Reliability of event-related EEG functional connectivity during visual entrainment: magnitude squared coherence and phase synchrony estimates

From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.

Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder

Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals' responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD. We examine the properties of well-established EEG preprocessing pipelines and consider factors leading to the discovery of sensitive and reliable biomarkers.

Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities

Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.

Altered brain functional networks after Quchi (LI 11) acupuncture: An EEG analysis

BACKGROUND

As a unique traditional Chinese medicine therapy, the central effect of acupuncture has received increasing attention. Functional brain networks can provide connectivity information among brain regions.

OBJECTIVE

The study goal is to explore the regulatory effect of acupuncture on the brain functional network.

METHODS

This paper analyzes the electroencephalography (EEG)-based power spectrum and brain functional network elicited by acupuncture at Quchi (LI 11).

RESULTS

The power spectrum results showed that acupuncture at LI 11 decreased the energy in the alpha frequency, mainly in the central region, left parietal lobe, left temporal lobe and left frontal lobe. Moreover, functional brain networks converted from the magnitude-squared coherence matrix in the alpha band are reconstructed. The results show that acupuncture did not alter the basic properties of the brain functional connection network. During acupuncture, the average node degree, average clustering coefficient, and small-world property of the brain functional connection network decreased after acupuncture compared with that before it. However, the average characteristic path length increased after acupuncture compared with before.

CONCLUSION

Acupuncture at LI 11 altered the brain's electrical activity. In the meantime, this acupuncture reduced the network's internal connectivity and information transfer efficiency.

The developmental trajectory of task-related frontal EEG theta/beta ratio in childhood

Attention control, the ability to focus on task-relevant information while blocking out irrelevant information, is crucial for successful task completion throughout development. However, the neurodevelopment of attention control during tasks remains underexplored, particularly from an electrophysiogical perpective. Therefore, the present study investigated the developmental trajectory of frontal TBR, a well-established EEG correlate of attention control, in a large sample of 5, 207 children aged 5-14 during a visuospatial working memory task. Results revealed that frontal TBR in tasks exhibited a different developmental trajectory (quadratic) compared to the baseline condition (linear). More importantly, we found that the association between task-related frontal TBR and age was modulated by task difficulty, with the age-related decrease in frontal TBR being more pronounced in more challenging conditions. Overall, by demonstrating a fine-grained age-related change in the frontal TBR based on a large dataset covering continuous age groups, our study provided electrophysiogical evidence about the maturation of attention control, suggesting potentially distinct developmental paths for attention control across the baseline and task conditions.

Prognostic Role of Hemispherical Functional Connectivity in Stroke: A Study via Graph Theory Versus Coherence of Electroencephalography Rhythms

BACKGROUND

The objective of the present study is to explore whether acute stroke may result in changes in brain network architecture by electroencephalography functional coupling analysis and graph theory.

METHODS

Ninety acute stroke patients and 110 healthy subjects were enrolled in different clinical centers in Rome, Italy, starting from 2013, and for each one electroencephalographies were recorded within <15 days from stroke onset. All patients were clinically evaluated through National Institutes of Health Stroke Scale, Barthel Index, and Action Research Arm Test in the acute stage and during the follow-up. Functional connectivity was assessed using Total Coherence and Small World (SW) by comparing the affected and the unaffected hemisphere between groups (Stroke versus Healthy). Correlations between connectivity and poststroke recovery scores have been carried out.

RESULTS

In stroke patients, network hemispheric asymmetry, in terms of Total Coherence, was mainly detected in the affected hemisphere with lower values in Delta, Theta, Alpha1, and Alpha2 (P=0.000001), whereas the unaffected hemisphere showed lower Total Coherence only in Delta and Theta (P=0.000001). SW revealed a significant difference only in the affected hemisphere in all electroencephalography bands (lower SW in Delta (P=0.000003), Theta (P=0.000003), Alpha1 (P=0.000203), and Alpha2 (P=0.028) and higher SW in Beta2 (P=0.000002) and Gamma (P=0.000002)). We also found significant correlations between SW and improvement in National Institutes of Health Stroke Scale (Theta SW: r=-0.2808), Barthel Index (Delta SW: r=0.3692; Theta SW: r=0.3844, Beta2 SW: r=-0.3589; Gamma SW: r=-04948), and Action Research Arm Test (Beta2 SW: r=-0.4274; Gamma SW: r=-0.4370).

CONCLUSIONS

These findings demonstrated changes in global functional connectivity and in the balance of network segregation and integration induced by acute stroke. The findings on the correlations between clinical outcome(s) and poststroke network architecture indicate the possibility to identify a predictive index of recovery useful to address and personalize the rehabilitation program.

The interplay between structural and functional connectivity in early stage Parkinson's disease patients

The mechanisms underlying cognitive disturbances in Parkinson's disease (PD) are poorly understood but likely to depend on the ongoing degenerative processes affecting structural and functional connectivity (FC). This pilot study examined patterns of FC alterations during a cognitive task using EEG and structural characteristics of white matter (WM) pathways connecting these activated regions in early-stage PD. Eleven PD patients and nine healthy controls (HCs) underwent EEG recording during an auditory oddball task and MRI scans. Source localization was performed and Gaussian mixture model was fitted to identify brain regions with high power during task performance. These areas served as seed regions for connectivity analysis. FC among these regions was assessed by measures of magnitude squared coherence (MSC), and phase-locking value (PLV), while structural connectivity was evaluated using fiber tracking based on diffusion tensor imaging (DTI). The paracentral lobule (PL), superior parietal lobule (SPL), superior and middle frontal gyrus (SMFG), parahippocampal gyrus, superior and middle temporal gyri (STG, MTG) demonstrated increased activation during task performance. Compared to HCs, PD showed lower FC between SMFG and PL and between SMFG and SPL in MSC (p = 0.012 and p = 0.036 respectively). No significant differences between the groups were observed in PLV and the measured DTI metrics along WM tracts. These findings demonstrate that in early PD, cognitive performance changes might be attributed to FC alterations, suggesting that FC is affected early on in the degenerative process, whereas structural damage is more prominent in advanced stages as a result of the disease burden accumulation.

EEG functional connectivity analysis in the source space

There is a growing interest in using electroencephalography (EEG) and source modeling to investigate functional interactions among cortical processes, particularly when dealing with pediatric populations. This paper introduces two pipelines that have been recently used to conduct EEG FC analysis in the cortical source space. The analytic streams of these pipelines can be summarized into the following steps: 1) cortical source reconstruction of high-density EEG data using realistic magnetic resonance imaging (MRI) models created with age-appropriate MRI templates; 2) segmentation of reconstructed source activities into brain regions of interest; and 3) estimation of FC in age-related frequency bands using robust EEG FC measures, such as weighted phase lag index and orthogonalized power envelope correlation. In this paper we demonstrate the two pipelines with resting-state EEG data collected from children at 12 and 36 months of age. We also discuss the advantages and limitations of the methods/techniques integrated into the pipelines. Given there is a need in the research community for open-access analytic toolkits that can be used for pediatric EEG data, programs and codes used for the current analysis are made available to the public.

Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions

BACKGROUND

Electrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric.

NEW METHODS

We undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity.

RESULTS

Power envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn't strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals.

CONCLUSIONS

The competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.

Resting State EEG Related to Mathematical Improvement After Spatial Training in Children

Spatial cognitive abilities, including mental rotation (MR) and visuo-spatial working memory (vsWM) are correlated with mathematical performance, and several studies have shown that training of these abilities can enhance mathematical performance. Here, we investigated the behavioral and neural correlates of MR and vsWM training combined with number line (NL) training. Fifty-seven children, aged 6–7, performed 25 days of NL training combined with either vsWM or MR and participated in an Electroencephalography (EEG)-session in school to measure resting state activity and steady-state visual evoked potentials during a vsWM task before and after training. Fifty children, aged 6–7, received usual teaching and acted as a control group. Compared to the control group, both training groups improved on a combined measure of mathematics. Cognitive improvement was specific to the training. Significant pre-post changes in resting state-EEG (rs-EEG), common to both training groups, were found for power as well as for coherence, with no significant differences in rs-EEG-changes between the vsWM and MR groups. Two of the common rs-EEG changes were correlated with mathematical improvement: (1) an increase in coherence between the central frontal lobe and the right parietal lobe in frequencies ranging from 16 to 25 Hz, and (2) an increase in coherence between the left frontal lobe and the right parietal lobe ranging from 23 to 25 Hz. These results indicate that changes in fronto-parietal coherence are related to an increase in mathematical performance, which thus might be a useful measure in further investigations of mathematical interventions in children.

Transcranial photobiomodulation changes topology, synchronizability, and complexity of resting state brain networks

Objective. Transcranial photobiomodulation (tPBM) is a recently proposed non-invasive brain stimulation approach with various effects on the nervous system from the cells to the whole brain networks. Specially in the neural network level, tPBM can alter the topology and synchronizability of functional brain networks. However, the functional properties of the neural networks after tPBM are still poorly clarified.Approach. Here, we employed electroencephalography and different methods (conventional and spectral) in the graph theory analysis to track the significant effects of tPBM on the resting state brain networks. The non-parametric statistical analysis showed that just one short-term tPBM session over right medial frontal pole can significantly change both topological (i.e. clustering coefficient, global efficiency, local efficiency, eigenvector centrality) and dynamical (i.e. energy, largest eigenvalue, and entropy) features of resting state brain networks.Main results. The topological results revealed that tPBM can reduce local processing, centrality, and laterality. Furthermore, the increased centrality of central electrode was observed.Significance. These results suggested that tPBM can alter topology of resting state brain network to facilitate the neural information processing. On the other hand, the dynamical results showed that tPBM reduced stability of synchronizability and increased complexity in the resting state brain networks. These effects can be considered in association with the increased complexity of connectivity patterns among brain regions and the enhanced information propagation in the resting state brain networks. Overall, both topological and dynamical features of brain networks suggest that although tPBM decreases local processing (especially in the right hemisphere) and disrupts synchronizability of network, but it can increase the level of information transferring and processing in the brain network.

Synchrony and Complexity in State-Related EEG Networks: An Application of Spectral Graph Theory

The brain may be considered as a synchronized dynamic network with several coherent dynamical units. However, concerns remain whether synchronizability is a stable state in the brain networks. If so, which index can best reveal the synchronizability in brain networks? To answer these questions, we tested the application of the spectral graph theory and the Shannon entropy as alternative approaches in neuroimaging. We specifically tested the alpha rhythm in the resting-state eye closed (rsEC) and the resting-state eye open (rsEO) conditions, a well-studied classical example of synchrony in neuroimaging EEG. Since the synchronizability of alpha rhythm is more stable during the rsEC than the rsEO, we hypothesized that our suggested spectral graph theory indices (as reliable measures to interpret the synchronizability of brain signals) should exhibit higher values in the rsEC than the rsEO condition. We performed two separate analyses of two different datasets (as elementary and confirmatory studies). Based on the results of both studies and in agreement with our hypothesis, the spectral graph indices revealed higher stability of synchronizability in the rsEC condition. The k-mean analysis indicated that the spectral graph indices can distinguish the rsEC and rsEO conditions by considering the synchronizability of brain networks. We also computed correlations among the spectral indices, the Shannon entropy, and the topological indices of brain networks, as well as random networks. Correlation analysis indicated that although the spectral and the topological properties of random networks are completely independent, these features are significantly correlated with each other in brain networks. Furthermore, we found that complexity in the investigated brain networks is inversely related to the stability of synchronizability. In conclusion, we revealed that the spectral graph theory approach can be reliably applied to study the stability of synchronizability of state-related brain networks.

In pursuit of visual attention: SSVEP frequency-tagging moving targets

Previous research has shown that visual attention does not always exactly follow gaze direction, leading to the concepts of overt and covert attention. However, it is not yet clear how such covert shifts of visual attention to peripheral regions impact the processing of the targets we directly foveate as they move in our visual field. The current study utilised the co-registration of eye-position and EEG recordings while participants tracked moving targets that were embedded with a 30 Hz frequency tag in a Steady State Visually Evoked Potentials (SSVEP) paradigm. When the task required attention to be divided between the moving target (overt attention) and a peripheral region where a second target might appear (covert attention), the SSVEPs elicited by the tracked target at the 30 Hz frequency band were significantly, but transiently, lower than when participants did not have to covertly monitor for a second target. Our findings suggest that neural responses of overt attention are only briefly reduced when attention is divided between covert and overt areas. This neural evidence is in line with theoretical accounts describing attention as a pool of finite resources, such as the perceptual load theory. Altogether, these results have practical implications for many real-world situations where covert shifts of attention may discretely reduce visual processing of objects even when they are directly being tracked with the eyes.

Using multiple short epochs optimises the stability of infant EEG connectivity parameters

Atypicalities in connectivity between brain regions have been implicated in a range of neurocognitive disorders. We require metrics to assess stable individual differences in connectivity in the developing brain, while facing the challenge of limited data quality and quantity. Here, we examine how varying core processing parameters can optimise the test–retest reliability of EEG connectivity measures in infants. EEG was recorded twice with a 1-week interval between sessions in 10-month-olds. EEG alpha connectivity was measured across different epoch lengths and numbers, with the phase lag index (PLI) and debiased weighted PLI (dbWPLI), for both whole-head connectivity and graph theory metrics. We calculated intra-class correlations between sessions for infants with sufficient data for both sessions (N’s = 19–41, depending on the segmentation method). Reliability for the whole brain dbWPLI was higher across many short epochs, whereas reliability for the whole brain PLI was higher across fewer long epochs. However, the PLI is confounded by the number of available segments. Reliability was higher for whole brain connectivity than graph theory metrics. Thus, segmenting available data into a high number of short epochs and calculating the dbWPLI is most appropriate for characterising connectivity in populations with limited availability of EEG data.

Reliability of Magnetoencephalography and High-Density Electroencephalography Resting-State Functional Connectivity Metrics

Resting-state connectivity, for example, based on magnetoencephalography (MEG) or electroencephalography (EEG), is a widely used method for characterizing brain networks and a promising imaging biomarker. However, there is no established standard as to which method, modality, and analysis variant is preferable and there is only limited knowledge on the reproducibility, an important prerequisite for clinical application. We conducted an MEG-/high-density (hd)-EEG-study on 22 young healthy adults, who were measured twice in a scan/rescan design after 7 ± 2 days. Reliability of resting-state (15 min, eyes-closed) connectivity in source space was calculated via intraclass correlation coefficient (ICC) in classical frequency bands (delta-gamma). We investigated the reliability of two commonly used connectivity metrics, namely the imaginary part of coherency and the weighted phase-lag index and the influence of frequency band, vigilance, and the number of trials. We found a strong increase of reliability with more trials and relatively mild effects of vigilance. Reliability was excellent in the alpha band for MEG, as well as hd-EEG (ICC >0.85); in the theta band, reliability was good for MEG and poor for EEG. Other frequency bands showed lower reliability, with delta band being the worst. Furthermore, we investigated the spatial reliability of resting-state connectivity in a vertex-based approach, which reached fair to good reliability (ICC up to 0.67) with 5 min of data. Our results indicate that excellent reliability of global connectivity is achievable in alpha band, and vertex-based connectivity was still fair to good. Moreover, electrophysiological resting-state studies could benefit from more data than used previously. MEG and hd-EEG were similar in their overall performance but showed frequency band-specific differences.

Assessing spatiotemporal variability of brain spontaneous activity by multiscale entropy and functional connectivity

Brain signaling occurs across a wide range of spatial and temporal scales, and analysis of brain signal variability and synchrony has attracted recent attention as markers of intelligence, cognitive states, and brain disorders. However, current technologies to measure brain signals in humans have limited resolutions either in space or in time and cannot fully capture spatiotemporal variability, leaving it untested whether temporal variability and spatiotemporal synchrony are valid and reliable proxy of spatiotemporal variability in vivo. Here we used optical voltage imaging in mice under anesthesia and wakefulness to monitor cortical voltage activity at both high spatial and temporal resolutions to investigate functional connectivity (FC, a measure of spatiotemporal synchronization), Multi-Scale Entropy (MSE, a measure of temporal variability), and their relationships to Regional Entropy (RE, a measure of spatiotemporal variability). We observed that across cortical space, MSE pattern can largely explain RE pattern at small and large temporal scales with high positive and negative correlation respectively, while FC pattern strongly negatively associated with RE pattern. The time course of FC and small scale MSE tightly followed that of RE, while large scale MSE was more loosely coupled to RE. fMRI and EEG data simulated by reducing spatiotemporal resolution of the voltage imaging data or considering hemodynamics yielded MSE and FC measures that still contained information about RE based on the high resolution voltage imaging data. This suggested that MSE and FC could still be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. Our results support the notion that FC and MSE are effective biomarkers for brain states, and provide a promising viewpoint to unify these two principal domains in human brain data analysis.

Differences in the synchronization of alpha oscillations between anterior and posterior brain regions

Alpha oscillations generated in the human brain have long been investigated in neuroscience. Synchronized oscillatory activities of numerous neural oscillators are thought to underlie alpha oscillations. The amplitude of alpha oscillations reflects the degree of synchronization, which is often modulated through alpha activation and inactivation, for example, in event-related synchronization and desynchronization. The range of synchronization can be estimated using the similarity between neighboring alpha oscillations. Yet, little is known about how the synchronization range of alpha oscillations varies with alpha activation/inactivation over different brain regions. To examine this, we recorded human electroencephalography (EEG) during different tasks used to modulate alpha oscillations. We found that the synchronization range of posterior alpha oscillations was reduced in the activation phase compared to that in the inactivation phase. In contrast, the synchronization range of anterior alpha oscillations was enhanced in the activation phase compared to that in the inactivation phase. The results imply that the mechanisms generating anterior and posterior alpha oscillations may be different.

How the visual brain detects emotional changes in facial expressions: Evidence from driven and intrinsic brain oscillations

The processing of facial expressions is often studied using static pictorial cues. Recent work, however, suggests that viewing changing expressions more robustly evokes physiological responses. Here, we examined the sensitivity of steady-state visual evoked potentials and intrinsic oscillatory brain activity to transient emotional changes in facial expressions. Twenty-two participants viewed sequences of grayscale faces periodically turned on and off at a rate of 17.5 Hz, to evoke flicker steady-state visual evoked potentials (ssVEPs) in visual cortex. Each sequence began with a neutral face (flickering for 2290 msec), immediately followed by a face from the same actor (also flickering for 2290 msec) with one of four expressions (happy, angry, fearful, or another neutral expression), followed by the initially presented neutral face (flickering for 1140 msec). The amplitude of the ssVEP and the power of intrinsic brain oscillations were analyzed, comparing the four expression-change conditions. We found a transient perturbation (reduction) of the ssVEP that was more pronounced after the neutral-to-angry change compared to the other conditions, at right posterior sensors. Induced alpha-band (8-13 Hz) power was reduced compared to baseline after each change. This reduction showed a central-occipital topography and was strongest in the subtlest and rarest neutral-to-neutral condition. Thus, the ssVEP indexed involvement of face-sensitive cortical areas in decoding affective expressions, whereas mid-occipital alpha power reduction reflected condition frequency rather than expression-specific processing, consistent with the role of alpha power changes in selective attention.

Face Perception in Social Anxiety: Visuocortical Dynamics Reveal Propensities for Hypervigilance or Avoidance

BACKGROUND

Theories of aberrant attentional processing in social anxiety, and anxiety disorders more broadly, have postulated an initial hypervigilance or facilitation to clinically relevant threats and consequent defensive avoidance. However, existing objective measurements utilized to explore this phenomenon lack the resolution to elucidate attentional dynamics, particularly covert influences.

METHODS

We utilized a continuous measure of visuocortical engagement, the steady-state visual evoked potential in response to naturalistic angry, fearful, happy, and neutral facial expressions. Participants were treatment-seeking patients with principal diagnoses of social anxiety circumscribed to performance situations (n = 21) or generalized across interaction contexts (n = 42), treatment-seeking patients with panic disorder with agoraphobia (n = 25), and 17 healthy participants.

RESULTS

At the principal disorder level, only circumscribed social anxiety patients showed sustained visuocortical facilitation to aversive facial expressions. Control participants as well as patients with panic disorder with agoraphobia and generalized social anxiety showed no bias. More finely stratifying the sample according to clinical judgment of social anxiety severity and interference revealed a linear increase in visuocortical bias to aversive expressions for all but the most severely impaired patients. This group showed an opposing sustained attentional disengagement.

CONCLUSIONS

Rather than shifts between covert vigilance and avoidance of aversive facial expressions, social anxiety appears to confer a sustained bias for one or the other. While vigilant attention reliably increases with social anxiety severity for the majority of patients, the most impaired patients show an opposing avoidance. These distinct patterns of attentional allocation could provide a powerful means of personalizing neuroscience-based interventions to modify attention bias and related impairment.

Using Event-Related Potentials and Startle to Evaluate Time Course in Anxiety and Depression

The National Institute of Mental Health's Research Domain Criteria initiative is a research framework designed toward understanding psychopathology as abnormalities of dimensional neurobehavioral constructs rather than in terms of DSM-defined categories. Research Domain Criteria constructs within the negative valence domain are particularly relevant for understanding anxiety and depressive disorders, which are pervasive, debilitating, and characterized by negative processing bias. One important direction for Research Domain Criteria research is investigating processes and parameters related to the time course (or chronometry) of negative valenced constructs. Two reliable methods for assessing chronometry are event-related potentials (ERPs) and startle blink. In this qualitative review, we examine ERP and startle studies of individuals with anxiety or depression or individuals vulnerable to affective disorders. The aim of the review is to highlight how these methods can inform the role of chronometry in the spectrum of anxiety and depression. ERP studies examining different chronometry facets of negative valenced responses have shown that transdiagnostic groups of individuals with internalizing psychopathologies exhibit abnormalities at early stages of processing. Startle reactivity studies have robustly differentiated fear-based disorders (e.g., panic disorder, social phobia) from other anxiety disorders (e.g., generalized anxiety disorder) and have also shown that different internalizing phenotypes exhibit different patterns of habituation. Findings lend support to the value of ERP and startle measures in identifying groups that cut across conventional classification systems. We also highlight methodological issues that can aid in the validity and reproducibility of ERP and startle findings and, ultimately, in the goal of developing more precise models of anxiety and depression.

Reliability of EEG Measures of Interaction: A Paradigm Shift Is Needed to Fight the Reproducibility Crisis

Measures of interaction (connectivity) of the EEG are at the forefront of current neuroscientific research. Unfortunately, test-retest reliability can be very low, depending on the measure and its estimation, the EEG-frequency of interest, the length of the signal, and the population under investigation. In addition, artifacts can hamper the continuity of the EEG signal, and in some clinical situations it is impractical to exclude artifacts. We aimed to examine factors that moderate test-retest reliability of measures of interaction. The study involved 40 patients with a range of neurological diseases and memory impairments (age median: 60; range 21–76; 40% female; 22 mild cognitive impairment, 5 subjective cognitive complaints, 13 temporal lobe epilepsy), and 20 healthy controls (age median: 61.5; range 23–74; 70% female). We calculated 14 measures of interaction based on the multivariate autoregressive model from two EEG-recordings separated by 2 weeks. We characterized test-retest reliability by correlating the measures between the two EEG-recordings for variations of data length, data discontinuity, artifact exclusion, model order, and frequency over all combinations of channels and all frequencies, individually for each subject, yielding a correlation coefficient for each participant. Excluding artifacts had strong effects on reliability of some measures, such as classical, real valued coherence (~0.1 before, ~0.9 after artifact exclusion). Full frequency directed transfer function was highly reliable and robust against artifacts. Variation of data length decreased reliability in relation to poor adjustment of model order and signal length. Variation of discontinuity had no effect, but reliabilities were different between model orders, frequency ranges, and patient groups depending on the measure. Pathology did not interact with variation of signal length or discontinuity. Our results emphasize the importance of documenting reliability, which may vary considerably between measures of interaction. We recommend careful selection of measures of interaction in accordance with the properties of the data. When only short data segments are available and when the signal length varies strongly across subjects after exclusion of artifacts, reliability becomes an issue. Finally, measures which show high reliability irrespective of the presence of artifacts could be extremely useful in clinical situations when exclusion of artifacts is impractical.

Reliability of EEG Interactions Differs between Measures and Is Specific for Neurological Diseases

Alterations of interaction (connectivity) of the EEG reflect pathological processes in patients with neurologic disorders. Nevertheless, it is questionable whether these patterns are reliable over time in different measures of interaction and whether this reliability of the measures is the same across different patient populations. In order to address this topic we examined 22 patients with mild cognitive impairment, five patients with subjective cognitive complaints, six patients with right-lateralized temporal lobe epilepsy, seven patients with left lateralized temporal lobe epilepsy, and 20 healthy controls. We calculated 14 measures of interaction from two EEG-recordings separated by 2 weeks. In order to characterize test-retest reliability, we correlated these measures for each group and compared the correlations between measures and between groups. We found that both measures of interaction as well as groups differed from each other in terms of reliability. The strongest correlation coefficients were found for spectrum, coherence, and full frequency directed transfer function (average rho > 0.9). In the delta (2–4 Hz) range, reliability was lower for mild cognitive impairment compared to healthy controls and left lateralized temporal lobe epilepsy. In the beta (13–30 Hz), gamma (31–80 Hz), and high gamma (81–125 Hz) frequency ranges we found decreased reliability in subjective cognitive complaints compared to mild cognitive impairment. In the gamma and high gamma range we found increased reliability in left lateralized temporal lobe epilepsy patients compared to healthy controls. Our results emphasize the importance of documenting reliability of measures of interaction, which may vary considerably between measures, but also between patient populations. We suggest that studies claiming clinical usefulness of measures of interaction should provide information on the reliability of the results. In addition, differences between patient groups in reliability of interactions in the EEG indicate the potential of reliability to serve as a new biomarker for pathological memory decline as well as for epilepsy. While the brain concert of information flow is generally variable, high reliability, and thus, low variability may reflect abnormal firing patterns.

Steady-state visual evoked potentials as a research tool in social affective neuroscience

Like many other primates, humans place a high premium on social information transmission and processing. One important aspect of this information concerns the emotional state of other individuals, conveyed by distinct visual cues such as facial expressions, overt actions, or by cues extracted from the situational context. A rich body of theoretical and empirical work has demonstrated that these socioemotional cues are processed by the human visual system in a prioritized fashion, in the service of optimizing social behavior. Furthermore, socioemotional perception is highly dependent on situational contexts and previous experience. Here, we review current issues in this area of research and discuss the utility of the steady-state visual evoked potential (ssVEP) technique for addressing key empirical questions. Methodological advantages and caveats are discussed with particular regard to quantifying time-varying competition among multiple perceptual objects, trial-by-trial analysis of visual cortical activation, functional connectivity, and the control of low-level stimulus features. Studies on facial expression and emotional scene processing are summarized, with an emphasis on viewing faces and other social cues in emotional contexts, or when competing with each other. Further, because the ssVEP technique can be readily accommodated to studying the viewing of complex scenes with multiple elements, it enables researchers to advance theoretical models of socioemotional perception, based on complex, quasinaturalistic viewing situations.

Using a dry electrode EEG device during balance tasks in healthy young-adult males: Test-retest reliability analysis

BACKGROUND

The analysis of brain activity during balance is an important topic in different fields of science. Given that all measurements involve an error that is caused by different agents, like the instrument, the researcher, or the natural human variability, a test-retest reliability evaluation of the electroencephalographic assessment is a needed starting point. However, there is a lack of information about the reliability of electroencephalographic measurements, especially in a new wireless device with dry electrodes.

OBJECTIVE

The current study aims to analyze the reliability of electroencephalographic measurements from a wireless device using dry electrodes during two different balance tests.

METHOD

Seventeen healthy male volunteers performed two different static balance tasks on a Biodex Balance Platform: (a) with two feet on the platform and (b) with one foot on the platform. Electroencephalographic data was recorded using Enobio (Neuroelectrics). The mean power spectrum of the alpha band of the central and frontal channels was calculated. Relative and absolute indices of reliability were also calculated.

RESULTS

In general terms, the intraclass correlation coefficient (ICC) values of all the assessed channels can be classified as excellent (>0.90). The percentage standard error of measurement oscillated from 0.54% to 1.02% and the percentage smallest real difference ranged from 1.50% to 2.82%.

CONCLUSION

Electroencephalographic assessment through an Enobio device during balance tasks has an excellent reliability. However, its utility was not demonstrated because responsiveness was not assessed.