EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales

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.

Altered Cortical Activity during a Finger Tap in People with Stroke

This study describes electroencephalography (EEG) measurements during a simple finger movement in people with stroke to understand how temporal patterns of cortical activation and network connectivity align with prolonged muscle contraction at the end of a task. We investigated changes in the EEG temporal patterns in the beta band (13-26 Hz) of people with chronic stroke (N = 10, 7 F/3 M) and controls (N = 10, 7 F/3 M), during and after a cued movement of the index finger. We quantified the change in beta band EEG power relative to baseline as activation at each electrode and the change in task-based phase-locking value (tbPLV) and beta band task-based coherence (tbCoh) relative to baseline coherence as connectivity between EEG electrodes. Finger movements were associated with a decrease in beta power (event related desynchronization (ERD)) followed by an increase in beta power (event related resynchronization (ERS)). The ERS in the post task period was lower in the stroke group (7%), compared to controls (44%) (p < 0.001) and the transition from ERD to ERS was delayed in the stroke group (1.43 s) compared to controls (0.90 s) in the C3 electrode (p = 0.007). In the same post movement period, the stroke group maintained a heightened tbPLV (p = 0.030 for time to baseline of the C3:Fz electrode pair) and did not show the decrease in connectivity in electrode pair C3:Fz that was observed in controls (tbPLV: p = 0.006; tbCoh: p = 0.023). Our results suggest that delays in cortical deactivation patterns following movement coupled with changes in the time course of connectivity between the sensorimotor and frontal cortices in the stroke group might explain clinical observations of prolonged muscle activation in people with stroke. This prolonged activation might be attributed to the combination of cortical reorganization and changes to sensory feedback post-stroke.

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups

Objective.Although motor imagery-based brain-computer interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state electroencephalography (EEG) network differs between BCI-literacy and -illiteracy.Approach.To address the issues above, we analyzed three large public EEG datasets using three functional connectivity and three effective connectivity metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for multivariate Granger causality. The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found.Significance.Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data

INTRODUCTION

High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets.

FINDINGS

Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software.

CONCLUSIONS

NET is freely available (https://github.com/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows.

Exploring the potential of combining transcranial magnetic stimulation and electroencephalography to investigate mild cognitive impairment and Alzheimer's disease: a systematic review

Transcranial magnetic stimulation (TMS) and electroencephalography (EEG) are non-invasive techniques used for neuromodulation and recording brain electrical activity, respectively. The integration of TMS-EEG has emerged as a valuable tool for investigating the complex mechanisms involved in age-related disorders, such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). By systematically synthesizing TMS-EEG studies, this review aims to shed light on the neurophysiological mechanisms underlying MCI and AD, while also exploring the practical applications of TMS-EEG in clinical settings. PubMed, ScienceDirect, and PsychInfo were selected as the databases for this review. The 22 eligible studies included a total of 592 individuals with MCI or AD as well as 301 cognitively normal adults. TMS-EEG assessments unveiled specific patterns of corticospinal excitability, plasticity, and brain connectivity that distinguished individuals on the AD spectrum from cognitively normal older adults. Moreover, the TMS-induced EEG features were observed to be correlated with cognitive performance and the presence of AD pathological biomarkers. The comprehensive examination of the existing studies demonstrates that the combination of TMS and EEG has yielded valuable insights into the neurophysiology of MCI and AD. This integration shows great potential for early detection, monitoring disease progression, and anticipating response to treatment. Future research is of paramount importance to delve into the potential utilization of TMS-EEG for treatment optimization in individuals with MCI and AD.

Mental workload evaluation using weighted phase lag index and coherence features extracted from EEG data

Electroencephalogram (EEG) represents an effective, non-invasive technology to study mental workload. However, volume conduction, a common EEG artifact, influences functional connectivity analysis of EEG data. EEG coherence has been used traditionally to investigate functional connectivity between brain areas associated with mental workload, while weighted Phase Lag Index (wPLI) is a measure that improves on coherence by reducing susceptibility to volume conduction, a common EEG artifact. The goal of this study was to compare two methods of functional connectivity analysis, wPLI and coherence, in the context of mental workload evaluation. The study involved model development for mental workload domains and comparing their performance using coherence-based features, wPLI-based features, and a combination of both. Generalized linear mixed-effects model (GLMM) with the least absolute shrinkage and selection operator (LASSO) feature selection method was used for model development. Results indicated that the model developed using a combination of both feature types demonstrated improved predictive performance across all mental workload domains, compared to models that used each feature type individually. The R2 values were 0.82 for perceived task complexity, 0.71 for distraction, 0.91 for mental demand, 0.85 for physical demand, 0.74 for situational stress, and 0.74 for temporal demand. Furthermore, task complexity and functional connectivity patterns in different brain areas were identified as significant contributors to perceived mental workload (p-value<0.05). Findings showed the potential of using EEG data for mental workload evaluation which suggests that combination of coherence and wPLI can improve the accuracy of mental workload domains prediction. Future research should aim to validate these results on larger, diverse datasets to confirm their generalizability and refine the predictive models.

Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with an alcohol use disorder

Research has identified clinical, genomic, and neurophysiological markers associated with suicide attempts (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA. We examined lifetime SA in 4,068 individuals with DSM-IV alcohol dependence from the Collaborative Study on the Genetics of Alcoholism (23% lifetime suicide attempt; 53% female; mean age: 38). Within participants with an AUD diagnosis, we explored risk across other clinical conditions, polygenic scores (PGS) for comorbid psychiatric problems, and neurocognitive functioning for lifetime suicide attempt. Participants with an AUD who had attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, and other substance use disorders compared to those who had not attempted suicide. Polygenic scores for suicide attempt, depression, and PTSD were associated with reporting a suicide attempt (ORs = 1.22 - 1.44). Participants who reported a SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small. Overall, individuals with an AUD who report a lifetime suicide attempt appear to experience greater levels of trauma, have more severe comorbidities, and carry polygenic risk for a variety of psychiatric problems. Our results demonstrate the need to further investigate suicide attempts in the presence of substance use disorders.

Bayesian reduced rank regression models generalizable neural fingerprints that differentiate between individuals in magnetoencephalography data

Recent magnetoencephalography (MEG) studies have reported that functional connectivity (FC) and power spectra can be used as neural fingerprints in differentiating individuals. Such studies have mainly used correlations between measurement sessions to distinguish individuals from each other. However, it has remained unclear whether such correlations might reflect a more generalizable principle of individually distinctive brain patterns. Here, we evaluated a machine-learning based approach, termed latent-noise Bayesian reduced rank regression (BRRR) as a means of modelling individual differences in the resting-state MEG data of the Human Connectome Project (HCP), using FC and power spectra as neural features. First, we verified that BRRR could model and reproduce the differences between metrics that correlation-based fingerprinting yields. We trained BRRR models to distinguish individuals based on data from one measurement and used the models to identify subsequent measurement sessions of those same individuals. The best performing BRRR models, using only 20 spatiospectral components, were able to identify subjects across measurement sessions with over 90% accuracy, approaching the highest correlation-based accuracies. Using cross-validation, we then determined whether that BRRR model could generalize to unseen subjects, successfully classifying the measurement sessions of novel individuals with over 80% accuracy. The results demonstrate that individual neurofunctional differences can be reliably extracted from MEG data with a low-dimensional predictive model and that the model is able to classify novel subjects.

Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals

Important efforts have been made to describe the neural and cognitive features of healthy and clinical populations. However, the neural and cognitive features of socially vulnerable individuals remain largely unexplored, despite their proneness to developing neurocognitive disorders. Socially vulnerable individuals can be characterised as socially deprived, having a low socioeconomic status, suffering from chronic social stress, and exhibiting poor social adaptation. While it is known that such individuals are likely to perform worse than their peers on executive function tasks, studies on healthy but socially vulnerable groups are lacking. In the current study, we explore whether neural power and connectivity signatures can characterise executive function performance in healthy but socially vulnerable individuals, shedding light on the impairing effects that chronic stress and social disadvantages have on cognition. We measured resting-state electroencephalography and executive functioning in 38 socially vulnerable participants and 38 matched control participants. Our findings indicate that while neural power was uninformative, lower delta and theta phase synchrony are associated with worse executive function performance in all participants, whereas delta phase synchrony is higher in the socially vulnerable group compared to the control group. Finally, we found that delta phase synchrony and years of schooling are the best predictors for belonging to the socially vulnerable group. Overall, these findings suggest that exposure to chronic stress due to socioeconomic factors and a lack of education are associated with changes in slow-wave neural connectivity and executive functioning.

Classification of Motor Imagery EEG signals using high resolution time-frequency representations and convolutional neural network

A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG signals present in the channels C3 and C4. A novel method for the selection of the value of number of cycles relative to the center frequency of the CMW is studied here for extracting the MI task features. The generated TFRs are given as input to a Convolutional neural network (CNN) for classifying them into left or right hand MI tasks. The proposed framework attains a classification accuracy of 82.2% on the BCI Competition IV dataset 2a, showing that the TFRs generated in this work give a higher classification accuracy than the baseline methods and other existing algorithms.

A distributed theta network of error generation and processing in aging

Based on previous concepts that a distributed theta network with a central "hub" in the medial frontal cortex is critically involved in movement regulation, monitoring, and control, the present study explored the involvement of this network in error processing with advancing age in humans. For that aim, the oscillatory neurodynamics of motor theta oscillations was analyzed at multiple cortical regions during correct and error responses in a sample of older adults. Response-related potentials (RRPs) of correct and incorrect reactions were recorded in a four-choice reaction task. RRPs were decomposed in the time-frequency domain to extract oscillatory theta activity. Motor theta oscillations at extended motor regions were analyzed with respect to power, temporal synchronization, and functional connectivity. Major results demonstrated that errors had pronounced effects on motor theta oscillations at cortical regions beyond the medial frontal cortex by being associated with (1) theta power increase in the hemisphere contra-lateral to the movement, (2) suppressed spatial and temporal synchronization at pre-motor areas contra-lateral to the responding hand, (2) inhibited connections between the medial frontal cortex and sensorimotor areas, and (3) suppressed connectivity and temporal phase-synchronization of motor theta networks in the posterior left hemisphere, irrespective of the hand, left, or right, with which the error was made. The distributed effects of errors on motor theta oscillations demonstrate that theta networks support performance monitoring. The reorganization of these networks with aging implies that in older individuals, performance monitoring is associated with a disengagement of the medial frontal region and difficulties in controlling the focus of motor attention and response selection.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-10018-4.

Motor oscillations reveal new correlates of error processing in the human brain

It has been demonstrated that during motor responses, the activation of the motor cortical regions emerges in close association with the activation of the medial frontal cortex implicated with performance monitoring and cognitive control. The present study explored the oscillatory neurodynamics of response-related potentials during correct and error responses to test the hypothesis that such continuous communication would modify the characteristics of motor potentials during performance errors. Electroencephalogram (EEG) was recorded at 64 electrodes in a four-choice reaction task and response-related potentials (RRPs) of correct and error responses were analysed. Oscillatory RRP components at extended motor areas were analysed in the theta (3.5-7 Hz) and delta (1-3 Hz) frequency bands with respect to power, temporal synchronization (phase-locking factor, PLF), and spatial synchronization (phase-locking value, PLV). Major results demonstrated that motor oscillations differed between correct and error responses. Error-related changes (1) were frequency-specific, engaging delta and theta frequency bands, (2) emerged already before response production, and (3) had specific regional topographies at posterior sensorimotor and anterior (premotor and medial frontal) areas. Specifically, the connectedness of motor and sensorimotor areas contra-lateral to the response supported by delta networks was substantially reduced during errors. Also, there was an error-related suppression of the phase stability of delta and theta oscillations at these areas. This synchronization reduction was accompanied by increased temporal synchronization of motor theta oscillations at bi-lateral premotor regions and by two distinctive error-related effects at medial frontal regions: (1) a focused fronto-central enhancement of theta power and (2) a separable enhancement of the temporal synchronization of delta oscillations with a localized medial frontal focus. Together, these observations indicate that the electrophysiological signatures of performance errors are not limited to the medial frontal signals, but they also involve the dynamics of oscillatory motor networks at extended cortical regions generating the movement. Also, they provide a more detailed picture of the medial frontal processes activated in relation to error processing.

Coherent activity within and between hemispheres: cortico-cortical connectivity revealed by rTMS of the right posterior parietal cortex

Introduction

Low frequency (1 Hz) repetitive transcranial stimulation (rTMS) applied over right posterior parietal cortex (rPPC) has been shown to reduce cortical excitability both of the stimulated area and of the interconnected contralateral homologous areas. In the present study, we investigated the whole pattern of intra- and inter-hemispheric cortico-cortical connectivity changes induced by rTMS over rPPC.

Methods

To do so, 14 healthy participants underwent resting state EEG recording before and after 30 min of rTMS at 1 Hz or sham stimulation over the rPPC (electrode position P6). Real stimulation was applied at 90% of motor threshold. Coherence values were computed on the electrodes nearby the stimulated site (i.e., P4, P8, and CP6) considering all possible inter- and intra-hemispheric combinations for the following frequency bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12Hz), low beta (12-20 Hz), high beta (20-30 Hz), and gamma (30-50 Hz).

Results and discussion

Results revealed a significant increase in coherence in delta, theta, alpha and beta frequency bands between rPPC and the contralateral homologous sites. Moreover, an increase in coherence in theta, alpha, beta and gamma frequency bands was found between rPPC and right frontal sites, reflecting the activation of the fronto-parietal network within the right hemisphere. Summarizing, subthreshold rTMS over rPPC revealed cortico-cortical inter- and intra-hemispheric connectivity as measured by the increase in coherence among these areas. Moreover, the present results further confirm previous evidence indicating that the increase of coherence values is related to intra- and inter-hemispheric inhibitory effects of rTMS. These results can have implications for devising evidence-based rehabilitation protocols after stroke.

Elevated phase amplitude coupling as a depression biomarker in epilepsy

Depression is prevalent in epilepsy patients and their intracranial brain activity recordings can be used to determine the types of brain activity that are associated with comorbid depression. We performed case-control comparison of spectral power and phase amplitude coupling (PAC) in 34 invasively monitored drug resistant epilepsy patients' brain recordings. The values of spectral power and PAC for one-minute segments out of every hour in a patient's study were correlated with pre-operative assessment of depressive symptoms by Beck Depression Inventory-II (BDI). We identified an elevated PAC signal (theta-alpha-beta phase (5-25 Hz)/gamma frequency (80-100 Hz) band) that is present in high BDI scores but not low BDI scores adult epilepsy patients in brain regions implicated in primary depression, including anterior cingulate cortex, amygdala and orbitofrontal cortex. Our results showed the application of PAC as a network-specific, electrophysiologic biomarker candidate for comorbid depression and its potential as treatment target for neuromodulation.

Errors elicit frontoparietal theta-gamma coupling that is modulated by endogenous estradiol levels

Cognitive control-related error monitoring is intimately involved in behavioral adaptation, learning, and individual differences in a variety of psychological traits and disorders. Accumulating evidence suggests that a focus on women's health and ovarian hormones is critical to the study of such cognitive brain functions. Here we sought to identify a novel index of error monitoring using a time-frequency based phase amplitude coupling (t-f PAC) measure and examine its modulation by endogenous levels of estradiol in females. Forty-three healthy, naturally cycling young adult females completed a flanker task while continuous electroencephalogram was recorded on four occasions across the menstrual cycle. Results revealed significant error-related t-f PAC between theta phase generated in fronto-central areas and gamma amplitude generated in parietal-occipital areas. Moreover, this error-related theta-gamma coupling was enhanced by endogenous levels of estradiol both within females across the cycle as well as between females with higher levels of average circulating estradiol. While the role of frontal midline theta in error processing is well documented, this paper extends the extant literature by illustrating that error monitoring involves the coordination between multiple distributed systems with the slow midline theta activity modulating the power of gamma-band oscillatory activity in parietal regions. They further show enhancement of inter-regional coupling by endogenous estradiol levels, consistent with research indicating modulation of cognitive control neural functions by the endocrine system in females. Together, this work identifies a novel neurophysiological marker of cognitive control-related error monitoring in females that has implications for neuroscience and women's health.

Functional connectivity between medial pulvinar and cortical networks as a predictor of arousal to noxious stimuli during sleep

The interruption of sleep by a nociceptive stimulus is favoured by an increase in the pre-stimulus functional connectivity between sensory and higher level cortical areas. In addition, stimuli inducing arousal also trigger a widespread electroencephalographic (EEG) response reflecting the coordinated activation of a large cortical network. Because functional connectivity between distant cortical areas is thought to be underpinned by trans-thalamic connections involving associative thalamic nuclei, we investigated the possible involvement of one principal associative thalamic nucleus, the medial pulvinar (PuM), in the sleeper's responsiveness to nociceptive stimuli. Intra-cortical and intra-thalamic signals were analysed in 440 intracranial electroencephalographic (iEEG) segments during nocturnal sleep in eight epileptic patients receiving laser nociceptive stimuli. The spectral coherence between the PuM and 10 cortical regions grouped in networks was computed during 5 s before and 1 s after the nociceptive stimulus and contrasted according to the presence or absence of an arousal EEG response. Pre- and post-stimulus phase coherence between the PuM and all cortical networks was significantly increased in instances of arousal, both during N2 and paradoxical (rapid eye movement [REM]) sleep. Thalamo-cortical enhancement in coherence involved both sensory and higher level cortical networks and predominated in the pre-stimulus period. The association between pre-stimulus widespread increase in thalamo-cortical coherence and subsequent arousal suggests that the probability of sleep interruption by a noxious stimulus increases when it occurs during phases of enhanced trans-thalamic transfer of information between cortical areas.

Aging alters functional connectivity of motor theta networks during sensorimotor reactions

OBJECTIVE

Both cognitive and primary motor networks alter with advancing age in humans. The networks activated in response to external environmental stimuli supported by theta oscillations remain less well explored. The present study aimed to characterize the effects of aging on the functional connectivity of response-related theta networks during sensorimotor tasks.

METHODS

Electroencephalographic signals were recorded in young and middle-to-older age adults during three tasks performed in two modalities, auditory and visual: a simple reaction task, a Go-NoGo task, and a choice-reaction task. Response-related theta oscillations were computed. The phase-locking value (PLV) was used to analyze the spatial synchronization of primary motor and motor control theta networks.

RESULTS

Performance was overall preserved in older adults. Independently of the task, aging was associated with reorganized connectivity of the contra-lateral primary motor cortex. In younger adults, it was synchronized with motor control regions (intra-hemispheric premotor/frontal and medial frontal). In older adults, it was only synchronized with intra-hemispheric sensorimotor regions.

CONCLUSIONS

Motor theta networks of older adults manifest a functional decoupling between the response-generating motor cortex and motor control regions, which was not modulated by task variables. The overall preserved performance in older adults suggests that the increased connectivity within the sensorimotor network is associated with an excessive reliance on sensorimotor feedback during movement execution compensating for a deficient cognitive regulation of motor regions during sensorimotor reactions.

SIGNIFICANCE

New evidence is provided for the reorganization of motor networks during sensorimotor reactions already at the transition from middle to old age.

The Hopf whole-brain model and its linear approximation

Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local dynamics have been proposed. Among them, nonlinear oscillators corresponding to a supercritical Hopf bifurcation have been used to link brain connectivity and collective phase and amplitude dynamics in different brain states. Here, we studied the linear fluctuations of this model to estimate its stationary statistics, i.e., the instantaneous and lagged covariances and the power spectral densities. This linear approximation-that holds in the case of heterogeneous parameters and time-delays-allows analytical estimation of the statistics and it can be used for fast parameter explorations to study changes in brain state, changes in brain activity due to alterations in structural connectivity, and modulations of parameter due to non-equilibrium dynamics.

Spatial Visual Imagery (SVI)-Based Electroencephalograph Discrimination for Natural CAD Manipulation

With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain-computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation.

Cortical effects of wrist tendon vibration during an arm tracking task in chronic stroke survivors: An EEG study

The purpose of this study was to characterize changes in cortical activity and connectivity in stroke survivors when vibration is applied to the wrist flexor tendons during a visuomotor tracking task. Data were collected from 10 chronic stroke participants and 10 neurologically-intact controls while tracking a target through a figure-8 pattern in the horizontal plane. Electroencephalography (EEG) was used to measure cortical activity (beta band desynchronization) and connectivity (beta band task-based coherence) with movement kinematics and performance error also being recorded during the task. All participants came into our lab on two separate days and performed three blocks (16 trials each, 48 total trials) of tracking, with the middle block including vibration or sham applied at the wrist flexor tendons. The order of the sessions (Vibe vs. Sham) was counterbalanced across participants to prevent ordering effects. During the Sham session, cortical activity increased as the tracking task progressed (over blocks). This effect was reduced when vibration was applied to controls. In contrast, vibration increased cortical activity during the vibration period in participants with stroke. Cortical connectivity increased during vibration, with larger effect sizes in participants with stroke. Changes in tracking performance, standard deviation of hand speed, were observed in both control and stroke groups. Overall, EEG measures of brain activity and connectivity provided insight into effects of vibration on brain control of a visuomotor task. The increases in cortical activity and connectivity with vibration improved patterns of activity in people with stroke. These findings suggest that reactivation of normal cortical networks via tendon vibration may be useful during physical rehabilitation of stroke patients.

Relationship between individual differences in pain empathy and task- and resting-state EEG

Pain empathy is a complex form of psychological inference that enables us to understand how others feel in the context of pain. Since pain empathy may be grounded in our own pain experiences, it exhibits huge inter-individual variability. However, the neural mechanisms behind the individual differences in pain empathy and its association with pain perception are still poorly understood. In this study, we aimed to characterize brain mechanisms associated with individual differences in pain empathy in adult participants (n = 24). The 32-channel electroencephalography (EEG) was recorded at rest and during a pain empathy task, and participants viewed static visual stimuli of the limbs submitted to painful and nonpainful stimulation to solicit empathy. The pain sensitivity of each participant was measured using a series of direct current stimulations. In our results, the N2 of Fz and the LPP of P3 and P4 were affected by painful pictures. We found that both delta and alpha bands in the frontal and parietal cortex were involved in the regulation of pain empathy. For the delta band, a close relationship was found between average power, either in the resting or task state, and individual differences in pain empathy. It suggested that the spectral power in Fz's delta band may reflect subjective pain empathy across individuals. For the alpha band, the functional connectivity between Fz and P3 under painful picture stimulation was correlated to individuals' pain sensitivity. It indicated that the alpha band may reflect individual differences in pain sensitivity and be involved in pain empathy processing. Our results suggested the distinct role of the delta and alpha bands of EEG signals in pain empathy processing and may deepen our understanding of the neural mechanisms underpinning pain empathy.

Functional connectivity changes in mild cognitive impairment: A meta-analysis of M/EEG studies

OBJECTIVE

Early synchrony alterations have been observed through electrophysiological techniques in Mild Cognitive Impairment (MCI), which is considered the intermediate phase between healthy aging (HC) and Alzheimer's disease (AD). However, the documented direction (hyper/hypo-synchronization), regions and frequency bands affected are inconsistent. This meta-analysis intended to elucidate existing evidence linked to potential neurophysiological biomarkers of AD.

METHODS

We conducted a random-effects meta-analysis that entailed the unbiased inclusion of Non-statistically Significant Unreported Effect Sizes ("MetaNSUE") of electroencephalogram (EEG) and magnetoencephalogram (MEG) studies investigating functional connectivity changes at rest along the healthy-pathological aging continuum, searched through PubMed, Scopus, Web of Science and PsycINFO databases until June 2023.

RESULTS

Of the 3852 articles extracted, we analyzed 12 papers, and we found an alpha synchrony decrease in MCI compared to HC, specifically between temporal-parietal (d = -0.26) and frontal-parietal areas (d = -0.25).

CONCLUSIONS

Alterations of alpha synchrony are present even at MCI stage.

SIGNIFICANCE

Synchrony measures may be promising for the detection of the first hallmarks of connectivity alterations, even at the prodromal stages of the AD, before clinical symptoms occur.

Infra-slow fluctuations in cortical potentials and respiration drive fast cortical EEG rhythms in sleeping and waking states

OBJECTIVE

Infra-slow fluctuations (ISF, 0.008-0.1 Hz) characterize hemodynamic and electric potential signals of human brain. ISFs correlate with the amplitude dynamics of fast (>1 Hz) neuronal oscillations, and may arise from permeability fluctuations of the blood-brain barrier (BBB). It is unclear if physiological rhythms like respiration drive or track fast cortical oscillations, and the role of sleep in this coupling is unknown.

METHODS

We used high-density full-band electroencephalography (EEG) in healthy human volunteers (N = 21) to measure concurrently the ISFs, respiratory pulsations, and fast neuronal oscillations during periods of wakefulness and sleep, and to assess the strength and direction of their phase-amplitude coupling.

RESULTS

The phases of ISFs and respiration were both coupled with the amplitude of fast neuronal oscillations, with stronger ISF coupling being evident during sleep. Phases of ISF and respiration drove the amplitude dynamics of fast oscillations in sleeping and waking states, with different contributions.

CONCLUSIONS

ISFs in slow cortical potentials and respiration together significantly determine the dynamics of fast cortical oscillations.

SIGNIFICANCE

We propose that these slow physiological phases play a significant role in coordinating cortical excitability, which is a fundamental aspect of brain function.

Electrical Source Imaging of Somatosensory Evoked Potentials from Intracranial EEG Signals

Stereoelectroencephalography (SEEG) records electrical brain activity with intracerebral electrodes. However, it has an inherently limited spatial coverage. Electrical source imaging (ESI) infers the position of the neural generators from the recorded electric potentials, and thus, could overcome this spatial undersampling problem. Here, we aimed to quantify the accuracy of SEEG ESI under clinical conditions. We measured the somatosensory evoked potential (SEP) in SEEG and in high-density EEG (HD-EEG) in 20 epilepsy surgery patients. To localize the source of the SEP, we employed standardized low resolution brain electromagnetic tomography (sLORETA) and equivalent current dipole (ECD) algorithms. Both sLORETA and ECD converged to similar solutions. Reflecting the large differences in the SEEG implantations, the localization error also varied in a wide range from 0.4 to 10 cm. The SEEG ESI localization error was linearly correlated with the distance from the putative neural source to the most activated contact. We show that it is possible to obtain reliable source reconstructions from SEEG under realistic clinical conditions, provided that the high signal fidelity recording contacts are sufficiently close to the source of the brain activity.

Characterizing brain stage-dependent pupil dynamics based on lateral hypothalamic activity

Pupil dynamics presents varied correlation features with brain activity under different vigilant levels. The modulation of brain dynamic stages can arise from the lateral hypothalamus (LH), where diverse neuronal cell types contribute to arousal regulation in opposite directions via the anterior cingulate cortex (ACC). However, the relationship of the LH and pupil dynamics has seldom been investigated. Here, we performed local field potential (LFP) recordings at the LH and ACC, and whole-brain fMRI with simultaneous fiber photometry Ca2+ recording in the ACC, to evaluate their correlation with brain state-dependent pupil dynamics. Both LFP and functional magnetic resonance imaging (fMRI) data showed various correlations to pupil dynamics across trials that span negative, null, and positive correlation values, demonstrating brain state-dependent coupling features. Our results indicate that the correlation of pupil dynamics with ACC LFP and whole-brain fMRI signals depends on LH activity, suggesting a role of the latter in brain dynamic stage regulation.

EEG functional connectivity in infants at elevated familial likelihood for autism spectrum disorder

BACKGROUND

Many studies have reported that autism spectrum disorder (ASD) is associated with atypical structural and functional connectivity. However, we know relatively little about the development of these differences in infancy.

METHODS

We used a high-density electroencephalogram (EEG) dataset pooled from two independent infant sibling cohorts, to characterize such neurodevelopmental deviations during the first years of life. EEG was recorded at 6 and 12 months of age in infants at typical (N = 92) or elevated likelihood for ASD (N = 90), determined by the presence of an older sibling with ASD. We computed the functional connectivity between cortical sources of EEG during video watching using the corrected imaginary part of phase-locking values.

RESULTS

Our main analysis found no significant association between functional connectivity and ASD, showing only significant effects for age, sex, age-sex interaction, and site. Given these null results, we performed an exploratory analysis and observed, at 12 months, a negative correlation between functional connectivity and ADOS calibrated severity scores for restrictive and repetitive behaviors (RRB).

LIMITATIONS

The small sample of ASD participants inherent to sibling studies limits diagnostic group comparisons. Also, results from our secondary exploratory analysis should be considered only as potential relationships to further explore, given their increased vulnerability to false positives.

CONCLUSIONS

These results are inconclusive concerning an association between EEG functional connectivity and ASD in infancy. Exploratory analyses provided preliminary support for a relationship between RRB and functional connectivity specifically, but these preliminary observations need corroboration on larger samples.

COVID-19 pandemic stressors are associated with reported increases in frequency of drunkenness among individuals with a history of alcohol use disorder

Some sources report increases in alcohol use have been observed since the start of the COVID-19 pandemic, particularly among women. Cross-sectional studies suggest that specific COVID-19-related stressful experiences (e.g., social disconnection) may be driving such increases in the general population. Few studies have explored these topics among individuals with a history of Alcohol Use Disorders (AUD), an especially vulnerable population. Drawing on recent data collected by the Collaborative Study on the Genetics of Alcoholism (COGA; COVID-19 study N = 1651, 62% women, age range: 30-91) in conjunction with AUD history data collected on the sample since 1990, we investigated associations of COVID-19 related stressors and coping activities with changes in drunkenness frequency since the start of the pandemic. Analyses were conducted for those without a history of AUD (N: 645) and three groups of participants with a history of AUD prior to the start of the pandemic: (1) those experiencing AUD symptoms (N: 606), (2) those in remission who were drinking (N: 231), and (3) those in remission who were abstinent (had not consumed alcohol for 5+ years; N: 169). Gender-stratified models were also examined. Exploratory analyses examined the moderating effects of 'problematic alcohol use' polygenic risk scores (PRS) and neural connectivity (i.e., posterior interhemispheric alpha EEG coherence) on associations between COVID-19 stressors and coping activities with changes in the frequency of drunkenness. Increases in drunkenness frequency since the start of the pandemic were higher among those with a lifetime AUD diagnosis experiencing symptoms prior to the start of the pandemic (14% reported increased drunkenness) when compared to those without a history of AUD (5% reported increased drunkenness). Among individuals in remission from AUD prior to the start of the pandemic, rates of increased drunkenness were 10% for those who were drinking pre-pandemic and 4% for those who had previously been abstinent. Across all groups, women reported nominally greater increases in drunkenness frequency when compared with men, although only women experiencing pre-pandemic AUD symptoms reported significantly greater rates of increased drunkenness since the start of the pandemic compared to men in this group (17% of women vs. 5% of men). Among those without a prior history of AUD, associations between COVID-19 risk and protective factors with increases in drunkenness frequency were not observed. Among all groups with a history of AUD (including those with AUD symptoms and those remitted from AUD), perceived stress was associated with increases in drunkenness. Among the remitted-abstinent group, essential worker status was associated with increases in drunkenness. Gender differences in these associations were observed: among women in the remitted-abstinent group, essential worker status, perceived stress, media consumption, and decreased social interactions were associated with increases in drunkenness. Among men in the remitted-drinking group, perceived stress was associated with increases in drunkenness, and increased relationship quality was associated with decreases in drunkenness. Exploratory analyses indicated that associations between family illness or death with increases in drunkenness and increased relationship quality with decreases in drunkenness were more pronounced among the remitted-drinking participants with higher PRS. Associations between family illness or death, media consumption, and economic hardships with increases in drunkenness and healthy coping with decreases in drunkenness were more pronounced among the remitted-abstinent group with lower interhemispheric alpha EEG connectivity. Our results demonstrated that only individuals with pre-pandemic AUD symptoms reported greater increases in drunkenness frequency since the start of the COVID-19 pandemic compared to those without a lifetime history of AUD. This increase was more pronounced among women than men in this group. However, COVID-19-related stressors and coping activities were associated with changes in the frequency of drunkenness among all groups of participants with a prior history of AUD, including those experiencing AUD symptoms, as well as abstinent and non-abstinent participants in remission. Perceived stress, essential worker status, media consumption, social connections (especially for women), and relationship quality (especially for men) are specific areas of focus for designing intervention and prevention strategies aimed at reducing pandemic-related alcohol misuse among this particularly vulnerable group. Interestingly, these associations were not observed for individuals without a prior history of AUD, supporting prior literature that demonstrates that widespread stressors (e.g., pandemics, terrorist attacks) disproportionately impact the mental health and alcohol use of those with a prior history of problems.

Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition

Inhibitory control processes are an important aspect of executive functions and goal-directed behavior. However, the mostly correlative nature of neurophysiological studies was not able to provide insights which aspects of neural dynamics can best predict whether an individual is confronted with a situation requiring the inhibition of a response. This is particularly the case when considering the complex spatio-temporal nature of neural processes captured by EEG data. In the current study, we ask whether independent spatial activity profiles in the EEG data are useful to predict whether an individual is confronted with a situation requiring response inhibition. We combine independent component analysis (ICA) with explainable artificial intelligence approaches (EEG-based deep learning) using data from a Go/Nogo task (N = 255 participants). We show that there are four dissociable spatial activity profiles important to classify Go and Nogo trials as revealed by deep learning. Of note, for all of these four independent activity profiles, neural activity in the time period between 300 and 550 ms after stimulus presentation was most informative. Source localization analyses further revealed regions in the pre-central gyrus (BA6), the middle frontal gyrus (BA10), the inferior frontal gyrus (BA46), and the insular cortex (BA13) were associated with the isolated spatial activity profiles. The data suggest concomitant processes being reflected in the identified time window. This has implications for the ongoing debate on the functional significance of event-related potential correlates of inhibitory control.

Texture-based brain networks for characterization of healthy subjects from MRI

Brain networks have been widely used to study the relationships between brain regions based on their dynamics using, e.g. fMRI or EEG, and to characterize their real physical connections using DTI. However, few studies have investigated brain networks derived from structural properties; and those have been based on cortical thickness or gray matter volume. The main objective of this work was to investigate the feasibility of obtaining useful information from brain networks derived from structural MRI, using texture features. We also wanted to verify if texture brain networks had any relation with established functional networks. T1-MR images were segmented using AAL and texture parameters from the gray-level co-occurrence matrix were computed for each region, for 760 subjects. Individual texture networks were used to evaluate the structural connections between regions of well-established functional networks; assess possible gender differences; investigate the dependence of texture network measures with age; and single out brain regions with different texture-network characteristics. Although around 70% of texture connections between regions belonging to the default mode, attention, and visual network were greater than the mean connection value, this effect was small (only between 7 and 15% of these connections were larger than one standard deviation), implying that texture-based morphology does not seem to subside function. This differs from cortical thickness-based morphology, which has been shown to relate to functional networks. Seventy-five out of 86 evaluated regions showed significant (ANCOVA, p < 0.05) differences between genders. Forty-four out of 86 regions showed significant (ANCOVA, p < 0.05) dependence with age; however, the R2 indicates that this is not a linear relation. Thalamus and putamen showed a very unique texture-wise structure compared to other analyzed regions. Texture networks were able to provide useful information regarding gender and age-related differences, as well as for singling out specific brain regions. We did not find a morphological texture-based subsidy for the evaluated functional brain networks. In the future, this approach will be extended to neurological patients to investigate the possibility of extracting biomarkers to help monitor disease evolution or treatment effectiveness.

Healthy ageing and cognitive impairment alter EEG functional connectivity in distinct frequency bands

Functional connectivity (FC) indicates the interdependencies between brain signals recorded from spatially distinct locations in different frequency bands, which is modulated by cognitive tasks and is known to change with ageing and cognitive disorders. Recently, the power of narrow-band gamma oscillations induced by visual gratings have been shown to reduce with both healthy ageing and in subjects with mild cognitive impairment (MCI). However, the impact of ageing/MCI on stimulus-induced gamma FC has not been well studied. We recorded electroencephalogram (EEG) from a large cohort (N = 229) of elderly subjects (>49 years) while they viewed large cartesian gratings to induce gamma oscillations and studied changes in alpha and gamma FC with healthy ageing (N = 218) and MCI (N = 11). Surprisingly, we found distinct differences across age and MCI groups in power and FC. With healthy ageing, alpha power did not change but FC decreased significantly. MCI reduced gamma but not alpha FC significantly compared with age and gender matched controls, even when power was matched between the two groups. Overall, our results suggest distinct effects of ageing and disease on EEG power and FC, suggesting different mechanisms underlying ageing and cognitive disorders.

A global pause generates nonselective response inhibition during selective stopping

Selective response inhibition may be required when stopping a part of a multicomponent action. A persistent response delay (stopping-interference effect) indicates nonselective response inhibition during selective stopping. This study aimed to elucidate whether nonselective response inhibition is the consequence of a global pause process during attentional capture or specific to a nonselective cancel process during selective stopping. Twenty healthy human participants performed a bimanual anticipatory response inhibition paradigm with selective stop and ignore signals. Frontocentral and sensorimotor beta-bursts were recorded with electroencephalography. Corticomotor excitability and short-interval intracortical inhibition in primary motor cortex were recorded with transcranial magnetic stimulation. Behaviorally, responses in the non-signaled hand were delayed during selective ignore and stop trials. The response delay was largest during selective stop trials and indicated that stopping-interference could not be attributed entirely to attentional capture. A stimulus-nonselective increase in frontocentral beta-bursts occurred during stop and ignore trials. Sensorimotor response inhibition was reflected in maintenance of beta-bursts and short-interval intracortical inhibition relative to disinhibition observed during go trials. Response inhibition signatures were not associated with the magnitude of stopping-interference. Therefore, nonselective response inhibition during selective stopping results primarily from a nonselective pause process but does not entirely account for the stopping-interference effect.

Identifying good practices for detecting inter-regional linear functional connectivity from EEG

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC. Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.

Analysis of Relation between Brainwave Activity and Reaction Time of Short-Haul Pilots Based on EEG Data

The purpose of this research is to examine and assess the relation between a pilot's concentration and reaction time with specific brain activity during short-haul flights. Participants took part in one-hour long flight sessions performed on the FNPT II class flight simulator. Subjects were instructed to respond to unexpected events that occurred during the flight. The brainwaves of each participant were recorded with the Emotiv EPOC+ Scientific Contextual EEG device. The majority of participants showed a statistically significant, positive correlation between Theta Power in the frontal lobe and response time. Additionally, most subjects exhibited statistically significant, positive correlations between band-power and reaction times in the Theta range for the temporal and parietal lobes. Statistically significant event-related changes (ERC) were observed for the majority of subjects in the frontal lobe for Theta frequencies, Beta waves in the frontal lobe and in all lobes for the Gamma band. Notably, significant ERC was also observed for Theta and Beta frequencies in the temporal and occipital Lobes, Alpha waves in the frontal, parietal and occipital lobes for most participants. A difference in brain activity patterns was observed, depending on the performance in time-restricted tasks.

Spike and wave discharges detection in genetic absence epilepsy rat from Strasbourg and patients with genetic generalized epilepsy

OBJECTIVE

Generalised spike and wave discharges (SWDs) are pathognomonic EEG signatures for diagnosing absence seizures in patients with Genetic Generalized Epilepsy (GGE). The Genetic Absence Epilepsy Rats from Strasbourg (GAERS) is one of the best-validated animal models of GGE with absence seizures.

METHODS

We developed an SWDs detector for both GAERS rodents and GGE patients with absence seizures using a neural network method. We included 192 24-hour EEG sessions recorded from 18 GAERS rats, and 24-hour scalp-EEG data collected from 11 GGE patients.

RESULTS

The SWDs detection performance on GAERS showed a sensitivity of 98.01% and a false positive (FP) rate of 0.96/hour. The performance on GGE patients showed 100% sensitivity in five patients, while the remaining patients obtained over 98.9% sensitivity. Moderate FP rates were seen in our patients with 2.21/hour average FP. The detector trained within our patient cohort was validated in an independent dataset, TUH EEG Seizure Corpus (TUSZ), that showed 100% sensitivity in 11 of 12 patients and 0.56/hour averaged FP.

CONCLUSIONS

We developed a robust SWDs detector that showed high sensitivity and specificity for both GAERS rats and GGE patients.

SIGNIFICANCE

This detector can assist researchers and neurologists with the time-efficient and accurate quantification of SWDs.

Evaluating brain spectral and connectivity differences between silent mind-wandering and trance states

Trance is an altered state of consciousness characterized by alterations in cognition. In general, trance states induce mental silence (i.e., cognitive thought reduction), and mental silence can induce trance states. Conversely, mind-wandering is the mind's propensity to stray its attention away from the task at hand and toward content irrelevant to the current moment, and its main component is inner speech. Building on the previous literature on mental silence and trance states and incorporating inverse source reconstruction advances, the study's objectives were to evaluate differences between trance and mind-wandering states using: (1) electroencephalography (EEG) power spectra at the electrode level, (2) power spectra at the area level (source reconstructed signal), and (3) EEG functional connectivity between these areas (i.e., how they interact). The relationship between subjective trance depths ratings and whole-brain connectivity during trance was also evaluated. Spectral analyses revealed increased delta and theta power in the frontal region and increased gamma in the centro-parietal region during mind-wandering, whereas trance showed increased beta and gamma power in the frontal region. Power spectra at the area level and pairwise comparisons of the connectivity between these areas demonstrated no significant difference between the two states. However, subjective trance depth ratings were inversely correlated with whole-brain connectivity in all frequency bands (i.e., deeper trance is associated with less large-scale connectivity). Trance allows one to enter mentally silent states and explore their neurophenomenological processes. Limitations and future directions are discussed.

Effect of Visual Feedback on Behavioral Control and Functional Activity During Bilateral Hand Movement

Previous researches state vision as a vital source of information for movement control and more precisely for accurate hand movement. Further, fine bimanual motor activity may be associated with various oscillatory activities within distinct brain areas and inter-hemispheric interactions. However, neural coordination among the distinct brain areas responsible to enhance motor accuracy is still not adequate. In the current study, we investigated task-dependent modulation by simultaneously measuring high time resolution electroencephalogram (EEG), electromyogram (EMG) and force along with bi-manual and unimanual motor tasks. The errors were controlled using visual feedback. To complete the unimanual tasks, the participant was asked to grip the strain gauge using the index finger and thumb of the right hand thereby exerting force on the connected visual feedback system. Whereas the bi-manual task involved finger abduction of the left index finger in two contractions along with visual feedback system and at the same time the right hand gripped using definite force on two conditions that whether visual feedback existed or not for the right hand. Primarily, the existence of visual feedback for the right hand significantly decreased brain network global and local efficiency in theta and alpha bands when compared with the elimination of visual feedback using twenty participants. Brain network activity in theta and alpha bands coordinates to facilitate fine hand movement. The findings may provide new neurological insight on virtual reality auxiliary equipment and participants with neurological disorders that cause movement errors requiring accurate motor training. The current study investigates task-dependent modulation by simultaneously measuring high time resolution electroencephalogram, electromyogram and force along with bi-manual and unimanual motor tasks. The findings show that visual feedback for right hand decreases the force root mean square error of right hand. Visual feedback for right hand decreases local and global efficiency of brain network in theta and alpha bands.

Effects of continuous tactile stimulation on auditory-evoked cortical responses depend on the audio-tactile phase

Auditory perception can benefit from stimuli in non-auditory sensory modalities, as for example in lip-reading. Compared with such visual influences, tactile influences are still poorly understood. It has been shown that single tactile pulses can enhance the perception of auditory stimuli depending on their relative timing, but whether and how such brief auditory enhancements can be stretched in time with more sustained, phase-specific periodic tactile stimulation is still unclear. To address this question, we presented tactile stimulation that fluctuated coherently and continuously at 4Hz with an auditory noise (either in-phase or anti-phase) and assessed its effect on the cortical processing and perception of an auditory signal embedded in that noise. Scalp-electroencephalography recordings revealed an enhancing effect of in-phase tactile stimulation on cortical responses phase-locked to the noise and a suppressive effect of anti-phase tactile stimulation on responses evoked by the auditory signal. Although these effects appeared to follow well-known principles of multisensory integration of discrete audio-tactile events, they were not accompanied by corresponding effects on behavioral measures of auditory signal perception. Our results indicate that continuous periodic tactile stimulation can enhance cortical processing of acoustically-induced fluctuations and mask cortical responses to an ongoing auditory signal. They further suggest that such sustained cortical effects can be insufficient for inducing sustained bottom-up auditory benefits.

Within-subject reproducibility varies in multi-modal, longitudinal brain networks

Network neuroscience provides important insights into brain function by analyzing complex networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. However, in order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first confirm that across all modalities, within-subject reproducibility is higher than between-subject reproducibility. We see high variability in the reproducibility of individual connections, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than connectivity in other frequency bands. Structural networks show a higher reliability than functional networks across network statistics, but synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the type of analysis should depend on whether or not one wants to take into account state-dependent fluctuations in connectivity.

The potential of electroencephalography coherence to predict the outcome of repetitive transcranial magnetic stimulation in insomnia disorder

BACKGROUND

It is unknown whether repetitive Transcranial Magnetic Stimulation (rTMS) could improve sleep quality by modulating electroencephalography (EEG) connectivity of insomnia disorder (ID) patients. Great heterogeneity had been found in the clinical outcomes of rTMS for ID. The study aimed to investigate the potential mechanisms of rTMS therapy for ID and develop models to predict clinical outcomes.

METHODS

In Study 1, 50 ID patients were randomly divided into active and sham groups, and subjected to 20 sessions of treatment with 1 Hz rTMS over the left dorsolateral prefrontal cortex. EEG during awake, Polysomnography, and clinical assessment were collected and analyzed before and after rTMS. In Study 2, 120 ID patients were subjected to active rTMS stimulation and were then separated into optimal and sub-optimal groups due to the median of Pittsburgh Sleep Quality Index reduction rate. Machine learning models were developed based on baseline EEG coherence to predict rTMS treatment effects.

RESULTS

In Study 1, decreased EEG coherence in theta and alpha bands were observed after rTMS treatment, and changes in theta band (F7-O1) coherence were correlated with changes in sleep efficiency. In Study 2, baseline EEG coherence in theta, alpha, and beta bands showed the potential to predict the treatment effects of rTMS for ID.

CONCLUSION

rTMS improved sleep quality of ID patients by modulating the abnormal EEG coherence. Baseline EEG coherence between certain channels in theta, alpha, and beta bands could act as potential biomarkers to predict the therapeutic effects.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

Functional connectivity based machine learning approach for autism detection in young children using MEG signals

Objective.Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.Approach.We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique.Main results.To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50-100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1-4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity.Significance.Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.

The Feature of Sleep Spindle Deficits in Patients With Schizophrenia With and Without Auditory Verbal Hallucinations

BACKGROUND

Previous sleep electroencephalography studies have detected abnormalities in sleep architecture and sleep spindle deficits in schizophrenia (SCZ), but the consistency of these results was not robust, which might be due to the small sample size and the influence of clinical factors such as the various medication therapies and symptom heterogeneity. This study aimed to regard auditory verbal hallucinations (AVHs) as a pointcut to downscale the heterogeneity of SCZ and explore whether some sleep architecture and spindle parameters were more severely impaired in SCZ patients with AVHs compared with those without AVHs.

METHODS

A total of 90 SCZ patients with AVHs, 92 SCZ patients without AVHs, and 91 healthy control subjects were recruited, and parameters of sleep architecture and spindle activities were compared between groups. The correlation between significant sleep parameters and clinical indicators was analyzed.

RESULTS

Deficits of sleep spindle activities at prefrontal electrodes and intrahemispheric spindle coherence were observed in both AVH and non-AVH groups, several of which were more serious in the AVH group. In addition, deficits of spindle activities at central and occipital electrodes and interhemispheric spindle coherence mainly manifested accompanying AVH symptoms, most of which were retained in the medication-naive first-episode patients, and were associated with Auditory Hallucination Rating Scale scores.

CONCLUSIONS

Our results suggest that the underlying mechanism of spindle deficits might be different between SCZ patients with and without AVHs. In the future, the sleep feature of SCZ patients with different symptoms and the influence of clinical factors, such as medication therapy, should be further illustrated.

Systematic Review of EEG Coherence in Alzheimer's Disease

BACKGROUND

Magnitude-squared coherence (MSCOH) is an electroencephalography (EEG) measure of functional connectivity. MSCOH has been widely applied to investigate pathological changes in patients with Alzheimer's disease (AD). However, significant heterogeneity exists between the studies using MSOCH.

OBJECTIVE

We systematically reviewed the literature on MSCOH changes in AD as compared to healthy controls to investigate the clinical utility of MSCOH as a marker of AD.

METHODS

We searched PubMed, Embase, and Scopus to identify studies reporting EEG MSCOH used in patients with AD. The identified studies were independently screened by two researchers and the data was extracted, which included cognitive scores, preprocessing steps, and changes in MSCOH across frequency bands.

RESULTS

A total of 35 studies investigating changes in MSCOH in patients with AD were included in the review. Alpha coherence was significantly decreased in patients with AD in 24 out of 34 studies. Differences in other frequency bands were less consistent. Some studies showed that MSCOH may serve as a diagnostic marker of AD.

CONCLUSION

Reduced alpha MSCOH is present in patients with AD and MSCOH may serve as a diagnostic marker. However, studies validating MSCOH as a diagnostic marker are needed.

The sleeping brain's connectivity and family environment: characterizing sleep EEG coherence in an infant cohort

Brain connectivity closely reflects brain function and behavior. Sleep EEG coherence, a measure of brain's connectivity during sleep, undergoes pronounced changes across development under the influence of environmental factors. Yet, the determinants of the developing brain's sleep EEG coherence from the child's family environment remain unknown. After characterizing high-density sleep EEG coherence in 31 healthy 6-month-old infants by detecting strongly synchronized clusters through a data-driven approach, we examined the association of sleep EEG coherence from these clusters with factors from the infant's family environment. Clusters with greatest coherence were observed over the frontal lobe. Higher delta coherence over the left frontal cortex was found in infants sleeping in their parents' room, while infants sleeping in a room shared with their sibling(s) showed greater delta coherence over the central parts of the frontal cortex, suggesting a link between local brain connectivity and co-sleeping. Finally, lower occipital delta coherence was associated with maternal anxiety regarding their infant's sleep. These interesting links between sleep EEG coherence and family factors have the potential to serve in early health interventions as a new set of targets from the child's immediate environment.

The role of visual association cortices during response selection processes in interference-modulated response stopping

Response inhibition and the ability to navigate distracting information are both integral parts of cognitive control and are imperative to adaptive behavior in everyday life. Thus far, research has only inconclusively been able to draw inferences regarding the association between response stopping and the effects of interfering information. Using a novel combination of the Simon task and a stop signal task, the current study set out to investigate the behavioral as well as the neurophysiological underpinnings of the relationship between response stopping and interference processing. We tested n = 27 healthy individuals and combined temporal EEG signal decomposition with source localization methods to delineate the precise neurophysiological dynamics and functional neuroanatomical structures associated with conflict effects on response stopping. The results showed that stopping performance was compromised by conflicts. Importantly, these behavioral effects were reflected by specific aspects of information coded in the neurophysiological signal, indicating that conflict effects during response stopping are not mediated via purely perceptual processes. Rather, it is the processing of specific, stop-relevant stimulus features in the sensory regions during response selection, which underlies the emergence of conflict effects in response stopping. The findings connect research regarding response stopping with overarching theoretical frameworks of perception-action integration.

Early EEG monitoring predicts clinical outcome in patients with moderate to severe traumatic brain injury

There is a need for reliable predictors in patients with moderate to severe traumatic brain injury to assist clinical decision making. We assess the ability of early continuous EEG monitoring at the intensive care unit (ICU) in patients with traumatic brain injury (TBI) to predict long term clinical outcome and evaluate its complementary value to current clinical standards. We performed continuous EEG measurements in patients with moderate to severe TBI during the first week of ICU admission. We assessed the Extended Glasgow Outcome Scale (GOSE) at 12 months, dichotomized into poor (GOSE 1-3) and good (GOSE 4-8) outcome. We extracted EEG spectral features, brain symmetry index, coherence, aperiodic exponent of the power spectrum, long range temporal correlations, and broken detailed balance. A random forest classifier using feature selection was trained to predict poor clinical outcome based on EEG features at 12, 24, 48, 72 and 96 h after trauma. We compared our predictor with the IMPACT score, the best available predictor, based on clinical, radiological and laboratory findings. In addition we created a combined model using EEG as well as the clinical, radiological and laboratory findings. We included hundred-seven patients. The best prediction model using EEG parameters was found at 72 h after trauma with an AUC of 0.82 (0.69-0.92), specificity of 0.83 (0.67-0.99) and sensitivity of 0.74 (0.63-0.93). The IMPACT score predicted poor outcome with an AUC of 0.81 (0.62-0.93), sensitivity of 0.86 (0.74-0.96) and specificity of 0.70 (0.43-0.83). A model using EEG and clinical, radiological and laboratory parameters resulted in a better prediction of poor outcome (p < 0.001) with an AUC of 0.89 (0.72-0.99), sensitivity of 0.83 (0.62-0.93) and specificity of 0.85 (0.75-1.00). EEG features have potential use for predicting clinical outcome and decision making in patients with moderate to severe TBI and provide complementary information to current clinical standards.

Sleep and cognition in aging dogs. A polysomnographic study

Introduction

Sleep is fundamental for cognitive homeostasis, especially in senior populations since clearance of amyloid beta (key in the pathophysiology of Alzheimer's disease) occurs during sleep. Some electroencephalographic characteristics of sleep and wakefulness have been considered a hallmark of dementia. Owners of dogs with canine cognitive dysfunction syndrome (a canine analog to Alzheimer's disease) report that their dogs suffer from difficulty sleeping. The aim of this study was to quantify age-related changes in the sleep-wakefulness cycle macrostructure and electroencephalographic features in senior dogs and to correlate them with their cognitive performance.

Methods

We performed polysomnographic recordings in 28 senior dogs during a 2 h afternoon nap. Percentage of time spent in wakefulness, drowsiness, NREM, and REM sleep, as well as latency to the three sleep states were calculated. Spectral power, coherence, and Lempel Ziv Complexity of the brain oscillations were estimated. Finally, cognitive performance was evaluated by means of the Canine Dementia Scale Questionnaire and a battery of cognitive tests. Correlations between age, cognitive performance and sleep-wakefulness cycle macrostructure and electroencephalographic features were calculated.

Results

Dogs with higher dementia scores and with worse performance in a problem-solving task spent less time in NREM and REM sleep. Additionally, quantitative electroencephalographic analyses showed differences in dogs associated with age or cognitive performance, some of them reflecting shallower sleep in more affected dogs.

Discussion

Polysomnographic recordings in dogs can detect sleep-wakefulness cycle changes associated with dementia. Further studies should evaluate polysomnography's potential clinical use to monitor the progression of canine cognitive dysfunction syndrome.

Effect of Electrical Muscle Stimulation and Resistance Exercise Intervention on Physical and Brain Function in Middle-Aged and Older Women

This study investigated the effectiveness of electrical muscle stimulation (EMS) with resistance exercise training (ERT) and resistance exercise training (RT) on physical and brain function in middle-aged and older women. Method: Forty-eight participants were randomly allocated into three groups: (i) ERT (n = 16), (ii) RT (n = 16), and (iii) control group (n = 16). The intervention session was 50 min long and performed three times/week for four weeks. The ERT group performed quadriceps setting, straight leg raises, and ankle pump exercises while constantly receiving EMS on their quadriceps muscle on both legs. The RT group performed the same exercise without EMS. Physical function was measured using skeletal muscle mass index (SMI), handgrip strength, gait speed, five times sit-to-stand test (FTSS) and timed up-and-go test (TUG). Brain function was assessed with electroencephalogram measurement of whole brain activity. Results: After four-week intervention, significant improvements were observed in SMI (p < 0.01), phase angle (p < 0.05), and gait speed (p < 0.05) in the ERT group compared to the control group. ERT also increased muscle strength (p < 0.05) and mobility in lower limbs as observed in FTSS and TUG tests (p < 0.05) at post-intervention compared to the baseline. In the ERT group, significant positive changes were observed in Beta1 band power, Theta band power, and Alpha1 band whole brain connectivity (p < 0.005) compared to the control group. Conclusions: Our findings showed that ERT can improve muscle and brain function in middle-aged and older adults during a four-week intervention program whereas significant improvements were not observed with RT. Therefore might be one of the feasible alternative intervention to RT for the prevention of muscle loss whilst improving brain function for middle-aged and older population.

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

Introduction

The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking.

Methods

Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review.

Results

Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour.

Conclusions and implications

FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.