Effect of EEG electrode number on epileptic source localization in pediatric patients

Exploring Electrocortical Signatures of Gait Adaptation: Differential Neural Dynamics in Slow and Fast Gait Adapters

Individuals exhibit significant variability in their ability to adapt locomotor skills, with some adapting quickly and others more slowly. Differences in brain activity likely contribute to this variability, but direct neural evidence is lacking. We investigated individual differences in electrocortical activity that led to faster locomotor adaptation rates. We recorded high-density electroencephalography while young, neurotypical adults adapted their walking on a split-belt treadmill and grouped them based on how quickly they restored their gait symmetry. Results revealed unique spectral signatures within the posterior parietal, bilateral sensorimotor, and right visual cortices that differ between fast and slow adapters. Specifically, fast adapters exhibited lower alpha power in the posterior parietal and right visual cortices during early adaptation, associated with quicker attainment of steady-state step length symmetry. Decreased posterior parietal alpha may reflect enhanced spatial attention, sensory integration, and movement planning to facilitate faster locomotor adaptation. Conversely, slow adapters displayed greater alpha and beta power in the right visual cortex during late adaptation, suggesting potential differences in visuospatial processing. Additionally, fast adapters demonstrated reduced spectral power in the bilateral sensorimotor cortices compared with slow adapters, particularly in the theta band, which may suggest variations in perception of the split-belt perturbation. These findings suggest that alpha and beta oscillations in the posterior parietal and visual cortices and theta oscillations in the sensorimotor cortex are related to the rate of gait adaptation.

Electrocortical theta activity may reflect sensory prediction errors during adaptation to a gradual gait perturbation

Locomotor adaptation to abrupt and gradual perturbations are likely driven by fundamentally different neural processes. The aim of this study was to quantify brain dynamics associated with gait adaptation to a gradually introduced gait perturbation, which typically results in smaller behavioral errors relative to an abrupt perturbation. Loss of balance during standing and walking elicits transient increases in midfrontal theta oscillations that have been shown to scale with perturbation intensity. We hypothesized there would be no significant change in anterior cingulate theta power (4-7 Hz) with respect to pre-adaptation when a gait perturbation is introduced gradually because the gradual perturbation acceleration and stepping kinematic errors are small relative to an abrupt perturbation. Using mobile electroencephalography (EEG), we measured gait-related spectral changes near the anterior cingulate, posterior cingulate, sensorimotor, and posterior parietal cortices as young, neurotypical adults (n = 30) adapted their gait to an incremental split-belt treadmill perturbation. Most cortical clusters we examined (>70%) did not exhibit changes in electrocortical activity between 2-50 Hz. However, we did observe gait-related theta synchronization near the left anterior cingulate cortex during strides with the largest errors, as measured by step length asymmetry. These results suggest gradual adaptation with small gait asymmetry and perturbation magnitude may not require significant cortical resources beyond normal treadmill walking. Nevertheless, the anterior cingulate may remain actively engaged in error monitoring, transmitting sensory prediction error information via theta oscillations.

Use, experience and perspectives of high-density EEG among Italian epilepsy centers: a national survey

INTRODUCTION

High-density EEG (hdEEG) is a validated tool in presurgical evaluation of people with epilepsy. The aim of this national survey is to estimate diffusion and knowledge of hdEEG to develop a network among Italian epilepsy centers.

METHODS

A survey of 16 items (and 15 additional items) was distributed nationwide by email to all members of the Italian League Against Epilepsy and the Italian Society of Clinical Neurophysiology. The data obtained were analyzed using descriptive statistics.

RESULTS

A total of 104 respondents were collected from 85 centers, 82% from the Centre-North of Italy; 27% of the respondents had a hdEEG. The main applications were for epileptogenic focus characterization in the pre-surgical evaluation (35%), biomarker research (35%) and scientific activity (30%). The greatest obstacles to hdEEG were economic resources (35%), acquisition of dedicated personnel (30%) and finding expertise (17%). Dissemination was limited by difficulties in finding expertise and dedicated personnel (74%) more than buying devices (9%); 43% of the respondents have already published hdEEG data, and 91% of centers were available to participate in multicenter hdEEG studies, helping in both pre-processing and analysis. Eighty-nine percent of respondents would be interested in referring patients to centers with established experience for clinical and research purposes.

CONCLUSIONS

In Italy, hdEEG is mainly used in third-level epilepsy centers for research and clinical purposes. HdEEG diffusion is limited not only by costs but also by lack of trained personnel. Italian centers demonstrated a high interest in educational initiatives on hdEEG as well as in clinical and research collaborations.

An electroencephalographic signature predicts craving for methamphetamine

Craving is central to methamphetamine use disorder (MUD) and both characterizes the disease and predicts relapse. However, there is currently a lack of robust and reliable biomarkers for monitoring craving and diagnosing MUD. Here, we seek to identify a neurobiological signature of craving based on individual-level functional connectivity pattern differences between healthy control and MUD subjects. We train high-density electroencephalography (EEG)-based models using data recorded during the resting state and then calculate imaginary coherence features between the band-limited time series across different brain regions of interest. Our prediction model demonstrates that eyes-open beta functional connectivity networks have significant predictive value for craving at the individual level and can also identify individuals with MUD. These findings advance the neurobiological understanding of craving through an EEG-tailored computational model of the brain connectome. Dissecting neurophysiological features provides a clinical avenue for personalized treatment of MUD.

Simulation study on high spatio-temporal resolution acousto-electrophysiological neuroimaging

Objective.Acousto-electrophysiological neuroimaging (AENI) is a technique hypothesized to record electrophysiological activity of the brain with millimeter spatial and sub-millisecond temporal resolution. This improvement is obtained by tagging areas with focused ultrasound (fUS). Due to mechanical vibration with respect to the measuring electrodes, the electrical activity of the marked region will be modulated onto the ultrasonic frequency. The region's electrical activity can subsequently be retrieved via demodulation of the measured signal. In this study, the feasibility of this hypothesized technique is tested.Approach.This is done by calculating the forward electroencephalography response under quasi-static assumptions. The head is simplified as a set of concentric spheres. Two sizes are evaluated representing human and mouse brains. Moreover, feasibility is assessed for wet and dry transcranial, and for cortically placed electrodes. The activity sources are modeled by dipoles, with their current intensity profile drawn from a power-law power spectral density.Results.It is shown that mechanical vibration modulates the endogenous activity onto the ultrasonic frequency. The signal strength depends non-linearly on the alignment between dipole orientation, vibration direction and recording point. The strongest signal is measured when these three dependencies are perfectly aligned. The signal strengths are in the pV-range for a dipole moment of 5 nAm and ultrasonic pressures within Food and Drug Administration (FDA)-limits. The endogenous activity can then be accurately reconstructed via demodulation. Two interference types are investigated: vibrational and static. Depending on the vibrational interference, it is shown that millimeter resolution signal detection is possible also for deep brain regions. Subsequently, successful demodulation depends on the static interference, that at MHz-range has to be sub-picovolt.Significance.Our results show that mechanical vibration is a possible underlying mechanism of acousto-electrophyisological neuroimaging. This paper is a first step towards improved understanding of the conditions under which AENI is feasible.

Aberrant hyperfocusing in schizophrenia indicated by elevated theta phase-gamma amplitude coupling

OBJECTIVE

We aimed to investigate electroencephalographic (EEG) markers of aberrant hyperfocusing, a novel framework of impaired selective attention, in schizophrenia patients by using theta phase-gamma amplitude coupling (TGC).

METHODS

Fifty-four schizophrenia patients and 73 healthy controls (HCs) underwent EEG recording during an auditory oddball paradigm. For the standard and target conditions, TGC was calculated using the source signals from 25 brain regions of interest (ROIs) related to attention networks and sensory processing; TGC values were then compared across groups and conditions using two-way analysis of covariance. Correlations of altered TGC with performance on the Trail Making Test Parts A and B (TMT-A/B), were explored.

RESULTS

Compared to HCs, schizophrenia patients showed elevated TGC in the left inferior frontal gyrus (IFG) and superior temporal gyrus in the standard condition but not in the target condition. Correlation analyses revealed that the TGC in the left IFG was positively correlated with the TMT-A/B completion times.

CONCLUSIONS

Aberrant hyperfocusing, as reflected by elevated TGC in attention-related brain regions, was related to behavioral performance on the TMT-A/B in schizophrenia patients.

SIGNIFICANCE

This study suggests that TGC is a electrophysiological marker for aberrant hyperfocusing of attentional processes that may result in cognitive impairments in schizophrenia patients.

Targeted density electrode placement achieves high concordance with traditional high-density EEG for electrical source imaging in epilepsy

OBJECTIVE

High-density (HD) electroencephalography (EEG) is increasingly used in presurgical epilepsy evaluation, but it is demanding in time and resources. To overcome these issues, we compared EEG source imaging (ESI) solutions with a targeted density and HD-EEG montage.

METHODS

HD-EEGs from patients undergoing presurgical evaluation were analyzed. A low-density recording was created by selecting the 25 electrodes of a standard montage from the 83 electrodes of the HD-EEG and adding 8-11 electrodes around the electrode with the highest amplitude interictal epileptiform discharges. The ESI solution from this "targeted" montage was compared to that from the HD-EEG using the distance between peak vertices, sublobar concordance and a qualitative similarity measure.

RESULTS

Fifty-eight foci of forty-three patients were included. The median distance between the peak vertices of the two montages was 13.2 mm, irrespective of focus' location. Tangential generators (n = 5/58) showed a higher distance than radial generators (p = 0.04). We found sublobar concordance in 54/58 of the foci (93%). Map similarity, assessed by an epileptologist, had a median score of 4/5.

CONCLUSIONS

ESI solutions obtained from a targeted density montage show high concordance with those calculated from HD-EEG.

SIGNIFICANCE

Requiring significantly fewer electrodes, targeted density EEG allows obtaining similar ESI solutions as traditional HD-EEG montage.

Feasibility of high-density electric source imaging in the presurgical workflow: Effect of number of spikes and automated spike detection

OBJECTIVE

Presurgical high-density electric source imaging (hdESI) of interictal epileptic discharges (IEDs) is only used by few epilepsy centers. One obstacle is the time-consuming workflow both for recording as well as for visual review. Therefore, we analyzed the effect of (a) an automated IED detection and (b) the number of IEDs on the accuracy of hdESI and time-effectiveness.

METHODS

In 22 patients with pharmacoresistant focal epilepsy receiving epilepsy surgery (Engel 1) we retrospectively detected IEDs both visually and semi-automatically using the EEG analysis software Persyst in 256-channel EEGs. The amount of IEDs, the Euclidean distance between hdESI maximum and resection zone, and the operator time were compared. Additionally, we evaluated the intra-individual effect of IED quantity on the distance between hdESI maximum of all IEDs and hdESI maximum when only a reduced amount of IEDs were included.

RESULTS

There was no significant difference in the number of IEDs between visually versus semi-automatically marked IEDs (74 ± 56 IEDs/patient vs 116 ± 115 IEDs/patient). The detection method of the IEDs had no significant effect on the mean distances between resection zone and hdESI maximum (visual: 26.07 ± 31.12 mm vs semi-automated: 33.6 ± 34.75 mm). However, the mean time needed to review the full datasets semi-automatically was shorter by 275 ± 46 min (305 ± 72 min vs 30 ± 26 min, P < 0.001). The distance between hdESI of the full versus reduced amount of IEDs of the same patient was smaller than 1 cm when at least a mean of 33 IEDs were analyzed. There was a significantly shorter intraindividual distance between resection zone and hdESI maximum when 30 IEDs were analyzed as compared to the analysis of only 10 IEDs (P < 0.001).

SIGNIFICANCE

Semi-automatized processing and limiting the amount of IEDs analyzed (~30-40 IEDs per cluster) appear to be time-saving clinical tools to increase the practicability of hdESI in the presurgical work-up.

Electrocortical activity correlated with locomotor adaptation during split-belt treadmill walking

Locomotor adaptation is crucial for daily gait adjustments to changing environmental demands and obstacle avoidance. Mobile brain imaging with high-density electroencephalography (EEG) now permits quantification of electrocortical dynamics during human locomotion. To determine the brain areas involved in human locomotor adaptation, we recorded high-density EEG from healthy, young adults during split-belt treadmill walking. We incorporated a dual-electrode EEG system and neck electromyography to decrease motion and muscle artefacts. Voluntary movement preparation and execution have been linked to alpha (8-13 Hz) and beta band (13-30 Hz) desynchronizations in the sensorimotor and posterior parietal cortices, whereas theta band (4-7 Hz) modulations in the anterior cingulate have been correlated with movement error monitoring. We hypothesized that relative to normal walking, split-belt walking would elicit: (1) decreases in alpha and beta band power in sensorimotor and posterior parietal cortices, reflecting enhanced motor flexibility; and (2) increases in theta band power in anterior cingulate cortex, reflecting instability and balance errors that will diminish with practice. We found electrocortical activity in multiple regions that was associated with stages of gait adaptation. Data indicated that sensorimotor and posterior parietal cortices had decreased alpha and beta band spectral power during early adaptation to split-belt treadmill walking that gradually returned to pre-adaptation levels by the end of the adaptation period. Our findings emphasize that multiple brain areas are involved in adjusting gait under changing environmental demands during human walking. Future studies could use these findings on healthy, young participants to identify dysfunctional supraspinal mechanisms that may be impairing gait adaptation. KEY POINTS: Identifying the location and time course of electrical changes in the brain correlating with gait adaptation increases our understanding of brain function and provides targets for brain stimulation interventions. Using high-density EEG in combination with 3D biomechanics, we found changes in neural oscillations localized near the sensorimotor, posterior parietal and cingulate cortices during split-belt treadmill adaptation. These findings suggest that multiple cortical mechanisms may be associated with locomotor adaptation, and their temporal dynamics can be quantified using mobile EEG. Results from this study can serve as a reference model to examine brain dynamics in individuals with movement disorders that cause gait asymmetry and reduced gait adaptation.

Normative brain mapping using scalp EEG and potential clinical application

A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.

Seeing our hand or a tool during visually-guided actions: Different effects on the somatosensory and visual cortices

The processing of proprioceptive information in the context of a conflict between visual and somatosensory feedbacks deteriorates motor performance. Previous studies have shown that seeing one's hand increases the weighting assigned to arm somatosensory inputs. In this light, we hypothesized that the sensory conflict, when tracing the contour of a shape with mirror-reversed vision, will be greater for participants who trace with a stylus seen in their hand (Hand group, n = 17) than for participants who trace with the tip of rod without seen their hand (Tool group, n = 15). Based on this hypothesis, we predicted that the tracing performance with mirror vision will be more deteriorated for the Hand group than for the Tool group, and we predicted a greater gating of somatosensory information for the Hand group to reduce the sensory conflict. The participants of both groups followed the outline of a shape in two visual conditions. Direct vision: the participants saw the hand or portion of a light 40 cm rod directly. Mirror Vision: the hand or the rod was seen through a mirror. We measured tracing performance using a digitizing tablet and the cortical activity with electroencephalography. Behavioral analyses revealed that the tracing performance of both groups was similarly impaired by mirror vision. However, contrasting the spectral content of the cortical oscillatory activity between the Mirror and Direct conditions, we observed that tracing with mirror vision resulted in significantly larger alpha (8-12 Hz) and beta (15-25 Hz) powers in the somatosensory cortex for participants of the Hand group. The somatosensory alpha and beta powers did not significantly differ between Mirror and Direct vision conditions for the Tool group. For both groups, tracing with mirror vision altered the activity of the visual cortex: decreased alpha power for the Hand group, decreased alpha and beta power for the Tool group. Overall, these results suggest that seeing the hand enhanced the sensory conflict when tracing with mirror vision and that the increase of alpha and beta powers in the somatosensory cortex served to reduce the weight assigned to somatosensory information. The increased activity of the visual cortex observed for both groups in the mirror vision condition suggests greater visual processing with increased task difficulty. Finally, the fact that the participants of the Tool group did not show better tracing performance than those of the Hand group suggests that tracing deterioration resulted from a sensorimotor conflict (as opposed to a visuo-proprioceptive conflict).

Normative brain mapping using scalp EEG and potential clinical application

A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.

Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study

Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of connectivity patterns in this so-called "resting-state" has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different analytical choices induce significant discrepancies in results and drawn conclusions, thereby hindering the reproducibility of neuroimaging research. Hence, our objective in this study was to shed light on the effect of analytical variability on outcome consistency by evaluating the implications of parameters involved in the EEG source connectivity analysis on the accuracy of resting-state networks (RSNs) reconstruction. We simulated, using neural mass models, EEG data corresponding to two RSNs, namely the default mode network (DMN) and dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low-resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure, high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed significant variability in the performance of the tested inverse solutions and connectivity measures. Such methodological variability and absence of analysis standardization represent a critical issue for neuroimaging studies that should be prioritized. We believe that this work could be useful for the field of electrophysiology connectomics, by increasing awareness regarding the challenge of variability in methodological approaches and its implications on reported results.

Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review

Background

Dementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification.

Objective

With the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues.

Methods

In this study, ProQuest, PubMed, IEEE Xplore, Springer Link, and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia-related disorders between January 2016 to December 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy.

Results

Out of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 peer-review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics.

Significance

This study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.

Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough?

OBJECTIVE

Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities.

METHODS

EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested.

RESULTS

The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated.

CONCLUSIONS

Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data.

SIGNIFICANCE

Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.

TMS combined with EEG: Recommendations and open issues for data collection and analysis

Transcranial magnetic stimulation (TMS) evokes neuronal activity in the targeted cortex and connected brain regions. The evoked brain response can be measured with electroencephalography (EEG). TMS combined with simultaneous EEG (TMS-EEG) is widely used for studying cortical reactivity and connectivity at high spatiotemporal resolution. Methodologically, the combination of TMS with EEG is challenging, and there are many open questions in the field. Different TMS-EEG equipment and approaches for data collection and analysis are used. The lack of standardization may affect reproducibility and limit the comparability of results produced in different research laboratories. In addition, there is controversy about the extent to which auditory and somatosensory inputs contribute to transcranially evoked EEG. This review provides a guide for researchers who wish to use TMS-EEG to study the reactivity of the human cortex. A worldwide panel of experts working on TMS-EEG covered all aspects that should be considered in TMS-EEG experiments, providing methodological recommendations (when possible) for effective TMS-EEG recordings and analysis. The panel identified and discussed the challenges of the technique, particularly regarding recording procedures, artifact correction, analysis, and interpretation of the transcranial evoked potentials (TEPs). Therefore, this work offers an extensive overview of TMS-EEG methodology and thus may promote standardization of experimental and computational procedures across groups.

Examining the Low- Resolution Electromagnetic Tomography Technique for EEG Brain Mapping

NeuroIS presents a new opportunity for information systems research. Often used neuroscience techniques include brain mapping with the functional magnetic resonance imaging (fMRI) device or eventrelated potential time-domain studies with the electroencephalogram (EEG). The critics of EEG consider the poor spatial resolution as justification for EEG's inadequacy to brain mapping studies. However, the low-resolution electromagnetic tomography (LORETA) technique provides strong estimation parameters allowing EEG to perform brain mapping. This paper presents EEG (with lower number of channels) and LORETA techniques as an effective approach for exploratory investigation specially when researchers are constrained with lack of resources (specially at significantly lower costs). We demonstrate the effectiveness of EEG using sLORETA with respect to fMRI as proof-of-concept approach to study IS phenomenon. The results of such studies can serve as a preliminary step for further analysis with the use of more sophisticated neuroscience devices. This can enhance IS research by taking advantage of both high temporal and spatial resolution leading to reduced estimation errors of neural activity and stronger basis for correlating neural activity and specific tasks. We also present a set of guidelines for using the LORETA family of techniques in IS research.

Spatio-temporal dynamics of oscillatory brain activity during the observation of actions and interactions between point-light agents

Predicting actions from non-verbal cues and using them to optimise one's response behaviour (i.e. interpersonal predictive coding) is essential in everyday social interactions. We aimed to investigate the neural correlates of different cognitive processes evolving over time during interpersonal predictive coding. Thirty-nine participants watched two agents depicted by moving point-light stimuli while an electroencephalogram (EEG) was recorded. One well-recognizable agent performed either a 'communicative' or an 'individual' action. The second agent either was blended into a cluster of noise dots (i.e. present) or was entirely replaced by noise dots (i.e. absent), which participants had to differentiate. EEG amplitude and coherence analyses for theta, alpha and beta frequency bands revealed a dynamic pattern unfolding over time: Watching communicative actions was associated with enhanced coupling within medial anterior regions involved in social and mentalising processes and with dorsolateral prefrontal activation indicating a higher deployment of cognitive resources. Trying to detect the agent in the cluster of noise dots without having seen communicative cues was related to enhanced coupling in posterior regions for social perception and visual processing. Observing an expected outcome was modulated by motor system activation. Finally, when the agent was detected correctly, activation in posterior areas for visual processing of socially relevant features was increased. Taken together, our results demonstrate that it is crucial to consider the temporal dynamics of social interactions and of their neural correlates to better understand interpersonal predictive coding. This could lead to optimised treatment approaches for individuals with problems in social interactions.

Applying correlation analysis to electrode optimization in source domain

In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.

EEG Resting-State Functional Networks in Amnestic Mild Cognitive Impairment

Background. Alzheimer's cognitive-behavioral syndrome is the result of impaired connectivity between nerve cells, due to misfolded proteins, which accumulate and disrupt specific brain networks. Electroencephalography, because of its excellent temporal resolution, is an optimal approach for assessing the communication between functionally related brain regions. Objective. To detect and compare EEG resting-state networks (RSNs) in patients with amnesic mild cognitive impairment (aMCI), and healthy elderly (HE). Methods. We recruited 125 aMCI patients and 70 healthy elderly subjects. One hundred and twenty seconds of artifact-free EEG data were selected and compared between patients with aMCI and HE. We applied standard low-resolution brain electromagnetic tomography (sLORETA)-independent component analysis (ICA) to assess resting-state networks. Each network consisted of a set of images, one for each frequency (delta, theta, alpha1/2, beta1/2). Results. The functional ICA analysis revealed 17 networks common to groups. The statistical procedure demonstrated that aMCI used some networks differently than HE. The most relevant findings were as follows. Amnesic-MCI had: i) increased delta/beta activity in the superior frontal gyrus and decreased alpha1 activity in the paracentral lobule (ie, default mode network); ii) greater delta/theta/alpha/beta in the superior frontal gyrus (i.e, attention network); iii) lower alpha in the left superior parietal lobe, as well as a lower delta/theta and beta, respectively in post-central, and in superior frontal gyrus(ie, attention network). Conclusions. Our study confirms sLORETA-ICA method is effective in detecting functional resting-state networks, as well as between-groups connectivity differences. The findings provide support to the Alzheimer's network disconnection hypothesis.

Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization

High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations are often manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrode configurations are not often selected according to their contribution to estimation accuracy. In this work, an optimization-based study is proposed to determine the minimum number of electrodes that can be used and to identify the optimal combinations of electrodes that can retain the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single- and multiple-source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that concurrently minimizes (1) the localization error for each source and (2) the number of required EEG electrodes. The method can be used for evaluating the source localization quality of low-density EEG systems (e.g. consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG datasets with known ground-truth. The experimental results show that optimal subsets with 6 electrodes can attain an equal or better accuracy than HD-EEG (with more than 200 channels) for a single source case. This happened when reconstructing a particular brain activity in more than 88% of the cases in synthetic signals and 63% in real signals, and in more than 88% and 73% of cases when considering optimal combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that optimized combinations of 8, 12 and 16 electrodes attained an equal or better accuracy than HD-EEG with 231 electrodes in at least 58%, 76%, and 82% of cases respectively. Additionally, for such electrode numbers, lower mean errors and standard deviations than with 231 electrodes were obtained.

Methods Used to Estimate EEG Source-Space Networks: A Comparative Simulation-Based Study

Along with the study of the brain activity evoked by external stimuli, an important advance in current neuroscience involves understanding the spontaneous brain activity that occurs during resting conditions. Interestingly, the identification of the connectivity patterns in "resting-state" has been the subject of a great number of electrophysiology-based studies. In this context, the Electroencephalography (EEG) source connectivity method enables estimating resting-state cortical networks from scalp-EEG recordings. However, there is still no consensus over a unified pipeline adapted in all cases (e.g., type of task, a priori on studied networks) and numerous methodological questions remain unanswered. In order to address this problem, we simulated, using neural mass models, EEG data corresponding to the default mode network (DMN), the most widely studied resting-state network, and tested the effect of different channel densities, two inverse solutions and two functional connectivity measures on the correspondence between the reconstructed networks and the reference networks. Results showed that increasing the number of electrodes enhances the accuracy of the network reconstruction, and that eLORETA/PLV led to better accuracy than other inverse solution/connectivity measure combinations in terms of the correlation between reconstructed and reference connectivity matrices. This work has a wide range of implications in the field of electrophysiology connectomics, and is a step towards a convergence and standardization of approaches in this emerging field.

Utility of Cognitive Neural Features for Predicting Mental Health Behaviors

Cognitive dysfunction underlies common mental health behavioral symptoms including depression, anxiety, inattention, and hyperactivity. In this study of 97 healthy adults, we aimed to classify healthy vs. mild-to-moderate self-reported symptoms of each disorder using cognitive neural markers measured with an electroencephalography (EEG). We analyzed source-reconstructed EEG data for event-related spectral perturbations in the theta, alpha, and beta frequency bands in five tasks, a selective attention and response inhibition task, a visuospatial working memory task, a Flanker interference processing task, and an emotion interference task. From the cortical source activation features, we derived augmented features involving co-activations between any two sources. Logistic regression on the augmented feature set, but not the original feature set, predicted the presence of psychiatric symptoms, particularly for anxiety and inattention with >80% sensitivity and specificity. We also computed current flow closeness and betweenness centralities to identify the “hub” source signal predictors. We found that the Flanker interference processing task was the most useful for assessing the connectivity hubs in general, followed by the inhibitory control go-nogo paradigm. Overall, these interpretable machine learning analyses suggest that EEG biomarkers collected on a rapid suite of cognitive assessments may have utility in classifying diverse self-reported mental health symptoms.

Cortical Source Analysis of Event-Related Potentials: A Developmental Approach

Cortical source analysis of electroencephalographic (EEG) signals has become an important tool in the analysis of brain activity. The aim of source analysis is to reconstruct the cortical generators (sources) of the EEG signal recorded on the scalp. The quality of the source reconstruction relies on the accuracy of the forward problem, and consequently the inverse problem. An accurate forward solution is obtained when an appropriate imaging modality (i.e., structural magnetic resonance imaging - MRI) is used to describe the head geometry, precise electrode locations are identified with 3D maps of the sensor positions on the scalp, and realistic conductivity values are determined for each tissue type of the head model. Together these parameters contribute to the definition of realistic head models. Here, we describe the steps necessary to reconstruct the cortical generators of the EEG signal recorded on the scalp. We provide an example of source reconstruction of event-related potentials (ERPs) during a face-processing task performed by a 6-month-old infant. We discuss the adjustments necessary to perform source analysis with measures different from the ERPs. The proposed pipeline can be applied to the investigation of different cognitive tasks in both younger and older participants.

On the Dynamics of Spatial Updating

Most of our knowledge on the human neural bases of spatial updating comes from functional magnetic resonance imaging (fMRI) studies in which recumbent participants moved in virtual environments. As a result, little is known about the dynamic of spatial updating during real body motion. Here, we exploited the high temporal resolution of electroencephalography (EEG) to investigate the dynamics of cortical activation in a spatial updating task where participants had to remember their initial orientation while they were passively rotated about their vertical axis in the dark. After the rotations, the participants pointed toward their initial orientation. We contrasted the EEG signals with those recorded in a control condition in which participants had no cognitive task to perform during body rotations. We found that the amplitude of the P₁N₁ complex of the rotation-evoked potential (RotEPs) (recorded over the vertex) was significantly greater in the Updating task. The analyses of the cortical current in the source space revealed that the main significant task-related cortical activities started during the N₁P₂ interval (136–303 ms after rotation onset). They were essentially localized in the temporal and frontal (supplementary motor complex, dorsolateral prefrontal cortex, anterior prefrontal cortex) regions. During this time-window, the right superior posterior parietal cortex (PPC) also showed significant task-related activities. The increased activation of the PPC became bilateral over the P₂N₂ component (303–470 ms after rotation onset). In this late interval, the cuneus and precuneus started to show significant task-related activities. Together, the present results are consistent with the general scheme that the first task-related cortical activities during spatial updating are related to the encoding of spatial goals and to the storing of spatial information in working memory. These activities would precede those involved in higher order processes also relevant for updating body orientation during rotations linked to the egocentric and visual representations of the environment.

Case Report: Laser Ablation Guided by State of the Art Source Imaging Ends an Adolescent's 16-Year Quest for Seizure Freedom

Epilepsy surgery is the most effective therapeutic approach for children with drug resistant epilepsy (DRE). Recent advances in neurosurgery, such as the Laser Interstitial Thermal Therapy (LITT), improved the safety and non-invasiveness of this method. Electric and magnetic source imaging (ESI/MSI) plays critical role in the delineation of the epileptogenic focus during the presurgical evaluation of children with DRE. Yet, they are currently underutilized even in tertiary epilepsy centers. Here, we present a case of an adolescent who suffered from DRE for 16 years and underwent surgery at Cook Children's Medical Center (CCMC). The patient was previously evaluated in a level 4 epilepsy center and treated with multiple antiseizure medications for several years. Presurgical evaluation at CCMC included long-term video electroencephalography (EEG), magnetoencephalography (MEG) with simultaneous conventional EEG (19 channels) and high-density EEG (256 channels) in two consecutive sessions, MRI, and fluorodeoxyglucose - positron emission tomography (FDG-PET). Video long-term EEG captured nine focal-onset clinical seizures with a maximal evolution over the right frontal/frontal midline areas. MRI was initially interpreted as non-lesional. FDG-PET revealed a small region of hypometabolism at the anterior right superior temporal gyrus. ESI and MSI performed with dipole clustering showed a tight cluster of dipoles in the right anterior insula. The patient underwent intracranial EEG which indicated the right anterior insular as seizure onset zone. Eventually LITT rendered the patient seizure free (Engel 1; 12 months after surgery). Retrospective analysis of ESI and MSI clustered dipoles found a mean distance of dipoles from the ablated volume ranging from 10 to 25 mm. Our findings highlight the importance of recent technological advances in the presurgical evaluation and surgical treatment of children with DRE, and the underutilization of epilepsy surgery in children with DRE.

Comparison of Spinal Cord Stimulation vs. Dorsal Root Ganglion Stimulation vs. Association of Both in Patients with Refractory Chronic Back and/or Lower Limb Neuropathic Pain: An International, Prospective, Randomized, Double-Blinded, Crossover Trial (BOOST-DRG Study)

While spinal cord stimulation (SCS) is a well-established therapy to address refractory persistent spinal pain syndrome after spinal surgery (PSPS-T2), its lack of spatial selectivity and reported discomfort due to positional effects can be considered as significant limitations. As alternatives, new waveforms, such as burst stimulation and different spatial neural targets, such as dorsal root ganglion stimulation (DRGS), have shown promising results. Comparisons between DRGS and standard SCS, or their combination, have never been studied on the same patients. "BOOST DRG" is the first prospective, randomized, double-blinded, crossover study to compare SCS vs. DRGS vs. SCS+DRGS. Sixty-six PSPS-T2 patients will be recruited internationally in three centers. Before crossing over, patients will receive each stimulation modality for 1 month, using tonic conventional stimulation. After 3 months, stimulation will consist in switching to burst for 1 month, and patients will choose which modality/waveform they receive and will then be reassessed at 6 and 12 months. In addition to our primary outcome based on pain rating, this study is designed to assess quality of life, functional disability, psychological distress, pain surface coverage, global impression of change, medication quantification, adverse events, brain functional imaging and electroencephalography, with the objective being to provide a multidimensional insight based on composite pain assessment.

Early and late signals of unexpected reward contribute to low extraversion and high disinhibition, respectively

Like socio-economic status and cognitive abilities, personality traits predict important life outcomes. Traits that reflect unusually low or high approach motivations, such as low extraversion and high disinhibition, are linked to various forms of mental disorder. Similarly, the dopamine system is theoretically linked to approach motivation traits and to various forms of mental disorder. Identifying neural contributions to extremes of such traits should map to neural sources of psychopathology, with dopamine a prime candidate. Notably, dopamine cells fire in response to unexpected reward, which suggests that the size of non-invasive, scalp-recorded potentials evoked by unexpected reward could reflect sensitivity in approach motivation traits. Here, we evaluated the validity of evoked electroencephalography (EEG) responses to unexpected reward in a monetary gain/loss task to assess approach motivation traits in 137 participants, oversampled for externalizing psychopathology symptoms. We demonstrated that over the 0–400 ms period in which feedback on the outcome was presented, responses evoked by unexpected reward contributed to all theoretically relevant approach motivation trait domains (disinhibition, extraversion and the behavioural activation system); and did so only at times when dopamine responses normally peak and reportedly code salience (70–100 ms) and valuation (200–300 ms). In particular, we linked “dopaminergic” salience and valuation to the psychopathology-related constructs of low extraversion (social anxiety) and high disinhibition (impulsivity) respectively, making the evoked potential components biomarker candidates for indexing aberrant processing of unexpected reward.

EEG source imaging concordance with intracranial EEG and epileptologist review in focal epilepsy

EEG source imaging is becoming widely used for the evaluation of medically refractory focal epilepsy. The validity of EEG source imaging has been established in several studies comparing source imaging to the surgical resection cavity and subsequent seizure freedom. We present a cohort of 87 patients and compare EEG source imaging of both ictal and interictal scalp EEG to the seizure onset zone on intracranial EEG. Concordance of EEG source imaging with intracranial EEG was determined on a sublobar level and was quantified by measuring the distance between the source imaging result and the centroid of the active seizure onset zone electrodes. The EEG source imaging results of a subgroup of 26 patients with high density 76-channel EEG were compared with the localization of three experienced epileptologists. Of 87 patients, 95% had at least one analysis concordant with intracranial EEG and 74% had complete concordance. There was a higher rate of complete concordance in temporal lobe epilepsy compared to extratemporal (89.3 and 62.8%, respectively, P = 0.015). Of the total 282 analyses performed on this cohort, higher concordance was also seen in temporal discharges (95%) compared to extratemporal (77%) (P = 0.0012), but no difference was seen comparing high-density EEG with standard (32-channel) EEG. Subgroup analysis of ictal waveforms showed greater concordance for ictal spiking, compared with rhythmic activity, paroxysmal fast activity, or obscured onset. Median distances from the dipole and maximum distributed source to a centroid of seizure onset zone electrodes were 30.0 and 32.5 mm, respectively, and the median distances from dipole and maximum distributed source to nearest seizure onset zone electrode were 22.8 and 21.7, respectively. There were significantly shorter distances in ictal spiking. There were shorter distances in patients with Engel Class 1 outcome from surgical resection compared to patients with worse outcomes. For the subgroup of 26 high-density EEG patients, EEG source localization had a significantly higher concordance (92% versus 65%), sensitivity (57% versus 35%) and positive predictive value (60% versus 36%) compared with epileptologist localization. Our study demonstrates good concordance between ictal and interictal source imaging and intracranial EEG. Temporal lobe discharges have higher concordance rates than extratemporal discharges. Importantly, this study shows that source imaging has greater agreement with intracranial EEG than visual review alone, supporting its role in surgical planning.

A high-density 256-channel cap for dry electroencephalography

High‐density electroencephalography (HD‐EEG) is currently limited to laboratory environments since state‐of‐the‐art electrode caps require skilled staff and extensive preparation. We propose and evaluate a 256‐channel cap with dry multipin electrodes for HD‐EEG. We describe the designs of the dry electrodes made from polyurethane and coated with Ag/AgCl. We compare in a study with 30 volunteers the novel dry HD‐EEG cap to a conventional gel‐based cap for electrode‐skin impedances, resting state EEG, and visual evoked potentials (VEP). We perform wearing tests with eight electrodes mimicking cap applications on real human and artificial skin. Average impedances below 900 kΩ for 252 out of 256 dry electrodes enables recording with state‐of‐the‐art EEG amplifiers. For the dry EEG cap, we obtained a channel reliability of 84% and a reduction of the preparation time of 69%. After exclusion of an average of 16% (dry) and 3% (gel‐based) bad channels, resting state EEG, alpha activity, and pattern reversal VEP can be recorded with less than 5% significant differences in all compared signal characteristics metrics. Volunteers reported wearing comfort of 3.6 ± 1.5 and 4.0 ± 1.8 for the dry and 2.5 ± 1.0 and 3.0 ± 1.1 for the gel‐based cap prior and after the EEG recordings, respectively (scale 1–10). Wearing tests indicated that up to 3,200 applications are possible for the dry electrodes. The 256‐channel HD‐EEG dry electrode cap overcomes the principal limitations of HD‐EEG regarding preparation complexity and allows rapid application by not medically trained persons, enabling new use cases for HD‐EEG.

Time Course of Odor Categorization Processing

The brain’s mechanisms for categorizing different odors have long been a research focus. Previous studies suggest that odor categorization may involve multiple neurological processes within the brain with temporal and spatial neuronal activation. However, there is limited evidence regarding temporally mediated mechanisms in humans, especially millisecond odor processing. Such mechanisms may be important because different brain areas may play different roles at a particular activation time during sensory processing. Here, we focused on how the brain categorizes odors at specific time intervals. Using multivariate electroencephalography (EEG) analysis, we found that similarly perceived odors induced similar EEG signals during 50–100, 150–200, and 350–400 ms at the theta frequency. We also found significant activation at 100–150 and 350–400 ms at the gamma frequency. At these two frequencies, significant activation was observed in some olfactory-associated areas, including the orbitofrontal cortex. Our findings provide essential evidence that specific periods may be related to odor quality processing during central olfactory processing.

Automated interictal source localisation based on high-density EEG

PURPOSE

To study the accuracy of automated interictal EEG source localisation based on high-density EEG, and to compare it to low-density EEG.

METHODS

Thirty patients operated for pharmacoresistant focal epilepsy were retrospectively examined. Twelve months after resective brain surgery, 18 were seizure-free or had 'auras' only, while 12 had persistence of disabling seizures. Presurgical 257-channel EEG lasting 3-20 h was down-sampled to 25, 40, and 204 channels for separate analyses. For each electrode setup, interictal spikes were detected, clustered, and averaged automatically before validation by an expert reviewer. An individual 6-layer finite difference head model and the standardised low-resolution electromagnetic tomography were used to localise the maximum source activity of the most prevalent spike. Sublobar concordance with the resected brain area was visually assessed and related to favourable vs. unfavourable postsurgical outcome.

RESULTS

Depending on the EEG setup, epileptic spikes were detected in 21-24 patients (70-80%). The median number of single spikes per average was 470 (range 17-15,066). Diagnostic sensitivity of EEG source localisation was 58-75%, specificity was 50-67%, and overall accuracy was 55-71%. There were no significant differences between low- and high-density EEG setups with 25 to 257 electrodes.

CONCLUSION

Automated high-density EEG source localisation provides meaningful information in the majority of cases. With hundreds of single spikes averaged, diagnostic accuracy is similar in high- and low-density EEG. Therefore, low-density EEG may be sufficient for interictal EEG source localisation if high numbers of spikes are available.

Improving surgical outcome with electric source imaging and high field magnetic resonance imaging

While most patients with focal epilepsy present with clear structural abnormalities on standard, 1.5 or 3 T MRI, some patients are MRI-negative. For those, quantitative MRI techniques, such as volumetry, voxel-based morphometry, and relaxation time measurements can aid in finding the epileptogenic focus. High-field MRI, just recently approved for clinical use by the FDA, increases the resolution and, in several publications, was shown to improve the detection of focal cortical dysplasias and mild cortical malformations. For those cases without any tissue abnormality in neuroimaging, even at 7 T, scalp EEG alone is insufficient to delimitate the epileptogenic zone. They may benefit from the use of high-density EEG, in which the increased number of electrodes helps improve spatial sampling. The spatial resolution of even low-density EEG can benefit from electric source imaging techniques, which map the source of the recorded abnormal activity, such as interictal epileptiform discharges, focal slowing, and ictal rhythm. These EEG techniques help localize the irritative, functional deficit, and seizure-onset zone, to better estimate the epileptogenic zone. Combining those technologies allows several drug-resistant cases to be submitted to surgery, increasing the odds of seizure freedom and providing a must needed hope for patients with epilepsy.

Mean-Field Modeling of Brain-Scale Dynamics for the Evaluation of EEG Source-Space Networks

Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called "electroencephalography (EEG) source connectivity" has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted to avoid pitfalls. Thus, a validation toolbox that provides flexible "ground truth" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the "EEG source connectivity" pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.

Disruption of the Prefrontal Cortex Improves Implicit Contextual Memory-Guided Attention: Combined Behavioral and Electrophysiological Evidence

Many studies have shown that the dorsolateral prefrontal cortex (DLPFC) plays an important role in top-down cognitive control over intentional and deliberate behavior. However, recent studies have reported that DLPFC-mediated top-down control interferes with implicit forms of learning. Here we used continuous theta-burst stimulation (cTBS) combined with electroencephalography to investigate the causal role of DLPFC in implicit contextual memory-guided attention. We aimed to test whether transient disruption of the DLPFC would interfere with implicit learning performance and related electrical brain activity. We applied neuronavigation-guided cTBS to the DLPFC or to the vertex as a control region prior to the performance of an implicit contextual learning task. We found that cTBS applied over the DLPFC significantly improved performance during implicit contextual learning. We also noted that beta-band (13-19 Hz) oscillatory power was reduced at fronto-central channels about 140 to 370 ms after visual stimulus onset in cTBS DLPFC compared with cTBS vertex. Taken together, our results provide evidence that DLPFC-mediated top-down control interferes with contextual memory-guided attention and beta-band oscillatory activity.

High-Density EEG in Current Clinical Practice and Opportunities for the Future

SUMMARY

High-density EEG (HD-EEG) recordings use a higher spatial sampling of scalp electrodes than a standard 10-20 low-density EEG montage. Although several studies have demonstrated improved localization of the epileptogenic cortex using HD-EEG, widespread implementation is impeded by cost, setup and interpretation time, and lack of specific or sufficient procedural billing codes. Despite these barriers, HD-EEG has been in use at several institutions for years. These centers have noted utility in a variety of clinical scenarios where increased spatial resolution from HD-EEG has been required, justifying the extra time and cost. We share select scenarios from several centers, using different recording techniques and software, where HD-EEG provided information above and beyond the standard low-density EEG. We include seven cases where HD-EEG contributed directly to current clinical care of epilepsy patients and highlight two novel techniques which suggest potential opportunities to improve future clinical care. Cases illustrate how HD-EEG allows clinicians to: case 1-lateralize falsely generalized interictal epileptiform discharges; case 2-improve localization of falsely generalized epileptic spasms; cases 3 and 4-improve localization of interictal epileptiform discharges in anatomic regions below the circumferential limit of standard low-density EEG coverage; case 5-improve noninvasive localization of the seizure onset zone in lesional epilepsy; cases 6 and 7-improve localization of the seizure onset zone to guide invasive investigation near eloquent cortex; case 8-identify epileptic fast oscillations; and case 9-map language cortex. Together, these nine cases illustrate that using both visual analysis and advanced techniques, HD-EEG can play an important role in clinical management.

Efficient high resolution sLORETA in brain source localization

Objective. Estimation of the source location within the brain from electroencephalography (EEG) and magnetoencephalography measures is a challenging task. Among the existing techniques in the field, which are known as brain imaging methods, standardized low-resolution brain electromagnetic tomography (sLORETA) is the most popular method due to its simplicity and high accuracy. However, in this work we illustrate that sLORETA is still noisy and the additive noise is causing the blurry image. The existing pre-fixed/manual thresholding process after sLORETA can partially take care of denoising. However, this ad-hoc theresholding can either remove so much of the desired data or leave much of the noise in the process. Manual correction to avoid such extreme cases can be time-consuming. The objective of this paper is to automate the denoising process in the form of adaptive thresholding. Approach. The proposed method, denoted by efficient high-resolution sLORETA (EHR-sLORETA), is based on minimizing the error between the desired denoised source and the source estimates. Main results. The approach is evaluated using synthetic EEG and real EEG data. spatial dispersion (SD), and mean square error (MSE) are used as metrics to provide the quantitative performance of the method. In addition, qualitative analysis of the method is provided for real EEG data. This proposed model demonstrates advantages over the existing methods in sense of accuracy and robustness with SD and MSE comparison. Significance. EHR-sLORETA could have a significant impact on clinical studies with source estimation task, as it improves the accuracy of source estimation and eliminates the need for manual thresholding.

Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity

BACKGROUND

Oscillations in the resting-state scalp electroencephalogram (EEG) represent various intrinsic brain activities. One of the characteristic EEG oscillations is the sensorimotor rhythm (SMR)-with its arch-shaped waveform in alpha- and betabands-that reflect sensorimotor activity. The representation of sensorimotor activity by the SMR depends on the signal-to-noise ratio achieved by EEG spatial filters.

NEW METHOD

We employed simultaneous recording of EEG and functional magnetic resonance imaging, and 10-min resting-state brain activities were recorded in 19 healthy volunteers. To compare the EEG spatial-filtering methods commonly used for extracting sensorimotor cortical activities, we assessed nine different spatial-filters: a default reference of EEG amplifier system, a common average reference (CAR), small-, middle- and large-Laplacian filters, and four types of bipolar manners (C3-Cz, C3-F3, C3-P3, and C3-T7). We identified the brain region that correlated with the EEG-SMR power obtained after each spatial-filtering method was applied. Subsequently, we calculated the proportion of the significant voxels in the sensorimotor cortex as well as the sensorimotor occupancy in all significant regions to examine the sensitivity and specificity of each spatial-filter.

RESULTS

The CAR and large-Laplacian spatial-filters were superior at improving the signal-to-noise ratios for extracting sensorimotor activity from the EEG-SMR signal.

COMPARISON WITH EXISTING METHODS

Our results are consistent with the spatial-filter selection to extract task-dependent activation for better control of EEG-SMR-based interventions. Our approach has the potential to identify the optimal spatial-filter for EEG-SMR.

CONCLUSIONS

Evaluating spatial-filters for extracting spontaneous sensorimotor activity from the EEG is a useful procedure for constructing more effective EEG-SMR-based interventions.

ELECTROENCEPHALOGRAPHY FEATURE ENHANCEMENT BASED ON ELECTRODE ACTIVITY RATIO FOR IDENTIFICATION

Purpose: Selecting the proper electrodes to capture EEG signals is a critical issue that affects overall classification accuracy. Using the pre-selected electrodes is impractical due to the variety of responses to a stimulus among individuals. Thus, discarding electrodes may lead to a loss of useful information. Methods: In this work, a novel algorithm is proposed to help address this problem by manipulating the feature values of each electrode individually according to its activity ratio. Plus, to improve EEG feature vectors that correspond to the electrodes’ energetic levels, the algorithm amplifies or dampens the feature values according to the energy ratio of each electrode individually. The algorithm was examined using a public dataset and statistical features. The test was performed using two different groups of electrodes: the first in a more specific area over the motor cortex with seven channels and the second in the area over and close to the motor cortex with twenty-one channels to prove the existence of useful information in adjacent electrodes. Further, the proposed method was tested using 109 subjects and various kinds of stimuli. Results: The algorithm enhanced the identification accuracy to approximately 10.4% in eye-opened and 7.8% in eye-closed stimuli in all frequency bands. Conclusions: The experiments revealed that using pre-selected electrodes may lead to a loss of valuable information that exists in discarded electrodes. It also showed that each electrode has its unique activity level, hence processing all the electrodes equally are impractical.

Balance task difficulty affects postural sway and cortical activity in healthy adolescents

Electroencephalographic (EEG) research indicates changes in adults’ low frequency bands of frontoparietal brain areas executing different balance tasks with increasing postural demands. However, this issue is unsolved for adolescents when performing the same balance task with increasing difficulty. Therefore, we examined the effects of a progressively increasing balance task difficulty on balance performance and brain activity in adolescents. Thirteen healthy adolescents aged 16–17 year performed tests in bipedal upright stance on a balance board with six progressively increasing levels of task difficulty. Postural sway and cortical activity were recorded simultaneously using a pressure sensitive measuring system and EEG. The power spectrum was analyzed for theta (4–7 Hz) and alpha-2 (10–12 Hz) frequency bands in pre-defined frontal, central, and parietal clusters of electrocortical sources. Repeated measures analysis of variance (rmANOVA) showed a significant main effect of task difficulty for postural sway (p < 0.001; d = 6.36). Concomitantly, the power spectrum changed in frontal, bilateral central, and bilateral parietal clusters. RmANOVAs revealed significant main effects of task difficulty for theta band power in the frontal (p < 0.001, d = 1.80) and both central clusters (left: p < 0.001, d = 1.49; right: p < 0.001, d = 1.42) as well as for alpha-2 band power in both parietal clusters (left: p < 0.001, d = 1.39; right: p < 0.001, d = 1.05) and in the central right cluster (p = 0.005, d = 0.92). Increases in theta band power (frontal, central) and decreases in alpha-2 power (central, parietal) with increasing balance task difficulty may reflect increased attentional processes and/or error monitoring as well as increased sensory information processing due to increasing postural demands. In general, our findings are mostly in agreement with studies conducted in adults. Similar to adult studies, our data with adolescents indicated the involvement of frontoparietal brain areas in the regulation of postural control. In addition, we detected that activity of selected brain areas (e.g., bilateral central) changed with increasing postural demands.

Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources

Brain networks are spatiotemporal phenomena that dynamically vary over time. Functional imaging approaches strive to noninvasively estimate these underlying processes. Here, we propose a novel source imaging approach that uses high-density EEG recordings to map brain networks. This approach objectively addresses the long-standing limitations of conventional source imaging techniques, namely, difficulty in objectively estimating the spatial extent, as well as the temporal evolution of underlying brain sources. We validate our approach by directly comparing source imaging results with the intracranial EEG (iEEG) findings and surgical resection outcomes in a cohort of 36 patients with focal epilepsy. To this end, we analyzed a total of 1,027 spikes and 86 seizures. We demonstrate the capability of our approach in imaging both the location and spatial extent of brain networks from noninvasive electrophysiological measurements, specifically for ictal and interictal brain networks. Our approach is a powerful tool for noninvasively investigating large-scale dynamic brain networks.

Noninvasive electromagnetic measurements are utilized effectively to estimate large scale dynamic brain networks. Sohrabpour et al. propose a novel electrophysiological source imaging approach to estimate the location and size of epileptogenic tissues in patients with epilepsy.

The Application of EEG Mu Rhythm Measures to Neurophysiological Research in Stuttering

Deficits in basal ganglia-based inhibitory and timing circuits along with sensorimotor internal modeling mechanisms are thought to underlie stuttering. However, much remains to be learned regarding the precise manner how these deficits contribute to disrupting both speech and cognitive functions in those who stutter. Herein, we examine the suitability of electroencephalographic (EEG) mu rhythms for addressing these deficits. We review some previous findings of mu rhythm activity differentiating stuttering from non-stuttering individuals and present some new preliminary findings capturing stuttering-related deficits in working memory. Mu rhythms are characterized by spectral peaks in alpha (8-13 Hz) and beta (14-25 Hz) frequency bands (mu-alpha and mu-beta). They emanate from premotor/motor regions and are influenced by basal ganglia and sensorimotor function. More specifically, alpha peaks (mu-alpha) are sensitive to basal ganglia-based inhibitory signals and sensory-to-motor feedback. Beta peaks (mu-beta) are sensitive to changes in timing and capture motor-to-sensory (i.e., forward model) projections. Observing simultaneous changes in mu-alpha and mu-beta across the time-course of specific events provides a rich window for observing neurophysiological deficits associated with stuttering in both speech and cognitive tasks and can provide a better understanding of the functional relationship between these stuttering symptoms. We review how independent component analysis (ICA) can extract mu rhythms from raw EEG signals in speech production tasks, such that changes in alpha and beta power are mapped to myogenic activity from articulators. We review findings from speech production and auditory discrimination tasks demonstrating that mu-alpha and mu-beta are highly sensitive to capturing sensorimotor and basal ganglia deficits associated with stuttering with high temporal precision. Novel findings from a non-word repetition (working memory) task are also included. They show reduced mu-alpha suppression in a stuttering group compared to a typically fluent group. Finally, we review current limitations and directions for future research.

Overview of EEG Montages and Principles of Localization

Montages are logical, orderly arrangements of electroencephalographic derivations or channels that are created to display activity over the entire head and to provide lateralizing and localizing information. Most often, bipolar and referential montages are used for routine electroencephalographic recordings. Common average and Laplacian montages can also be helpful in some situations. Because each type of montage has certain strengths and limitations, the ACNS guidelines recommend the use of multiple classes of montages for each electroencephalographic recording. A variety of factors need to be considered for localization by scalp electroencephalogram, but in clinical practice, a three-step approach can be used to localize an interictal epileptiform discharge by visual inspection using a standard set of scalp electrodes and conventional montages. The ACNS guideline provides a number of standard and suggested montages, but, depending on the clinical situation, additional montages can be designed using the electrodes within the 10-20 system or by placing additional electrodes.

Safety of slow-pulsed transcranial electrical stimulation in acute spike suppression

We examined the effects of slow‐pulsed transcranial electrical stimulation (TES) in suppressing epileptiform discharges in seven adults with refractory epilepsy. An MRI‐based realistic head model was constructed for each subject and co‐registered with 256‐channel dense EEG (dEEG). Interictal spikes were localized, and TES targeted the cortical source of each subject's principal spike population. Targeted spikes were suppressed in five subject's (29/35 treatment days overall), and nontargeted spikes were suppressed in four subjects. Epileptiform activity did not worsen. This study suggests that this protocol, designed to induce long‐term depression (LTD), is safe and effective in acute suppression of interictal epileptiform discharges.