Towards the utilization of EEG as a brain imaging tool

Neural substrates of early executive function development

In the last decade, advances in neuroimaging technologies have given rise to a large number of research studies that investigate the neural underpinnings of executive function (EF). EF has long been associated with the prefrontal cortex (PFC) and involves both a unified, general element, as well as the distinct, separable elements of working memory, inhibitory control and set shifting. We will highlight the value of utilising advances in neuroimaging techniques to uncover answers to some of the most pressing questions in the field of early EF development. First, this review will explore the development and neural substrates of each element of EF. Second, the structural, anatomical and biochemical changes that occur in the PFC during infancy and throughout childhood will be examined, in order to address the importance of these changes for the development of EF. Third, the importance of connectivity between regions of the PFC and other brain areas in EF development is reviewed. Finally, throughout this review more recent developments in neuroimaging techniques will be addressed, alongside the implications for further elucidating the neural substrates of early EF development in the future.

Rapid Extraction of Emotion Regularities from Complex Scenes in the Human Brain

Adaptive behavior requires the rapid extraction of behaviorally relevant information in the environment, with particular emphasis on emotional cues. However, the speed of emotional feature extraction from complex visual environments is largely undetermined. Here we use objective electrophysiological recordings in combination with frequency tagging to demonstrate that the extraction of emotional information from neutral, pleasant, or unpleasant naturalistic scenes can be completed at a presentation speed of 167 ms (i.e., 6 Hz) under high perceptual load. Emotional compared to neutral pictures evoked enhanced electrophysiological responses with distinct topographical activation patterns originating from different neural sources. Cortical facilitation in early visual cortex was also more pronounced for scenes with pleasant compared to unpleasant or neutral content, suggesting a positivity offset mechanism dominating under conditions of rapid scene processing. These results significantly advance our knowledge of complex scene processing in demonstrating rapid integrative content identification, particularly for emotional cues relevant for adaptive behavior in complex environments.

Steady state visual evoked potentials in reading aloud: Effects of lexicality, frequency and orthographic familiarity

The present study explored the possibility to use Steady-State Visual Evoked Potentials (SSVEPs) as a tool to investigate the core mechanisms in visual word recognition. In particular, we investigated three benchmark effects of reading aloud: lexicality (words vs. pseudowords), frequency (high-frequency vs. low-frequency words), and orthographic familiarity ('familiar' versus 'unfamiliar' pseudowords). We found that words and pseudowords elicited robust SSVEPs. Words showed larger SSVEPs than pseudowords and high-frequency words showed larger SSVEPs than low-frequency words. SSVEPs were not sensitive to orthographic familiarity. We further localized the neural generators of the SSVEP effects. The lexicality effect was located in areas associated with early level of visual processing, i.e. in the right occipital lobe and in the right precuneus. Pseudowords produced more activation than words in left sensorimotor areas, rolandic operculum, insula, supramarginal gyrus and in the right temporal gyrus. These areas are devoted to speech processing and/or spelling-to-sound conversion. The frequency effect involved the left temporal pole and orbitofrontal cortex, areas previously implicated in semantic processing and stimulus-response associations respectively, and the right postcentral and parietal inferior gyri, possibly indicating the involvement of the right attentional network.

EEG Source Imaging: A Practical Review of the Analysis Steps

The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.

Brain oscillations reflecting pain-related behavior in freely moving rats

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We comprehensively characterized the physiological properties of pain-related brain oscillations in freely moving rats and provided a foundation for the animal-to-human translation of experimental findings.

Recording oscillatory brain activity holds great promise in pain research. However, experimental results are variable and often difficult to reconcile. Some of these inconsistencies arise from the use of hypothesis-driven analysis approaches that (1) do not assess the consistency of the observed responses within and across individuals, and (2) do not fully exploit information sampled across the entire cortex. Here, we address these issues by recording the electrocorticogram directly from the brain surface of 12 freely moving rats. Using a hypothesis-free approach, we isolated brain oscillations induced by graded nociceptive stimuli and characterized their relation to pain-related behavior. We isolated 4 responses, one phase-locked event-related potential, 2 non–phase-locked event-related synchronizations, and one non–phase-locked event-related desynchronization (ERD), in different frequency bands (δ/θ-ERD, θ/α–event-related synchronization, and gamma-band event-related synchronization). All responses except the δ/θ-ERD correlated with pain-related behavior at within-subject level. Notably, the gamma-band event-related synchronization was the only response that reliably correlated with pain-related behavior between subjects. These results comprehensively characterize the physiological properties of the brain oscillations elicited by nociceptive stimuli in freely moving rodents and provide a foundational work to improve the translation of experimental animal findings to human physiology and pathophysiology.

The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of Signal Spectral Features, Mental States Classification and Usability

One century after the first recording of human electroencephalographic (EEG) signals, EEG has become one of the most used neuroimaging techniques. The medical devices industry is now able to produce small and reliable EEG systems, enabling a wide variety of applications also with no-clinical aims, providing a powerful tool to neuroscientific research. However, these systems still suffer from a critical limitation, consisting in the use of wet electrodes, that are uncomfortable and require expertise to install and time from the user. In this context, dozens of different concepts of EEG dry electrodes have been recently developed, and there is the common opinion that they are reaching traditional wet electrodes quality standards. However, although many papers have tried to validate them in terms of signal quality and usability, a comprehensive comparison of different dry electrode types from multiple points of view is still missing. The present work proposes a comparison of three different dry electrode types, selected among the main solutions at present, against wet electrodes, taking into account several aspects, both in terms of signal quality and usability. In particular, the three types consisted in gold-coated single pin, multiple pins and solid-gel electrodes. The results confirmed the great standards achieved by dry electrode industry, since it was possible to obtain results comparable to wet electrodes in terms of signals spectra and mental states classification, but at the same time drastically reducing the time of montage and enhancing the comfort. In particular, multiple-pins and solid-gel electrodes overcome gold-coated single-pin-based ones in terms of comfort.

Electrical Neuroimaging of Music Processing in Pianists With and Without True Absolute Pitch

True absolute pitch (AP), labeling of pitches with semitone precision without a reference, is classically studied using isolated tones. However, AP is acquired and has its function within complex dynamic musical contexts. Here we examined event-related brain responses and underlying cerebral sources to endings of short expressive string quartets, investigating a homogeneous population of young highly trained pianists with half of them possessing true-AP. The pieces ended regularly or contained harmonic transgressions at closure that participants appraised. Given the millisecond precision of ERP analyses, this experimental plan allowed examining whether AP alters music processing at an early perceptual, or later cognitive level, or both, and which cerebral sources underlie differences with non-AP musicians. We also investigated the impact of AP on general auditory cognition. Remarkably, harmonic transgression sensitivity did not differ between AP and non-AP participants, and differences for auditory cognition were only marginal. The key finding of this study is the involvement of a microstate peaking around 60 ms after musical closure, characterizing AP participants. Concurring sources were estimated in secondary auditory areas, comprising the planum temporale, all transgression conditions collapsed. These results suggest that AP is not a panacea to become a proficient musician, but a rare perceptual feature.

Brain and Cognitive Mechanisms of Top–Down Attentional Control in a Multisensory World: Benefits of Electrical Neuroimaging

In real-world environments, information is typically multisensory, and objects are a primary unit of information processing. Object recognition and action necessitate attentional selection of task-relevant from among task-irrelevant objects. However, the brain and cognitive mechanisms governing these processes remain not well understood. Here, we demonstrate that attentional selection of visual objects is controlled by integrated top–down audiovisual object representations (“attentional templates”) while revealing a new brain mechanism through which they can operate. In multistimulus (visual) arrays, attentional selection of objects in humans and animal models is traditionally quantified via “the N2pc component”: spatially selective enhancements of neural processing of objects within ventral visual cortices at approximately 150–300 msec poststimulus. In our adaptation of Folk et al.'s [Folk, C. L., Remington, R. W., & Johnston, J. C. Involuntary covert orienting is contingent on attentional control settings. Journal of Experimental Psychology: Human Perception and Performance, 18, 1030–1044, 1992] spatial cueing paradigm, visual cues elicited weaker behavioral attention capture and an attenuated N2pc during audiovisual versus visual search. To provide direct evidence for the brain, and so, cognitive, mechanisms underlying top–down control in multisensory search, we analyzed global features of the electrical field at the scalp across our N2pcs. In the N2pc time window (170–270 msec), color cues elicited brain responses differing in strength and their topography. This latter finding is indicative of changes in active brain sources. Thus, in multisensory environments, attentional selection is controlled via integrated top–down object representations, and so not only by separate sensory-specific top–down feature templates (as suggested by traditional N2pc analyses). We discuss how the electrical neuroimaging approach can aid research on top–down attentional control in naturalistic, multisensory settings and on other neurocognitive functions in the growing area of real-world neuroscience.

Practice-induced functional plasticity in inhibitory control interacts with aging

Inhibitory control deficits represent a key aspect of the cognitive declines associated with aging. Practicing inhibitory control has thus been advanced as a potential approach to compensate for age-induced neurocognitive impairments. Yet, the functional brain changes associated with practicing inhibitory control tasks in older adults and whether they differ from those observed in young populations remains unresolved. We compared electrical neuroimaging analyses of ERPs recorded during a Go/NoGo practice session with a Group (Young; Older adults) by Session (Beginning; End of the practice) design to identify whether the practice of an inhibition task in older adults reinforces already implemented compensatory activity or reduce it by enhancing the functioning of the brain networks primarily involved in the tasks. We observed an equivalent small effect of practice on performance in the two age-groups. The topographic ERP analyses and source estimations revealed qualitatively different effects of the practice over the N2 and P3 ERP components, respectively driven by a decrease in supplementary motor area activity and an increase in left ventrolateral prefrontal activity in the older but not in the young adults with practice. Our results thus indicate that inhibition task practice in older adults increases age-related divergences in the underlying functional processes.

Subcortical electrophysiological activity is detectable with high-density EEG source imaging

Subcortical neuronal activity is highly relevant for mediating communication in large-scale brain networks. While electroencephalographic (EEG) recordings provide appropriate temporal resolution and coverage to study whole brain dynamics, the feasibility to detect subcortical signals is a matter of debate. Here, we investigate if scalp EEG can detect and correctly localize signals recorded with intracranial electrodes placed in the centromedial thalamus, and in the nucleus accumbens. Externalization of deep brain stimulation (DBS) electrodes, placed in these regions, provides the unique opportunity to record subcortical activity simultaneously with high-density (256 channel) scalp EEG. In three patients during rest with eyes closed, we found significant correlation between alpha envelopes derived from intracranial and EEG source reconstructed signals. Highest correlation was found for source signals in close proximity to the actual recording sites, given by the DBS electrode locations. Therefore, we present direct evidence that scalp EEG indeed can sense subcortical signals.

Electroencephalography (EEG) allows the measurement of electrical signals associated with brain activity, but it is unclear if EEG can accurately measure subcortical activity. Here, the authors show that source dynamics, reconstructed from scalp EEG, correlate with activity recorded from human thalamus and nucleus accumbens.

Handling anisotropic conductivities in the EEG forward problem with a symmetric formulation

The electroencephalography (EEG) forward problem, the computation of the electric potential generated by a known electric current source configuration in the brain, is a key step of EEG source analysis. In this problem, it is often desired to model the anisotropic conductivity profiles of the skull and of the white matter. These profiles, however, cannot be handled by standard surface integral formulations and the use of volume finite elements is required. Leveraging on the representation theorem using an anisotropic fundamental solution, this paper proposes a modified symmetric formulation for solving the EEG forward problem by a surface integral equation which can take into account anisotropic conductivity profiles. A set of numerical results is presented to corroborate theoretical treatments and to show the impact of the proposed approach on both canonical and real case scenarios.

Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest

We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.

Neural correlates of reality filtering in schizophrenia spectrum disorder

BACKGROUND

A false sense of reality is a characteristic of schizophrenia spectrum disorders (SSD). Reality confusion may also emanate from posterior orbitofrontal cortex (OFC) lesions, as evident in confabulations that patients act upon and disorientation. This confusion can be measured by repeated runs of a continuous recognition task (CRT): patients increase their false positive rate from the second run on, failing to realize that an item is not a repetition within the current run. Correct handling of these stimuli, a faculty called orbitofrontal reality filtering (ORFi), induces a distinct frontal potential at 200-300 ms, the "ORFi potential". Patients with schizophrenia have been reported to fail in this task, too. Here, we explored the electrophysiology of ORFi in SSD.

METHODS

Evoked potentials, source, and connectivity analyses derived from high-density electroencephalograms of 17 patients with SSD and 15 age-matched healthy controls performing two runs of a CRT.

RESULTS

Although the patients obtained normal performance, they did not normally express the frontal potential typical of ORFi between 200 and 300 ms. Coherence analysis demonstrated virtually absent functional connectivity in the theta band within the memory network in this period. Source analysis showed increased activity in left medial temporal and prefrontal regions in patients.

CONCLUSIONS

SSD patients appear to invoke compensatory resources to handle the challenges of reality filtering. An abnormal ORFi potential may be an early biomarker of SSD.

EEG recording during an emotional face-matching task in children of mothers with interpersonal violence-related posttraumatic stress disorder

The aim of this study was to examine the effects of maternal interpersonal violence-related posttraumatic disorder (IPV-PTSD) on child appraisal of emotion, as measured by high-density electroencephalography (HD-EEG) during an Emotional Face-matching Task (EFMT). We recorded HD-EEG in 47 children of mothers with and without IPV-PTSD during an Emotional Face-matching Task (EFMT). Mothers and children each performed the EFMT. Behavioral results demonstrated that both mothers who were directly exposed to violent events, and their children, presented attentional bias toward negative emotions when processing facial stimuli. EEG findings confirmed differences in emotion appraisal between children of IPV-PTSD mothers and non-PTSD controls at scalp-level and in terms of source localization upon which children of IPV-PTSD mothers demonstrated decreased activation of the right dorsolateral prefrontal cortex (dlPFC) in response to angry and fearful faces as compared to non-PTSD children with respect to the N170 component. Our study, to our knowledge, is the first to show that maternal IPV-PTSD significantly affects a mother's own and her child's neural activity in response to facial expressions of negative emotion. These findings are potentially important to the development and study of effective interventions to interrupt intergenerational cycles of violence and trauma.

Spatial localization of EEG electrodes using 3D scanning

OBJECTIVE

A reliable reconstruction of neural activity using high-density electroencephalography (EEG) requires an accurate spatial localization of EEG electrodes aligned to the structural magnetic resonance (MR) image of an individual's head. Current technologies for electrode positioning, such as electromagnetic digitization, are yet characterized by non-negligible localization and co-registration errors. In this study, we propose an automated method for spatial localization of EEG electrodes using 3D scanning, a non-invasive and easy-to-use technology with potential applications in clinical settings.

APPROACH

Our method consists of three main steps: (1) the 3D scan is ambient light-corrected and spatially aligned to the head surface extracted from the anatomical MR image; (2) electrode positions are identified by segmenting the 3D scan based on predefined colour and topological properties; (3) electrode labelling is performed by aligning an EEG montage template to the electrode positions. The performance of the method was assessed on data collected in eight participants wearing high-density EEG caps with 128 sensors, from three different manufacturers. We estimated the co-registration error using the distance between the MR-based head shape and the closest 3D scan points. Also, we quantified the positioning error using the distance between the detected electrode positions and the corresponding locations manually selected on the 3D scan data.

MAIN RESULTS

For all participants and EEG caps, we obtained a median error of co-registration below 3.0 mm and of spatial localization below 1.4 mm. The method based on 3D scanning data was significantly more precise compared to the electromagnetic digitization technique, and the total time required for obtaining electrode positions was reduced by about half.

SIGNIFICANCE

We have introduced a method to automatically detect EEG electrodes based on 3D scanning information. We believe that our work can contribute to a more effective, reliable and widespread use of high-density EEG as brain imaging tool.

EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms

Background: The few previous studies on resting-state electroencephalography (EEG) microstates in depressive patients suggest altered temporal characteristics of microstates compared to those of healthy subjects. We tested whether resting-state microstate temporal characteristics could capture large-scale brain network dynamic activity relevant to depressive symptomatology. Methods: To evaluate a possible relationship between the resting-state large-scale brain network dynamics and depressive symptoms, we performed EEG microstate analysis in 19 patients with moderate to severe depression in bipolar affective disorder, depressive episode, and recurrent depressive disorder and in 19 healthy controls. Results: Microstate analysis revealed six classes of microstates (A-F) in global clustering across all subjects. There were no between-group differences in the temporal characteristics of microstates. In the patient group, higher depressive symptomatology on the Montgomery-Åsberg Depression Rating Scale correlated with higher occurrence of microstate A (Spearman's rank correlation, r = 0.70, p < 0.01). Conclusion: Our results suggest that the observed interindividual differences in resting-state EEG microstate parameters could reflect altered large-scale brain network dynamics relevant to depressive symptomatology during depressive episodes. Replication in larger cohort is needed to assess the utility of the microstate analysis approach in an objective depression assessment at the individual level.

Neural Processing of Dynamic Animated Social Interactions in Young Children With Autism Spectrum Disorder: A High-Density Electroencephalography Study

Background: Atypical neural processing of social visual information contributes to impaired social cognition in autism spectrum disorder. However, evidence for early developmental alterations in neural processing of social contingencies is scarce. Most studies in the literature have been conducted in older children and adults. Here, we aimed to investigate alterations in neural processing of social visual information in children with autism spectrum disorder compared to age-matched typically developing peers. Methods: We used a combination of 129-channel electroencephalography and high-resolution eye-tracking to study differences in the neural processing of dynamic cartoons containing human-like social interactions between 14 male children with autism spectrum disorder and 14 typically developing male children, aged 2-5 years. Using a microstate approach, we identified four prototypical maps in both groups and compared the temporal characteristics and inverse solutions (activation of neural sources) of these maps between groups. Results: Inverse solutions of the group maps that were most dominant during free viewing of the dynamic cartoons indicated decreased prefrontal and cingulate activation, impaired activation of the premotor cortex, and increased activation of parietal, temporal, occipital, and cerebellar regions in children with autism spectrum disorder compared to their typically developing peers. Conclusions: Our findings suggest that impairments in brain regions involved in processing social contingencies embedded in dynamic cartoons are present from an early age in autism spectrum disorder. To the best of our knowledge, this is the first study to investigate neural processing of social interactions of children with autism spectrum disorder using dynamic semi-naturalistic stimuli.

THE NEUROPHENOMENOLOGY OF OUT-OF-BODY EXPERIENCES INDUCED BY HYPNOTIC SUGGESTIONS

Inducing out-of-body experiences in hypnosis (H-OBEs) offers an almost unique opportunity to investigate them under controlled conditions. OBEs were induced as an imaginative task in a resting condition (I-OBE) or in hypnosis (H-OBE) in a group of 15 high hypnotizable subjects. A 32-channel EEG was recorded, and the spectral power and imaginary coherence of each frequency band and each couple of electrodes were calculated. At the end of each session, the Phenomenology of Consciousness Inventory (PCI) was administered to assess the phenomenological aspects of the subjects' experience. Significantly higher scores in the altered state, positive affect altered experience, and attention subdimensions of the PCI were reported in H-OBE than in I-OBE, which were associated with a significant decrease of power in beta and gamma band activity in right parieto-temporal derivations. These results suggest that the H-OBE may offer a useful experimental model of spontaneous OBEs.

The Potential for a Speech Brain-Computer Interface Using Chronic Electrocorticography

A brain-computer interface (BCI) is a technology that uses neural features to restore or augment the capabilities of its user. A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. Such a technology would potentially restore communication and improve quality of life for locked-in patients and other patients with severe communication disorders. There have been many recent developments in neural decoders, neural feature extraction, and brain recording modalities facilitating BCI for the control of prosthetics and in automatic speech recognition (ASR). Indeed, ASR and related fields have developed significantly over the past years, and many lend many insights into the requirements, goals, and strategies for speech BCI. Neural speech decoding is a comparatively new field but has shown much promise with recent studies demonstrating semantic, auditory, and articulatory decoding using electrocorticography (ECoG) and other neural recording modalities. Because the neural representations for speech and language are widely distributed over cortical regions spanning the frontal, parietal, and temporal lobes, the mesoscopic scale of population activity captured by ECoG surface electrode arrays may have distinct advantages for speech BCI, in contrast to the advantages of microelectrode arrays for upper-limb BCI. Nevertheless, there remain many challenges for the translation of speech BCIs to clinical populations. This review discusses and outlines the current state-of-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail.

Differentiation of Types of Visual Agnosia Using EEG

Visual recognition deficits are the hallmark symptom of visual agnosia, a neuropsychological disorder typically associated with damage to the visual system. Most research into visual agnosia focuses on characterizing the deficits through detailed behavioral testing, and structural and functional brain scans are used to determine the spatial extent of any cortical damage. Although the hierarchical nature of the visual system leads to clear predictions about the temporal dynamics of cortical deficits, there has been little research on the use of neuroimaging methods with high temporal resolution to characterize the temporal profile of agnosia deficits. Here, we employed high-density electroencephalography (EEG) to investigate alterations in the temporal dynamics of the visual system in two individuals with visual agnosia. In the context of a steady state visual evoked potential paradigm (SSVEP), individuals viewed pattern-reversing checkerboards of differing spatial frequency, and we assessed the responses of the visual system in the frequency and temporal domain. JW, a patient with early visual cortex damage, showed impaired SSVEP response relative to a control group and to the second patient (SM) who had right temporal lobe damage. JW also showed lower decoding accuracy for early visual responses (around 100 ms). SM, whose lesion is more anterior in the visual system, showed good decoding accuracy initially but low decoding after 500 ms. Overall, EEG and multivariate decoding methods can yield important insights into the temporal dynamics of visual responses in individuals with visual agnosia.

Tuning characteristics of low-frequency EEG to positions and velocities in visuomotor and oculomotor tracking tasks

Movement decoders exploit the tuning of neural activity to various movement parameters with the ultimate goal of controlling end-effector action. Invasive approaches, typically relying on spiking activity, have demonstrated feasibility. Results of recent functional neuroimaging studies suggest that information about movement parameters is even accessible non-invasively in the form of low-frequency brain signals. However, their spatiotemporal tuning characteristics to single movement parameters are still unclear. Here, we extend the current understanding of low-frequency electroencephalography (EEG) tuning to position and velocity signals. We recorded EEG from 15 healthy participants while they performed visuomotor and oculomotor pursuit tracking tasks. Linear decoders, fitted to EEG signals in the frequency range of the tracking movements, predicted positions and velocities with moderate correlations (0.2–0.4; above chance level) in both tasks. Predictive activity in terms of decoder patterns was significant in superior parietal and parieto-occipital areas in both tasks. By contrasting the two tracking tasks, we found that predictive activity in contralateral primary sensorimotor and premotor areas exhibited significantly larger tuning to end-effector velocity when the visuomotor tracking task was performed.

Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis

In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~ 80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth.

Artifacts in EEG of simultaneous EEG-fMRI: pulse artifact remainders in the gradient artifact template are a source of artifact residuals after average artifact subtraction

OBJECTIVE

The simultaneous application of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) opens up new ways to investigate the human brain. The EEG recordings of simultaneous EEG-fMRI, however, are overlaid to a great degree by fMRI related artifacts and an artifact reduction is mandatory before any EEG analysis. The most severe artifacts-the gradient artifact and the pulse artifact-are repetitive. Average artifact subtraction (AAS) technique exploits the repetitiveness and is presumably the most often used artifact reduction technique. In this method artifact templates are calculated by averaging over adjacent artifact epochs and subsequently the templates are subtracted to reduce the artifacts. Although the AAS technique is one of the best performing methods, artifact residuals are usually present in the resulting EEG after applying the AAS technique. This work aims at identifying sources of the artifact residuals.

APPROACH

Application of the AAS technique to artificial EEG that is contaminated with artificial fMRI related artifacts.

MAIN RESULTS

A new source of artifact residuals was identified. It was found that the AAS technique itself adds artifacts to the EEG during gradient artifact reduction, because the gradient artifact template is corrupted by pulse artifact remainders.

SIGNIFICANCE

This work shows that using a standard number of 25 epochs to calculate the gradient artifact template-as suggested by the inventors of AAS-results in substantial artifact residuals and consequently to a low EEG quality. Furthermore, the work discusses how potential solutions to this problem have serious side effects such as loss of adaptivity of the AAS technique. Hence, this problem must be considered carefully already in the design of simultaneous EEG-fMRI experiments.

A Finite-Difference Solution for the EEG Forward Problem in Inhomogeneous Anisotropic Media

Accurate source localization of electroencephalographic (EEG) signals requires detailed information about the geometry and physical properties of head tissues. Indeed, these strongly influence the propagation of neural activity from the brain to the sensors. Finite difference methods (FDMs) are head modelling approaches relying on volumetric data information, which can be directly obtained using magnetic resonance (MR) imaging. The specific goal of this study is to develop a computationally efficient FDM solution that can flexibly integrate voxel-wise conductivity and anisotropy information. Given the high computational complexity of FDMs, we pay particular attention to attain a very low numerical error, as evaluated using exact analytical solutions for spherical volume conductor models. We then demonstrate the computational efficiency of our FDM numerical solver, by comparing it with alternative solutions. Finally, we apply the developed head modelling tool to high-resolution MR images from a real experimental subject, to demonstrate the potential added value of incorporating detailed voxel-wise conductivity and anisotropy information. Our results clearly show that the developed FDM can contribute to a more precise head modelling, and therefore to a more reliable use of EEG as a brain imaging tool.

A Primer on Electroencephalography and Event-Related Potentials for Organizational Neuroscience

Electroencephalography (EEG) was the first of the noninvasive brain measures in neuroscience. Technical advances over the last 100 years or so have rendered EEG a true brain imaging technique. Here, we provide an accessible primer on the biophysics of EEG, on measurement aspects, and on the analysis of EEG data. We use the example of event-related potentials (ERPs), although the issues apply equally to other varieties of EEG signals, and provide an overview of analytic methods at the base of the so-called electrical neuroimaging framework. We detail the interpretational strengths of electrical neuroimaging for organizational researchers and describe some domains of ongoing technical developments. We likewise emphasize practical considerations with the use of EEG in more real-world settings. This primer is intended to provide organizational researchers specifically, and novices more generally, an access point to understanding how EEG may be applied in their research.

Sounds enhance visual completion processes

Everyday vision includes the detection of stimuli, figure-ground segregation, as well as object localization and recognition. Such processes must often surmount impoverished or noisy conditions; borders are perceived despite occlusion or absent contrast gradients. These illusory contours (ICs) are an example of so-called mid-level vision, with an event-related potential (ERP) correlate at ∼100-150 ms post-stimulus onset and originating within lateral-occipital cortices (the IC_effect). Presently, visual completion processes supporting IC perception are considered exclusively visual; any influence from other sensory modalities is currently unknown. It is now well-established that multisensory processes can influence both low-level vision (e.g. detection) as well as higher-level object recognition. By contrast, it is unknown if mid-level vision exhibits multisensory benefits and, if so, through what mechanisms. We hypothesized that sounds would impact the IC_effect. We recorded 128-channel ERPs from 17 healthy, sighted participants who viewed ICs or no-contour (NC) counterparts either in the presence or absence of task-irrelevant sounds. The IC_effect was enhanced by sounds and resulted in the recruitment of a distinct configuration of active brain areas over the 70-170 ms post-stimulus period. IC-related source-level activity within the lateral occipital cortex (LOC), inferior parietal lobe (IPL), as well as primary visual cortex (V1) were enhanced by sounds. Moreover, the activity in these regions was correlated when sounds were present, but not when absent. Results from a control experiment, which employed amodal variants of the stimuli, suggested that sounds impact the perceived brightness of the IC rather than shape formation per se. We provide the first demonstration that multisensory processes augment mid-level vision and everyday visual completion processes, and that one of the mechanisms is brightness enhancement. These results have important implications for the design of treatments and/or visual aids for low-vision patients.

Removing deep brain stimulation artifacts from the electroencephalogram: Issues, recommendations and an open-source toolbox

A major question for deep brain stimulation (DBS) research is understanding how DBS of one target area modulates activity in different parts of the brain. EEG gives privileged access to brain dynamics, but its use with implanted patients is limited since DBS adds significant high-amplitude electrical artifacts that can completely obscure neural activity measured using EEG. Here, we systematically review and discuss the methods available for removing DBS artifacts. These include simple techniques such as oversampling, antialiasing analog filtering and digital low-pass filtering, which are necessary but typically not sufficient to fully remove DBS artifacts when each is used in isolation. We also cover more advanced methods, including techniques tracking outliers in the frequency-domain, which can be effective, but are rarely used. The reason for that is twofold: First, it requires advanced skills in signal processing since no user friendly tool for removing DBS artifacts is currently available. Second, it involves fine-tuning to avoid over-aggressive filtering. We highlight an open-source toolbox incorporating most artifact removal methods, allowing users to combine different strategies.

Steady-state evoked potentials distinguish brain mechanisms of self-paced versus synchronization finger tapping

Sensorimotor synchronization (SMS) requires aligning motor actions to external events and represents a core part of both musical and dance performances. In the current study, to isolate the brain mechanisms involved in synchronizing finger tapping with a musical beat, we compared SMS to pure self-paced finger tapping and listen-only conditions at different tempi. We analyzed EEG data using frequency domain steady-state evoked potentials (SSEPs) to identify sustained electrophysiological brain activity during repetitive tasks. Behavioral results revealed different timing modes between SMS and self-paced finger tapping, associated with distinct scalp topographies, thus suggesting different underlying brain sources. After subtraction of the listen-only brain activity, SMS was compared to self-paced finger tapping. Resulting source estimations showed stronger activation of the left inferior frontal gyrus during SMS, and stronger activation of the bilateral inferior parietal lobule during self-paced finger tapping. These results point to the left inferior frontal gyrus as a pivot for perception-action coupling. We discuss our findings in the context of the ongoing debate about SSEPs interpretation given the variety of brain events contributing to SSEPs and similar EEG frequency responses.

System neuroscience: Past, present, and future

In this review, we discuss first the anatomical and lesion studies that allowed the localization of fundamental functions in the cerebral cortex of primates including humans. Subsequently, we argue that the years from the end of the Second World War until the end of the last century represented the "golden age" of system neuroscience. In this period, the mechanisms-not only the localization-underlying sensory, and in particular visual functions were described, followed by those underlying cognitive functions and housed in temporal, parietal, and premotor areas. At the end of the last century, brain imaging techniques were developed that allowed the assessment of the functions of different cortical areas in a more precise and sophisticated way. However, brain imaging tells little about the neural mechanisms underlying functions. Furthermore, the brain imaging suffers from 3 major problems: time is absent, the data are merely correlative and the testing is often not ecological. We conclude our review discussing the possibility that these pitfalls might be overcome by using intracortical recordings (eg stereo-EEG), which have millisecond time resolution, allow direct electrical stimulation of specific sites, and finally enable to study patients while freely moving.

Brain mechanisms for perceiving illusory lines in humans

Illusory contours (ICs) are perceptions of visual borders despite absent contrast gradients. The psychophysical and neurobiological mechanisms of IC processes have been studied across species and diverse brain imaging/mapping techniques. Nonetheless, debate continues regarding whether IC sensitivity results from a (presumably) feedforward process within low-level visual cortices (V1/V2) or instead are processed first within higher-order brain regions, such as lateral occipital cortices (LOC). Studies in animal models, which generally favour a feedforward mechanism within V1/V2, have typically involved stimuli inducing IC lines. By contrast, studies in humans generally favour a mechanism where IC sensitivity is mediated by LOC and have typically involved stimuli inducing IC forms or shapes. Thus, the particular stimulus features used may strongly contribute to the model of IC sensitivity supported. To address this, we recorded visual evoked potentials (VEPs) while presenting human observers with an array of 10 inducers within the central 5°, two of which could be oriented to induce an IC line on a given trial. VEPs were analysed using an electrical neuroimaging framework. Sensitivity to the presence vs. absence of centrally-presented IC lines was first apparent at ∼200 ms post-stimulus onset and was evident as topographic differences across conditions. We also localized these differences to the LOC. The timing and localization of these effects are consistent with a model of IC sensitivity commencing within higher-level visual cortices. We propose that prior observations of effects within lower-tier cortices (V1/V2) are the result of feedback from IC sensitivity that originates instead within higher-tier cortices (LOC).

EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

OBJECTIVE

In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions.

APPROACH

We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method.

MAIN RESULTS

Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction.

SIGNIFICANCE

This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

Source-Modeling Auditory Processes of EEG Data Using EEGLAB and Brainstorm

Electroencephalography (EEG) source localization approaches are often used to disentangle the spatial patterns mixed up in scalp EEG recordings. However, approaches differ substantially between experiments, may be strongly parameter-dependent, and results are not necessarily meaningful. In this paper we provide a pipeline for EEG source estimation, from raw EEG data pre-processing using EEGLAB functions up to source-level analysis as implemented in Brainstorm. The pipeline is tested using a data set of 10 individuals performing an auditory attention task. The analysis approach estimates sources of 64-channel EEG data without the prerequisite of individual anatomies or individually digitized sensor positions. First, we show advanced EEG pre-processing using EEGLAB, which includes artifact attenuation using independent component analysis (ICA). ICA is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals and is further a powerful tool to attenuate stereotypical artifacts (e.g., eye movements or heartbeat). Data submitted to ICA are pre-processed to facilitate good-quality decompositions. Aiming toward an objective approach on component identification, the semi-automatic CORRMAP algorithm is applied for the identification of components representing prominent and stereotypic artifacts. Second, we present a step-wise approach to estimate active sources of auditory cortex event-related processing, on a single subject level. The presented approach assumes that no individual anatomy is available and therefore the default anatomy ICBM152, as implemented in Brainstorm, is used for all individuals. Individual noise modeling in this dataset is based on the pre-stimulus baseline period. For EEG source modeling we use the OpenMEEG algorithm as the underlying forward model based on the symmetric Boundary Element Method (BEM). We then apply the method of dynamical statistical parametric mapping (dSPM) to obtain physiologically plausible EEG source estimates. Finally, we show how to perform group level analysis in the time domain on anatomically defined regions of interest (auditory scout). The proposed pipeline needs to be tailored to the specific datasets and paradigms. However, the straightforward combination of EEGLAB and Brainstorm analysis tools may be of interest to others performing EEG source localization.

Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG

We investigated the influence of processing steps in the estimation of multivariate directed functional connectivity during seizures recorded with intracranial EEG (iEEG) on seizure-onset zone (SOZ) localization. We studied the effect of (i) the number of nodes, (ii) time-series normalization, (iii) the choice of multivariate time-varying connectivity measure: Adaptive Directed Transfer Function (ADTF) or Adaptive Partial Directed Coherence (APDC) and (iv) graph theory measure: outdegree or shortest path length. First, simulations were performed to quantify the influence of the various processing steps on the accuracy to localize the SOZ. Afterwards, the SOZ was estimated from a 113-electrodes iEEG seizure recording and compared with the resection that rendered the patient seizure-free. The simulations revealed that ADTF is preferred over APDC to localize the SOZ from ictal iEEG recordings. Normalizing the time series before analysis resulted in an increase of 25-35% of correctly localized SOZ, while adding more nodes to the connectivity analysis led to a moderate decrease of 10%, when comparing 128 with 32 input nodes. The real-seizure connectivity estimates localized the SOZ inside the resection area using the ADTF coupled to outdegree or shortest path length. Our study showed that normalizing the time-series is an important pre-processing step, while adding nodes to the analysis did only marginally affect the SOZ localization. The study shows that directed multivariate Granger-based connectivity analysis is feasible with many input nodes (> 100) and that normalization of the time-series before connectivity analysis is preferred.

When Wine and Apple Both Help the Production of Grapes: ERP Evidence for Post-lexical Semantic Facilitation in Picture Naming

Background: Producing a word in referential naming requires to select the right word in our mental lexicon among co-activated semantically related words. The mechanisms underlying semantic context effects during speech planning are still controversial, particularly for semantic facilitation which investigation remains under-represented in contrast to the plethora of studies dealing with interference. Our aim is to study the time-course of semantic facilitation in picture naming, using a picture-word "interference" paradigm and event-related potentials (ERPs). Methods: We compared two different types of semantic relationships, associative and categorical, in a single word priming and a double word priming paradigm. The primes were presented visually with a long negative Stimulus Onset Asynchrony (SOA), which is expected to cause facilitation. Results: Shorter naming latencies were observed after both associative and categorical primes, as compared to unrelated primes, and even shorter latencies after two primes. Electrophysiological results showed relatively late modulations of waveform amplitudes for both types of primes (beginning ~330 ms post picture onset with a single prime and ~275 ms post picture onset with two primes), corresponding to a shift in latency of similar topographic maps across conditions. Conclusion: The present results are in favor of a post-lexical locus of semantic facilitation for associative and categorical priming in picture naming and confirm that semantic facilitation is as relevant as semantic interference to inform on word production. The post-lexical locus argued here might be related to self-monitoting or/and to modulations at the level of word-form planning, without excluding the participation of strategic processes.

Recurrence of task-related electroencephalographic activity during post-training quiet rest and sleep

Offline reactivation of task-related neural activity has been demonstrated in animals but is difficult to directly observe in humans. We sought to identify potential electroencephalographic (EEG) markers of offline memory processing in human subjects by identifying a set of characteristic EEG topographies ("microstates") that occurred as subjects learned to navigate a virtual maze. We hypothesized that these task-related microstates would appear during post-task periods of rest and sleep. In agreement with this hypothesis, we found that one task-related microstate was increased in post-training rest and sleep compared to baseline rest, selectively for subjects who actively learned the maze, and not in subjects performing a non-learning control task. Source modeling showed that this microstate was produced by activity in temporal and parietal networks, which are known to be involved in spatial navigation. For subjects who napped after training, the increase in this task-related microstate predicted the magnitude of subsequent change in performance. Our findings demonstrate that task-related EEG patterns re-emerge during post-training rest and sleep.

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis.

Contributions of Intraindividual and Interindividual Differences to Multisensory Processes

Most evidence on the neural and perceptual correlates of sensory processing derives from studies that have focused on only a single sensory modality and averaged the data from groups of participants. Although valuable, such studies ignore the substantial interindividual and intraindividual differences that are undoubtedly at play. Such variability plays an integral role in both the behavioral/perceptual realms and in the neural correlates of these processes, but substantially less is known when compared with group-averaged data. Recently, it has been shown that the presentation of stimuli from two or more sensory modalities (i.e., multisensory stimulation) not only results in the well-established performance gains but also gives rise to reductions in behavioral and neural response variability. To better understand the relationship between neural and behavioral response variability under multisensory conditions, this study investigated both behavior and brain activity in a task requiring participants to discriminate moving versus static stimuli presented in either a unisensory or multisensory context. EEG data were analyzed with respect to intraindividual and interindividual differences in RTs. The results showed that trial-by-trial variability of RTs was significantly reduced under audiovisual presentation conditions as compared with visual-only presentations across all participants. Intraindividual variability of RTs was linked to changes in correlated activity between clusters within an occipital to frontal network. In addition, interindividual variability of RTs was linked to differential recruitment of medial frontal cortices. The present findings highlight differences in the brain networks that support behavioral benefits during unisensory versus multisensory motion detection and provide an important view into the functional dynamics within neuronal networks underpinning intraindividual performance differences.

Non-invasive imaging modalities to study neurodegenerative diseases of aging brain

The aim of this article is to highlight current approaches for imaging elderly brain, indispensable for cognitive neuroscience research with emphasis on the basic physical principles of various non-invasive neuroimaging techniques. The first part of this article presents a quick overview of the primary non-invasive neuroimaging modalities used by cognitive neuroscientists such as transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), electroencephalography (EEG), magnetoencephalography (MEG), single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance spectroscopic imaging (MRSI), Profusion imaging, functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS) and diffusion tensor imaging (DTI) along with tractography and connectomics. The second part provides a comprehensive overview of different multimodality imaging techniques for various cognitive neuroscience studies of aging brain.

Neural correlates of economic value and valuation context: an event-related potential study

The value of environmental cues and internal states is continuously evaluated by the human brain, and it is this subjective value that largely guides decision making. The present study aimed to investigate the initial value attribution process, specifically the spatiotemporal activation patterns associated with values and valuation context, using electroencephalographic event-related potentials (ERPs). Participants completed a stimulus rating task in which everyday household items marketed up to a price of £4 were evaluated with respect to their desirability or material properties. The subjective values of items were evaluated as willingness to pay (WTP) in a Becker-DeGroot-Marschak auction. On the basis of the individual's subjective WTP values, the stimuli were divided into high- and low-value items. Source dipole modeling was applied to estimate the cortical sources underlying ERP components modulated by subjective values (high vs. low WTP) and the evaluation condition (value-relevant vs. value-irrelevant judgments). Low-WTP items and value-relevant judgments both led to a more pronounced N2 visual evoked potential at right frontal scalp electrodes. Source activity in right anterior insula and left orbitofrontal cortex was larger for low vs. high WTP at ∼200 ms. At a similar latency, source activity in right anterior insula and right parahippocampal gyrus was larger for value-relevant vs. value-irrelevant judgments. A stronger response for low- than high-value items in anterior insula and orbitofrontal cortex appears to reflect aversion to low-valued item acquisition, which in an auction experiment would be perceived as a relative loss. This initial low-value bias occurs automatically irrespective of the valuation context. NEW & NOTEWORTHY We demonstrate the spatiotemporal characteristics of the brain valuation process using event-related potentials and willingness to pay as a measure of subjective value. The N2 component resolves values of objects with a bias toward low-value items. The value-related changes of the N2 component are part of an automatic valuation process.