Localization of the sources of EEG delta, theta, alpha and beta frequency bands using the FFT dipole approximation

Altered Cortical Information Interaction During Respiratory Events in Children with Obstructive Sleep Apnea-Hypopnea Syndrome

Obstructive sleep apnea-hypopnea syndrome (OSAHS) significantly impairs children's growth and cognition. This study aims to elucidate the pathophysiological mechanisms underlying OSAHS in children, with a particular focus on the alterations in cortical information interaction during respiratory events. We analyzed sleep electroencephalography before, during, and after events, utilizing Symbolic Transfer Entropy (STE) for brain network construction and information flow assessment. The results showed a significant increase in STE after events in specific frequency bands during N2 and rapid eye movement (REM) stages, along with increased STE during N3 stage events. Moreover, a noteworthy rise in the information flow imbalance within and between hemispheres was found after events, displaying unique patterns in central sleep apnea and hypopnea. Importantly, some of these alterations were correlated with symptom severity. These findings highlight significant changes in brain region coordination and communication during respiratory events, offering novel insights into OSAHS pathophysiology in children.

Antinociceptive effects of Raphanus sativus sprouts involve the opioid and 5-HT1A serotonin receptors, cAMP/cGMP pathways, and the central activity of sulforaphane

Raphanus sativus L. cv. Sango, commonly known as red radish, is widely consumed around the world as a vegetable, but its benefit in pain relief is not sufficiently investigated. This study aimed to evaluate the antinociceptive effects of R. sativus and a possible mechanism of action. An aqueous extract of R. sativus sprouts (AERSS) was investigated by parenteral (10, 30, and 100 mg kg-1, i.p.) and enteral (500 mg kg-1, p.o.) administration in the neurogenic and inflammatory phases of the formalin test, where gastric damage was also evaluated as a possible adverse effect. Ketorolac (5 mg kg-1, i.p.) was used as the reference drug. Endogenous opioid and 5-HT1A serotonin receptors, as well as the cAMP/NO-cGMP pathways, were explored in the study of a possible mechanism of action by using their corresponding antagonists: naloxone, 1 mg kg-1, i.p., WAY100635, 1 mg kg-1, i.p., and enzymatic activators or inhibitors, respectively. Sulforaphane (SFN), a known bioactive metabolite, was analyzed using electroencephalography (EEG) to evidence its central involvement. A significant and dose-dependent antinociceptive activity was observed with the AERSS resembling the antinociceptive effect of the reference drug, with an equivalent significant response with a dose of 500 mg kg-1, p.o. without causing gastric damage. The participation of the endogenous opioid and 5-HT1A serotonin receptors at central and peripheral levels was also observed, with a differential participation of cAMP/NO-cGMP. SFN as one metabolite produced significant changes in the EEG analysis, reinforcing its effects on the CNS. Our preclinical evidence supports the benefits of consuming Raphanus sativus cv. Sango sprouts for pain relief.

Obsessive-compulsive disorder (OCD) is associated with increased electroencephalographic (EEG) delta and theta oscillatory power but reduced delta connectivity

Obsessive-Compulsive Disorder (OCD) is a mental health condition causing significant decline in the quality of life of sufferers and the limited knowledge on the pathophysiology hinders successful treatment. The aim of the current study was to examine electroencephalographic (EEG) findings of OCD to broaden our understanding of the disease. Resting-state eyes-closed EEG data was recorded from 25 individuals with OCD and 27 healthy controls (HC). The 1/f arrhythmic activity was removed prior to computing oscillatory powers of all frequency bands (delta, theta, alpha, beta, gamma). Cluster-based permutation was used for between-group statistical analyses, and comparisons were performed for the 1/f slope and intercept parameters. Functional connectivity (FC) was measured using coherence and debiased weighted phase lag index (d-wPLI), and statistically analyzed using the Network Based Statistic method. Compared to HC, the OCD group showed increased oscillatory power in the delta and theta bands in the fronto-temporal and parietal brain regions. However, there were no significant between-group findings in other bands or 1/f parameters. The coherence measure showed significantly reduced FC in the delta band in OCD compared to HC but the d-wPLI analysis showed no significant differences. OCD is associated with raised oscillatory power in slow frequency bands in the fronto-temporal brain regions, which agrees with the previous literature and therefore is a potential biomarker. Although delta coherence was found to be lower in OCD, due to inconsistencies found between measures and the previous literature, further research is required to ascertain definitive conclusions.

Neuromodulation of Neural Oscillations in Health and Disease

Using EEG and local field potentials (LFPs) as an index of large-scale neural activities, research has been able to associate neural oscillations in different frequency bands with markers of cognitive functions, goal-directed behavior, and various neurological disorders. While this gives us a glimpse into how neurons communicate throughout the brain, the causality of these synchronized network activities remains poorly understood. Moreover, the effect of the major neuromodulatory systems (e.g., noradrenergic, cholinergic, and dopaminergic) on brain oscillations has drawn much attention. More recent studies have suggested that cross-frequency coupling (CFC) is heavily responsible for mediating network-wide communication across subcortical and cortical brain structures, implicating the importance of neurotransmitters in shaping coordinated actions. By bringing to light the role each neuromodulatory system plays in regulating brain-wide neural oscillations, we hope to paint a clearer picture of the pivotal role neural oscillations play in a variety of cognitive functions and neurological disorders, and how neuromodulation techniques can be optimized as a means of controlling neural network dynamics. The aim of this review is to showcase the important role that neuromodulatory systems play in large-scale neural network dynamics, informing future studies to pay close attention to their involvement in specific features of neural oscillations and associated behaviors.

EEG Resting-State Functional Networks in Amnestic Mild Cognitive Impairment

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

Detection of Different Hosts From a Distance Alters the Behaviour and Bioelectrical Activity of Cuscuta racemosa

In our study, we investigated some physiological and ecological aspects of the life of Cuscuta racemosa Mart. (Convolvulaceae) plants with the hypothesis that they recognise different hosts at a distance from them, and they change their survival strategy depending on what they detect. We also hypothesised that, as an attempt of prolonging their survival through photosynthesis, the synthesis of chlorophylls (a phenomenon not completely explained in these parasitic plants) would be increased if the plants don't detect a host. We quantified the pigments related to photosynthesis in different treatments and employed techniques such as electrophysiological time series recording, analyses of the complexity of the obtained signals, and machine learning classification to test our hypotheses. The results demonstrate that the absence of a host increases the amounts of chlorophyll a, chlorophyll b, and β-carotene in these plants, and the content varied depending on the host presented. Besides, the electrical signalling of dodders changes according to the species of host perceived in patterns detectable by machine learning techniques, suggesting that they recognise from a distance different host species. Our results indicate that electrical signalling might underpin important processes such as foraging in plants. Finally, we found evidence for a likely process of attention in the dodders toward the host plants. This is probably to be the first empirical evidence for attention in plants and has important implications on plant cognition studies.

Neural processing of iterated prisoner's dilemma outcomes indicates next-round choice and speed to reciprocate cooperation

The iterated prisoner's dilemma (iPD) game is a well-established model for testing how people cooperate, and the neural processes that unfold after its distinct outcomes have been partly described. Recent theoretical models suggest evolution favors intuitive cooperation, which raises questions on the behavioral but also neural timelines involved. We studied the outcome/feedback stage of iPD rounds with electroencephalography (EEG) methods. Results showed that neural signals associated with this stage also relate to future choice, in an outcome-dependent manner: (i) after zero-gain "sucker's payoffs" (unreciprocated cooperation), a participant's decision thereafter relates to changes to the feedback-related negativity (FRN); (ii) after one-sided non-cooperation (participant wins at co-player's expense), by the P3; (iii) after mutual cooperation, by late frontal delta-band modulations. Critically, faster reciprocation behavior towards a co-player's choice to cooperate was predicted, on a single-trial basis, by players' P3 and frontal delta modulations at the immediately preceding trial. Delta-band signaling is discussed in relation to homeostatic regulation processing in the literature. The findings relate the early outcome/feedback stage to subsequent decisional processes in the iPD, providing a first neural account of the brief timelines implied in heuristic modes of cooperation.

Spectral power of interictal EEG in the diagnosis and prognosis of idiopathic generalized epilepsies

INTRODUCTION

Idiopathic generalized epilepsies (IGE) are characterized by generalized interictal epileptiform discharges (IEDs) on a normal background electroencephalography (EEG). However, the yield of IEDs can be low. Approximately 20% of patients with IGE fail to achieve seizure control with antiepileptic drug (AED) treatment. Currently, there are no reliable prognostic markers for early identification of drug-resistant epilepsy (DRE). We examined spectral power of the interictal EEG in patients with IGE and healthy controls, to identify potential diagnostic and prognostic biomarkers of IGE.

METHODS

A 64-channel EEG was recorded under standard conditions in patients with well-controlled IGE (WC-IGE, n = 19), drug-resistant IGE (DR-IGE, n = 18), and age-matched controls (n = 20). After preprocessing, fast Fourier transform was performed to obtain 1D frequency spectra for each EEG channel. The 1D spectra (averaged over channels) and 2D topographic maps (averaged over canonical frequency bands) were computed for each participant. Power spectra in the 3 cohorts were compared using one-way analysis of variance (ANOVA), and power spectra images were compared using T-contrast tests. A post hoc analysis compared peak alpha power between the groups.

RESULTS

Compared with controls, participants with IGE had higher interictal EEG spectral power in the delta band in the midline central region, in the theta band in the midline, in the beta band over the left hemisphere, and in the gamma band over right hemisphere and left central regions. There were no differences in spectral power between cohorts with WC-IGE and DR-IGE. Peak alpha power was lower in WC-IGE and DR-IGE than controls.

CONCLUSIONS

Electroencephalography spectral power analysis could form part of a clinically useful diagnostic biomarker for IGE; however, it did not correlate with response to AED in this study.

Neural Oscillations: Understanding a Neural Code of Pain

Neural oscillations play an important role in the integration and segregation of brain regions that are important for brain functions, including pain. Disturbances in oscillatory activity are associated with several disease states, including chronic pain. Studies of neural oscillations related to pain have identified several functional bands, especially alpha, beta, and gamma bands, implicated in nociceptive processing. In this review, we introduce several properties of neural oscillations that are important to understand the role of brain oscillations in nociceptive processing. We also discuss the role of neural oscillations in the maintenance of efficient communication in the brain. Finally, we discuss the role of neural oscillations in healthy and chronic pain nociceptive processing. These data and concepts illustrate the key role of regional and interregional neural oscillations in nociceptive processing underlying acute and chronic pains.

Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials

We propose an evolutionary state space model (E-SSM) for analyzing high dimensional brain signals whose statistical properties evolve over the course of a non-spatial memory experiment. Under E-SSM, brain signals are modeled as mixtures of components (e.g., AR(2) process) with oscillatory activity at pre-defined frequency bands. To account for the potential non-stationarity of these components (since the brain responses could vary throughout the entire experiment), the parameters are allowed to vary over epochs. Compared with classical approaches such as independent component analysis and filtering, the proposed method accounts for the entire temporal correlation of the components and accommodates non-stationarity. For inference purpose, we propose a novel computational algorithm based upon using Kalman smoother, maximum likelihood and blocked resampling. The E-SSM model is applied to simulation studies and an application to a multi-epoch local field potentials (LFP) signal data collected from a non-spatial (olfactory) sequence memory task study. The results confirm that our method captures the evolution of the power for different components across different phases in the experiment and identifies clusters of electrodes that behave similarly with respect to the decomposition of different sources. These findings suggest that the activity of different electrodes does change over the course of an experiment in practice; treating these epoch recordings as realizations of an identical process could lead to misleading results. In summary, the proposed method underscores the importance of capturing the evolution in brain responses over the study period.

An Investigation of Early Detection of Driver Drowsiness Using Ensemble Machine Learning Based on Hybrid Sensing

Drowsy driving is one of the main causes of traffic accidents. To reduce such accidents, early detection of drowsy driving is needed. In previous studies, it was shown that driver drowsiness affected driving performance, behavioral indices, and physiological indices. The purpose of this study is to investigate the feasibility of classification of the alert states of drivers, particularly the slightly drowsy state, based on hybrid sensing of vehicle-based, behavioral, and physiological indicators with consideration for the implementation of these identifications into a detection system. First, we measured the drowsiness level, driving performance, physiological signals (from electroencephalogram and electrocardiogram results), and behavioral indices of a driver using a driving simulator and driver monitoring system. Next, driver alert and drowsy states were identified by machine learning algorithms, and a dataset was constructed from the extracted indices over a period of 10 s. Finally, ensemble algorithms were used for classification. The results showed that the ensemble algorithm can obtain 82.4% classification accuracy using hybrid methods to identify the alert and slightly drowsy states, and 95.4% accuracy classifying the alert and moderately drowsy states. Additionally, the results show that the random forest algorithm can obtain 78.7% accuracy when classifying the alert vs. slightly drowsy states if physiological indicators are excluded and can obtain 89.8% accuracy when classifying the alert vs. moderately drowsy states. These results represent the feasibility of highly accurate early detection of driver drowsiness and the feasibility of implementing a driver drowsiness detection system based on hybrid sensing using non-contact sensors.

Electrophysiology of obsessive compulsive disorder: A systematic review of the electroencephalographic literature

Obsessive-compulsive disorder (OCD) is a chronic disease that causes significant decline in the quality of life of those affected. Due to our limited understanding of the underlying pathophysiology of OCD, successful treatment remains elusive. Although many have studied the pathophysiology of OCD through electroencephalography (EEG), limited attempts have been made to synthesize and interpret their findings. To bridge this gap, we conducted a comprehensive literature review using Medline/PubMed and considered the 65 most relevant studies published before June 2018. The findings are categorised into quantitative EEG, sleep related EEG and event related potentials (ERPs). Increased frontal asymmetry, frontal slowing and an enhancement in the ERP known as error related negativity (ERN) were consistent findings in OCD. However, sleep EEG and other ERP (P3 and N2) findings were inconsistent. Additionally, we analysed the usefulness of ERN as a potential candidate endophenotype. We hypothesize that dysfunctional frontal circuitry and overactive performance monitoring are the major underlying impairments in OCD. Additionally, we conceptualized that defective fronto-striato-thalamic circuitry causing poor cerebral functional connectivity gives rise to the OCD behavioural manifestations. Finally, we have discussed transcranial magnetic stimulation and EEG (TMS-EEG) applications in future research to further our knowledge of the underlying pathophysiology of OCD.

Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging

The individual alpha peak frequency (IAPF) of the human electroencephalography (EEG) typically experiences slowing with increasing age. Despite this hallmark change, studies that investigate modulations of conventional EEG alpha power and connectivity by aging and age-related neuropathology neglect to account for intergroup differences in IAPF. To investigate the relationship of age-related IAPF slowing with EEG power and connectivity, we recorded eyes-closed resting-state EEG in 37 young adults and 32 older adults. We replicated the finding of a slowed IAPF in older adults. IAPF values were significantly correlated with the frequency of maximum global connectivity and the means of their distributions did not differ, suggesting that connectivity was highest at the IAPF. Older adults expressed reduced global EEG power and connectivity at the conventional upper alpha band (10-12 Hz) compared with young adults. By contrast, groups had equivalent power and connectivity at the IAPF. The results suggest that conventional spectral boundaries may be biased against older adults or any group with a slowed IAPF. We conclude that investigations of alpha activity in aging and age-related neuropathology should be adapted to the IAPF of the individual and that previous findings should be interpreted with caution. EEG in the dominant alpha range may be unsuitable for examining cortico-cortical connectivity due to its subcortical origins.

Pleasure: The missing link in the regulation of sleep

Although largely unrecognized by sleep scholars, sleeping is a pleasure. This report aims first, to fill the gap: sleep, like food, water and sex, is a primary reinforcer. The levels of extracellular mesolimbic dopamine show circadian oscillations and mark the "wanting" for pro-homeostatic stimuli. Further, the dopamine levels decrease during waking and are replenished during sleep, in opposition to sleep propensity. The wanting of sleep, therefore, may explain the homeostatic and circadian regulation of sleep. Accordingly, sleep onset occurs when the displeasure of excessive waking is maximal, coinciding with the minimal levels of mesolimbic dopamine. Reciprocally, sleep ends after having replenished the limbic dopamine levels. Given the direct relation between waking and mesolimbic dopamine, sleep must serve primarily to gain an efficient waking. Pleasant sleep (i.e. emotional sleep), can only exist in animals capable of feeling emotions. Therefore, although sleep-like states have been described in invertebrates and primitive vertebrates, the association sleep-pleasure clearly marks a difference between the sleep of homeothermic vertebrates and cool blooded animals.

Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study Using a Brain-Computer Interface

For the sake of establishing the neural correlates of phrase quadrature perception in harmonic rhythm, a musical experiment has been designed to induce music-evoked stimuli related to one important aspect of harmonic rhythm, namely the phrase quadrature. Brain activity is translated to action through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. The results of processing the acquired signals are in line with previous studies that use different musical parameters to induce emotions. Indeed, our experiment shows statistical differences in theta and alpha bands between the fulfillment and break of phrase quadrature, an important cue of harmonic rhythm, in two classical sonatas.

Distinct and flexible rates of online control

Elliott et al. (Hum Mov Sci 10:393-418, 1991) proposed a pseudocontinuous model of online control whereby overlapping corrections lead to the appearance of smooth kinematic profiles in the presence of online feedback. More recently, it was also proposed that online control is not a singular process [see Elliott et al. (Psychol Bull 136(6):1023-1044, 2010)]. However, support for contemporary models of online control were based on methodologies that were not designed to be sensitive to different online control sub-processes. The current study sought to evaluate the possibility of multiple distinct (i.e., visual and non-visual) mechanisms contributing to the control of reaching movements completed in either a full-vision, a no-vision, or a no-vision memory-guided condition. Frequency domain analysis was applied to the acceleration traces of reaching movements. In an attempt to elicit a modulation in the online control mechanisms, these movements were completed at two levels of spatio-temporal constraint, namely with 10 and 30 cm target distances. One finding was that performance in the full-vision relative to both no-vision conditions could be distinguished via two distinct frequency peaks. Increases in the peak magnitude at the lower frequencies were associated with visuomotor mechanisms and increases in the peak magnitude at the higher frequencies were associated with non-visual mechanisms. In addition, performance to the 30-cm target led to a lower peak at a lower frequency relative to the 10 cm target, indicating that the iterative rates of visuomotor control mechanisms are flexible and sensitive to the spatio-temporal constraints of the associated movement.

Connectome pattern alterations with increment of mental fatigue in one-hour driving simulation

The importance of understanding mental fatigue can be seen from many studies that started back in past decades. It is only until recent years has mental fatigue been explored through connectivity network analysis using graph theory. Although previous studies have revealed certain properties of the mental fatigue network via graph theory, some of these findings seemingly conflict with one another. The differences in findings could be due to mental fatigue being caused by various factors or being analyzed using different methods. So, in this study, to further understand the functional connectivity of driving fatigue, a weighted and undirected connectivity matrix would be constructed before applying graph theory to identify the biomarker from the network property. To obtain data for analysis, a 64-channel EEG cap was used to record the brain signals of subjects undergoing a one-hour driving simulation. Using the recorded EEG signal, a connectivity matrix was constructed using a synchronous method known as phase lag index (PLI) for the graph theory analysis. Results from this graph theory analysis showed that the synchronous network had increased clustering coefficient and decreased path length with the accumulation of mental fatigue. Furthermore, by calculating clustering coefficient regionally, its results revealed that the significant increase occurred mainly in the parietal and occipital regions of the brain.

Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form

This paper introduces the neural correlates of phrase rhythm. In short, phrase rhythm is the rhythmic aspect of phrase construction and the relationships between phrases. For the sake of establishing the neural correlates, a musical experiment has been designed to induce music-evoked stimuli related to phrase rhythm. Brain activity is monitored through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. Our experiment shows statistical differences in theta and alpha bands in the phrase rhythm variations of two classical sonatas, one in bipartite form and the other in rondo form.

Comparison of connectivity analyses for resting state EEG data

OBJECTIVE

In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated.

APPROACH

The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory.

MAIN RESULTS

The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization.

SIGNIFICANCE

Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Spontaneous dynamics of neural networks in deep layers of prefrontal cortex

Cortical systems maintain and process information through the sustained activation of recurrent local networks of neurons. Layer 5 is known to have a major role in generating the recurrent activation associated with these functions, but relatively little is known about its intrinsic dynamics at the mesoscopic level of large numbers of neighboring neurons. Using calcium imaging, we measured the spontaneous activity of networks of deep-layer medial prefrontal cortical neurons in an acute slice model. Inferring the simultaneous activity of tens of neighboring neurons, we found that while the majority showed only sporadic activity, a subset of neurons engaged in sustained delta frequency rhythmic activity. Spontaneous activity under baseline conditions was weakly correlated between pairs of neurons, and rhythmic neurons showed little coherence in their oscillations. However, we consistently observed brief bouts of highly synchronous activity that must be attributed to network activity. NMDA-mediated stimulation enhanced rhythmicity, synchrony, and correlation within these local networks. These results characterize spontaneous prefrontal activity at a previously unexplored spatiotemporal scale and suggest that medial prefrontal cortex can act as an intrinsic generator of delta oscillations.NEW & NOTEWORTHY Using calcium imaging and a novel analytic framework, we characterized the spontaneous and NMDA-evoked activity of layer 5 prefrontal cortex at a largely unexplored spatiotemporal scale. Our results suggest that the mPFC microcircuitry is capable of intrinsically generating delta oscillations and sustaining synchronized network activity that is potentially relevant for understanding its contribution to cognitive processes.

The predictive roles of neural oscillations in speech motor adaptability

The human speech system exhibits a remarkable flexibility by adapting to alterations in speaking environments. While it is believed that speech motor adaptation under altered sensory feedback involves rapid reorganization of speech motor networks, the mechanisms by which different brain regions communicate and coordinate their activity to mediate adaptation remain unknown, and explanations of outcome differences in adaption remain largely elusive. In this study, under the paradigm of altered auditory feedback with continuous EEG recordings, the differential roles of oscillatory neural processes in motor speech adaptability were investigated. The predictive capacities of different EEG frequency bands were assessed, and it was found that theta-, beta-, and gamma-band activities during speech planning and production contained significant and reliable information about motor speech adaptability. It was further observed that these bands do not work independently but interact with each other suggesting an underlying brain network operating across hierarchically organized frequency bands to support motor speech adaptation. These results provide novel insights into both learning and disorders of speech using time frequency analysis of neural oscillations.

Your Brain on Art: Emergent Cortical Dynamics During Aesthetic Experiences

The brain response to conceptual art was studied with mobile electroencephalography (EEG) to examine the neural basis of aesthetic experiences. In contrast to most studies of perceptual phenomena, participants were moving and thinking freely as they viewed the exhibit The Boundary of Life is Quietly Crossed by Dario Robleto at the Menil Collection-Houston. The brain activity of over 400 subjects was recorded using dry-electrode and one reference gel-based EEG systems over a period of 3 months. Here, we report initial findings based on the reference system. EEG segments corresponding to each art piece were grouped into one of three classes (complex, moderate, and baseline) based on analysis of a digital image of each piece. Time, frequency, and wavelet features extracted from EEG were used to classify patterns associated with viewing art, and ranked based on their relevance for classification. The maximum classification accuracy was 55% (chance = 33%) with delta and gamma features the most relevant for classification. Functional analysis revealed a significant increase in connection strength in localized brain networks while subjects viewed the most aesthetically pleasing art compared to viewing a blank wall. The direction of signal flow showed early recruitment of broad posterior areas followed by focal anterior activation. Significant differences in the strength of connections were also observed across age and gender. This work provides evidence that EEG, deployed on freely behaving subjects, can detect selective signal flow in neural networks, identify significant differences between subject groups, and report with greater-than-chance accuracy the complexity of a subject's visual percept of aesthetically pleasing art. Our approach, which allows acquisition of neural activity “in action and context,” could lead to understanding of how the brain integrates sensory input and its ongoing internal state to produce the phenomenon which we term aesthetic experience.

Decoding human swallowing via electroencephalography: a state-of-the-art review

Swallowing and swallowing disorders have garnered continuing interest over the past several decades. Electroencephalography (EEG) is an inexpensive and non-invasive procedure with very high temporal resolution which enables analysis of short and fast swallowing events, as well as an analysis of the organizational and behavioral aspects of cortical motor preparation, swallowing execution and swallowing regulation. EEG is a powerful technique which can be used alone or in combination with other techniques for monitoring swallowing, detection of swallowing motor imagery for diagnostic or biofeedback purposes, or to modulate and measure the effects of swallowing rehabilitation. This paper provides a review of the existing literature which has deployed EEG in the investigation of oropharyngeal swallowing, smell, taste and texture related to swallowing, cortical pre-motor activation in swallowing, and swallowing motor imagery detection. Furthermore, this paper provides a brief review of the different modalities of brain imaging techniques used to study swallowing brain activities, as well as the EEG components of interest for studies on swallowing and on swallowing motor imagery. Lastly, this paper provides directions for future swallowing investigations using EEG.

Joint Coupling of Awake EEG Frequency Activity and MRI Gray Matter Volumes in the Psychosis Dimension: A BSNIP Study

BACKGROUND

Many studies have examined either electroencephalogram (EEG) frequency activity or gray matter volumes (GMV) in various psychoses [including schizophrenia (SZ), schizoaffective (SZA), and psychotic bipolar disorder (PBP)]. Prior work demonstrated similar EEG and gray matter abnormalities in both SZ and PBP. Integrating EEG and GMV and jointly analyzing the combined data fully elucidates the linkage between the two and may provide better biomarker- or endophenotype-specificity for a particular illness. Joint exploratory investigations of EEG and GMV are scarce in the literature and the relationship between the two in psychosis is even less explored. We investigated a joint multivariate model to test whether the linear relationship or linkage between awake EEG (AEEG) frequency activity and GMV is abnormal across the psychosis dimension and if such effects are also present in first-degree relatives.

METHODS

We assessed 607 subjects comprising 264 probands [105 SZ, 72 SZA, and 87 PBP], 233 of their first degree relatives [82 SZ relatives (SZR), 71 SZA relatives (SZAR), and 80 PBP relatives (PBPR)], and 110 healthy comparison subjects (HC). All subjects underwent structural MRI (sMRI) and EEG scans. Frequency activity and voxel-based morphometric GMV were derived from EEG and sMRI data, respectively. Seven AEEG frequency and gray matter components were extracted using Joint independent component analysis (jICA). The loading coefficients (LC) were examined for group differences using analysis of covariance. Further, the LCs were correlated with psychopathology scores to identify relationship with clinical symptoms.

RESULTS

Joint ICA revealed a single component differentiating SZ from HC (p < 0.006), comprising increased posterior alpha activity associated with decreased volume in inferior parietal lobe, supramarginal, parahippocampal gyrus, middle frontal, inferior temporal gyri, and increased volume of uncus and culmen. No components were aberrant in either PBP or SZA or any relative group. No significant association was identified with clinical symptom measures.

CONCLUSION

Our data suggest that a joint EEG and GMV model yielded a biomarker specific to SZ, not abnormal in PBP or SZA. Alpha activity was related to both increased and decreased volume in different cortical structures. Additionally, the joint model failed to identify endophenotypes across psychotic disorders.

A statistically robust EEG re-referencing procedure to mitigate reference effect

BACKGROUND

The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures that modify the recorded traces and seek to minimize the impact of reference electrode activity upon functions of the original EEG recordings.

NEW METHOD

We provide a novel, statistically robust procedure that adapts a robust maximum-likelihood type estimator to the problem of reference estimation, reduces the influence of neural activity from the re-referencing operation, and maintains good performance in a wide variety of empirical scenarios.

RESULTS

The performance of the proposed and existing re-referencing procedures are validated in simulation and with examples of EEG recordings. To facilitate this comparison, channel-to-channel correlations are investigated theoretically and in simulation.

COMPARISON WITH EXISTING METHODS

The proposed procedure avoids using data contaminated by neural signal and remains unbiased in recording scenarios where physical references, the common average reference (CAR) and the reference estimation standardization technique (REST) are not optimal.

CONCLUSION

The proposed procedure is simple, fast, and avoids the potential for substantial bias when analyzing low-density EEG data.

Early prediction of medication refractoriness in children with idiopathic epilepsy based on scalp EEG analysis

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

Theta band activity in response to emotional expressions and its relationship with gamma band activity as revealed by MEG and advanced beamformer source imaging

Neuronal oscillations in the theta and gamma bands have been shown to be important for cognition. Here we examined the temporal and spatial relationship between the two frequency bands in emotional processing using magnetoencephalography and an advanced dynamic beamformer source imaging method called synthetic aperture magnetometry. We found that areas including the amygdala, visual and frontal cortex showed significant event-related synchronization in both bands, suggesting a functional association of neuronal oscillations in the same areas in the two bands. However, while the temporal profile in both bands was similar in the amygdala, the peak in gamma band power was much earlier within both visual and frontal areas. Our results do not support a traditional view that the localizations of lower and higher frequencies are spatially distinct. Instead, they suggest that in emotional processing, neuronal oscillations in the gamma and theta bands may reflect, at least in visual and frontal cortex either different but related functional processes or, perhaps more probably, different computational components of the same functional process.

Decreased movement-related beta desynchronization and impaired post-movement beta rebound in amyotrophic lateral sclerosis

OBJECTIVE

This study explored event-related desynchronization (ERD) and synchronization (ERS) in amyotrophic lateral sclerosis (ALS) to quantify cortical sensorimotor processes during volitional movements. We furthermore compared ERD/ERS measures with clinical scores and movement-related cortical potential (MRCP) amplitudes.

METHODS

Electroencephalograms were recorded while 21 ALS patients and 19 controls performed two self-paced motor tasks: sniffing and right index finger flexion. Based on Wavelet analysis the alpha and beta frequency bands were selected for subsequent evaluation.

RESULTS

Patients generated significantly smaller resting alpha spectral power density (SPD) and smaller beta ERD compared to controls. Additionally patients exhibited merely unilateral post-movement ERS (beta rebound) whereas this phenomenon was bilateral in controls. ERD/ERS amplitudes did not correlate with corresponding MRCPs for either patients or controls.

CONCLUSIONS

The smaller resting alpha SPD and beta ERD and asymmetrical appearance of beta ERS in patients compared to controls could be the result of pyramidal cell degeneration and/or corpus callosum involvement in ALS.

SIGNIFICANCE

These results support the notion of reduced movement preparation in ALS involving also areas outside the motor cortex. Furthermore post-movement cortical inhibition seems to be impaired in ALS. ERD/ERS and MRCP are found to be independent measures of cortical motor functions in ALS.

Spectral changes in spontaneous MEG activity across the lifespan

OBJECTIVE

The aim of this study is to explore the spectral patterns of spontaneous magnetoencephalography (MEG) activity across the lifespan.

APPROACH

Relative power (RP) in six frequency bands (delta, theta, alpha, beta-1, beta-2 and gamma) was calculated in a sample of 220 healthy subjects with ages ranging from 7 to 84 years.

MAIN RESULTS

A significant RP decrease in low-frequency bands (i.e. delta and theta) and a significant increase in high bands (mainly beta-1 and beta-2) were found from childhood to adolescence. This trend was observed until the sixth decade of life, though only slight changes were found. Additionally, healthy aging was characterized by a power increase in low-frequency bands. Our results show that spectral changes across the lifespan may follow a quadratic relationship in delta, theta, alpha, beta-2 and gamma bands with peak ages being reached around the fifth or sixth decade of life.

SIGNIFICANCE

Our findings provide original insights into the definition of the 'normal' behavior of age-related MEG spectral patterns. Furthermore, our study can be useful for the forthcoming MEG research focused on the description of the abnormalities of different brain diseases in comparison to cognitive decline in normal aging.

Resting state EEG abnormalities in autism spectrum disorders

Autism spectrum disorders (ASD) are a group of complex and heterogeneous developmental disorders involving multiple neural system dysfunctions. In an effort to understand neurophysiological substrates, identify etiopathophysiologically distinct subgroups of patients, and track outcomes of novel treatments with translational biomarkers, EEG (electroencephalography) studies offer a promising research strategy in ASD. Resting-state EEG studies of ASD suggest a U-shaped profile of electrophysiological power alterations, with excessive power in low-frequency and high-frequency bands, abnormal functional connectivity, and enhanced power in the left hemisphere of the brain. In this review, we provide a summary of recent findings, discuss limitations in available research that may contribute to inconsistencies in the literature, and offer suggestions for future research in this area for advancing the understanding of ASD.

Coherent delta-band oscillations between cortical areas correlate with decision making

Coherent oscillations in the theta-to-gamma frequency range have been proposed as a mechanism that coordinates neural activity in large-scale cortical networks in sensory, motor, and cognitive tasks. Whether this mechanism also involves coherent oscillations at delta frequencies (1-4 Hz) is not known. Rather, delta oscillations have been associated with slow-wave sleep. Here, we show coherent oscillations in the delta frequency band between parietal and frontal cortices during the decision-making component of a somatosensory discrimination task. Importantly, the magnitude of this delta-band coherence is modulated by the different decision alternatives. Furthermore, during control conditions not requiring decision making, delta-band coherences are typically much reduced. Our work indicates an important role for synchronous activity in the delta frequency band when large-scale, distant cortical networks coordinate their neural activity during decision making.

Cross-frequency coupling in real and virtual brain networks

Information processing in the brain is thought to rely on the convergence and divergence of oscillatory behaviors of widely distributed brain areas. This information flow is captured in its simplest form via the concepts of synchronization and desynchronization and related metrics. More complex forms of information flow are transient synchronizations and multi-frequency behaviors with metrics related to cross-frequency coupling (CFC). It is supposed that CFC plays a crucial role in the organization of large-scale networks and functional integration across large distances. In this study, we describe different CFC measures and test their applicability in simulated and real electroencephalographic (EEG) data obtained during resting state. For these purposes, we derive generic oscillator equations from full brain network models. We systematically model and simulate the various scenarios of CFC under the influence of noise to obtain biologically realistic oscillator dynamics. We find that (i) specific CFC-measures detect correctly in most cases the nature of CFC under noise conditions, (ii) bispectrum (BIS) and bicoherence (BIC) correctly detect the CFCs in simulated data, (iii) empirical resting state EEG show a prominent delta-alpha CFC as identified by specific CFC measures and the more classic BIS and BIC. This coupling was mostly asymmetric (directed) and generally higher in the eyes closed (EC) than in the eyes open (EO) condition. In conjunction, these two sets of measures provide a powerful toolbox to reveal the nature of couplings from experimental data and as such allow inference on the brain state dependent information processing. Methodological advantages of using CFC measures and theoretical significance of delta and alpha interactions during resting and other brain states are discussed.

Multi-scale order recurrence quantification analysis of EEG signals evoked by manual acupuncture in healthy subjects

To explore the effects of manual acupuncture (MA) on brain activities, we design an experiment that acupuncture at acupoint ST36 of right leg with four different frequencies to obtain electroencephalograph (EEG) signals. Many studies have demonstrated that the complexity of EEG can reflect the states of brain function, so we propose to adopt order recurrence quantification analysis combined with discrete wavelet transform, to analyze the dynamical characteristics of different EEG rhythms under acupuncture, further to explore the effects of MA on the complexity of brain activities from multi-scale point of view. By analyzing the complexity of five EEG rhythms, it is found that the complexity of delta rhythm during acupuncture is lower than before acupuncture, and for alpha rhythm that is higher, but for beta, theta and gamma rhythms there are no obvious changes. All of those effects are especially obvious during acupuncture with frequency of 200 times/min. Furthermore, the determinism extracted from delta, alpha and gamma rhythms can be regarded as a characteristic parameter to distinguish the state acupuncture at 200 times/min and the state before acupuncture. These results can provide a theoretical support for selecting appropriate acupuncture frequency for patients in clinical, and the proposed methods have the potential of exploring the effects of acupuncture on brain activities.

Finding thalamic BOLD correlates to posterior alpha EEG

Oscillatory electrical brain activity in the alpha (8-13 Hz) band is a prominent feature of human electroencephalography (EEG) during alert wakefulness, and is commonly thought to arise primarily from the occipital and parietal parts of the cortex. While the thalamus is considered to play a supportive role in the generation and modulation of cortical alpha rhythms, its precise function remains controversial and incompletely understood. To address this, we evaluated the correlation between the blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) signals in the thalamus and the spontaneous modulation of posterior alpha rhythms based on EEG-fMRI data acquired concurrently during an eyes-closed task-free condition. We observed both negative and positive correlations in the thalamus. The negative correlations were mostly seen within the visual thalamus, with a preference for the pulvinar over lateral geniculate nuclei. The positive correlations were found at the anterior and medial dorsal nuclei. Through functional connectivity analysis of the fMRI data, the pulvinar was found to be functionally associated with the same widespread cortical visual areas where the fMRI signals were negatively correlated with the posterior alpha modulation. In contrast, the dorsal nuclei were part of a distinct functional network that included brain stem, cingulate cortex and cerebellum. These observations are consistent with previous animal electrophysiology studies and the notion that the visual thalamus, and the pulvinar in particular, is intimately involved in the generation and spontaneous modulation of posterior alpha rhythms, facilitated by its reciprocal and widespread interaction with the cortical visual areas. We further postulate that the anterior and medial dorsal nuclei, being part of the ascending neuromodulatory system, may indirectly modulate cortical alpha rhythms by affecting vigilance and arousal levels.

EEG delta oscillations as a correlate of basic homeostatic and motivational processes

Functional significance of delta oscillations is not fully understood. One way to approach this question would be from an evolutionary perspective. Delta oscillations dominate the EEG of waking reptiles. In humans, they are prominent only in early developmental stages and during slow-wave sleep. Increase of delta power has been documented in a wide array of developmental disorders and pathological conditions. Considerable evidence on the association between delta waves and autonomic and metabolic processes hints that they may be involved in integration of cerebral activity with homeostatic processes. Much evidence suggests the involvement of delta oscillations in motivation. They increase during hunger, sexual arousal, and in substance users. They also increase during panic attacks and sustained pain. In cognitive domain, they are implicated in attention, salience detection, and subliminal perception. This evidence shows that delta oscillations are associated with evolutionary old basic processes, which in waking adults are overshadowed by more advanced processes associated with higher frequency oscillations. The former processes rise in activity, however, when the latter are dysfunctional.

Effect of levetiracetam monotherapy on background EEG activity and cognition in drug-naïve epilepsy patients

OBJECTIVE

To investigate the cognitive effect of levetiracetam (LEV) monotherapy with quantitative electroencephalogram (EEG) analysis and neuropsychological (NP) tests.

METHODS

Twenty-two drug-naïve epilepsy patients were enrolled. EEG recordings were performed before and after LEV therapy. Relative power of discrete frequency bands was computed, as well as alpha peak frequency (APF) at occipital electrodes. Eighteen patients performed a battery of NP tests twice across LEV treatment.

RESULTS

LEV therapy decreased the power of delta (1-3 Hz, p<0.01) and theta (3-7 Hz, p<0.05) bands and increased that of alpha-2 (10-13 Hz, p<0.05) and beta-2 (19-24 Hz, p<0.05) bands. Region-specific spectral change was observed: delta power change was significant in fronto-polar region, theta in anterior region, alpha-2 in broad region, and beta-2 in left fronto-central region. APF change was not significant. Improvement in diverse NP tests requiring attention, working memory, language and executive function was observed. Change in theta, alpha-2, and beta-2 power was correlated with improvement in several NP tests.

CONCLUSIONS

Our data suggest LEV is associated with acceleration of background EEG frequencies and improved cognitive function. Change in frequency band power could predict improvement in several cognitive domains across LEV therapy.

SIGNIFICANCE

Combined study of quantitative EEG analysis and NP tests can be useful in identifying cognitive effect of antiepileptic drugs.

Driving and driven architectures of directed small-world human brain functional networks

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome.

Cross-frequency coupling of brain oscillations in studying motivation and emotion

Research has shown that brain functions are realized by simultaneous oscillations in various frequency bands. In addition to examining oscillations in pre-specified bands, interactions and relations between the different frequency bandwidths is another important aspect that needs to be considered in unraveling the workings of the human brain and its functions. In this review we provide evidence that studying interdependencies between brain oscillations may be a valuable approach to study the electrophysiological processes associated with motivation and emotional states. Studies will be presented showing that amplitude-amplitude coupling between delta-alpha and delta-beta oscillations varies as a function of state anxiety and approach-avoidance-related motivation, and that changes in the association between delta-beta oscillations can be observed following successful psychotherapy. Together these studies suggest that cross-frequency coupling of brain oscillations may contribute to expanding our understanding of the neural processes underlying motivation and emotion.

Antiepileptic drugs modify power of high EEG frequencies and their neural generators

BACKGROUND

The clinical and molecular effects of antiepileptic drugs (AEDs) have been extensively investigated. Much less is known about their effects on human electrophysiology.

METHODS

Topographic analysis in the frequency domain has been used to analyze 104 electroencephalogram (EEG) epochs of 52 patients presenting with first-ever generalized seizure, with normal MRI and EEG. Patients were treated with valproate, arbamazepine, or lamotrigine in monotherapy (each group n = 13). Thirteen patients without medication served as a control group.

RESULTS

Carbamazepine and lamotrigine, both sodium-channel modulators, altered brain topography in the gamma range in the same frequency bands (50-60 Hz). Valproate, which has multiple actions on sodium and calcium channels as well as GABA turnover, modified brain topography in the low gamma range (30-40 Hz). No such changes were found in the control group. For all AEDs, the neural generators were shifted more anteriorly in medial temporal through to inferior frontal regions.

CONCLUSION

Decreased gamma-power and anterior shift of neural generators after AED introduction reflect AED influence on human electrophysiology.