EEG Source Imaging: A Practical Review of the Analysis Steps

Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

Brain source localization has been consistently implemented over the recent years to elucidate complex brain operations, pairing the high temporal resolution of the EEG with the high spatial estimation of the estimated sources. This review paper aims to present the basic principles of Electrical source imaging (ESI) in the context of the recent progress for solving the forward and the inverse problems, and highlight the advantages and limitations of the different approaches. As such, a synthesis of the current state-of-the-art methodological aspects is provided, offering a complete overview of the present advances with regard to the ESI solutions. Moreover, the new dimensions for the analysis of the brain processes are indicated in terms of clinical and cognitive ESI applications, while the prevailing challenges and limitations are thoroughly discussed, providing insights for future approaches that could help to alleviate methodological and technical shortcomings.

EEG hyperscanning in motor rehabilitation: a position paper

Studying the human brain during interpersonal interaction allows us to answer many questions related to motor control and cognition. For instance, what happens in the brain when two people walking side by side begin to change their gait and match cadences? Adapted from the neuroimaging techniques used in single-brain measurements, hyperscanning (HS) is a technique used to measure brain activity from two or more individuals simultaneously. Thus far, HS has primarily focused on healthy participants during social interactions in order to characterize inter-brain dynamics. Here, we advocate for expanding the use of this electroencephalography hyperscanning (EEG-HS) technique to rehabilitation paradigms in individuals with neurological diagnoses, namely stroke, spinal cord injury (SCI), Parkinson's disease (PD), and traumatic brain injury (TBI). We claim that EEG-HS in patient populations with impaired motor function is particularly relevant and could provide additional insight on neural dynamics, optimizing rehabilitation strategies for each individual patient. In addition, we discuss future technologies related to EEG-HS that could be developed for use in the clinic as well as technical limitations to be considered in these proposed settings.

ConvDip: A Convolutional Neural Network for Better EEG Source Imaging

The electroencephalography (EEG) is a well-established non-invasive method in neuroscientific research and clinical diagnostics. It provides a high temporal but low spatial resolution of brain activity. To gain insight about the spatial dynamics of the EEG, one has to solve the inverse problem, i.e., finding the neural sources that give rise to the recorded EEG activity. The inverse problem is ill-posed, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipole sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture, that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. It produces plausible inverse solutions for real EEG recordings from human participants. (4) The trained network needs <40 ms for a single prediction. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG data, with high relevance for clinical applications, e.g., in epileptology and real-time applications.

RT-NET: real-time reconstruction of neural activity using high-density electroencephalography

High-density electroencephalography (hdEEG) has been successfully used for large-scale investigations of neural activity in the healthy and diseased human brain. Because of their high computational demand, analyses of source-projected hdEEG data are typically performed offline. Here, we present a real-time noninvasive electrophysiology toolbox, RT-NET, which has been specifically developed for online reconstruction of neural activity using hdEEG. RT-NET relies on the Lab Streaming Layer for acquiring raw data from a large number of EEG amplifiers and for streaming the processed data to external applications. RT-NET estimates a spatial filter for artifact removal and source activity reconstruction using a calibration dataset. This spatial filter is then applied to the hdEEG data as they are acquired, thereby ensuring low latencies and computation times. Overall, our analyses show that RT-NET can estimate real-time neural activity with performance comparable to offline analysis methods. It may therefore enable the development of novel brain–computer interface applications such as source-based neurofeedback.

Resting-state electroencephalographic functional network alterations in major depressive disorder following magnetic seizure therapy

Magnetic seizure therapy (MST) is emerging as a safe and well-tolerated experimental intervention for major depressive disorder (MDD), with very minimal cognitive side-effects. However, the underlying mechanism of action of MST remains uncertain. Here, we used resting-state electroencephalography (RS-EEG) to characterise the physiological effects of MST for treatment resistant MDD. We recorded RS-EEG in 21 patients before and after an open label trial of MST applied over the prefrontal cortex using a bilateral twin coil. RS-EEG was analysed for changes in functional connectivity, network topology, and spectral power. We also ran further baseline comparisons between the MDD patients and a cohort of healthy controls (n = 22). Network-based connectivity analysis revealed a functional subnetwork of significantly increased theta connectivity spanning frontal and parieto-occipital channels following MST. The change in theta connectivity was further found to predict clinical response to treatment. An additional widespread subnetwork of reduced beta connectivity was also elucidated. Graph-based topological analyses showed an increase in functional network segregation and reduction in integration in the theta band, with a decline in segregation in the beta band. Finally, delta and theta power were significantly elevated following treatment, while gamma power declined. No baseline differences between MDD patients and healthy subjects were observed. These results highlight widespread changes in resting-state brain dynamics following a course of MST in MDD patients, with changes in theta connectivity providing a potential physiological marker of treatment response. Future prospective studies are required to confirm these initial findings.

Inferior parietal lobule involved in representation of "what" in a delayed-action Libet task

Intentional motor action is typically characterized by the decision about the timing, and the selection of the action variant, known as the "what" component. We compared free action selection with instructed action, where the movement type was externally cued, in order to investigate the action selection and action representation in a Libet's task. Temporal and spatial locus of these processes was examined using the combination of high-density electroencephalography, topographic analysis of variance, and source reconstruction. Instructed action, engaging representation of the response movement, was associated with distinct negativity at the parietal and centro-parietal channels starting around 750 ms before the movement, which has a source particularly in the bilateral inferior parietal lobule. This suggests that in delayed-action tasks, the process of action representation in the inferior parietal lobule may play an important part in the larger parieto-frontal activity responsible for movement selection.

Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies

Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.

Patient-Tailored, Home-Based Non-invasive Brain Stimulation for Memory Deficits in Dementia Due to Alzheimer's Disease

Alzheimer's disease (AD) is an irreversible, progressive brain disorder that can cause dementia (Alzheimer's disease-related dementia, ADRD) with growing cognitive disability and vast physical, emotional, and financial pressures not only on the patients but also on caregivers and families. Loss of memory is an early and very debilitating symptom in AD patients and a relevant predictor of disease progression. Data from rodents, as well as human studies, suggest that dysregulation of specific brain oscillations, particularly in the hippocampus, is linked to memory deficits. Animal and human studies demonstrate that non-invasive brain stimulation (NIBS) in the form of transcranial alternating current stimulation (tACS) allows to reliably and safely interact with ongoing oscillatory patterns in the brain in specific frequencies. We developed a protocol for patient-tailored home-based tACS with an instruction program to train a caregiver to deliver daily sessions of tACS that can be remotely monitored by the study team. We provide a discussion of the neurobiological rationale to modulate oscillations and a description of the study protocol. Data of two patients with ADRD who have completed this protocol illustrate the feasibility of the approach and provide pilot evidence on the safety of the remotely-monitored, caregiver-administered, home-based tACS intervention. These findings encourage the pursuit of a large, adequately powered, randomized controlled trial of home-based tACS for memory dysfunction in ADRD.

Synchronous Brain Dynamics Establish Brief States of Communality in Distant Neuronal Populations

Intrinsic brain dynamics co-fluctuate between distant regions in an organized manner during rest, establishing large-scale functional networks. We investigate these brain dynamics on a millisecond time scale by focusing on electroencephalographic (EEG) source analyses. While synchrony is thought of as a neuronal mechanism grouping distant neuronal populations into assemblies, the relevance of simultaneous zero-lag synchronization between brain areas in humans remains largely unexplored. This negligence is because of the confound of volume conduction, leading inherently to temporal dependencies of source estimates derived from scalp EEG [and magnetoencephalography (MEG)], referred to as spatial leakage. Here, we focus on the analyses of simultaneous, i.e., quasi zero-lag related, synchronization that cannot be explained by spatial leakage phenomenon. In eighteen subjects during rest with eyes closed, we provide evidence that first, simultaneous synchronization is present between distant brain areas and second, that this long-range synchronization is occurring in brief epochs, i.e., 54-80 ms. Simultaneous synchronization might signify the functional convergence of remote neuronal populations. Given the simultaneity of distant regions, these synchronization patterns might relate to the representation and maintenance, rather than processing of information. This long-range synchronization is briefly stable, not persistently, indicating flexible spatial reconfiguration pertaining to the establishment of particular, re-occurring states. Taken together, we suggest that the balance between temporal stability and spatial flexibility of long-range, simultaneous synchronization patterns is characteristic of the dynamic coordination of large-scale functional brain networks. As such, quasi zero-phase related EEG source fluctuations are physiologically meaningful if spatial leakage is considered appropriately.

The perspectives of mapping and monitoring of the sense of self in neurosurgical patients

Surgical treatment of tumors, epileptic foci or of vascular origin, requires a detailed individual pre-surgical workup and intra-operative surveillance of brain functions to minimize the risk of post-surgical neurological deficits and decline of quality of life. Most attention is attributed to language, motor functions, and perception. However, higher cognitive functions such as social cognition, personality, and the sense of self may be affected by brain surgery. To date, the precise localization and the network patterns of brain regions involved in such functions are not yet fully understood, making the assessment of risks of related post-surgical deficits difficult. It is in the interest of neurosurgeons to understand with which neural systems related to selfhood and personality they are interfering during surgery. Recent neuroscience research using virtual reality and clinical observations suggest that the insular cortex, medial prefrontal cortex, and temporo-parietal junction are important components of a neural system dedicated to self-consciousness based on multisensory bodily processing, including exteroceptive and interoceptive cues (bodily self-consciousness (BSC)). Here, we argue that combined extra- and intra-operative approaches using targeted cognitive testing, functional imaging and EEG, virtual reality, combined with multisensory stimulations, may contribute to the assessment of the BSC and related cognitive aspects. Although the usefulness of particular biomarkers, such as cardiac and respiratory signals linked to virtual reality, and of heartbeat evoked potentials as a surrogate marker for intactness of multisensory integration for intra-operative monitoring has to be proved, systemic and automatized testing of BSC in neurosurgical patients will improve future surgical outcome.

Multi-Angular Electroretinography (maERG): Topographic mapping of the retinal function combining real and virtual electrodes

GOAL

The full-field electroretinogram (ffERG) is an objective tool to assess global retinal function, though as it is currently done, it is unable to localize sources of retinal dysfunction or damage. To overcome this, we have developed a new way to record multiple spatial derivations of the ERG using the rotating capability of the eye, thus creating virtual electrodes. We have termed this the multi-angular ERG (or maERG). With only 3 real electrodes and 11 varying gaze positions, we create 33 virtual electrodes.

METHODS

We created a realistic electrophysiological and anatomical eye model (i.e. forward model) to reconstruct the retinal activity (i.e. inverse problem) from the 33 virtual electrodes. We simulated 2 pathological scenarios (central and peripheral scotomas), which were compared to their respective theoretical source configurations using an Area under Receiver Operator Characteristic curve metric.

RESULTS

Our simulations show that the low-resolution brain electromagnetic tomography algorithm (LORETA) is the best method tested to reconstruct retinal sources when compared to the Minimum Norm Estimate algorithm. Furthermore, a signal to noise ratio of 50dB is needed to accurately reconstruct the retinas functional map.

CONCLUSION

Our proposed maERG recording method, combined with our solution to the electromagnetic inverse problem enables us to reconstruct the functional map of the human retina.

SIGNIFICANCE

We believe that this new functional retinal imaging technique will permit earlier detection of retinal malfunction and consequently optimize the clinical monitoring of patients affected with retinopathies.

Meditation training modulates brain electric microstates and felt states of awareness

Meditation practice is believed to foster states of mindful awareness and mental quiescence in everyday life. If so, then the cultivation of these qualities with training ought to leave its imprint on the activity of intrinsic functional brain networks. In an intensive longitudinal study, we investigated associations between meditation practitioners' experiences of felt mindful awareness and changes in the spontaneous electrophysiological dynamics of functional brain networks. Experienced meditators were randomly assigned to complete 3 months of full‐time training in focused‐attention meditation (during an initial intervention) or to serve as waiting‐list controls and receive training second (during a later intervention). We collected broadband electroencephalogram (EEG) during rest at the beginning, middle, and end of the two training periods. Using a data‐driven approach, we segmented the EEG into a time series of transient microstate intervals based on clustering of topographic voltage patterns. Participants also provided daily reports of felt mindful awareness and mental quiescence, and reported daily on four experiential qualities of their meditation practice during training. We found that meditation training led to increases in mindful qualities of awareness, which corroborate contemplative accounts of deepening mental calm and attentional focus. We also observed reductions in the strength and duration of EEG microstates across both interventions. Importantly, changes in the dynamic sequencing of microstates were associated with daily increases in felt attentiveness and serenity during training. Our results connect shifts in subjective qualities of meditative experience with the large‐scale dynamics of whole brain functional EEG networks at rest.

Computational Models in Electroencephalography

Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.

Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging

Many methods applied to data acquired by various imaging modalities have been evaluated for their benefit in localizing lesions in magnetic resonance (MR) negative epilepsy patients. No approach has proven to be a stand-alone method with sufficiently high sensitivity and specificity. The presented study addresses the potential benefit of the automated fusion of results of individual methods in presurgical evaluation. We collected electrophysiological, MR, and nuclear imaging data from 137 patients with pharmacoresistant MR-negative/inconclusive focal epilepsy. A subgroup of 32 patients underwent surgical treatment with known postsurgical outcomes and histopathology. We employed a Gaussian mixture model to reveal several classes of gray matter tissue. Classes specific to epileptogenic tissue were identified and validated using the surgery subgroup divided into two disjoint sets. We evaluated the classification accuracy of the proposed method at a voxel-wise level and assessed the effect of individual methods. The training of the classifier resulted in six classes of gray matter tissue. We found a subset of two classes specific to tissue located in resected areas. The average classification accuracy (i.e., the probability of correct classification) was significantly higher than the level of chance in the training group (0.73) and even better in the validation surgery subgroup (0.82). Nuclear imaging, diffusion-weighted imaging, and source localization of interictal epileptic discharges were the strongest methods for classification accuracy. We showed that the automatic fusion of results can identify brain areas that show epileptogenic gray matter tissue features. The method might enhance the presurgical evaluations of MR-negative epilepsy patients.

Frontal activation as a key for deciphering context congruity and valence during visual perception: An electrical neuroimaging study

The object-context associations and the valence are two important stimulus attributes that influence visual perception. The current study investigates the neural sources associated with schema congruent and incongruent object-context associations within positive, negative, and neutral valence during an intermittent binocular rivalry task with simultaneous high-density EEG recording. Cortical sourceswere calculated using the sLORETA algorithm in 150 ms after stimulus onset (Stim + 150) and 400 ms before response (Resp-400) time windows. No significant difference in source activity was found between congruent and incongruent associations in any of the valence categories in the Stim + 150 ms window indicating that immediately after stimulus presentation the basic visual processing remains the same for both. In the Resp-400 ms window, different frontal regions showed higher activity for incongruent associations with different valence such as the superior frontal gyrus showed significantly higher activations for negative while the middle and medial frontal gyrus showed higher activations for neutral and finally, the inferior frontal gyrus showed higher activations for positive valence. Besides replicating the previous knowledge of frontal activations in response to context congruity, the current study provides further evidence for the sensitivity of the frontal lobe to the valence associated with the incongruent stimuli.

Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG current source dipoles

OBJECTIVE

Growing evidence suggests that EEG electrode (sensor) potential time series (PTS) of slow cortical potentials (SCPs) hold motor neural correlates that can be used for motion trajectory prediction (MTP), commonly by multiple linear regression (mLR). It is not yet known whether arm-joint trajectories can be reliably decoded from current sources, computed from sensor data, from which brain areas they can be decoded and using which neural features.

APPROACH

In this study, the PTS of 44 sensors were fed into sLORETA source localization software to compute current source activity in 30 regions of interest (ROIs) found in a recent meta-analysis to be engaged in action execution, motor imagery and motor preparation. The current sources PTS and band-power time series (BTS) in several frequency bands and time lags were used to predict actual and imagined trajectories in 3D space of the three velocity components of the hand, elbow and shoulder of nine subjects using an mLR model.

MAIN RESULTS

For all arm joints and movement types, current source SCPs PTS contributed most to trajectory reconstruction with time lags 150ms, 116ms and 84ms providing the highest contribution, and current source BTS in any of the tested frequency bands was not informative. Person's correlation coefficient (r) averaged across movement types, arm joints and velocity components using source data was slightly lower than using sensor data (r=0.25 and r=0.28, respectively). For each ROI, the three current source dipoles had different contribution to the reconstruction of each of the three velocity components.

SIGNIFICANCE

Overall, our results demonstrate the feasibility of predicting of actual and imagined 3D trajectories of all arm joints from current sources, computed from scalp EEG. These findings may be used by developers of a future BCI as a validated set of contributing ROIs.

Neuronal Effects of Listening to Entrainment Music Versus Preferred Music in Patients With Chronic Cancer Pain as Measured via EEG and LORETA Imaging

Previous studies examining EEG and LORETA in patients with chronic pain discovered an overactivation of high theta (6–9 Hz) and low beta (12–16 Hz) power in central regions. MEG studies with healthy subjects correlating evoked nociception ratings and source localization described delta and gamma changes according to two music interventions. Using similar music conditions with chronic pain patients, we examined EEG in response to two different music interventions for pain. To study this process in-depth we conducted a mixed-methods case study approach, based on three clinical cases. Effectiveness of personalized music therapy improvisations (entrainment music – EM) versus preferred music on chronic pain was examined with 16 participants. Three patients were randomly selected for follow-up EEG sessions three months post-intervention, where they listened to recordings of the music from the interventions provided during the research. To test the difference of EM versus preferred music, recordings were presented in a block design: silence, their own composed EM (depicting both “pain” and “healing”), preferred (commercially available) music, and a non-participant’s EM as a control. Participants rated their pain before and after the EEG on a 1–10 scale. We conducted a detailed single case analysis to compare all conditions, as well as a group comparison of entrainment-healing condition versus preferred music condition. Power spectrum and according LORETA distributions focused on expected changes in delta, theta, beta, and gamma frequencies, particularly in sensory-motor and central regions. Intentional moment-by-moment attention on the sounds/music rather than on pain and decreased awareness of pain was experienced from one participant. Corresponding EEG analysis showed accompanying power changes in sensory-motor regions and LORETA projection pointed to insula-related changes during entrainment-pain music. LORETA also indicated involvement of visual-spatial, motor, and language/music improvisation processing in response to his personalized EM which may reflect active recollection of creating the EM. Group-wide analysis showed common brain responses to personalized entrainment-healing music in theta and low beta range in right pre- and post-central gyrus. We observed somatosensory changes consistent with processing pain during entrainment-healing music that were not seen during preferred music. These results may depict top–down neural processes associated with active coping for pain.

Spatiotemporal Characteristics of Neural Dynamics in Theta Oscillations Related to the Inhibition of Habitual Behavior

The human brain carries out cognitive control for the inhibition of habitual behaviors by suppressing some familiar but inappropriate behaviors instead of engaging specific goal-directed behavior flexibly in a given situation. To examine the characteristics of neural dynamics related to such inhibition of habitual behaviors, we used a modified rock–paper–scissors (RPS) task that consisted of a basic, a lose-, and a win-conditioned game. Spectral and phase synchrony analyses were conducted to examine the acquired electroencephalogram signals across the entire brain during all RPS tasks. Temporal variations in frontal theta power activities were directly in line with the stream of RPS procedures in accordance with the task conditions. The lose-conditioned RPS task gave rise to increases in the local frontal power and global phase-synchronized pairs of theta oscillations. The activation of the global phase-synchronized network preceded the activation of frontal theta power. These results demonstrate that the frontal regions play a pivotal role in the inhibition of habitual behaviors—stereotyped and ingrained stimulus–response mappings that have been established over time. This study suggests that frontal theta oscillations may be engaged during the cognitive inhibition of habitual behaviors and that these oscillations characterize the degree of cognitive load required to inhibit habitual behaviors.

Associations between self-reported spontaneous thought and temporal sequences of EEG microstates

Thought dynamically evolves from one moment to the next even in the absence of external stimulation. The extent to which patterns of spontaneous thought covary with time-varying fluctuations in intrinsic brain activity is of great interest but remains unknown. We conducted novel analyses of data originally reported by Portnova et al. (2019) to examine associations between the intrinsic dynamics of EEG microstates and self-reported thought measured using the Amsterdam Resting-State Questionnaire (ARSQ). Accordingly, the millisecond fluctuations of microstates were associated with specific dimensions of thought. We evaluated the reliability of these findings by combining our results with those of another study using meta-analysis. Importantly, we extended this investigation using multivariate methods to evaluate multidimensional thought profiles of individuals and their links to sequences of successive microstates. Thought profiles were identified based on hierarchical clustering of ARSQ ratings and were distinguished in terms of the temporal ordering of successive microstates based on sequence analytic methods. These findings demonstrate the relevance of assessing spontaneous thought for understanding intrinsic brain activity and the novel use of sequence analysis for characterizing microstate dynamics. Integrating the phenomenological view from within remains crucial for understanding the functional significance of intrinsic large-scale neurodynamics.

Seizures and Sepsis: A Narrative Review

Patients with sepsis-associated encephalopathy (SAE) can develop convulsive or nonconvulsive seizures. The cytokine storm and the overwhelming systemic inflammation trigger the electric circuits that promote seizures. Several neurologic symptoms, associated with this disease, range from mild consciousness impairment to coma. Focal or generalized convulsive seizures are frequent in sepsis, although nonconvulsive seizures (NCS) are often misdiagnosed and prevalent in SAE. In order to map the trigger zone in all patients that present focal or generalized seizures and also to detect NCS, EEG is indicated but continuous EEG (cEEG) is not very widespread; timing, duration, and efficacy of this tool are still unknown. The long-term risk of seizures in survivors is increased. The typical stepwise approach of seizures management begins with benzodiazepines and follows with anticonvulsants up to anesthetic drugs such as propofol or thiopental, which are able to induce burst suppression and interrupt the pathological electrical circuits. This narrative review discusses pathophysiology, clinical presentation, diagnosis and treatment of seizures in sepsis.

A novel approach of CNN for human motor imagery recognition using the virtual electrode pairs

In order to develop an efficient brain-computer interface system, the brain activity measured by electroencephalography needs to be accurately decoded. In this paper, a motor imagery classification approach is proposed, combining virtual electrodes on the cortex layer with a convolutional neural network; this can effectively improve the decoding performance of the brain-computer interface system. A three layer (cortex, skull, and scalp) head volume conduction model was established by using the symmetric boundary element method to map the scalp signal to the cortex area. Nine pairs of virtual electrodes were created on the cortex layer, and the features of the time and frequency sequence from the virtual electrodes were extracted by performing time-frequency analysis. Finally, the convolutional neural network was used to classify motor imagery tasks. The results show that the proposed approach is convergent in both the training model and the test model. Based on the Physionet motor imagery database, the averaged accuracy can reach 98.32% for a single subject, while the averaged values of accuracy, Kappa, precision, recall, and F1-score on the group-wise are 96.23%, 94.83%, 96.21%, 96.13%, and 96.14%, respectively. Based on the High Gamma database, the averaged accuracy has achieved 96.37% and 91.21% at the subject and group levels, respectively. Moreover, this approach is superior to those of other studies on the same database, which suggests robustness and adaptability to individual variability.

Reconfiguration of Electroencephalography Microstate Networks after Breath-Focused, Digital Meditation Training

Sustained attention and working memory were improved in young adults after they engaged in a recently developed, closed-loop, digital meditation practice. Whether this type of meditation also has a sustained effect on dominant resting-state networks is currently unknown. In this study, we examined the resting brain states before and after a period of breath-focused, digital meditation training versus placebo using an electroencephalography (EEG) microstate approach. We found topographical changes in postmeditation rest, compared with baseline rest, selectively for participants who were actively involved in the meditation training and not in participants who engaged with an active, expectancy-match, placebo control paradigm. Our results suggest a reorganization of brain network connectivity after 6 weeks of intensive meditation training in brain areas, mainly including the right insula, the superior temporal gyrus, the superior parietal lobule, and the superior frontal gyrus bilaterally. These findings provide an opening for the development of a novel noninvasive treatment of neuropathological states by low-cost, breath-focused, digital meditation practice, which can be monitored by the EEG microstate approach.

Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis

While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics.

Variability and effect sizes of intracranial current source density estimations during pain: Systematic review, experimental findings, and future perspectives

Pain arises from the integration of sensory and cognitive processes in the brain, resulting in specific patterns of neural oscillations that can be characterized by measuring electrical brain activity. Current source density (CSD) estimation from low-resolution brain electromagnetic tomography (LORETA) and its standardized (sLORETA) and exact (eLORETA) variants, is a common approach to identify the spatiotemporal dynamics of the brain sources in physiological and pathological pain-related conditions. However, there is no consensus on the magnitude and variability of clinically or experimentally relevant effects for CSD estimations. Here, we systematically examined reports of sample size calculations and effect size estimations in all studies that included the keywords pain, and LORETA, sLORETA, or eLORETA in Scopus and PubMed. We also assessed the reliability of LORETA CSD estimations during non-painful and painful conditions to estimate hypothetical sample sizes for future experiments using CSD estimations. We found that none of the studies included in the systematic review reported sample size calculations, and less than 20% reported measures of central tendency and dispersion, which are necessary to estimate effect sizes. Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. These results provide a comprehensive summary of the effect sizes observed using LORETA in pain research, and this information can be used by clinicians and researchers to improve settings and designs of future pain studies.

Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning

Pain is an undesirable sensory experience that can induce depression and limit individuals' activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective and difficult to judge objectively, particularly during activity. Here, we developed a system to objectively judge pain levels in walking subjects by recording their quantitative electroencephalography (qEEG) and analyzing data by machine learning. To do so, we enrolled 23 patients who had undergone total hip replacement for pain, and recorded their qEEG during a five-minute walk via a wearable device with a single electrode placed over the Fp1 region, based on the 10-20 Electrode Placement System, before and three months after surgery. We also assessed subject hip pain using a numerical rating scale. Brain wave amplitude differed significantly among subjects with different levels of hip pain at frequencies ranging from 1 to 35 Hz. qEEG data were also analyzed by a support vector machine using the Radial Basis Functional Kernel, a function used in machine learning. That approach showed that an individual's hip pain during walking can be recognized and subdivided into pain quartiles with 79.6% recognition Accuracy. Overall, we have devised an objective and non-invasive tool to monitor an individual's pain during walking.

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.

Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.

Evaluation of smartphone interactions on drivers' brain function and vehicle control in an immersive simulated environment

Smartphones and other modern technologies have introduced multiple new forms of distraction that color the modern driving experience. While many smartphone functions aim to improve driving by providing the driver with real-time navigation and traffic updates, others, such as texting, are not compatible with driving and are often the cause of accidents. Because both functions elicit driver attention, an outstanding question is the degree to which drivers' naturalistic interactions with navigation and texting applications differ in regard to brain and behavioral indices of distracted driving. Here, we employed functional near-infrared spectroscopy to examine the cortical activity that occurs under parametrically increasing levels of smartphone distraction during naturalistic driving. Our results highlight a significant increase in bilateral prefrontal and parietal cortical activity that occurs in response to increasingly greater levels of smartphone distraction that, in turn, predicts changes in common indices of vehicle control.

The activity and connectivity of the facial emotion processing neural circuitry in bipolar disorder: a systematic review

BACKGROUND

Facial emotion processing abnormalities may be a trait feature of bipolar disorder (BD). These social cognitive impairments may be due to alterations in the neural processing of facial affective information in visual ("core"), and limbic and prefrontal ("extended") networks, however, the precise neurobiological mechanism(s) underlying these symptoms are unclear.

METHODS

We conducted a systematic review to appraise the literature on the activity and connectivity of the facial emotion processing neural circuitry in BD. Two reviewers undertook a search of the electronic databases PubMed, Scopus and PsycINFO, to identify relevant literature published since inception up until September 2019. Study eligibility criteria included; BD participants, neuroimaging, and facial emotion processing tasks.

RESULTS

Out of an initial yield of 6121 articles, 66 were eligible for inclusion in this review. We identified differences in neural activity and connectivity within and between occipitotemporal, limbic, and prefrontal regions, in response to facial affective stimuli, in BD compared to healthy controls.

LIMITATIONS

The methodologies used across studies varied considerably.

CONCLUSIONS

The findings from this review suggest abnormalities in both the activity and connectivity of facial emotion processing neural circuitry in BD. It is recommended that future research aims to further define the connectivity and spatiotemporal course of neural events within and between occipitotemporal, limbic, and prefrontal regions.

Spatiotemporal Processing of Bimodal Odor Lateralization in the Brain Using Electroencephalography Microstates and Source Localization

The neuronal cascade related to the perception of either purely olfactory or trigeminal airborne chemicals has been investigated using electroencephalography (EEG) microstate analyses and source localization. However, most airborne chemicals are bimodal in nature, encompassing both properties. Moreover, there is an ongoing debate regarding whether there is one dominant nostril, and this could be investigated using these multichannel EEG methods. In this study, 18 right-handed, healthy participants (13 females) were monorhinally stimulated using an olfactometer with the bimodal component acetic acid during continuous EEG recording. Participants indicated the side of stimulation, the confidence in their decision, and rated the strength of the evoked perception. EEG microstate clustering determined four distinct maps and successive backfitting procedures, and source estimations revealed a network that evolved from visual-spatial processing areas to brain areas related to basic olfactory and trigeminal sensations (e.g., thalamus, cingulate cortex, insula, parahippocampal, and pre-/post-central gyri) and resulted in activation of areas involved in multisensory integration (e.g., frontal-temporal areas). Right-nostril stimulation was associated with faster microstate transition and longer involvement of the superior temporal gyrus, which was previously linked to chemical localization and provides evidence for a potential nostril dominance. The results describe for the first time the processing cascade of bimodal odor perception using microstate analyses and demonstrate its feasibility to further investigate potential nostril dominance.

Characterization of Source-Localized EEG Activity During Sustained Deep-Tissue Pain

Musculoskeletal pain is a clinical condition that is characterized by ongoing pain and discomfort in the deep tissues such as muscle, bones, ligaments, nerves, and tendons. In the last decades, it was subject to extensive research due to its high prevalence. Still, a quantitative description of the electrical brain activity during musculoskeletal pain is lacking. This study aimed to characterize intracranial current source density (CSD) estimations during sustained deep-tissue experimental pain. Twenty-three healthy volunteers received three types of tonic stimuli for three minutes each: computer-controlled cuff pressure (1) below pain threshold (sustained deep-tissue no-pain, SDTnP), (2) above pain threshold (sustained deep-tissue pain, SDTP) and (3) vibrotactile stimulation (VT). The CSD in response to these stimuli was calculated in seven regions of interest (ROIs) likely involved in pain processing: contralateral anterior cingulate cortex, contralateral primary somatosensory cortex, bilateral anterior insula, contralateral dorsolateral prefrontal cortex, posterior parietal cortex and contralateral premotor cortex. Results showed that participants exhibited an overall increase in spectral power during SDTP in all seven ROIs compared to both SDTnP and VT, likely reflecting the differences in the salience of these stimuli. Moreover, we observed a difference is CSD due to the type of stimulus, likely reflecting somatosensory discrimination of stimulus intensity. These results describe the different contributions of neural oscillations within these brain regions in the processing of sustained deep-tissue pain.

Electroencephalographic Correlates of Obsessive-Compulsive Disorder

This chapter reviews EEG research in Obsessive-Compulsive Disorder (OCD), focusing on Event-Related Potentials (ERPs) such as the Contingent Negative Variation, N2, Error-Related Negativity, the feedback Error-Related Negativity and the Readiness Potential and their neural bases. The functional significance, utility and correlation of these ERPs with OCD symptoms will be discussed, alongside novel theories for integrating the research findings. I will consider hypotheses including goal-directed behaviour, overreliance on habits, dissociations between action and knowledge, and excessive intolerance of uncertainty in the context of EEG studies, thus providing a comprehensive framework of the electroencephalographic literature concerning OCD.

EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review

Introduction: Electrophysiological (EEG) abnormalities in subjects with schizophrenia have been largely reported. In the last decades, research has shifted to the identification of electrophysiological alterations in the prodromal and early phases of the disorder, focusing on the prediction of clinical and functional outcome. The identification of neuronal aberrations in subjects with a first episode of psychosis (FEP) and in those at ultra high-risk (UHR) or clinical high-risk (CHR) to develop a psychosis is crucial to implement adequate interventions, reduce the rate of transition to psychosis, as well as the risk of irreversible functioning impairment. The aim of the review is to provide an up-to-date synthesis of the electrophysiological findings in the at-risk mental state and early stages of schizophrenia. Methods: A systematic review of English articles using Pubmed, Scopus, and PsychINFO was undertaken in July 2020. Additional studies were identified by hand-search. Electrophysiological studies that included at least one group of FEP or subjects at risk to develop psychosis, compared to healthy controls (HCs), were considered. The heterogeneity of the studies prevented a quantitative synthesis. Results: Out of 319 records screened, 133 studies were included in a final qualitative synthesis. Included studies were mainly carried out using frequency analysis, microstates and event-related potentials. The most common findings included an increase in delta and gamma power, an impairment in sensory gating assessed through P50 and N100 and a reduction of Mismatch Negativity and P300 amplitude in at-risk mental state and early stages of schizophrenia. Progressive changes in some of these electrophysiological measures were associated with transition to psychosis and disease course. Heterogeneous data have been reported for indices evaluating synchrony, connectivity, and evoked-responses in different frequency bands. Conclusions: Multiple EEG-indices were altered during at-risk mental state and early stages of schizophrenia, supporting the hypothesis that cerebral network dysfunctions appear already before the onset of the disorder. Some of these alterations demonstrated association with transition to psychosis or poor functional outcome. However, heterogeneity in subjects' inclusion criteria, clinical measures and electrophysiological methods prevents drawing solid conclusions. Large prospective studies are needed to consolidate findings concerning electrophysiological markers of clinical and functional outcome.

Electroencephalographic Correlates and Predictors of Treatment Outcome in OCD: A Brief Narrative Review

Electroencephalography (EEG) measures the brain's electrical activity with high temporal resolution. In comparison to neuroimaging modalities such as MRI or PET, EEG is relatively cheap, non-invasive, portable, and simple to administer, making it an attractive tool for clinical deployment. Despite this, studies utilizing EEG to investigate obsessive-compulsive disorder (OCD) are relatively sparse. This contrasts with a robust literature using other brain imaging methodologies. The present review examines studies that have used EEG to examine predictors and correlates of response in OCD and draws tentative conclusions that may guide much needed future work. Key findings include a limited literature base; few studies have attempted to predict clinical change from EEG signals, and they are confounded by the effects of both pharmacotherapy and psychotherapy. The most robust literature, consisting of several studies, has examined event-related potentials, including the P300, which several studies have reported to be abnormal at baseline in OCD and to normalize with treatment; but even here the literature is quite heterogeneous, and more work is needed. With more robust research, we suggest that the relatively low cost and convenience of EEG, especially in comparison to fMRI and PET, make it well-suited to the development of feasible personalized treatment algorithms.

A Review on P300 in Obsessive-Compulsive Disorder

Neuropsychological studies indicate the presence of cognitive changes in patients with obsessive-compulsive disorder (OCD). Indeed, OCD may be included among the dysfunctions of the frontal lobes and their connections with the limbic system, associative cortex, and basal ganglia. P300 is a positive component of the human event-related potential (ERP); it is associated with processes of encoding, identification, and categorization constituting, as a whole, the superior cortical function of information processing. Thus, P300 explores several areas that are implicated in OCD pathophysiology. Our aim is to review all relevant studies on the P300 component of the human ERP in order to recognize any significant central nervous system (CNS) correlate of cognitive dysfunction in OCD. A PubMed-based literature search resulted in 35 articles assessing P300 in OCD and reporting neurophysiological correlates of response inhibition, cortical hyperarousal, and over-focused attention. A decreased P300 amplitude was reported in both adult and pediatric patients, with a trend toward normalization after pharmacological treatment. Source localization studies disclosed an association between P300 abnormalities and the functioning of brain regions involved in the pathophysiology of OCD. Moreover, studies converge on the evidence of neurophysiological dysfunction in the frontal areas with impairment of the normal inhibitory processes in OCD. At least some of these electrophysiological correlates might reflect the obsessive thoughts and compulsions that characterize this disorder. These findings may also support cognitive-behavioral therapy (CBT) approaches on over-focused attention and inflexibility of compulsive behaviors, which should be associated to pharmacological treatment in these patients.

Source Analysis of Triphasic Waves Using Quantitative Neuroimaging

PURPOSE

Triphasic waves (TWs), a common EEG pattern, are considered a subtype of generalized periodic discharges. Most patients with TWs present with an altered level of consciousness, and the TW pattern is believed to represent thalamocortical dysfunction. However, the exact meaning and mechanism of TWs remain unclear. The objective of the current study was to evaluate the source of TWs using EEG source imaging and computerized tomography.

METHODS

Twenty-eight patients with TWs were investigated. Source analysis was performed on the averaged TWs for each individual, and source maps were extracted. Normalization and automatic segmentation of gray matter were performed on computerized tomography scans before analysis. Finally, voxelwise correlation analyses were conducted between EEG source maps and gray matter volumes.

RESULTS

Source analyses showed that the anterior cingulate cortex was mainly involved in TWs (16/28 patients, 57%). Correlation analyses showed moderate positive and negative correlations between source location and gray matter volumes for the posterior cingulate (T = 2.85; volume = 6,533 mm3; r = 0.53; P = 0.002) and the superior frontal gyrus (T = 2.54; volume = 18,167 mm3; r = -0.48; P < 0.0001), respectively.

CONCLUSIONS

The results suggest that the anterior cingulate is involved in the origin of TWs. Furthermore, the volumes of posterior brain regions were positively correlated with TWs, indicating a possible preservation of these structures. Conversely, the volumes of anterior regions were negatively correlated with TWs. These findings may indicate a structural pattern necessary for the generation of the abnormal network responsible for TWs.

Is EEG Suitable for Marketing Research? A Systematic Review

Background: In the past decade, marketing studies have greatly benefited from the adoption of neuroscience techniques to explore conscious and unconscious drivers of consumer behavior. Electroencephalography (EEG) is one of the most frequently applied neuroscientific techniques for marketing studies, thanks to its low cost and high temporal resolution. Objective: We present an overview of EEG applications in consumer neuroscience. The aim of this review is to facilitate future research and to highlight reliable approaches for deriving research and managerial implications. Method: We conducted a systematic review by querying five databases for the titles of articles published up to June 2020 with the terms [EEG] AND [neuromarketing] OR [consumer neuroscience]. Results: We screened 264 abstracts and analyzed 113 articles, classified based on research topics (e.g., product characteristics, pricing, advertising attention and memorization, rational, and emotional messages) and characteristics of the experimental design (tasks, stimuli, participants, additional techniques). Conclusions: This review highlights the main applications of EEG to consumer neuroscience research and suggests several ways EEG technique can complement traditional experimental paradigms. Further research areas, including consumer profiling and social consumer neuroscience, have not been sufficiently explored yet and would benefit from EEG techniques to address unanswered questions.

A newly developed free software tool set for averaging electroencephalogram implemented in the Perl programming language

Background

Considering the need for daily activity analysis of older adults, development of easy-to-use, free electroencephalogram (EEG) analysis tools are desired in order to decrease barriers to accessing this technology and increase the entry of a wide range of new researchers.

New method

We describe a newly developed tool set for EEG analysis, enabling import, average, waveform display and iso-potential scalp topographies, utilizing the programming language Perl.

Results

The basic processing, including average, display waveforms, and isopotential scalp topography was implemented in the current system. The validation was examined by making difference waveforms between the results using the current analysis system and a commercial software.

Comparison with Existing Method(s): The current software tool set consists of free software. The scripts are easily editable by any user and there are no black boxes.

Conclusions

The currently reported procedures provide an easy-to-begin, flexible, readable, easy-to-modify basic tool set for EEG analysis and is expected to recruit new EEG researchers.

Detection of Simulated Tactile Gratings by Electro-Static Friction Show a Dependency on Bar Width for Blind and Sighted Observers, and Preliminary Neural Correlates in Sighted Observers

The three-dimensional micro-structure of physical surfaces produces frictional forces that provide sensory cues about properties of felt surfaces such as roughness. This tactile information activates somatosensory cortices, and frontal and temporal brain regions. Recent advances in haptic-feedback technologies allow the simulation of surface micro-structures via electro-static friction to produce touch sensations on otherwise flat screens. These sensations may benefit those with visual impairment or blindness. The primary aim of the current study was to test blind and sighted participants' perceptual sensitivity to simulated tactile gratings. A secondary aim was to explore which brain regions were involved in simulated touch to further understand the somatosensory brain network for touch. We used a haptic-feedback touchscreen which simulated tactile gratings using digitally manipulated electro-static friction. In Experiment 1, we compared blind and sighted participants' ability to detect the gratings by touch alone as a function of their spatial frequency (bar width) and intensity. Both blind and sighted participants showed high sensitivity to detect simulated tactile gratings, and their tactile sensitivity functions showed both linear and quadratic dependency on spatial frequency. In Experiment 2, using functional magnetic resonance imaging, we conducted a preliminary investigation to explore whether brain activation to physical vibrations correlated with blindfolded (but sighted) participants' performance with simulated tactile gratings outside the scanner. At the neural level, blindfolded (but sighted) participants' detection performance correlated with brain activation in bi-lateral supplementary motor cortex, left frontal cortex and right occipital cortex. Taken together with previous studies, these results suggest that there are similar perceptual and neural mechanisms for real and simulated touch sensations.

Microstate ERP Analyses to Pinpoint the Articulatory Onset in Speech Production

The use of electroencephalography (EEG) to study overt speech production has increased substantially in the past 15 years and the alignment of evoked potential (ERPs) on the response onset has become an extremely useful method to target “latest” stages of speech production. Yet, response-locked ERPs raise a methodological issue: on which event should the point of alignment be placed? Response-locked ERPs are usually aligned to the vocal (acoustic) onset, although it is well known that articulatory movements may start up to a hundred milliseconds prior to the acoustic onset and that this “articulatory onset to acoustic onset interval” (AAI) depends on the phoneme properties. Given the previously reported difficulties to measure the AAI, the purpose of this study was to determine if the AAI could be reliably detected with EEG-microstates. High-density EEG was recorded during delayed speech production of monosyllabic pseudowords with four different onset consonants. Whereas the acoustic response onsets varied depending on the onset consonant, the response-locked spatiotemporal EEG analysis revealed a clear asynchrony of the same sequence of microstates across onset consonants. A specific microstate, the latest observed in the ERPs locked to the vocal onset, presented longer duration for phonemes with longer acoustic response onsets. Converging evidences seemed to confirm that this microstate may be related to the articulatory onset of motor execution: its scalp topography corresponded to those previously associated with muscle activity and source localization highlighted the involvement of motor areas. Finally, the analyses on the duration of such microstate in single trials further fit with the AAI intervals for specific phonemes reported in previous studies. These results thus suggest that a particular ERP-microstate is a reliable index of articulation onset and of the AAI.

Cortical Patterns of Pleasurable Musical Chills Revealed by High-Density EEG

Music has the capacity to elicit strong positive feelings in humans by activating the brain's reward system. Because group emotional dynamics is a central concern of social neurosciences, the study of emotion in natural/ecological conditions is gaining interest. This study aimed to show that high-density EEG (HD-EEG) is able to reveal patterns of cerebral activities previously identified by fMRI or PET scans when the subject experiences pleasurable musical chills. We used HD-EEG to record participants (11 female, 7 male) while listening to their favorite pleasurable chill-inducing musical excerpts; they reported their subjective emotional state from low pleasure up to chills. HD-EEG results showed an increase of theta activity in the prefrontal cortex when arousal and emotional ratings increased, which are associated with orbitofrontal cortex activation localized using source localization algorithms. In addition, we identified two specific patterns of chills: a decreased theta activity in the right central region, which could reflect supplementary motor area activation during chills and may be related to rhythmic anticipation processing, and a decreased theta activity in the right temporal region, which may be related to musical appreciation and could reflect the right superior temporal gyrus activity. The alpha frontal/prefrontal asymmetry did not reflect the felt emotional pleasure, but the increased frontal beta to alpha ratio (measure of arousal) corresponded to increased emotional ratings. These results suggest that EEG may be a reliable method and a promising tool for the investigation of group musical pleasure through musical reward processing.

High-density Electric Source Imaging of interictal epileptic discharges: How many electrodes and which time point?

OBJECTIVE

To assess the value of caudal EEG electrodes over cheeks and neck for high-density electric source imaging (ESI) in presurgical epilepsy evaluation, and to identify the best time point during averaged interictal epileptic discharges (IEDs) for optimal ESI accuracy.

METHODS

We retrospectively examined presurgical 257-channel EEG recordings of 45 patients with pharmacoresistant focal epilepsy. By stepwise removal of cheek and neck electrodes, averaged IEDs were downsampled to 219, 204, and 156 EEG channels. Additionally, ESI at the IED's half-rise was compared to other time points. The respective sources of maximum activity were compared to the resected brain area and postsurgical outcome.

RESULTS

Caudal channels had disproportionately more artefacts. In 30 patients with favourable outcome, the 204-channel array yielded the most accurate results with ESI maxima < 10 mm from the resection in 67% and inside affected sublobes in 83%. Neither in temporal nor in extratemporal cases did the full 257-channel setup improve ESI accuracy. ESI was most accurate at 50% of the IED's rising phase.

CONCLUSION

Information from cheeks and neck electrodes did not improve high-density ESI accuracy, probably due to higher artefact load and suboptimal biophysical modelling.

SIGNIFICANCE

Very caudal EEG electrodes should be used for ESI with caution.

Abnormalities in electroencephalographic microstates are state and trait markers of major depressive disorder

Neuroimaging studies have shown that major depressive disorder (MDD) is characterized by abnormal neural activity and connectivity. However, hemodynamic imaging techniques lack the temporal resolution needed to resolve the dynamics of brain mechanisms underlying MDD. Moreover, it is unclear whether putative abnormalities persist after remission. To address these gaps, we used microstate analysis to study resting-state brain activity in major depressive disorder (MDD). Electroencephalographic (EEG) "microstates" are canonical voltage topographies that reflect brief activations of components of resting-state brain networks. We used polarity-insensitive k-means clustering to segment resting-state high-density (128-channel) EEG data into microstates. Data from 79 healthy controls (HC), 63 individuals with MDD, and 30 individuals with remitted MDD (rMDD) were included. The groups produced similar sets of five microstates, including four widely-reported canonical microstates (A-D). The proportion of microstate D was decreased in MDD and rMDD compared to the HC group (Cohen's d = 0.63 and 0.72, respectively) and the duration and occurrence of microstate D was reduced in the MDD group compared to the HC group (Cohen's d = 0.43 and 0.58, respectively). Among the MDD group, proportion and duration of microstate D were negatively correlated with symptom severity (Spearman's rho = -0.34 and -0.46, respectively). Finally, microstate transition probabilities were nonrandom and the MDD group, relative to the HC and the rMDD groups, exhibited multiple distinct transition probabilities, primarily involving microstates A and C. Our findings highlight both state and trait abnormalities in resting-state brain activity in MDD.

Dissecting Neuronal Activation on a Brain-Wide Scale With Immediate Early Genes

Visualizing neuronal activation on a brain-wide scale yet with cellular resolution is a fundamental technical challenge for neuroscience. This would enable analyzing how different neuronal circuits are disrupted in pathology and how they could be rescued by pharmacological treatments. Although this goal would have appeared visionary a decade ago, recent technological advances make it eventually feasible. Here, we review the latest developments in the fields of genetics, sample preparation, imaging, and image analysis that could be combined to afford whole-brain cell-resolution activation mapping. We show how the different biochemical and optical methods have been coupled to study neuronal circuits at different spatial and temporal scales, and with cell-type specificity. The inventory of techniques presented here could be useful to find the tools best suited for a specific experiment. We envision that in the next years, mapping of neuronal activation could become routine in many laboratories, allowing dissecting the neuronal counterpart of behavior.

Auditory and Visual Tasks Influence the Temporal Dynamics of EEG Microstates During Post-encoding Rest

Re-activations of task-dependent patterns of neural activity take place during post-encoding periods of wakeful rest and sleep. However, it is still unclear how the temporal dynamics of brain states change during these periods, which are shaped by prior conscious experiences. Here, we examined the very brief periods of wakeful rest immediately after encoding and recognition of auditory and visual stimuli, by applying the EEG microstate analysis, in which the global variance of the EEG is explained by only a few prototypical topographies. We identified the dominant brain states of sub-second duration during the tasks-dependent periods of rest, finding that the temporal dynamics-represented here by two temporal parameters: the frequency of occurrence and the fraction of time coverage-of three task-related microstate classes changed compared to wakeful rest. This study provides evidence of experience-dependent temporal changes in post-encoding periods of resting brain activity, which can be captured using the EEG microstates approach.

Inverse modelling to reduce crosstalk in high density surface electromyogram

Surface electromyogram (EMG) has a relatively large detection volume, so that it could include contributions both from the target muscle of interest and from nearby regions (i.e., crosstalk). This interference can prevent a correct interpretation of the activity of the target muscle, limiting the use of surface EMG in many fields. To counteract the problem, selective spatial filters have been proposed, but they reduce the representativeness of the data from the target muscle. A better solution would be to discard only crosstalk from the signal recorded in monopolar configuration (thus, keeping most information on the target muscle). An inverse modelling approach is here proposed to estimate the contributions of different muscles, in order to focus on the one of interest. The method is tested with simulated monopolar EMGs from superficial nearby muscles contracted at different force levels (either including or not model perturbations and noise), showing statistically significant improvements in information extraction from the data. The median over the entire dataset of the mean squared error in representing the EMG of the muscle under the detection electrode was reduced from 11.2% to 4.4% of the signal energy (5.3% if noisy data were processed); the median bias in conduction velocity estimation (from 3 monopolar channels aligned to the muscle fibres) was decreased from 2.12 to 0.72 m/s (1.1 m/s if noisy data were processed); the median absolute error in the estimation of median frequency was reduced from 1.02 to 0.67 Hz in noise free conditions and from 1.52 to 1.45 Hz considering noisy data.

Local Sleep Slow-Wave Activity Colocalizes With the Ictal Symptomatogenic Zone in a Patient With Reflex Epilepsy: A High-Density EEG Study

Background: Slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep reflects synaptic potentiation during preceding wakefulness. Epileptic activity may induce increases in state-dependent SWA in human brains, therefore, localization of SWA may prove useful in the presurgical workup of epileptic patients. We analyzed high-density electroencephalography (HDEEG) data across vigilance states from a reflex epilepsy patient with a clearly localizable ictal symptomatogenic zone to provide a proof-of-concept for the testability of this hypothesis.

Methods: Overnight HDEEG recordings were obtained in the patient during REM sleep, NREM sleep, wakefulness, and during a right facial motor seizure then compared to 10 controls. After preprocessing, SWA (i.e., delta power; 1–4 Hz) was calculated at each channel. Scalp level and source reconstruction analyses were computed. We assessed for statistical differences in maximum SWA between the patient and controls within REM sleep, NREM sleep, wakefulness, and seizure. Then, we completed an identical statistical comparison after first subtracting intrasubject REM sleep SWA from that of NREM sleep, wakefulness, and seizure SWA.

Results: The topographical analysis revealed greater left hemispheric SWA in the patient vs. controls in all vigilance states except REM sleep (which showed a right hemispheric maximum). Source space analysis revealed increased SWA in the left inferior frontal cortex during NREM sleep and wakefulness. Ictal data displayed poor source-space localization. Comparing each state to REM sleep enhanced localization accuracy; the most clearly localizing results were observed when subtracting REM sleep from wakefulness.

Conclusion: State-dependent SWA during NREM sleep and wakefulness may help to identify aspects of the potential epileptogenic zone. Future work in larger cohorts may assess the clinical value of sleep SWA to help presurgical planning.

Brain hothubs and dark functional networks: correlation analysis between amplitude and connectivity for Broca's aphasia

Source localization and functional brain network modeling are methods of identifying critical regions during cognitive tasks. The first activity estimates the relative differences of the signal amplitudes in regions of interest (ROI) and the second activity measures the statistical dependence among signal fluctuations. We hypothesized that the source amplitude–functional connectivity relationship decouples or reverses in persons having brain impairments. Five Broca’s aphasics with five matched cognitively healthy controls underwent overt picture-naming magnetoencephalography scans. The gamma-band (30–45 Hz) phase-locking values were calculated as connections among the ROIs. We calculated the partial correlation coefficients between the amplitudes and network measures and detected four node types, including hothubs with high amplitude and high connectivity, coldhubs with high connectivity but lower amplitude, non-hub hotspots, and non-hub coldspots. The results indicate that the high-amplitude regions are not necessarily highly connected hubs. Furthermore, the Broca aphasics utilized different hothub sets for the naming task. Both groups had dark functional networks composed of coldhubs. Thus, source amplitude–functional connectivity relationships could help reveal functional reorganizations in patients. The amplitude–connectivity combination provides a new perspective for pathological studies of the brain’s dark functional networks.