resting-state EEG
Research Papers
EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD
Adults with attention-deficit/hyperactivity disorder (ADHD) have been described as having altered resting-state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR). However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behaviour (mediational endophenotypes). Resting-state eyes-open and eyes-closed EEG was measured from 38 adults with ADHD, 45 first-degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1-45 Hz) and TBR could classify participants into ADHD, first-degree relatives and/or control groups. Random-label permutation was used to quantify any bias in the analysis. Eyes-open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic = 0.71-0.77). The best predictors of ADHD status were increased power in delta, theta and low-alpha over centro-parietal regions, and in frontal low-beta and parietal mid-beta. TBR did not successfully classify ADHD status. Elevated eyes-open power in delta, theta, low-alpha and low-beta distinguished first-degree relatives from controls (area under receiver operating characteristic = 0.68-0.72), suggesting that these features may be a mediational endophenotype for adult ADHD. Resting-state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD.
View Full Paper →Parsing heterogeneity in attention-deficit hyperactivity disorder using EEG-based subgroups.
Background: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous condition for which multiple efforts to characterize brain state differences are underway. The objective of this study was to identify distinct subgroups of resting electroencephalography (EEG) profiles among children with and without ADHD and subsequently provide extensive clinical characterization of the subgroups. Methods: Latent class analysis was used with resting state EEG recorded from a large sample of 781 children with and without ADHD (N = 620 ADHD, N = 161 Control), aged 618 years old. Behavioral and cognitive characteristics of the latent classes were derived from semistructured diagnostic interviews, parent completed behavior rating scales, and cognitive test performance. Results: A five-class solution was the best fit for the data, of which four classes had a defining spectral power elevation. The distribution of ADHD and control subjects was similar across classes suggesting there is no one resting state EEG profile for children with or without ADHD. Specific latent classes demonstrated distinct behavioral and cognitive profiles. Those with elevated slow-wave activity (i.e. delta and theta band) had higher levels of externalizing behaviors and cognitive deficits. Latent subgroups with elevated alpha and beta power had higher levels of internalizing behaviors, emotion dysregulation, and intact cognitive functioning. Conclusions: There is population-level heterogeneity in resting state EEG subgroups, which are associated with distinct behavioral and cognitive profiles. EEG measures may be more useful biomarkers of ADHD outcome or treatment response rather than diagnosis. Keywords: Electrophysiology; ADHD; resting state; latent class analysis.
View Full Paper →Short and long-term effects of sham-controlled prefrontal EEG-neurofeedback training in healthy subjects
Objective: In this study we evaluated long-term effects of frontal beta EEG-neurofeedback training (E-NFT) on healthy subjects. We hypothesized that E-NFT can change frontal beta activity in the long-term and that changes in frontal beta EEG activity are accompanied by altered cognitive performance. Methods: 25 healthy subjects were included and randomly assigned to active or sham E-NFT. On average the subjects underwent 15 E-NFT training sessions with a training duration of 45 min. Resting-state EEG was recorded prior to E-NFT training (t1) and in a 3-year follow-up (t3). Results: Compared to sham E-NFT, which was used for the control group, real E-NFT increased beta activity in a predictable way. This increase was maintained over a period of three years post training. However, E-NFT did not result in significantly improved cognitive performance. Conclusion: Based on our results, we conclude that EEG-NFT can selectively modify EEG beta activity both in short and long-term. Significance: This is a sham controlled EEG neurofeedback study demonstrating long-term effects in resting state EEG
View Full Paper →The Efficacy of Neurofeedback in Patients with Major Depressive Disorder: An Open Labeled Prospective Study
Abstract The purpose of this study was to evaluate the effect of neurofeedback on depressive symptoms and electrophysiological disturbances in patients with major depressive disorder. We recruited participants suffering from depression to evaluate efficacy of left prefrontal beta with alpha/theta training. An 8-week, prospective, openlabel study was undertaken. Twenty participants were recruited. The treatment protocol was twice or three times a week training of beta at F3 with alpha/theta at Pz for 8 weeks. When every visit, patients were received beta training for 30 min, and then alpha/theta training for 30 min. Baseline, 4 and 8 week scores of; the Hamilton rating scale for Depression (HAM-D), the Hamilton rating scale for Anxiety (HAM-A), the Beck Depression Inventory (BDI)-II, the Beck Anxiety Inventory (BAI), Clinical global impression-severity (CGI-S), and pre- and posttreatment resting state EEGs were compared. Interhemispheric alpha power asymmetry (A score) was computed for homologous sites F3–F4. Pre- and post-training clinical assessments revealed significant improvements in HAM– D, HAM-A, BDI, and CGI-S scores. Cumulative response rates by HAM-D were 35.0 and 75.0 % at 4 and 8 weeks, respectively, corresponding cumulative remission rates by HAM-D were 15.0 and 55.0 %, respectively. No significant differences were found between pre- and post-treatment A score. Neurofeedback treatment could improve depressive symptoms significantly. In addition, anxiety symptoms and clinical illness severity decreased significantly after neurofeedback treatment. Despite its several limitations, such as, small sample size and lack of a control group, this study suggested neurofeedback has significant effects in patients with major depressive disorder. Neurofeedback Beta training Depression Asymmetry score
View Full Paper →Test–retest reliability of resting EEG spectra validates a statistical signature of persons
Objective When EEG is recorded in humans, the question arises whether the resting EEG remains stable. We compared the inter-individual variation in spectral observables to the intra-individual stability over more than a year. Methods We recorded resting EEG in 55 healthy adults with eyes closed. In 20 persons EEG was recorded in a second session with retest intervals 12–40 months. For electrodes AFz, Cz and Pz α peak frequency and α peak height were transformed into Z-scores. We compared the curve shape of power spectra by first aligning α peaks to 10Hz and then regressing spectra pairwise onto each other to calculate a t-value. The t-value and differences of Z-scores for all pairs of sessions were entered into a generalized linear model (GLM) where binary output represents the recognition probability. The results were cross-validated by out-of-sample testing. Results Of the 40 sessions, 35 were correctly matched. The shape of power spectra contributed most to recognition. Out of all 2960 pairwise comparisons 99.5% were correct, with sensitivity 88% and specificity 99.5%. Conclusions Our statistical apparatus allows to identify those spectral EEG observables which qualify as statistical signature of a person. Significance The effect of external factors on EEG observables can be contrasted against their normal variability over time.
View Full Paper →Ready to Optimize Your Brain?
Schedule a free consultation to discuss resting-state eeg and how neurofeedback training can help
Or call us directly at 855-88-BRAIN
View Programs & Pricing →