EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

How do the resting EEG preprocessing states affect the outcomes of postprocessing?

Plenty of artifact removal tools and pipelines have been developed to correct the resting EEG waves and discover scientific values behind. Without expertised visual inspection, it is susceptible to derive improper preprocessing, resulting in either insufficient preprocessed EEG (IPE) or excessive preprocessed EEG (EPE). However, little is known about the impacts of IPE or EPE on postprocessing in the temporal, frequency, and spatial domains, particularly as to the spectra and the functional connectivity analysis. Here, the clean EEG (CE) with linear and quasi-stationary assumption was synthesized as ground truth based on the New-York head model and the multivariate autoregressive model. Later, IPE and EPE were simulated by injecting Gaussian noise and losing brain components, respectively. Spectral homogeneities of all EEGs were evaluated by the proposed Parallel LOg Spectra index (PaLOSi). Then, the impacts on postprocessing were quantified by the IPE/EPE deviation from CE as to the temporal statistics, multichannel power, cross spectra, scalp EEG network properties, and source dispersion. Lastly, the association between PaLOSi and varying trends of postprocessing outcomes was analyzed with evolutionary preprocessing states. We found that compared with CE: 1) IPE (EPE) temporal statistics deviated more greatly with more noise injected (brain activities discarded); 2) IPE (EPE) power was higher (lower), and IPE power was almost parallel to that of CE across frequencies, while EPE power deviation decreased with higher frequencies; IPE cross spectra deviated more greatly than EPE, except for β band; 3) derived from 7 coupling measures, IPE (EPE) network had lower (higher) transmission efficiency and worse (better) integration ability; 4) IPE sources distributed more dispersedly with greater strength while EPE sources activated more focally with lower amplitudes; 5) PaLOSi was consistently correlated with varying trends of investigated postprocessing for both simulated and real data. This study shed light on how the postprocessing outcomes are affected by the preprocessing states and PaLOSi is a promising quality control metric for creating normative EEG databases.

Stationarity assessment of resting state condition via permutation entropy on EEG recordings

The analysis of electrophysiological recordings of the human brain in resting state is a key experimental technique in neuroscience. Resting state is the default condition to characterize brain dynamics. Its successful implementation relies both on the capacity of subjects to comply with the requirement of staying awake while not performing any cognitive task, and on the capacity of the experimenter to validate that compliance. Here we propose a novel approach, based on permutation entropy, to assess the reliability of the resting state hypothesis by evaluating its stability during a recording. We combine the calculation of permutation entropy with a method to estimate its uncertainty out of a single time series. The approach is showcased on electroencephalographic data recorded from young and elderly subjects and considering eyes-closed and eyes-opened resting state conditions. Besides highlighting the reliability of the approach, the results show higher instability in elderly subjects, hinting at qualitative differences between age groups in the distribution of unstable brain activity. The method can be applied to other kinds of electrophysiological data, like magnetoencephalographic recordings. In addition, provided that suitable hardware and software processing units are used, the implementation of the method can be translated into a real time one.

Long-term impact of maternal prenatal smoking on EEG brain activity and internalizing/externalizing problem symptoms in young adults

OBJECTIVE

The objective of this study was to investigate the impact of smoking during pregnancy on the development of the child. While previous research has established its detrimental effects during early childhood, understanding potential long-term consequences into adulthood remains limited. This study specifically aimed to explore the influence of prenatal smoking exposure on brain activity and whether internalizing and externalizing symptoms are influenced by prenatal smoking exposure in a cohort of young adults.

METHODS

Utilizing data from 176 participants (mean age M = 24.68, SD = 0.49) and their mothers enrolled in a longitudinal risk study (MARS), we employed Generalized Additive Mixed Models (GAMMs) to analyze electroencephalography (EEG) power at rest and behavioral outcomes derived from the Young Adult-Self-Report (YASR) scales. Both covariate-unadjusted and -adjusted models were used, taking into account participant variables such as sex and age, as well as maternal factors like psychopathology and alcohol consumption, in addition to smoking and alcohol intake by the participants themselves.

RESULTS

The study revealed a significant impact of prenatal smoking on delta and theta band power, indicating decreased slower brain activity in prenatally exposed individuals compared to unexposed counterparts. Additionally, individuals exposed to prenatal smoking exhibited significantly higher levels of externalizing behavior. While this association was strongly influenced by maternal psychopathology, the child's gender, and the child's own substance use, the effect on delta power band remained after adjusting for covariates.

CONCLUSION

The findings suggest that prenatal smoking exposure may have enduring effects on brain activity patterns in young adulthood. Conversely, the influence on externalizing behaviors depended on familial factors (maternal psychopathology) and the lifestyle of the individual (substance use).

Clinical study on the intervention effect of digital therapy on children with attention deficit hyperactivity disorder (ADHD)

To evaluate the efficacy and safety of digital therapy for children aged 6 to 12 years with attention deficit hyperactivity disorder (ADHD). From January to March 2023, 52 children aged 6 to 12 years with attention deficit hyperactivity disorder (ADHD) from Wuhan Children's Hospital, Hubei Province, China were selected for intervention using the "MindPro1" attention training software developed by Jiangsu Ruinao Qizhi Medical Technology Co., Ltd. Before the intervention, the children were in a stable treatment state, and no modifications were made to the original treatment plan during the MindPro1 intervention. Subjects with severe mental illness or other conditions that may affect the implementation and evaluation of disease treatment were excluded. Subjects completed the planned 4-week intervention, and changes in attention-related variables were assessed using the Test of Attention Variables (TOVA) and the parent version of the 18-item SNAP-IV scale (Swanson, Nolan, and Pelham, version IV scale) before and after the intervention. After 4 weeks of intervention, the lower limit of the 95% confidence interval of the response rate of the 18-item SNAP-IV-Parent scale, which was ≥ 30% improvement from baseline, was higher than 27.5% (better than similar products on the market); the SNAP-IV parent score improved (P < 0.001), with statistical significance; the TOVA-ACS score improved (P < 0.05), with statistical significance. The acceptance rate of parents of children was 100%, and the average compliance rate was 95%. There were 4 cases (7.69%) of adverse reactions that may be related to the device in this trial, which recovered spontaneously within 2 days of discontinuation, and no serious adverse events occurred. After 4 weeks of treatment with ADHD auxiliary treatment software, the objective attention assessment data and attention function assessment scale were significantly improved. Parents had a high acceptance of the software, the average compliance rate of participants was high, and the incidence of related adverse events was low and mild.

Detection of short-lasting and ictal spike-and-wave discharges in around-the-ears EEG recordings in children with absence epilepsy

PURPOSE

Long-term ambulatory EEG recordings can improve the monitoring of absence epilepsy in children, but signal quality and increased review workload are a concern. We evaluated the feasibility of around-the-ears EEG arrays (cEEGrids) to capture 3-Hz short-lasting and ictal spike-and-wave discharges and assessed the performance of automated detection software in cEEGrids data. We compared patterns of bilateral synchronisation between short-lasting and ictal spike-and-wave discharges.

METHODS

We recruited children with suspected generalised epilepsy undergoing routine video-EEG monitoring and performed simultaneous cEEGrids recordings. We used ASSYST software to detect short-lasting 3-Hz spike-and-wave discharges (1-3 s) and ictal spike-and-wave discharges in the cEEGrids data. We assessed data quality and sensitivity of cEEGrids for spike-and-wave discharges in routine EEG. We determined the sensitivity and false detection rate for automated spike-and-wave discharge detection in cEEGrids data. We compared bihemispheric synchrony across the onset of short-lasting and ictal spike-and-wave discharges using the mean phase coherence in the 2-4 Hz frequency band.

RESULTS

We included nine children with absence epilepsy (median age = 11 y, range 8-15 y, nine females) and recorded 4 h and 27 min of cEEGrids data. The recordings from seven participants were suitable for quantitative analysis, containing 82 spike-and-wave discharges. The cEEGrids captured 58 % of all spike-and-wave discharges (median individual sensitivity: 100 %, range: 47-100 %). ASSYST detected 82 % of all spike-and-wave discharges (median: 100 %, range: 41-100 %) with a false detection rate of 48/h (median: 6/h, range: 0-154/h). The mean phase coherence significantly increased during short-lasting and ictal spike-and-wave discharges in the 500-ms pre-onset to 1-s post-onset interval.

CONCLUSIONS

cEEGrids are of variable quality for monitoring spike-and-wave discharges in children with absence epilepsy. ASSYST could facilitate the detection of short-lasting and ictal spike-and-wave discharges with clear periodic structures but with low specificity. A similar course of bihemispheric synchrony between short-lasting and ictal spike-and-wave discharges indicates that cortico-thalamic driving may be relevant for both types of spike-and-wave discharges.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

Cognitive assessment using ERP in child and adolescent psychiatry: Difficulties and opportunities

Event related potentials (ERPs) represent powerful tools to investigate cognitive functioning in child and adolescent psychiatry. So far, the available body of research has largely focused on advancements in analysis methods, with little attention given to the perspective of assessment. The aim of this brief report is to provide recommendations for cognitive ERPs assessment that can be applied across diagnostic categories in child and adolescent psychiatry research. First, we discuss major issues for ERPs testing using examples from common psychiatric disorders. We conclude by summing up our recommendations for methodological standards and highlighting the potential role of ERPs in the field.