Resting-state alpha and gamma activity in affective disorder with ADHD symptoms: Comparison between bipolar disorder and major depressive disorder

Distinguishing major depressive disorder from bipolar disorder using alpha-band activity in resting-state electroencephalogram

BACKGROUND

Bipolar disorder (BD) and major depressive disorder (MDD) share overlapping depressive symptoms, which pose challenges for achieving rapid and accurate differential diagnosis in clinical practice. This study aims to investigate whether alpha sub-band activity in electroencephalogram (EEG) can serve as a discriminative feature between MDD and BD, thereby improving diagnostic accuracy in mood disorders.

METHODS

This study recruited a total of 103 participants, comprising 37 patients diagnosed with MDD, 36 patients with BD, and 30 healthy controls (HC). All participants were matched in terms of gender and age. EEG data were acquired during both eyes-open and eyes-closed states over a 5-minute duration to examine whether different sub-oscillations in the alpha band can differentiate between MDD and BD.

RESULTS

We found that at the group level, the peak frequency of the HC group was in the low alpha band, the BD group in the medium alpha band, and the MDD group in the high alpha band. Our results indicate that the MDD and BD groups display the most pronounced differences in the high alpha band, irrespective of whether the eyes are open or closed. In contrast, the HC group exhibits some distinctions from the MDD and BD groups in the low alpha band.

CONCLUSIONS

This study provides novel insights into the differential characteristics of alpha sub-band oscillations in MDD from BD as compared to healthy controls. These observations suggest distinct neural signatures for MDD and BD, highlighting the potential value of alpha sub-band analyses in diagnostic classification.

Quantitative EEG Insights Into A Hundred Adult ADHD Patients: A Deep Dive Into Test of Variables of Attention (TOVA) Correlations and Attention Dynamics

OBJECTIVE

This study aimed to enhance the diagnostic accuracy of attention-deficit/hyperactivity disorder (ADHD) by integrating quantitative electroencephalography (qEEG) power bands with the test of variables of attention (TOVA) and self-reported psychiatric symptoms. We examined the relationship between TOVA scores, qEEG findings-particularly the theta-beta ratio-and comorbid psychiatric conditions to assess their role in refining ADHD diagnoses according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V).

METHOD

A total of 100 participants were assessed using TOVA, qEEG, and psychological scales, including the Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), Maudsley Obsessive-Compulsive Inventory (MOCI), and the Mood Disorder Questionnaire (MDQ). Participants were categorized into groups based on their Attention Comparison Scores (ACS) above or below the zero threshold. Mann-Whitney U-tests, correlation analyses, and predictive modeling using automatic linear modeling (ALM) were conducted to evaluate group differences, age-related changes, and predictor variables for attention performance.

RESULTS

All participants met the diagnostic criteria for ADHD. Among them, 37% exhibited anxiety, 60% depression, 26% obsessive-compulsive, and 35% mood disorder symptoms. The group with ACS above zero was significantly older (p = 0.034) and performed better on all Tests of Variables of Attention (TOVA) measures (p < 0.05). Age negatively correlated with attention scores (r = -0.371, p < 0.001), response time variability (r = -0.241, p = 0.016), and response time (r = -0.311, p = 0.002). qEEG showed significant age-related changes in theta-to-beta and delta-to-beta ratios (p < 0.005). TOVA and qEEG ratios, particularly beta and delta activity, predicted attention and response time variability, with adjusted R2 values between 71.5% and 87.1%.

CONCLUSION

The findings highlight that attention performance in ADHD is shaped by age, neuropsychological factors, and qEEG-measured brain activity. Higher attention scores correlate with better TOVA results, particularly in response time and error rates. Age-related declines in attention align with reductions in theta-to-beta and delta-to-beta ratios. Predictive modeling underscores the value of combining TOVA and qEEG to identify key predictors like response time variability, omission errors, and specific beta and delta activity. This integration enhances the evaluation of attention deficits and brain dynamics, benefiting both clinical and research applications.

Neurophysiological Correlates of Dynamic Beat Tracking in Individuals With Williams Syndrome

BACKGROUND

Williams syndrome (WS) is a neurodevelopmental disorder characterized by hypersociability, heightened auditory sensitivities, attention deficits, and strong musical interests despite differences in musical skills. Behavioral studies have reported that individuals with WS exhibit variable beat and rhythm perception skills.

METHODS

We sought to investigate the neural basis of beat tracking in individuals with WS using electroencephalography. Twenty-seven adults with WS and 16 age-matched, typically developing control subjects passively listened to musical rhythms with accents on either the first or second tone of the repeating pattern, leading to distinct beat percepts.

RESULTS

Consistent with the role of beta and gamma oscillations in rhythm processing, individuals with WS and typically developing control subjects showed strong evoked neural activity in both the beta (13-30 Hz) and gamma (31-55 Hz) frequency bands in response to beat onsets. This neural response was somewhat more distributed across the scalp for individuals with WS. Compared with typically developing control subjects, individuals with WS exhibited significantly greater amplitude of auditory evoked potentials (P1-N1-P2 complex) and modulations in evoked alpha (8-12 Hz) activity, reflective of sensory and attentional processes. Individuals with WS also exhibited markedly stable neural responses over the course of the experiment, and these responses were significantly more stable than those of control subjects.

CONCLUSIONS

These results provide neurophysiological evidence for dynamic beat tracking in WS and coincide with the atypical auditory phenotype and attentional difficulties seen in this population.

Fronto-lateral alpha power asymmetry in panic disorder

Resting state alpha power asymmetry in frontal and temporal regions has been reported in various clinical populations, possibly indicating deficits in prefrontal control. In panic disorder (PD), results regarding alpha asymmetric activity to date have been mixed. This study compared 55 PD patients and 42 healthy controls (HC) with regards to resting state alpha power asymmetry. Our results show more right-than-left fronto-lateral alpha power in PD, whereas at other sites and in HC no significant differences were detected. These results support the notion of altered neurobiological processes in PD that possibly represent a vulnerability to the experience of panic attacks. Further studies are needed to clarify potential causal implications of this finding in the genesis of PD, as well as to specify the functional significance of fronto-lateral alpha power asymmetry in PD.

A novel quantitative electroencephalography subtype with high alpha power in ADHD: ADHD or misdiagnosed ADHD?

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.