A Comparative Study of Standardized Infinity Reference and Average Reference for EEG of Three Typical Brain States

Exercise-induced central and peripheral sympathetic activity in a community-based group of epilepsy patients differ from healthy controls

Ictal and interictal activity within the autonomic nervous system is characterized by a sympathetic overshoot in people with epilepsy. This autonomic dysfunction is assumed to be driven by alterations in the central autonomic network. In this study, exercise-induced changes of the interrelation of central and peripheral autonomic activity in patients with epilepsy was assessed. 21 patients with epilepsy (16 seizure-free), and 21 healthy matched controls performed an exhaustive bicycle ergometer test. Immediately before and after the exercise test, resting state electroencephalography measurements (Brain Products GmbH, 128-channel actiCHamp) of 5 min were carried out to investigate functional connectivity assessed by phase locking value in source space for whole brain, central autonomic network and visual network. Additionally, 1-lead ECG (Brain products GmbH) was performed to analyze parasympathetic (root mean square of successive differences (RMSSD) of the heart rate variability) and sympathetic activity (electrodermal activity (meanEDA)). MeanEDA increased (p < 0.001) and RMSSD decreased (p < 0.001) from pre to post-exercise in both groups. Correlation coefficients of meanEDA and central autonomic network functional connectivity differed significantly between the groups (p = 0.004) after exercise. Both patients with epilepsy and normal control subjects revealed the expected physiological peripheral autonomic responses to acute exhaustive exercise, but alterations of the correlation between central autonomic and peripheral sympathetic activity may indicate a different sympathetic reactivity after exercise in patients with epilepsy. The clinical relevance of this finding and its modulators (seizures, anti-seizure medication, etc.) still needs to be elucidated.

EEG is better left alone

Automated preprocessing methods are critically needed to process the large publicly-available EEG databases, but the optimal approach remains unknown because we lack data quality metrics to compare them. Here, we designed a simple yet robust EEG data quality metric assessing the percentage of significant channels between two experimental conditions within a 100 ms post-stimulus time range. Because of volume conduction in EEG, given no noise, most brain-evoked related potentials (ERP) should be visible on every single channel. Using three publicly available collections of EEG data, we showed that, with the exceptions of high-pass filtering and bad channel interpolation, automated data corrections had no effect on or significantly decreased the percentage of significant channels. Referencing and advanced baseline removal methods were significantly detrimental to performance. Rejecting bad data segments or trials could not compensate for the loss in statistical power. Automated Independent Component Analysis rejection of eyes and muscles failed to increase performance reliably. We compared optimized pipelines for preprocessing EEG data maximizing ERP significance using the leading open-source EEG software: EEGLAB, FieldTrip, MNE, and Brainstorm. Only one pipeline performed significantly better than high-pass filtering the data.

Self- and Situation-Focused Reappraisal are not homogeneous: Evidence from behavioral and brain networks

Reappraisal is an effective emotion regulation strategy which can be divided into self- and situation-focused subtypes. Previous studies have produced inconsistent findings on the moderating effects and neural mechanisms of reappraisal; thus, further research is necessary to clarify these inconsistencies. In this study, a total of 44 participants were recruited and randomly assigned to two groups. 23 participants were assigned to the self-focused group, while 21 participants were assigned to the situation-focused group. The participants' resting EEG data were collected for 6 minutes before the experiment began, followed by an emotional regulation task. During this task, participants were asked to view emotion-provoking images under four emotion regulation conditions (View, Watch, Increase, and Decrease). Late positive potential (LPP) was obtained when these emotional images were observed. LPP is an effective physiological indicator of emotion regulation, enabling this study to explore emotion regulation under different reappraisal strategies, as well as the functional connectivity and node efficiency within the brain. It was found that, in terms of the effect on emotion regulation, situation-focused reappraisal was significantly better than self-focused reappraisal at enhancing the valence of negative emotion, while self-focused reappraisal was significantly better than situation-focused reappraisal at increasing the arousal of negative emotion. In terms of neural mechanisms, multiple brain regions such as the anterior cingulate cortex, the frontal lobe, the parahippocampal gyrus, parts of the temporal lobe, and parts of the parietal lobe were involved in both reappraisal processes. In addition, there were some differences in brain regions associated with different forms of cognitive reappraisal. Self-focused reappraisal was associated with the posterior cingulate gyrus, fusiform gyrus, and lingual gyrus, and situation-focused reappraisal was associated with the parietal lobule, anterior central gyrus, and angular gyrus. In conclusion, this research demonstrates that self- and situation-focused reappraisal are not homogenous in terms of their effects and neural mechanisms and clarifies the uncertainties over their regulatory effects. Different types of reappraisal activate different brain regions when used, and the functional connectivity or node efficiency of these brain regions seems to be a suitable indicator for assessing the effects of different types of reappraisal.

Sparsely Wiring Connectivity in the Upper Beta Band Characterizes the Brains of Top Swimming Athletes

Human brains are extremely energy costly in neural connections and activities. However, it is unknown what is the difference in the brain connectivity between top athletes with long-term professional trainings and age-matched controls. Here we ask whether long-term training can lower brain-wiring cost while have better performance. Since elite swimming requires athletes to move their arms and legs at different tempos in time with high coordination skills, we selected an eye-hand-foot complex reaction (CR) task to examine the relations between the task performance and the brain connections and activities, as well as to explore the energy cost-efficiency of top athletes. Twenty-one master-level professional swimmers and 23 age-matched non-professional swimmers as controls were recruited to perform the CR task with concurrent 8-channel EEG recordings. Reaction time and accuracy of the CR task were recorded. Topological network analysis of various frequency bands was performed using the phase lag index (PLI) technique to avoid volume conduction effects. The wiring number of connections and mean frequency were calculated to reflect the wiring and activity cost, respectively. Results showed that professional athletes demonstrated better eye-hand-foot coordination than controls when performing the CR task, indexing by faster reaction time and higher accuracy. Comparing to controls, athletes' brain demonstrated significantly less connections and weaker correlations in upper beta frequency band between the frontal and parietal regions, while demonstrated stronger connectivity in the low theta frequency band between sites of F3 and Cz/C4. Additionally, athletes showed highly stable and low eye-blinking rates across different reaction performance, while controls had high blinking frequency with high variance. Elite athletes' brain may be characterized with energy efficient sparsely wiring connections in support of superior motor performance and better cognitive performance in the eye-hand-foot complex reaction task.

Negative Correlation Between Functional Connectivity and Small-Worldness in the Alpha Frequency Band of a Healthy Brain

The aim of the study was to analyze the relationship between resting state electroencephalographic (EEG) alpha functional connectivity (FC) and small-world organization. For that purpose, Pearson correlation was calculated between FC and small-worldness (SW). Three undirected FC measures were used: magnitude-squared coherence (MSC), imaginary part of coherency (ICOH), and synchronization likelihood (SL). As a result, statistically significant negative correlation occurred between FC and SW for all three FC measures. Small-worldness of MSC and SL were mostly above 1, but lower than 1 for ICOH, suggesting that functional EEG networks did not have small-world properties. Based on the results of the current study, we suggest that decreased alpha small-world organization is compensated with increased connectivity of alpha oscillations in a healthy brain.

Altered brain network topology related to working memory in internet addiction

BACKGROUND AND AIMS

The working memory (WM) ability of internet addicts and the topology underlying the WM processing in internet addiction (IA) are poorly understood. In this study, we employed a graph theoretical framework to characterize the topological properties of the IA brain network in the source cortical space during WM task.

METHODS

A sample of 24 subjects with IA and 23 matched healthy controls (HCs) performed visual 2-back task. Exact Low Resolution Electromagnetic Tomography was adopted to project the pre-processed EEG signals into source space. Subsequently, Lagged phase synchronization was calculated between all pairs of Brodmann areas, the graph theoretical approaches were then employed to estimate the brain topological properties of all participants during the WM task.

RESULTS

We found better WM behavioral performance in IA subjects compared with the HCs. Moreover, compared to the HC group, more integrated and hierarchical brain network was revealed in the IA subjects in alpha band. And altered regional centrality was mainly resided in frontal and limbic lobes. In addition, significant relationships between the IA severity and the significant altered graph indices were found.

CONCLUSIONS

In conclusion, these findings provide evidence to support the notion that altered topological configuration may underline changed WM function observed in IA.

Interictal structural and functional connectivity in idiopathic generalized epilepsy: A systematic review of graph theoretical studies

The evaluation of the role of anomalous neuronal networks in epilepsy using a graph theoretical approach is of growing research interest. There is currently no consensus on optimal methods for performing network analysis, and it is possible that variations in study methodology account for diverging findings. This review focuses on global functional and structural interictal network characteristics in people with idiopathic generalized epilepsy (IGE) with the aim of appraising the methodological approaches used and assessing for meaningful consensus. Thirteen studies were included in the review. Data were heterogenous and not suitable for meta-analysis. Overall, there is a suggestion that the cerebral neuronal networks of people with IGE have different global structural and functional characteristics to people without epilepsy. However, the nature of the aberrations is inconsistent with some studies demonstrating a more regular network configuration in IGE, and some, a more random topology. There is greater consistency when different data modalities and connectivity subtypes are compared separately, with a tendency towards increased small-worldness of networks in functional electroencephalography/magnetoencephalography (EEG/MEG) studies and decreased small-worldness of networks in structural studies. Prominent variation in study design at all stages is likely to have contributed to differences in study outcomes. Despite increasing literature surrounding neuronal network analysis, systematic methodological studies are lacking. Absence of consensus in this area significantly limits comparison of results from different studies, and the ability to draw firm conclusions about network characteristics in IGE.

Sensor Level Functional Connectivity Topography Comparison Between Different References Based EEG and MEG

Sensor-level functional connectivity topography (sFCT) contributes significantly to our understanding of brain networks. sFCT can be constructed using either electroencephalography (EEG) or magnetoencephalography (MEG). Here, we compared sFCT within the EEG modality and between EEG and MEG modalities. We first used simulations to look at how different EEG references-including the Reference Electrode Standardization Technique (REST), average reference (AR), linked mastoids (LM), and left mastoid references (LR)-affect EEG-based sFCT. The results showed that REST decreased the reference effects on scalp EEG recordings, making REST-based sFCT closer to the ground truth (sFCT based on ideal recordings). For the inter-modality simulation comparisons, we compared each type of EEG-sFCT with MEG-sFCT using three metrics to quantize the differences: Relative Error (RE), Overlap Rate (OR), and Hamming Distance (HD). When two sFCTs are similar, RE and HD are low, while OR is high. Results showed that among all reference schemes, EEG-and MEG-sFCT were most similar when the EEG was REST-based and the EEG and MEG were recorded simultaneously. Next, we analyzed simultaneously recorded MEG and EEG data from publicly available face-recognition experiments using a similar procedure as in the simulations. The results showed (1) if MEG-sFCT is the standard, REST-and LM-based sFCT provided results closer to this standard in the terms of HD; (2) REST-based sFCT and MEG-sFCT had the highest similarity in terms of RE; (3) REST-based sFCT had the most overlapping edges with MEG-sFCT in terms of OR. This study thus provides new insights into the effect of different reference schemes on sFCT and the similarity between MEG and EEG in terms of sFCT.

Severe neonatal hyperbilirubinemia induces temporal and occipital lobe seizures

To examine the origin of seizures induced by severe neonatal hyperbilirubinemia, The EEG characteristics of seizures were analyzed in newborns with and without severe neonatal hyperbilirubinemia. Fisher’s exact test was used to determine the specificity. In total, 931 patients had a total serum bilirubin (TSB) level of 340–425 μmol/L, only 2 of whom had seizures. Compared to patients with hyperbilirubinemia and a TSB level of 340–425 μmol/L, those with a TSB level >425 μmol/L had a significant risk of seizure (OR = 213.2, 95% CI = 113.0–405.8, P<0.001). Of all 28 patients with severe hyperbilirubinemia and seizure, 26 had seizures that originated in the temporal and/or occipital lobe. In seizure patients without severe hyperbilirubinemia, origination in the temporal/occipital and other lobes occurred in 19 and 117 cases, respectively. Compared to the risk of seizure origination in the temporal and/or occipital lobe in other diseases, the risk in patients with severe hyperbilirubinemia was increased by approximately 80 times (OR = 80.1, 95% CI = 28.3–226.4, P<0.001). Severe neonatal hyperbilirubinemia can selectively induce temporal and occipital lobe seizures. This is the first report of a new syndrome with the same etiology and electrophysiological features as epilepsy.