EEG Source Imaging: A Practical Review of the Analysis Steps

Electromagnetic field enhanced flow state: Insights from electrophysiological measures, self-reported experiences, and gameplay

The intersection of neuroscience and technology hinges on the development of wearable devices and electrodes that can augment brain networks to improve cognitive capabilities such as learning and concentration. The capacity to enhance networks associated with these functions above baseline capabilities, holds the potential to benefit numerous individuals. The purpose of this study was to determine if electromagnetic field exposure modeled from physiological data would increase instances of flow in participants playing a computer game. The flow state refers to a subjective state of optimal performance experienced by individuals during a variety of tasks. For this study, participants (n = 39, 18-65 years, nfemale = 20) played the arcade game Snake for two ten-minute periods (each with a ten-minute rest period immediately following). For one of the trials, an electromagnetic field was applied bilaterally to the temporal lobes, with the other serving as the control. Brain activity was measured using quantitative electroencephalography, flow experience was measured using the Flow Short Scale and game play scores were also recorded. Results showed deceased beta 1 (12-16 Hz) activity in the left cuneus [t = 4.650, p < 0.01] and left precuneus [t = 4.603, p < 0.01], left posterior cingulate [t = 4.521, p < 0.05], insula [t = 4.234, p < 0.05], and parahippocampal gyrus [t = 4.113, p < 0.05] for trials when the field was active, compared to controls during rest periods. Results from the Flow Short Scale showed a statistically significant difference in mean "concentration ease" scores across electromagnetic field conditions, irrespective of difficulty [t = 2.131, p < 0.05]. In the EMF exposure trials, there was no discernible experience effect; participants with prior experience in the game Snake did not exhibit significantly better performance compared to those without prior experience. This anticipated effect was observed in control conditions. The comparable performance observed between novices and experienced players in the EMF condition indicate a noteworthy learning curve for novices. In all, these results provide evidence supporting the ability of EMF patterned from amygdaloid firing (6-20 Hz) to elicit neurological correlates of flow in brain regions previously reported in the literature, facilitate concentration, and subtly improve game scores. The possibility for wearable devices to support learning, concentration, and focus are discussed.

EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints

One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on L 1 and L 2 norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.

Localizing hidden Interictal Epileptiform Discharges with simultaneous intracerebral and scalp high-density EEG recordings

BACKGROUND

Scalp EEG is one of the main tools in the clinical evaluation of epilepsy. In some cases intracranial Interictal Epileptiform Discharges (IEDs) are not visible from the scalp. Recent studies have shown the feasibility of revealing them in the EEG if their timings are extracted from simultaneous intracranial recordings, but their potential for the localization of the epileptogenic zone is not yet well defined.

NEW METHOD

We recorded simultaneous high-density EEG (HD-EEG) and stereo-electroencephalography (SEEG) during interictal periods in 8 patients affected by drug-resistant focal epilepsy. We identified IEDs in the SEEG and systematically analyzed the time-locked signals on the EEG by means of evoked potentials, topographical analysis and Electrical Source Imaging (ESI). The dataset has been standardized and is being publicly shared.

RESULTS

Our results showed that IEDs that were not clearly visible at single-trials could be uncovered by averaging, in line with previous reports. They also showed that their topographical voltage distributions matched the position of the SEEG electrode where IEDs had been identified, and that ESI techniques can reconstruct it with an accuracy of ∼2 cm. Finally, the present dataset provides a reference to test the accuracy of different methods and parameters.

COMPARISON WITH EXISTING METHODS

Our study is the first to systematically compare ESI methods on simultaneously recorded IEDs, and to share a public resource with in-vivo data for their evaluation.

CONCLUSIONS

Simultaneous HD-EEG and SEEG recordings can unveil hidden IEDs whose origins can be reconstructed using topographical and ESI analyses, but results depend on the selected methods and parameters.

Understanding effects of observing affordance-driven action during motor imagery through EEG analysis

The aim of this paper is to investigate the impact of observing affordance-driven action during motor imagery. Affordance-driven action refers to actions that are initiated based on the properties of objects and the possibilities they offer for interaction. Action observation (AO) and motor imagery (MI) are two forms of motor simulation that can influence motor responses. We examined combined AO + MI, where participants simultaneously engaged in AO and MI. Two different kinds of combined AO + MI were employed. Participants imagined and observed the same affordance-driven action during congruent AO + MI, whereas in incongruent AO + MI, participants imagined the actual affordance-driven action while observing a distracting affordance involving the same object. EEG data were analyzed for the N2 component of event-related potential (ERP). Our study found that the N2 ERP became more negative during congruent AO + MI, indicating strong affordance-related activity. The maximum source current density (0.00611 μ A/mm2 ) using Low-Resolution Electromagnetic Tomography (LORETA) was observed during congruent AO + MI in brain areas responsible for planning motoric actions. This is consistent with prefrontal cortex and premotor cortex activity for AO + MI reported in the literature. The stronger neural activity observed during congruent AO + MI suggests that affordance-driven actions hold promise for neurorehabilitation.

XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging due to its ill-posed nature. To achieve a unique solution, it is essential to apply sophisticated regularization constraints to restrict the solution space. Traditionally, the design of regularization terms is based on assumptions about the spatiotemporal structure of the underlying source dynamics. In this paper, we propose a novel paradigm for ESI via an Explainable Deep Learning framework, termed as XDL-ESI, which connects the iterative optimization algorithm with deep learning architecture by unfolding the iterative updates with neural network modules. The proposed framework has the advantages of (1) establishing a data-driven approach to model the source solution structure instead of using hand-crafted regularization terms; (2) improving the robustness of source solutions by introducing a topological loss that leverages the geometric spatial information applying varying penalties on distinct localization errors; (3) improving the reconstruction efficiency and interpretability as it inherits the advantages from both the iterative optimization algorithms (interpretability) and deep learning approaches (function approximation). The proposed XDL-ESI framework provides an efficient, accurate, and interpretable paradigm to solve the ESI inverse problem with satisfactory performance in both simulated data and real clinical data. Specially, this approach is further validated using simultaneous EEG and intracranial EEG (iEEG).

Loneliness and brain rhythmic activity in resting state: an exploratory report

Recent studies using resting-state functional magnetic resonance imaging have shown that loneliness is associated with altered blood oxygenation in several brain regions. However, the relationship between loneliness and changes in neuronal rhythm activity in the brain remains unclear. To evaluate brain rhythm, we conducted an exploratory resting-state electroencephalogram (EEG) study of loneliness. We recorded resting-state EEG signals from 139 participants (94 women; mean age = 19.96 years) and analyzed power spectrum density (PSD) and functional connectivity (FC) in both the electrode and source spaces. The PSD analysis revealed significant correlations between loneliness scores and decreased beta-band powers, which may indicate negative emotion, attention, reward, and/or sensorimotor processing. The FC analysis revealed a trend of alpha-band FC associated with individuals' loneliness scores. These findings provide new insights into the neural basis of loneliness, which will facilitate the development of neurobiologically informed interventions for loneliness.

EEG markers of successful allocentric spatial working memory maintenance in humans

Several brain regions in the frontal, occipital and medial temporal lobes are known to contribute to spatial information processing. In contrast, the oscillatory patterns contributing to allocentric spatial working memory maintenance are poorly understood, especially in humans. Here, we tested twenty-three 21- to 32-year-old and twenty-two 64- to 76-year-old healthy right-handed adults in a real-world, spatial working memory task and recorded electroencephalographic (EEG) activity during the maintenance period. We established criteria for designating recall trials as perfect (no errors) or failed (errors and random search) and identified 8 young and 13 older adults who had at least 1 perfect and 1 failed trial amongst 10 recall trials. Individual alpha frequency-based analyses were used to identify oscillatory patterns during the maintenance period of perfect and failed trials. Spectral scalp topographies showed that individual theta frequency band relative power was stronger in perfect than in failed trials in the frontal midline and posterior regions. Similarly, gamma band (30-40 Hz) relative power was stronger in perfect than in failed trials over the right motor cortex. Exact low-resolution brain electromagnetic tomography in the frequency domain identified greater theta power in perfect than in failed trials in the secondary visual area (BA19) and greater gamma power in perfect than in failed trials in the right supplementary motor area. The findings of this exploratory study suggest that theta oscillations in the occipital lobe and gamma oscillations in the secondary motor cortex (BA6) play a particular role in successful allocentric spatial working memory maintenance.

Effect of Inverse Solutions, Connectivity Measures, and Node Sizes on EEG Source Network: A Simultaneous EEG Study

Brain network provides an essential perspective for studying normal and pathological brain activities. Reconstructing the brain network in the source space becomes more needed, for example, as a target in non-invasive neuromodulation. Precise estimating source activities from the scalp EEG is still challenging because it is an ill-posed question and because of the volume conduction effect. There is no consensus on how to reconstruct the EEG source network. This study uses simultaneous scalp EEG and stereo-EEG to investigate the effect of inverse solutions, connectivity measures, and node sizes on the reconstruction of the source network. We evaluated the performance of different methods on both source activity and network. Numerical simulation was also carried out for comparison. The weighted phase-lag index (wPLI) method achieved significantly better performance on the reconstructed networks in source space than five other connectivity measures (directed transfer function (DTF), partial directed coherence (PDC), efficient effective connectivity (EEC), Pearson correlation coefficient (PCC), and amplitude envelope correlation (AEC)). There is no significant difference between the inverse solutions (standardized low-resolution brain electromagnetic tomography (sLORETA), weighted minimum norm estimate (wMNE), and linearly constrained minimum variance (LCMV) beamforming) on the reconstructed source networks. The source network based on signal phases can fit intracranial activities better than signal waveform properties or causality. Our study provides a basis for reconstructing source space networks from scalp EEG, especially for future neuromodulation research.

Stereo electroencephalography-guided radiofrequency ablation in focal epilepsia partialis continua: illustrative case

BACKGROUND

Epilepsia partialis continua (EPC) is a variant of focal motor status epilepticus that can occur as a single or repetitive episode with progressive or nonprogressive characteristics.

OBSERVATIONS

The authors describe the feasibility of identifying focal EPC in a 33-year-old woman using video electroencephalography (VEEG), electroencephalography source localization, [18F]fluorodeoxyglucose positron emission tomography, magnetic resonance imaging, and psychiatric and neuropsychological assessments and of treating it with stereo electroencephalography-guided radiofrequency (SEEG-RF) ablation. EPC comprised recurrent myoclonus of the right thigh and iliopsoas with a progressive pain syndrome after left anterior-temporo-mesial resection. Switching between VEEG under regular and epidural block helped to define myoclonus as the presenting ictal symptom with a suspected seizure onset zone in the left parietal paramedian lobule. After the epileptic network was identified, SEEG-RF ablation abolished all seizures. No correlation was found between pain and VEEG/SEEG abnormalities. Rehabilitation began 3 days after the SEEG-RF ablation. By 1 year of follow-up, the patient had no EPC and could walk with assistance in rehabilitation; however, due to the abrupt abolishment of EPC and underlying psychological factors, the patient perceived her pain as overriding, which prevented her from walking.

LESSONS

The application of SEEG-RF ablation is an efficient therapeutic option for focal EPC with special concerns regarding concurrent nonepileptic pain. https://thejns.org/doi/10.3171/CASE23611.

Visual perspective taking neural processing in forensic cases with high density EEG

This EEG study aims at dissecting the differences in the activation of neural generators between borderline personality disorder patients with court-ordered measures (BDL-COM) and healthy controls in visual perspective taking. We focused on the distinction between mentalizing (Avatar) and non-mentalizing (Arrow) stimuli as well as self versus other-perspective in the dot perspective task (dPT) in a sample of 15 BDL-COM cases and 54 controls, all of male gender. BDL-COM patients showed a late and diffuse right hemisphere involvement of neural generators contrasting with the occipitofrontal topography observed in controls. For Avatars only and compared to controls, the adoption of Self perspective involved a lower EEG activity in the left inferior frontal, right middle temporal cortex and insula in BDL-COM patients prior to 80 ms post-stimulus. When taking the Other-perspective, BDL-COM patients also showed a lower activation of superior frontal, right inferior temporal and fusiform cortex within the same time frame. The beta oscillation power was significantly lower in BDL-COM patients than controls between 400 and 1300 ms post stimulus in the Avatar-Other condition. These results indicate that BDL-COM patients display both altered topography of EEG activation patterns and reduced abilities to mobilize beta oscillations during the treatment of mentalistic stimuli in dPT.

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

Further characterisation of late somatosensory evoked potentials using electroencephalogram and magnetoencephalogram source imaging

Beside the well-documented involvement of secondary somatosensory area, the cortical network underlying late somatosensory evoked potentials (P60/N60 and P100/N100) is still unknown. Electroencephalogram and magnetoencephalogram source imaging were performed to further investigate the origin of the brain cortical areas involved in late somatosensory evoked potentials, using sensory inputs of different strengths and by testing the correlation between cortical sources. Simultaneous high-density electroencephalograms and magnetoencephalograms were performed in 19 participants, and electrical stimulation was applied to the median nerve (wrist level) at intensity between 1.5 and 9 times the perceptual threshold. Source imaging was undertaken to map the stimulus-induced brain cortical activity according to each individual brain magnetic resonance imaging, during three windows of analysis covering early and late somatosensory evoked potentials. Results for P60/N60 and P100/N100 were compared with those for P20/N20 (early response). According to literature, maximal activity during P20/N20 was found in central sulcus contralateral to stimulation site. During P60/N60 and P100/N100, activity was observed in contralateral primary sensorimotor area, secondary somatosensory area (on both hemispheres) and premotor and multisensory associative cortices. Late responses exhibited similar characteristics but different from P20/N20, and no significant correlation was found between early and late generated activities. Specific clusters of cortical activities were activated with specific input/output relationships underlying early and late somatosensory evoked potentials. Cortical networks, partly common to and distinct from early somatosensory responses, contribute to late responses, all participating in the complex somatosensory brain processing.

Peak alpha frequency and electroencephalographic microstates are correlated with aggression in schizophrenia

Large scale retrospective studies have shown an association between schizophrenia and risk of violence. Overall, this increase in risk is small and does not justify or support stigmatizing public perceptions or media depictions of people with schizophrenia. Nonetheless, in some situations, some symptoms of schizophrenia can increase the risk of violent behavior. Prediction of this behavior would allow high impact preventive interventions. However, to date the neurobiological correlates of violent behavior in schizophrenia are not well understood, precluding the development of prognostic biomarkers. We used electroencephalography to measure alpha activity and microstates from 31 patients with schizophrenia and 18 age matched controls. Participants also completed multiple assessments of current aggressive tendencies and their lifetime history of aggressive acts. We found that individual alpha peak frequency was negatively correlated with aggression scores in both patients and controls (largest Spearman's r = -0.45). Furthermore, this result could be replicated in data taken from a single frontal channel suggesting that this may be possible to obtain in routine clinical settings (largest Spearman's r = -0.40). We also found that transitions between microstates corresponding to auditory and visual networks were inversely correlated with aggression scores. Finally, we found that, within patients, aggression was correlated with the degree of randomness between microstate transitions. This suggests that aggression is related to inappropriate switching between large scale brain networks and subsequent failure to appropriately integrate complicated environmental and internal stimuli. By elucidating some of the electrophysiological correlates of aggression, these data facilitate the development of prognostic biomarkers.

Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation

Motor imagery-based Brain-Computer Interfaces (MI-BCIs) have gained a lot of attention due to their potential usability in neurorehabilitation and neuroprosthetics. However, the accurate recognition of MI patterns in electroencephalography signals (EEG) is hindered by several data-related limitations, which restrict the practical utilization of these systems. Moreover, leveraging deep learning (DL) models for MI decoding is challenged by the difficulty of accessing user-specific MI-EEG data on large scales. Simulated MI-EEG signals can be useful to address these issues, providing well-defined data for the validation of decoding models and serving as a data augmentation approach to improve the training of DL models. While substantial efforts have been dedicated to implementing effective data augmentation strategies and model-based EEG signal generation, the simulation of neurophysiologically plausible EEG-like signals has not yet been exploited in the context of data augmentation. Furthermore, none of the existing approaches have integrated user-specific neurophysiological information during the data generation process. Here, we present PySimMIBCI, a framework for generating realistic MI-EEG signals by integrating neurophysiologically meaningful activity into biophysical forward models. By means of PySimMIBCI, different user capabilities to control an MI-BCI can be simulated and fatigue effects can be included in the generated EEG. Results show that our simulated data closely resemble real data. Moreover, a proposed data augmentation strategy based on our simulated user-specific data significantly outperforms other state-of-the-art augmentation approaches, enhancing DL models performance by up to 15%.

Exploring the Association Between EEG Microstates During Resting-State and Error-Related Activity in Young Children

The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates - whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity - during both a go/no-go task and resting-state in 90, 4-8-year-old children. The mean amplitude of the ERN was quantified during the -64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same -64 to 108 ms period (i.e., error-related microstate 3), and to greater anxiety risk as measured by parent-reported behavioral inhibition. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.

Neural Correlates of Motor/Tactile Imagery and Tactile Sensation in a BCI paradigm: A High-Density EEG Source Imaging Study

Complementary to brain-computer interface (BCI) based on motor imagery (MI) task, sensory imagery (SI) task provides a way for BCI construction using brain activity from somatosensory cortex. The underlying neurophysiological correlation between SI and MI was unclear and difficult to measure through behavior recording. In this study, we investigated the underlying neurodynamic of motor/tactile imagery and tactile sensation tasks through a high-density electroencephalogram (EEG) recording, and EEG source imaging was used to systematically explore the cortical activation differences and correlations between the tasks. In the experiment, participants were instructed to perform the left and right hand tasks in MI paradigm, sensory stimulation (SS) paradigm and SI paradigm. The statistical results demonstrated that the imagined MI and SI tasks differed from each other within ipsilateral sensorimotor scouts, frontal and right temporal areas in α bands, whereas real SS and imagined SI showed a similar activation pattern. The similarity between SS and SI may provide a way to train the BCI system, while the difference between MI and SI may provide a way to integrate the discriminative information between them to enhance BCI performance. The combination of the tasks and its underlying neurodynamic would provide a new approach for BCI designation for a wider application. BCI studies concentrate on the hybrid decoding method combining MI or SI with SS, but the underlining neurophysiological correlates between them were unclear. MI and SI differed from each other within the ipsilateral sensorimotor cortex in alpha bands. This is a first study to investigate the neurophysiological relationship between MI and SI through an EEG source imaging approach from high-density EEG recording.

Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study

Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.

Sensing behavior change in chronic pain: a scoping review of sensor technology for use in daily life

ABSTRACT

Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided "ground truth" for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field.

Robust memory of face moral values is encoded in the human caudate tail: a simultaneous EEG-fMRI study

Moral judgements about people based on their actions is a key component that guides social decision making. It is currently unknown how positive or negative moral judgments associated with a person's face are processed and stored in the brain for a long time. Here, we investigate the long-term memory of moral values associated with human faces using simultaneous EEG-fMRI data acquisition. Results show that only a few exposures to morally charged stories of people are enough to form long-term memories a day later for a relatively large number of new faces. Event related potentials (ERPs) showed a significant differentiation of remembered good vs bad faces over centerofrontal electrode sites (value ERP). EEG-informed fMRI analysis revealed a subcortical cluster centered on the left caudate tail (CDt) as a correlate of the face value ERP. Importantly neither this analysis nor a conventional whole-brain analysis revealed any significant coding of face values in cortical areas, in particular the fusiform face area (FFA). Conversely an fMRI-informed EEG source localization using accurate subject-specific EEG head models also revealed activation in the left caudate tail. Nevertheless, the detected caudate tail region was found to be functionally connected to the FFA, suggesting FFA to be the source of face-specific information to CDt. A further psycho-physiological interaction analysis also revealed task-dependent coupling between CDt and dorsomedial prefrontal cortex (dmPFC), a region previously identified as retaining emotional working memories. These results identify CDt as a main site for encoding the long-term value memories of faces in humans suggesting that moral value of faces activates the same subcortical basal ganglia circuitry involved in processing reward value memory for objects in primates.

The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks

Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting-state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting-state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model-based, as well as individual head model-based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.

Rhythmic Attention and ADHD: A Narrative and Systematic Review

In recent decades, a growing body of evidence has confirmed the existence of rhythmic fluctuations in attention, but the effect of inter-individual variations in these attentional rhythms has yet to be investigated. The aim of this review is to identify trends in the attention deficit/hyperactivity disorder (ADHD) literature that could be indicative of between-subject differences in rhythmic attention. A narrative review of the rhythmic attention and electrophysiological ADHD research literature was conducted, and the commonly-reported difference in slow-wave power between ADHD subjects and controls was found to have the most relevance to an understanding of rhythmic attention. A systematic review of the literature examining electrophysiological power differences in ADHD was then conducted to identify studies with conditions similar to those utilised in the rhythmic attention research literature. Fifteen relevant studies were identified and reviewed. The most consistent finding in the studies reviewed was for no spectral power differences between ADHD subjects and controls. However, the strongest trend in the studies reporting power differences was for higher power in the delta and theta frequency bands and lower power in the alpha band. In the context of rhythmic attention, this trend is suggestive of a slowing in the frequency and/or increase in the amplitude of the attentional oscillation in a subgroup of ADHD subjects. It is suggested that this characteristic electrophysiological modulation could be indicative of a global slowing of the attentional rhythm and/or an increase in the rhythmic recruitment of neurons in frontal attention networks in individuals with ADHD.

Optimal filters for ERP research I: A general approach for selecting filter settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

Cross-modal plasticity in children with cochlear implant: converging evidence from EEG and functional near-infrared spectroscopy

Over the first years of life, the brain undergoes substantial organization in response to environmental stimulation. In a silent world, it may promote vision by (i) recruiting resources from the auditory cortex and (ii) making the visual cortex more efficient. It is unclear when such changes occur and how adaptive they are, questions that children with cochlear implants can help address. Here, we examined 7-18 years old children: 50 had cochlear implants, with delayed or age-appropriate language abilities, and 25 had typical hearing and language. High-density electroencephalography and functional near-infrared spectroscopy were used to evaluate cortical responses to a low-level visual task. Evidence for a 'weaker visual cortex response' and 'less synchronized or less inhibitory activity of auditory association areas' in the implanted children with language delays suggests that cross-modal reorganization can be maladaptive and does not necessarily strengthen the dominant visual sense.

An Analytical Approach for Naturalistic Cooperative and Competitive EEG-Hyperscanning Data: A Proof-of-Concept Study

Investigating the neural mechanisms underlying both cooperative and competitive joint actions may have a wide impact in many social contexts of human daily life. An effective pipeline of analysis for hyperscanning data recorded in a naturalistic context with a cooperative and competitive motor task has been missing. We propose an analytical pipeline for this type of joint action data, which was validated on electroencephalographic (EEG) signals recorded in a proof-of-concept study on two dyads playing cooperative and competitive table tennis. Functional connectivity maps were reconstructed using the corrected imaginary part of the phase locking value (ciPLV), an algorithm suitable in case of EEG signals recorded during turn-based competitive joint actions. Hyperbrain, within-, and between-brain functional connectivity maps were calculated in three frequency bands (i.e., theta, alpha, and beta) relevant during complex motor task execution and were characterized with graph theoretical measures and a clustering approach. The results of the proof-of-concept study are in line with recent findings on the main features of the functional networks sustaining cooperation and competition, hence demonstrating that the proposed pipeline is promising tool for the analysis of joint action EEG data recorded during cooperation and competition using a turn-based motor task.

EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10-10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.

Asynchronous behavioral and neurophysiological changes in word production in the adult lifespan

Behavioral and brain-related changes in word production have been claimed to predominantly occur after 70 years of age. Most studies investigating age-related changes in adulthood only compared young to older adults, failing to determine whether neural processes underlying word production change at an earlier age than observed in behavior. This study aims to fill this gap by investigating whether changes in neurophysiological processes underlying word production are aligned with behavioral changes. Behavior and the electrophysiological event-related potential patterns of word production were assessed during a picture naming task in 95 participants across five adult lifespan age groups (ranging from 16 to 80 years old). While behavioral performance decreased starting from 70 years of age, significant neurophysiological changes were present at the age of 40 years old, in a time window (between 150 and 220 ms) likely associated with lexical-semantic processes underlying referential word production. These results show that neurophysiological modifications precede the behavioral changes in language production; they can be interpreted in line with the suggestion that the lexical-semantic reorganization in mid-adulthood influences the maintenance of language skills longer than for other cognitive functions.

Using transcranial alternating current stimulation to enhance working memory skills in youths with 22q11.2 deletion syndrome: A randomized double-blind sham-controlled study

Abnormal cognitive development, particularly working memory (WM) deficits, is among the first apparent manifestations of psychosis. Yet, cognitive impairment only shows limited response to current pharmacological treatment. Alternative interventions to target cognition are highly needed in individuals at high risk for psychosis, like carriers of 22q11.2 deletion syndrome (22q11.2DS). Here we applied theta-tuned transcranial alternating current stimulation (tACS) between frontal and temporal regions during a visual WM task in 34 deletion carriers. We conducted a double-blind sham-controlled study over three consecutive days. The stimulation parameters were derived from individual structural MRI scan and HD-EEG data acquired at baseline (Day 1) to model current intensity and individual preferential theta peak. Participants were randomized to either sham or tACS (Days 2 and 3) and then completed a visual WM task and a control task. Our findings reveal that tACS was safe and well-tolerated among participants. We found a significantly increased accuracy in the visual WM but not the control task following tACS. Moreover, this enhancement in WM accuracy was greater after tACS than during tACS, indicating stronger offline effects than online effects. Our study therefore supports the application of repeated sessions of brain stimulation in 22q11.2DS.

Super-resolution techniques for biomedical applications and challenges

Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.

EEG Microstates as Markers for Cognitive Impairments in Fragile X Syndrome

Fragile X syndrome (FXS) is one of the most common inherited causes of intellectual disabilities. While there is currently no cure for FXS, EEG is considered an important method to investigate the pathophysiology and evaluate behavioral and cognitive treatments. We conducted EEG microstate analysis to investigate resting brain dynamics in FXS participants. Resting-state recordings from 70 FXS participants and 71 chronological age-matched typically developing control (TDC) participants were used to derive microstates via modified k-means clustering. The occurrence, mean global field power (GFP), and global explained variance (GEV) of microstate C were significantly higher in the FXS group compared to the TDC group. The mean GFP was significantly negatively correlated with non-verbal IQ (NVIQ) in the FXS group, where lower NVIQ scores were associated with greater GFP. In addition, the occurrence, mean duration, mean GFP, and GEV of microstate D were significantly greater in the FXS group than the TDC group. The mean GFP and occurrence of microstate D were also correlated with individual alpha frequencies in the FXS group, where lower IAF frequencies accompanied greater microstate GFP and occurrence. Alterations in microstates C and D may be related to the two well-established cognitive characteristics of FXS, intellectual disabilities and attention impairments, suggesting that microstate parameters could serve as markers to study cognitive impairments and evaluate treatment outcomes in this population. Slowing of the alpha peak frequency and its correlation to microstate D parameters may suggest changes in thalamocortical dynamics in FXS, which could be specifically related to attention control. (250 words).

EEG Spatial-temporal Dynamics of Resting-state Activity in Young Women with Anorexia Nervosa: Preliminary Evidence

The aim of this study was to provide preliminary evidence on temporal dynamics of resting-state brain networks in youth with anorexia nervosa (AN) using electroencephalography (EEG). Resting-state EEG data were collected in 18 young women with AN and 18 healthy controls (HC). Between-group differences in brain networks were assessed using microstates analyses. Five microstates were identified across all subjects (A, B, C, D, E). Using a single set of maps representative of the whole dataset, group differences were identified for microstates A, C, and E. A common-for-all template revealed a relatively high degree of consistency in results for reduced time coverage of microstate C, but also an increased presence of microstate class E. AN and HC had different microstate transition probabilities, largely involving microstate A. Using LORETA, for microstate D, we found that those with AN had augmented activations in the left frontal inferior operculum, left insula, and bilateral paracentral lobule, compared with HC. For microstate E, AN had augmented activations in the para-hippocampal gyrus, caudate, pallidum, cerebellum, and cerebellar vermis. Our findings suggest altered microstates in young women with AN associated with integration of sensory and bodily signals, monitoring of internal/external mental states, and self-referential processes. Future research should examine how EEG-derived microstates could be applied to develop diagnostic and prognostic models of AN.

Cardiac interoception in Anorexia Nervosa: A resting-state heartbeat-evoked potential study

OBJECTIVE

A deficit in interoception - the ability to perceive, interpret and integrate afferent signals about the physiological state of the body - has been shown in Anorexia Nervosa (AN), and linked to altered hunger sensations, body dysmorphia, and abnormal emotional awareness. The present high-density electroencephalography (hdEEG) study aims to assess cardiac interoception in AN and to investigate its neural correlates, using an objective neurophysiological measure.

METHOD

Heartbeat-evoked potentials (HEPs) were computed from 5 min of resting-state EEG and electrocardiogram (ECG) data and compared between individuals with AN (N = 22) and healthy controls (HC) (N = 19) with waveform, topographic, and source imaging analyses.

RESULTS

Differences in the cortical representation of heartbeats were present between AN and HC at a time window of 332-348 ms after the ECG R-peak. Source imaging analyses revealed a right-sided hypoactivation in AN of brain regions linked to interoceptive processing, such as the anterior cingulate and orbitofrontal areas.

CONCLUSIONS

To the best of our knowledge, this is the first study using hdEEG to localise the underlying sources of HEPs in AN. Results point to altered interoceptive processing during resting-state in AN. As our participants had a short duration of illness, this might not be the consequence of prolonged starvation. Interventions targeted at interoception could provide an additional tool to facilitate recovery.

Functional Connectivity Alterations in Patients with Post-stroke Epilepsy Based on Source-level EEG and Graph Theory

We investigated the differences in functional connectivity based on the source-level electroencephalography (EEG) analysis between stroke patients with and without post-stroke epilepsy (PSE). Thirty stroke patients with PSE and 35 stroke patients without PSE were enrolled. EEG was conducted during a resting state period. We used a Brainstorm program for source estimation and the connectivity matrix. Data were processed according to EEG frequency bands. We used a BRAPH program to apply a graph theoretical analysis. In the beta band, radius and diameter were increased in patients with PSE than in those without PSE (2.699 vs. 2.579, adjusted p = 0.03; 2.261 vs. 2.171, adjusted p = 0.03). In the low gamma band, radius was increased in patients with PSE than in those without PSE (2.808 vs. 2.617, adjusted p = 0.03). In the high gamma band, the radius, diameter, average eccentricity, and characteristic path length were increased (1.828 vs. 1.559, adjusted p < 0.01; 2.653 vs. 2.306, adjusted p = 0.01; 2.212 vs. 1.913, adjusted p < 0.01; 1.425 vs. 1.286, adjusted p = 0.01), whereas average strength, mean clustering coefficient, and transitivity were decreased in patients with PSE than in those without PSE (49.955 vs. 55.055, adjusted p < 0.01; 0.727 vs. 0.810, adjusted p < 0.01; 1.091 vs. 1.215, adjusted p < 0.01). However, in the delta, theta, and alpha bands, none of the functional connectivity measures were different between groups. We demonstrated significant alterations of functional connectivity in patients with PSE, who have decreased segregation and integration in brain network, compared to those without PSE.

Imaging of brain electric field networks

We present a method for direct imaging of the electric field networks in the human brain from electroencephalography (EEG) data with much higher temporal and spatial resolution than functional MRI (fMRI), without the concomitant distortions. The method is validated using simultaneous EEG/fMRI data in healthy subjects, intracranial EEG data in epilepsy patients, and in a direct comparison with standard EEG analysis in a well-established attention paradigm. The method is then demonstrated on a very large cohort of subjects performing a standard gambling task designed to activate the brain's 'reward circuit'. The technique uses the output from standard EEG systems and thus has potential for immediate benefit to a broad range of important basic scientific and clinical questions concerning brain electrical activity, but also provides an inexpensive and portable alternative to function MRI (fMRI).

Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study

Introduction

The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification.

Methods

The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs).

Results

Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states.

Discussion

In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.

Relative band power in assessing temporary neurological dysfunction post- type A aortic dissection surgery: a prospective study

Temporary neurological dysfunction (TND), a common complication following surgical repair of Type A Aortic Dissection (TAAD), is closely associated with increased mortality and long-term cognitive impairment. Currently, effective treatment options for TND remain elusive. Therefore, we sought to investigate the potential of postoperative relative band power (RBP) in predicting the occurrence of postoperative TND, with the aim of identifying high-risk patients prior to the onset of TND. We conducted a prospective observational study between February and December 2022, involving 165 patients who underwent surgical repair for TAAD at our institution. Bedside Quantitative electroencephalography (QEEG) was utilized to monitor the post-operative brain electrical activity of each participant, recording changes in RBP (RBP Delta, RBP Theta, RBP Beta and RBP Alpha), and analyzing their correlation with TND. Univariate and multivariate analyses were employed to identify independent risk factors for TND. Subsequently, line graphs were generated to estimate the incidence of TND. The primary outcome of interest was the development of TND, while secondary outcomes included intensive care unit (ICU) admission and length of hospital stay. A total of 165 patients were included in the study, among whom 68 (41.2%) experienced TND. To further investigate the independent risk factors for postoperative TND, we conducted both univariate and multivariate logistic regression analyses on all variables. In the univariate regression analysis, we identified age (Odds Ratio [OR], 1.025; 95% CI, 1.002-1.049), age ≥ 60 years (OR, 2.588; 95% CI, 1.250-5.475), hemopericardium (OR, 2.767; 95% CI, 1.150-7.009), cardiopulmonary bypass (CPB) (OR, 1.007; 95% CI, 1.001-1.014), RBP Delta (OR, 1.047; 95% CI, 1.020-1.077), RBP Alpha (OR, 0.853; 95% CI, 0.794-0.907), and Beta (OR, 0.755; 95% CI, 0.649-0.855) as independent risk factors for postoperative TND. Further multivariate regression analyses, we discovered that CPB time ≥ 180 min (OR, 1.021; 95% CI, 1.011-1.032), RBP Delta (OR, 1.168; 95% CI, 1.105-1.245), and RBP Theta (OR, 1.227; 95% CI, 1.135-1.342) emerged as independent risk factors. TND patients had significantly longer ICU stays (p < 0.001), and hospital stays (p = 0.002). We obtained the simplest predictive model for TND, consisting of three variables (CPB time ≥ 180 min, RBP Delta, RBP Theta, upon which we constructed column charts. The areas under the receiver operating characteristic (AUROC) were 0.821 (0.755, 0.887). Our study demonstrates that postoperative RBP monitoring can detect changes in brain function in patients with TAAD during the perioperative period, providing clinicians with an effective predictive method that can help improve postoperative TND in TAAD patients. These findings have important implications for improving clinical care in this population.Trial registration ChiCTR2200055980. Registered 30th Jan. 2022. This trial was registered before the first participant was enrolled.

High-density electroencephalographic functional networks in genetic generalized epilepsy: Preserved whole-brain topology hides local reorganization

OBJECTIVE

Genetic generalized epilepsy (GGE) accounts for approximately 20% of adult epilepsy cases and is considered a disorder of large brain networks, involving both hemispheres. Most studies have not shown any difference in functional whole-brain network topology when compared to healthy controls. Our objective was to examine whether this preserved global network topology could hide local reorganizations that balance out at the global network level.

METHODS

We recorded high-density electroencephalograms from 20 patients and 20 controls, and reconstructed the activity of 118 regions. We computed functional connectivity in windows free of interictal epileptiform discharges in broad, delta, theta, alpha, and beta frequency bands, characterized the network topology, and used the Hub Disruption Index (HDI) to quantify the topological reorganization. We examined the generalizability of our results by reproducing a 25-electrode clinical system.

RESULTS

Our study did not reveal any significant change in whole-brain network topology among GGE patients. However, the HDI was significantly different between patients and controls in all frequency bands except alpha (p < .01, false discovery rate [FDR] corrected, d < -1), and accompanied by an increase in connectivity in the prefrontal regions and default mode network. This reorganization suggests that regions that are important in transferring the information in controls were less so in patients. Inversely, the crucial regions in patients are less so in controls. These findings were also found in delta and theta frequency bands when using 25 electrodes (p < .001, FDR corrected, d < -1).

SIGNIFICANCE

In GGE patients, the overall network topology is similar to that of healthy controls but presents a balanced local topological reorganization. This reorganization causes the prefrontal areas and default mode network to be more integrated and segregated, which may explain executive impairment associated with GGE. Additionally, the reorganization distinguishes patients from controls even when using 25 electrodes, suggesting its potential use as a diagnostic tool.

Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high-spatial resolution

Connectome spectrum electromagnetic tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.

Mapping of the central sulcus using non-invasive ultra-high-density brain recordings

Brain mapping is vital in understanding the brain's functional organization. Electroencephalography (EEG) is one of the most widely used brain mapping approaches, primarily because it is non-invasive, inexpensive, straightforward, and effective. Increasing the electrode density in EEG systems provides more neural information and can thereby enable more detailed and nuanced mapping procedures. Here, we show that the central sulcus can be clearly delineated using a novel ultra-high-density EEG system (uHD EEG) and somatosensory evoked potentials (SSEPs). This uHD EEG records from 256 channels with an inter-electrode distance of 8.6 mm and an electrode diameter of 5.9 mm. Reconstructed head models were generated from T1-weighted MRI scans, and electrode positions were co-registered to these models to create topographical plots of brain activity. EEG data were first analyzed with peak detection methods and then classified using unsupervised spectral clustering. Our topography plots of the spatial distribution from the SSEPs clearly delineate a division between channels above the somatosensory and motor cortex, thereby localizing the central sulcus. Individual EEG channels could be correctly classified as anterior or posterior to the central sulcus with 95.2% accuracy, which is comparable to accuracies from invasive intracranial recordings. Our findings demonstrate that uHD EEG can resolve the electrophysiological signatures of functional representation in the brain at a level previously only seen from surgically implanted electrodes. This novel approach could benefit numerous applications, including research, neurosurgical mapping, clinical monitoring, detection of conscious function, brain-computer interfacing (BCI), rehabilitation, and mental health.

Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation

EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.

Altered connectivity between the central executive network and the salience network in delusion-prone individuals: A resting state eLORETA report

Although the Triple Network (TN) model has been proposed as a valid neurophysiological framework for conceptualizing delusion-like experiences, the neurodynamics of TN in relation to delusion proneness have been relatively understudied in nonclinical samples so far. Therefore, the main aim of the current study was to investigate the functional connectivity of resting state electroencephalography (EEG) in subjects with high levels of delusion proneness. Twenty-one delusion-prone (DP) individuals and thirty-seven non-delusion prone (N-DP) individuals were included in the study. The exact Low-Resolution Electromagnetic Tomography (eLORETA) software was used for all EEG analyses. Compared to N-DP participants, DP individuals showed an increas of theta connectivity (T = 3.618; p = 0.045) between the Salience Network (i.e., the left anterior insula) and the Central Executive Network (i.e., the left posterior parietal cortex). Increased theta connectivity was also positively correlated with the frequency of delusional experiences (rho = 0.317; p = 0.015). Our results suggest that increased theta connectivity between the Salience Network and the Central Executive Network may underline brain correlates of altered resting state salience detection, information processing, and cognitive control processes typical of delusional thinking.

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.

High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting

Objective. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution.Approach. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (N= 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated.Main results.For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was found to be superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e. 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0 ± 4.0 mm). In addition, somatosensory evoked magnetic fields obtained by Magnetoencephalography was 25.3 ± 8.5 mm using three-layer sphere and sLORETA.Significance. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.

Yield of non-invasive imaging in MRI-negative focal epilepsy

OBJECTIVE

The absence of MRI-lesion reduces considerably the probability of having an excellent outcome (International League Against Epilepsies [ILAE] class I-II) after epilepsy surgery. Surgical success in magnetic-resonance imaging (MRI)-negative cases relies therefore mainly on non-invasive techniques such as positron-emission tomography (PET), subtraction ictal/inter-ictal single-photon-emission-computed-tomography co-registered to MRI (SISCOM), electric source imaging (ESI) and morphometric MRI analysis (MAP). We were interested in identifying the optimal imaging technique or combination to achieve post-operative class I-II in patients with MRI-negative focal epilepsy.

METHODS

We identified 168 epileptic patients without MRI lesion. Thirty-three (19.6%) were diagnosed with unifocal epilepsy, underwent surgical resection and follow-up ⩾ 2 years. Sensitivity, specificity, predictive values, and diagnostic odds ratio (OR) were calculated for each technique individually and in combination (after co-registration).

RESULTS

23/33 (70%) were free of disabling seizures (75.0% with temporal and 61.5% extratemporal lobe epilepsy). None of the individual modalities presented an OR > 1.5, except ESI if only patients with interictal epileptiform discharges (IEDs) were considered (OR 3.2). On a dual combination, SISCOM with ESI presented the highest outcome (OR = 6). MAP contributed to detecting indistinguishable focal cortical dysplasia in particular in extratemporal epilepsies with a sensitivity of 75%. Concordance of PET, ESI on interictal epileptic discharges, and SISCOM was associated with the highest chance for post-operative seizure control (OR = 11).

CONCLUSION

If MRI is negative, the chances to benefit from epilepsy surgery are almost as high as in lesional epilepsy, provided that multiple established non-invasive imaging tools are rigorously applied and co-registered together.

Consistency of electrical source imaging in presurgical evaluation of epilepsy across different vigilance states

OBJECTIVE

The use of electrical source imaging (ESI) in assessing the source of interictal epileptic discharges (IEDs) is gaining increasing popularity in presurgical work-up of patients with drug-resistant focal epilepsy. While vigilance affects the ability to locate IEDs and identify the epileptogenic zone, we know little about its impact on ESI.

METHODS

We studied overnight high-density electroencephalography recordings in focal drug-resistant epilepsy. IEDs were marked visually in each vigilance state, and examined in the sensor and source space. ESIs were calculated and compared between all vigilance states and the clinical ground truth. Two conditions were considered within each vigilance state, an unequalized and an equalized number of IEDs.

RESULTS

The number, amplitude, and duration of IEDs were affected by the vigilance state, with N3 sleep presenting the highest number, amplitude, and duration for both conditions (P < 0.001), while signal-to-noise ratio only differed in the unequalized condition (P < 0.001). The vigilance state did not affect channel involvement (P > 0.05). ESI maps showed no differences in distance, quality, extent, or maxima distances compared to the clinical ground truth for both conditions (P > 0.05). Only when an absolute reference (wakefulness) was used, the channel involvement (P < 0.05) and ESI source extent (P < 0.01) were impacted during rapid-eye-movement (REM) sleep. Clustering of amplitude-sensitive and -insensitive ESI maps pointed to amplitude rather than the spatial profile as the driver (P < 0.05).

INTERPRETATION

IED ESI results are stable across vigilance states, including REM sleep, if controlled for amplitude and IED number. ESI is thus stable and invariant to the vigilance state.

Maximizing signal-to-noise ratio and statistical power in ERP measurement: Single sites versus multi-site average clusters

One important decision in every event-related potential (ERP) experiment is which electrode site(s) to use in quantifying the ERP component of interest. A common approach is to measure the ERP from a single electrode site, typically the site where the ERP component is largest. Alternatively, two or more electrode sites in a given spatial region are averaged together, and the ERP is measured from the resulting multi-site cluster. The goal of the present study was to systematically compare these two measurement approaches across a range of outcome measures and ERP components to determine whether measuring from a single electrode site or an average of multiple sites yields consistently better results. We examined seven common ERP components from the open-source ERP CORE dataset that span a range of neurocognitive processes: N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). For each component, we compared ERP amplitude, noise level, signal-to-noise ratio, and effect size at two single electrode sites and four multi-site clusters. We also used a Monte Carlo approach to simulate within-participant and between-groups experiments with known effect magnitudes to compare statistical power at single sites and multi-site clusters. Overall, measuring from a multi-site cluster produced results that were as good as or better than measuring from a single electrode site across analyses and components, indicating that the cluster-based measurement approach may be beneficial in quantifying ERPs from a range of neurocognitive domains.

Complexity of STG signals and linguistic rhythm: a methodological study for EEG data

The superior temporal and the Heschl's gyri of the human brain play a fundamental role in speech processing. Neurons synchronize their activity to the amplitude envelope of the speech signal to extract acoustic and linguistic features, a process known as neural tracking/entrainment. Electroencephalography has been extensively used in language-related research due to its high temporal resolution and reduced cost, but it does not allow for a precise source localization. Motivated by the lack of a unified methodology for the interpretation of source reconstructed signals, we propose a method based on modularity and signal complexity. The procedure was tested on data from an experiment in which we investigated the impact of native language on tracking to linguistic rhythms in two groups: English natives and Spanish natives. In the experiment, we found no effect of native language but an effect of language rhythm. Here, we compare source projected signals in the auditory areas of both hemispheres for the different conditions using nonparametric permutation tests, modularity, and a dynamical complexity measure. We found increasing values of complexity for decreased regularity in the stimuli, giving us the possibility to conclude that languages with less complex rhythms are easier to track by the auditory cortex.

Evaluating and benchmarking the EEG signal quality of high-density, dry MXene-based electrode arrays against gelled Ag/AgCl electrodes

Objective.To evaluate the signal quality of dry MXene-based electrode arrays (also termed 'MXtrodes') for electroencephalographic (EEG) recordings where gelled Ag/AgCl electrodes are a standard.Approach.We placed 4 × 4 MXtrode arrays and gelled Ag/AgCl electrodes on different scalp locations. The scalp was cleaned with alcohol and rewetted with saline before application. We recorded from both electrode types simultaneously while participants performed a vigilance task.Main results.The root mean squared amplitude of MXtrodes was slightly higher than that of Ag/AgCl electrodes (.24-1.94 uV). Most MXtrode pairs had slightly lower broadband spectral coherence (.05 to .1 dB) and Delta- and Theta-band timeseries correlation (.05 to .1 units) compared to the Ag/AgCl pair (p< .001). However, the magnitude of correlation and coherence was high across both electrode types. Beta-band timeseries correlation and spectral coherence were higher between neighboring MXtrodes in the array (.81 to .84 units) than between any other pair (.70 to .75 units). This result suggests the close spacing of the nearest MXtrodes (3 mm) more densely sampled high spatial-frequency topographies. Event-related potentials were more similar between MXtrodes (ρ⩾ .95) than equally spaced Ag/AgCl electrodes (ρ⩽ .77,p< .001). Dry MXtrode impedance (x̄= 5.15 KΩ cm2) was higher and more variable than gelled Ag/AgCl electrodes (x̄= 1.21 KΩ cm2,p< .001). EEG was also recorded on the scalp across diverse hair types.Significance.Dry MXene-based electrodes record EEG at a quality comparable to conventional gelled Ag/AgCl while requiring minimal scalp preparation and no gel. MXtrodes can record independent signals at a spatial density four times higher than conventional electrodes, including through hair, thus opening novel opportunities for research and clinical applications that could benefit from dry and higher-density configurations.

Brain-behavior analysis of transcranial direct current stimulation effects on a complex surgical motor task

Transcranial Direct Current Stimulation (tDCS) has demonstrated its potential in enhancing surgical training and performance compared to sham tDCS. However, optimizing its efficacy requires the selection of appropriate brain targets informed by neuroimaging and mechanistic understanding. Previous studies have established the feasibility of using portable brain imaging, combining functional near-infrared spectroscopy (fNIRS) with tDCS during Fundamentals of Laparoscopic Surgery (FLS) tasks. This allows concurrent monitoring of cortical activations. Building on these foundations, our study aimed to explore the multi-modal imaging of the brain response using fNIRS and electroencephalogram (EEG) to tDCS targeting the right cerebellar (CER) and left ventrolateral prefrontal cortex (PFC) during a challenging FLS suturing with intracorporeal knot tying task. Involving twelve novices with a medical/premedical background (age: 22-28 years, two males, 10 females with one female with left-hand dominance), our investigation sought mechanistic insights into tDCS effects on brain areas related to error-based learning, a fundamental skill acquisition mechanism. The results revealed that right CER tDCS applied to the posterior lobe elicited a statistically significant (q < 0.05) brain response in bilateral prefrontal areas at the onset of the FLS task, surpassing the response seen with sham tDCS. Additionally, right CER tDCS led to a significant (p < 0.05) improvement in FLS scores compared to sham tDCS. Conversely, the left PFC tDCS did not yield a statistically significant brain response or improvement in FLS performance. In conclusion, right CER tDCS demonstrated the activation of bilateral prefrontal brain areas, providing valuable mechanistic insights into the effects of CER tDCS on FLS peformance. These insights motivate future investigations into the effects of CER tDCS on error-related perception-action coupling through directed functional connectivity studies.

First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation

Introduction

One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.

Methods

We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.

Results

Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.

Discussion

It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.

Technical Considerations in EEG Source Imaging

SUMMARY

EEG source imaging is an established technique for identifying the origin of interictal and ictal epileptiform discharges in patients with epilepsy, and it is an important tool in neurophysiology research. Accurate and reliable EEG source imaging requires appropriate choices of how the head, skull, and scalp are modeled, and understanding of the different approaches to modeling is important to guide these choices. Similarly, numerous different approaches to modeling the electrical sources within the brain exist, and appropriate understanding of the strengths and limitations of each are essential to obtaining accurate, reliable, and interpretable solutions. This review aims to describe the essential theoretical basis for these head and source models while also discussing the practical implications of each in clinical or research applications.