c) were from the ADHD group (48% and 47%, respectively). Conversely, group (b) mostly consisted of the participants from the NOS group (59%), while group (d) had the largest proportion of participants from the NT group (62%). These results indicate that children with ADHD does not neurophysiologically constitute a homogenous group. We also identified a new subtype with increased alpha power in addition to those commonly reported in ADHD. Given the QEEG characteristics with increased alpha power, we should consider the possibility that this subtype may be caused by childhood depression. In conclusion, we believe that these QEEG subtypes of ADHD are expected to provide valuable information for accurately diagnosing ADHD.

This study aimed to investigate the effectiveness of a quantitative electroencephalography (qEEG) biomarker in predicting the response to pharmacological treatment in patients with anxiety disorder. A total of 86 patients were diagnosed with anxiety disorder according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition, and subsequently treated with antidepressants. After 8–12 weeks, the participants were divided into treatment-resistant (TRS) and treatment-response (TRP) groups based on their Clinical Global Impressions-Severity (CGI-S) scores. We obtained the absolute-EEG measurements for 19-channels and analyzed qEEG findings according to the frequency range: delta, theta, alpha, and beta. The beta-wave was subdivided into low-beta, beta, and high-beta waves. The theta-beta ratio (TBR) was calculated, and an analysis of covariance was performed. Of the 86 patients with anxiety disorder, 56 patients (65%) were classified in the TRS group. The TRS and TRP groups did not differ in terms of age, sex, or medication-dosage. However, the baseline CGI-S was higher in the TRP group. After calibration by covariates, the TRP group showed higher beta-waves in T3 and T4, and a lower TBR, especially in T3 and T4, than the TRS group. These results indicate that patients with a lower TBR and higher beta and high-beta waves in T3 and T4 are more likely to respond to medication.

Objective Diagnosis of anxiety has relied primarily on self-report. This study aimed to investigate the neural correlates of anxiety with quantitative electroencephalography (qEEG) focusing on the state and trait anxiety defined according to the Research Domain Criteria framework existing across the differential diagnosis, rather than focusing on the diagnosis.Methods A total of 41 participants who visited a psychiatric clinic underwent resting state EEG and completed the State-Trait Anxiety Inventory. The absolute power of six frequency bands were analyzed: delta (1–4 Hz), theta (4–8 Hz), alpha (8–10 Hz), fast alpha (10–13.5 Hz), beta (13.5–30 Hz), and gamma (30–80 Hz).Results State anxiety scores were significantly negatively correlated with absolute gamma power in frontal (Fz, r=-0.484) and central (Cz, r=-0.523) regions, while trait anxiety scores were significantly negatively correlated with absolute gamma power in frontal (Fz, r= -0.523), central (Cz, r=-0.568), parietal (P7, r=-0.500; P8, r=-0.541), and occipital (O1, r=-0.510; O2, r=-0.480) regions.Conclusion The present study identified the significantly negative correlations between the anxiety level and gamma band power in fronto-central and posterior regions assessed at resting status. Further studies to confirm our findings and identify the neural correlates of anxiety are needed.

Objectives: This study investigated the effectiveness of a quantitative electroencephalography(qEEG) biomarker in predicting the response to pharmacological treatment in patients with anxiety disorder.Methods: A total of 86 patients were diagnosed with anxiety disorder by using the Diagnostic and Statistical Manual of Mental Disorders 5th edition and treated with antidepressants. After 8-12weeks, the participants were divided into treatment-resistant(TRS) and treatment-response(TRP) groups on the basis of the Clinical Global Impressions-Severity(CGI-S) score. We obtained the absolute-EEG measurements for 19-channels and analyzed qEEG findings according to the Hz range: delta, theta, alpha, and beta. The beta-wave was subdivided into low-beta, beta, and high-beta. The theta-beta ratio(TBR) was calculated, and an analysis of covariance was performed.Results: Among the 86 patients with anxiety disorder, 65% were classified in the TRS group. The TRS and TRP groups did not differ regarding age, sex, and medication-dosage. However, the baseline CGI-S was higher in the TRP. After calibration by covariates, the TRP showed a higher beta-wave and high-beta-waves in T3 and T4. The TRP showed a lower TBR, especially in T3 and T4. Conclusion: These results indicate that patients with a lower TBR and higher beta and high-beta waves in T3 and T4 are more likely to respond to medication.