Coherence, phase differences, phase shift, and phase lock in EEG/ERP analyses

EEG-based major depressive disorder recognition by neural oscillation and asymmetry

Background

Major Depressive Disorder (MDD) is a pervasive mental health issue with significant diagnostic challenges. Electroencephalography (EEG) offers a non-invasive window into the neural dynamics associated with MDD, yet the diagnostic efficacy is contingent upon the appropriate selection of EEG features and brain regions.

Methods

In this study, resting-state EEG signals from both eyes-closed and eyes-open conditions were analyzed. We examined band power across various brain regions, assessed the asymmetry of band power between the hemispheres, and integrated these features with clinical characteristics of MDD into a diagnostic regression model.

Results

Regression analysis found significant predictors of MDD to be beta2 (16-24 Hz) power in the Prefrontal Cortex (PFC) with eyes open (B = 20.092, p = 0.011), beta3 (24-40 Hz) power in the Medial Occipital Cortex (MOC) (B = -12.050, p < 0.001), and beta2 power in the Right Medial Frontal Cortex (RMFC) with eyes closed (B = 24.227, p < 0.001). Asymmetries in beta1 (12-16 Hz) power with eyes open (B = 28.047, p = 0.018), and in alpha (8-12 Hz, B = 9.004, p = 0.013) and theta (4-8 Hz, B = -13.582, p = 0.008) with eyes closed were also significant predictors.

Conclusion

The study confirms the potential of multi-region EEG analysis in improving the diagnostic precision for MDD. By including both neurophysiological and clinical data, we present a more robust approach to understanding and identifying this complex disorder.

Limitations

The research is limited by the sample size and the inherent variability in EEG signal interpretation. Future studies with larger cohorts and advanced analytical techniques are warranted to validate and refine these findings.

Coherence in event-related EEG oscillations in patients with Alzheimer's disease dementia and amnestic mild cognitive impairment

Objectives Working memory performances are based on brain functional connectivity, so that connectivity may be deranged in individuals with mild cognitive impairment (MCI) and patients with dementia due to Alzheimer's disease (ADD). Here we tested the hypothesis of abnormal functional connectivity as revealed by the imaginary part of coherency (ICoh) at electrode pairs from event-related electroencephalographic oscillations in ADD and MCI patients. Methods The study included 43 individuals with MCI, 43 with ADD, and 68 demographically matched healthy controls (HC). Delta, theta, alpha, beta, and gamma bands event-related ICoh was measured during an oddball paradigm. Inter-hemispheric, midline, and intra-hemispheric ICoh values were compared in ADD, MCI, and HC groups. Results The main results of the present study can be summarized as follows: (1) A significant increase of midline frontal and temporal theta coherence in the MCI group as compared to the HC group; (2) A significant decrease of theta, delta, and alpha intra-hemispheric coherence in the ADD group as compared to the HC and MCI groups; (3) A significant decrease of theta midline coherence in the ADD group as compared to the HC and MCI groups; (4) Normal inter-hemispheric coherence in the ADD and MCI groups. Conclusions Compared with the MCI and HC, the ADD group showed disrupted event-related intra-hemispheric and midline low-frequency band coherence as an estimate of brain functional dysconnectivity underlying disabilities in daily living. Brain functional connectivity during attention and short memory demands is relatively resilient in elderly subjects even with MCI (with preserved abilities in daily activities), and it shows reduced efficiency at multiple operating oscillatory frequencies only at an early stage of ADD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09920-0.

EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs

Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.

Measuring multivariate phase synchronization with symbolization and permutation

Phase synchronization is an important mechanism for the information processing of neurons in the brain. Most of the current phase synchronization measures are bivariate and focus on the synchronization between pairs of time series. However, these methods do not provide a full picture of global interactions in neural systems. Considering the prevalence and importance of multivariate neural signal analysis, there is an urgent need to quantify global phase synchronization (GPS) in neural networks. Therefore, we propose a new measure named symbolic phase difference and permutation entropy (SPDPE), which symbolizes the phase difference in multivariate neural signals and estimates GPS according to the permutation patterns of the symbolic sequences. The performance of SPDPE was evaluated using simulated data generated by Kuramoto and Rössler model. The results demonstrate that SPDPE exhibits low sensitivity to data length and outperforms existing methods in accurately characterizing GPS and effectively resisting noise. Moreover, to validate the method with real data, it was applied to classify seizures and non-seizures by calculating the GPS of stereoelectroencephalography (SEEG) data recorded from the onset zones of ten epilepsy patients. We believe that SPDPE will improve the estimation of GPS in many applications, such as EEG-based brain-computer interfaces, brain modeling, and simultaneous EEG-fMRI analysis.

EEG coherences of the fronto-limbic circuit between patients with major depressive disorder and healthy controls

BACKGROUND

Imaging studies found that patients with major depressive disorder (MDD) showed abnormal functional connectivity in the fronto-limbic circuit, including the prefrontal cortex (PFC), anterior cingulate cortex (ACC), and limbic system (amygdala). This study used electroencephalography (EEG) coherence as an indicator of functional connectivity in the fronto-limbic circuit and examined the group differences between the MDD group and healthy controls (HC group), and the associations between EEG coherence and depressive symptoms.

METHODS

125 and 132 participants in the MDD and HC groups have measured the symptoms of depression and anxiety, and delta, theta, alpha, and beta1-beta4 EEG coherences in the fronto-limbic circuit and examined the differences between the two groups, and the associations between the EEG coherence and depressive symptoms were examined.

RESULTS

Lower theta, alpha, beta1, beta3, and beta4 coherence in the fronto-limbic circuit and higher beta2 coherence between the PFC and limbic system in the MDD group than in the HC group. Negative correlations between delta, theta, beta1, beta3, and beta4 coherence and total depression, cognitive depression, and somatic depression; positive correlations between beta2 coherences in the PFC and limbic system, and total depression and cognitive depression scores in the MDD group.

LIMITATIONS

Whether low EEG coherence in the fronto-limbic circuit is applicable to other subtypes of MDD requires further study.

CONCLUSIONS

Low EEG coherences in the fronto-limbic circuit were related to depressive symptoms, and increased functional connectivity in the fronto-limbic circuit can be applied by neurofeedback in future studies.

The possible role of early-stage phase-locked neural activities in speech-in-noise perception in human adults across age and hearing loss

Ageing affects auditory neural phase-locked activities which could increase the challenges experienced during speech-in-noise (SiN) perception by older adults. However, evidence for how ageing affects SiN perception through these phase-locked activities is still lacking. It is also unclear whether influences of ageing on phase-locked activities in response to different acoustic properties have similar or different mechanisms to affect SiN perception. The present study addressed these issues by measuring early-stage phase-locked encoding of speech under quiet and noisy backgrounds (speech-shaped noise (SSN) and multi-talker babbles) in adults across a wide age range (19-75 years old). Participants passively listened to a repeated vowel whilst the frequency-following response (FFR) to fundamental frequency that has primary subcortical sources and cortical phase-locked response to slowly-fluctuating acoustic envelopes were recorded. We studied how these activities are affected by age and age-related hearing loss and how they are related to SiN performances (word recognition in sentences in noise). First, we found that the effects of age and hearing loss differ for the FFR and slow-envelope phase-locking. FFR was significantly decreased with age and high-frequency (≥ 2 kHz) hearing loss but increased with low-frequency (< 2 kHz) hearing loss, whilst the slow-envelope phase-locking was significantly increased with age and hearing loss across frequencies. Second, potential relationships between the types of phase-locked activities and SiN perception performances were also different. We found that the FFR and slow-envelope phase-locking positively corresponded to SiN performance under multi-talker babbles and SSN, respectively. Finally, we investigated how age and hearing loss affected SiN perception through phase-locked activities via mediation analyses. We showed that both types of activities significantly mediated the relation between age/hearing loss and SiN perception but in distinct manners. Specifically, FFR decreased with age and high-frequency hearing loss which in turn contributed to poorer SiN performance but increased with low-frequency hearing loss which in turn contributed to better SiN performance under multi-talker babbles. Slow-envelope phase-locking increased with age and hearing loss which in turn contributed to better SiN performance under both SSN and multi-talker babbles. Taken together, the present study provided evidence for distinct neural mechanisms of early-stage auditory phase-locked encoding of different acoustic properties through which ageing affects SiN perception.

Methodological considerations when measuring and analyzing auditory steady-state responses with multi-channel EEG

The auditory steady-state response (ASSR) has been traditionally recorded with few electrodes and is often measured as the voltage difference between mastoid and vertex electrodes (vertical montage). As high-density EEG recording systems have gained popularity, multi-channel analysis methods have been developed to integrate the ASSR signal across channels. The phases of ASSR across electrodes can be affected by factors including the stimulus modulation rate and re-referencing strategy, which will in turn affect the estimated ASSR strength. To explore the relationship between the classical vertical-montage ASSR and whole-scalp ASSR, we applied these two techniques to the same data to estimate the strength of ASSRs evoked by tones with sinusoidal amplitude modulation rates of around 40, 100, and 200 Hz. The whole-scalp methods evaluated in our study, with either linked-mastoid or common-average reference, included ones that assume equal phase across all channels, as well as ones that allow for different phase relationships. The performance of simple averaging was compared to that of more complex methods involving principal component analysis. Overall, the root-mean-square of the phase locking values (PLVs) across all channels provided the most efficient method to detect ASSR across the range of modulation rates tested here.

Interictal Spikes as an EEG Biomarker of Cognitive Impairment

SUMMARY

Although interictal spikes (IISs) are a well-established EEG biomarker for epilepsy, whether they are also a biomarker of cognitive deficits is unclear. Interictal spikes are dynamic events consisting of a synchronous discharge of neurons producing high frequency oscillations and a succession of action potentials which disrupt the ongoing neural activity. There are robust data showing that IISs result in transitory cognitive impairment with the type of deficit specific to the cognitive task and anatomic location of the IIS. Interictal spike, particularly if frequent and widespread, can impair cognitive abilities, through interference with waking learning and memory and memory consolidation during sleep. Interictal spikes seem to be particularly concerning in the developing brain where animal data suggest that IISs can lead to adverse cognitive effects even after the disappearance of the spikes. Whether a similar phenomenon occurs in human beings is unclear. Thus, although IISs are a clear biomarker of transitory cognitive impairment, currently, they lack sensitivity and specificity as a biomarker for enduring cognitive impairment.

Electroencephalographic assessment of infant spinal anesthesia: A pilot prospective observational study

INTRODUCTION

Spinal anesthesia is utilized as an alternative to general anesthesia in infants for some surgeries. After spinal anesthesia, infants often become less conscious without administration of sedative medications. The aim of this study was to assess electroencephalographic (EEG) correlates after spinal anesthesia in a cohort of infants.

PATIENTS AND METHODS

This pilot study included 12 infants who underwent spinal anesthesia. Unprocessed electroencephalography was recorded. The electroencephalogram was interpreted by four neurologists. Processed analyses compared electroencephalogram changes 30 min after spinal anesthesia to baseline.

RESULTS

Following spinal anesthesia, all 12 infants became sedated. Electroencephalography in all 12 demonstrated Stage 2 sleep with the appearance of sleep spindles (12-14 Hz) in the frontal and central leads in 8/12 (67%) of subjects. The median time to onset of sleep spindles was 24.7 interquartile range (21.2, 29.9) min. The duration of sleep spindles was 25.1 interquartile range (5.8, 99.8) min. Voltage attenuation and background slowing were the most common initial changes. Compared to baseline, the electroencephalogram 30 min after spinal anesthesia showed significantly increased absolute delta power (p = 0.02) and gamma power (p < 0.0001); decreases in beta (p = 0.0006) and higher beta (p < 0.0001) were also observed. The Fast Fourier Transform power ratio difference for delta/beta was increased (p = 0.03). Increased coherence was noted in the delta (p = 0.02) and theta (p = 0.04) bandwidths.

DISCUSSION

Spinal anesthesia in infants is associated with increased electroencephalographic slow wave activity and decreased beta activity compared to the awake state, with appearance of sleep spindles suggestive of normal sleep. The etiology and significance of the observed voltage attenuation and background slowing remains unclear.

CONCLUSIONS

The EEG signature of infant spinal anesthesia is distinct from that seen with general anesthesia and is consistent with normal sleep. Further investigation is required to better understand the etiology of these findings. Our preliminary findings contribute to the understanding of the brain effects of spinal anesthesia in early development.

Psilocin, LSD, mescaline, and DOB all induce broadband desynchronization of EEG and disconnection in rats with robust translational validity

Serotonergic psychedelics are recently gaining a lot of attention as a potential treatment of several neuropsychiatric disorders. Broadband desynchronization of EEG activity and disconnection in humans have been repeatedly shown; however, translational data from animals are completely lacking. Therefore, the main aim of our study was to assess the effects of tryptamine and phenethylamine psychedelics (psilocin 4 mg/kg, LSD 0.2 mg/kg, mescaline 100 mg/kg, and DOB 5 mg/kg) on EEG in freely moving rats. A system consisting of 14 cortical EEG electrodes, co-registration of behavioral activity of animals with subsequent analysis only in segments corresponding to behavioral inactivity (resting-state-like EEG) was used in order to reach a high level of translational validity. Analyses of the mean power, topographic brain-mapping, and functional connectivity revealed that all of the psychedelics irrespective of the structural family induced overall and time-dependent global decrease/desynchronization of EEG activity and disconnection within 1-40 Hz. Major changes in activity were localized on the large areas of the frontal and sensorimotor cortex showing some subtle spatial patterns characterizing each substance. A rebound of occipital theta (4-8 Hz) activity was detected at later stages after treatment with mescaline and LSD. Connectivity analyses showed an overall decrease in global connectivity for both the components of cross-spectral and phase-lagged coherence. Since our results show almost identical effects to those known from human EEG/MEG studies, we conclude that our method has robust translational validity.

Anticipated synchronization in human EEG data: Unidirectional causality with negative phase lag

Understanding the functional connectivity of the brain has become a major goal of neuroscience. In many situations the relative phase difference, together with coherence patterns, has been employed to infer the direction of the information flow. However, it has been recently shown in local field potential data from monkeys the existence of a synchronized regime in which unidirectionally coupled areas can present both positive and negative phase differences. During the counterintuitive regime, called anticipated synchronization (AS), the phase difference does not reflect the causality. Here we investigate coherence and causality at the alpha frequency band (f∼10 Hz) between pairs of electroencephalogram (EEG) electrodes in humans during a GO/NO-GO task. We show that human EEG signals can exhibit anticipated synchronization, which is characterized by a unidirectional influence from an electrode A to an electrode B, but the electrode B leads the electrode A in time. To the best of our knowledge, this is the first verification of AS in EEG signals and in the human brain. The usual delayed synchronization (DS) regime is also present between many pairs. DS is characterized by a unidirectional influence from an electrode A to an electrode B and a positive phase difference between A and B which indicates that the electrode A leads the electrode B in time. Moreover we show that EEG signals exhibit diversity in the phase relations: the pairs of electrodes can present in-phase, antiphase, or out-of-phase synchronization with a similar distribution of positive and negative phase differences.

Transcranial photobiomodulation changes topology, synchronizability, and complexity of resting state brain networks

Objective. Transcranial photobiomodulation (tPBM) is a recently proposed non-invasive brain stimulation approach with various effects on the nervous system from the cells to the whole brain networks. Specially in the neural network level, tPBM can alter the topology and synchronizability of functional brain networks. However, the functional properties of the neural networks after tPBM are still poorly clarified.Approach. Here, we employed electroencephalography and different methods (conventional and spectral) in the graph theory analysis to track the significant effects of tPBM on the resting state brain networks. The non-parametric statistical analysis showed that just one short-term tPBM session over right medial frontal pole can significantly change both topological (i.e. clustering coefficient, global efficiency, local efficiency, eigenvector centrality) and dynamical (i.e. energy, largest eigenvalue, and entropy) features of resting state brain networks.Main results. The topological results revealed that tPBM can reduce local processing, centrality, and laterality. Furthermore, the increased centrality of central electrode was observed.Significance. These results suggested that tPBM can alter topology of resting state brain network to facilitate the neural information processing. On the other hand, the dynamical results showed that tPBM reduced stability of synchronizability and increased complexity in the resting state brain networks. These effects can be considered in association with the increased complexity of connectivity patterns among brain regions and the enhanced information propagation in the resting state brain networks. Overall, both topological and dynamical features of brain networks suggest that although tPBM decreases local processing (especially in the right hemisphere) and disrupts synchronizability of network, but it can increase the level of information transferring and processing in the brain network.

Brain State-dependent Gain Modulation of Corticospinal Output in the Active Motor System

The communication through coherence hypothesis suggests that only coherently oscillating neuronal groups can interact effectively and predicts an intrinsic response modulation along the oscillatory rhythm. For the motor cortex (MC) at rest, the oscillatory cycle has been shown to determine the brain's responsiveness to external stimuli. For the active MC, however, the demonstration of such a phase-specific modulation of corticospinal excitability (CSE) along the rhythm cycle is still missing. Motor evoked potentials in response to transcranial magnetic stimulation (TMS) over the MC were used to probe the effect of cortical oscillations on CSE during several motor conditions. A brain-machine interface (BMI) with a robotic hand orthosis allowed investigating effects of cortical activity on CSE without the confounding effects of voluntary muscle activation. Only this BMI approach (and not active or passive hand opening alone) revealed a frequency- and phase-specific cortical modulation of CSE by sensorimotor beta-band activity that peaked once per oscillatory cycle and was independent of muscle activity. The active MC follows an intrinsic response modulation in accordance with the communication through coherence hypothesis. Furthermore, the BMI approach may facilitate and strengthen effective corticospinal communication in a therapeutic context, for example, when voluntary hand opening is no longer possible after stroke.

Electrographic Features of Spontaneous Recurrent Seizures in a Mouse Model of Extended Hippocampal Kindling

Epilepsy is a chronic neurological disorder characterized by spontaneous recurrent seizures (SRS) and comorbidities. Kindling through repetitive brief stimulation of a limbic structure is a commonly used model of temporal lobe epilepsy. Particularly, extended kindling over a period up to a few months can induce SRS, which may simulate slowly evolving epileptogenesis of temporal lobe epilepsy. Currently, electroencephalographic (EEG) features of SRS in rodent models of extended kindling remain to be detailed. We explored this using a mouse model of extended hippocampal kindling. Intracranial EEG recordings were made from the kindled hippocampus and unstimulated hippocampal, neocortical, piriform, entorhinal, or thalamic area in individual mice. Spontaneous EEG discharges with concurrent low-voltage fast onsets were observed from the two corresponding areas in nearly all SRS detected, irrespective of associated motor seizures. Examined in brain slices, epileptiform discharges were induced by alkaline artificial cerebrospinal fluid in the hippocampal CA3, piriform and entorhinal cortical areas of extended kindled mice but not control mice. Together, these in vivo and in vitro observations suggest that the epileptic activity involving a macroscopic network may generate concurrent discharges in forebrain areas and initiate SRS in hippocampally kindled mice.

Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach

We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive-compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.

Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms

BACKGROUND

Functional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Functional disruption during infancy could provide earlier markers of ASD, thus providing a crucial opportunity to improve developmental outcomes. Using a whole-brain multivariate approach, we asked whether electroencephalography measures of neural connectivity at 3 months of age predict autism symptoms at 18 months.

METHODS

Spontaneous electroencephalography data were collected from 65 infants with and without familial risk for ASD at 3 months of age. Neural connectivity patterns were quantified using phase coherence in the alpha range (6-12 Hz). Support vector regression analysis was used to predict ASD symptoms at age 18 months, with ASD symptoms quantified by the Toddler Module of the Autism Diagnostic Observation Schedule, Second Edition.

RESULTS

Autism Diagnostic Observation Schedule scores predicted by support vector regression algorithms trained on 3-month electroencephalography data correlated highly with Autism Diagnostic Observation Schedule scores measured at 18 months (r = .76, p = .02, root-mean-square error = 2.38). Specifically, lower frontal connectivity and higher right temporoparietal connectivity at 3 months predicted higher ASD symptoms at 18 months. The support vector regression model did not predict cognitive abilities at 18 months (r = .15, p = .36), suggesting specificity of these brain patterns to ASD.

CONCLUSIONS

Using a data-driven, unbiased analytic approach, neural connectivity across frontal and temporoparietal regions at 3 months predicted ASD symptoms at 18 months. Identifying early neural differences that precede an ASD diagnosis could promote closer monitoring of infants who show signs of neural risk and provide a crucial opportunity to mediate outcomes through early intervention.

Relations between frontal EEG maturation and inhibitory control in preschool in the prediction of children's early academic skills

Maturation of the prefrontal cortex (PFC) across the first few years of life is thought to underlie the emergence of inhibitory control (IC) abilities, which may play an important role in children's early academic success. In this growth curve modeling study (N = 364), we assessed developmental change in children's resting-state electroencephalogram (EEG) activity (6-9 Hz 'alpha' power) from 10 months to 4 years and examined whether the initial levels or amount of change in frontal alpha power were associated with children's IC at age 4 and indirectly academic skills at age 6. Results indicated that greater increases in frontal alpha power across the study period were associated with better IC, and indirectly with better performance on Woodcock-Johnson tests of reading and math achievement at age 6. Similar associations between change in EEG and age 4 vocabulary were observed but did not mediate an association with academic skills. Similar analyses with posterior alpha power showed no associations with IC. Findings underscore the significance of frontal lobe maturation from infancy to early childhood for children's intellectual development.

EEG coherence during subjectively-rated psychological state variations

BACKGROUND

Neurofeedback training aims to develop awareness and control of psychological states in order to self-regulate brain activity and while used widely therapeutically, important questions remain unanswered. Central to these aims is an assumed association between the live EEG-based feedback and the subjective experience of a psychological state. To date, there is little evidence to support this relationship. Previous studies examining the association between an EEG index and subjective experience have explored only the presence or absence of the state, or merely assumed state variations. The current study aims to examine this association by considering how different levels of a psychological state (i.e., attention) are reflected in EEG coherence.

METHODS

Our approach aims to allow comparisons of EEG coherence between psychological states (attention vs. rest), and also within subjectively-rated levels of a psychological state (attention) through a purpose-designed questionnaire. Thirty healthy adult participants performed a resting eyes-open (REO) and attention modulation task, while 28 channels of EEG were recorded. Levels within the psychological state were subjectively-attested by participants on a trial-by-trial basis.

RESULTS

The main analyses examined the effect of subjectively-rated attention levels (SRALs) on EEG coherence, with results suggesting that high and low SRALs may be represented by: 1) different levels of alpha and theta coherence at anterior and posterior electrodes of the frontal lobe bilaterally, and 2) different levels of alpha coherence between central and parietal lobes, also bilaterally.

DISCUSSION

These findings provide partial, preliminary evidence for EEG correlates of SRALs. These findings may have implications for understanding underlying mechanisms of NFT, which is an underdeveloped area.

Aiming at ecological validity-Midfrontal theta oscillations in a toy gun shooting task

Laboratory electroencephalography (EEG) studies have already provided important insights into the neuronal mechanisms of performance monitoring. However, to our knowledge no study so far has examined neuronal correlates of performance monitoring using an ecologically valid task outside a typical laboratory setting. Therefore, we examined midfrontal theta and the feedback-related negativity (FRN) using mobile EEG in a physical shooting task within an ecologically valid environment with highly dynamical visual feedback. Participants shot a target using a toy gun while moving and looking around freely. Shots that missed the target evoked stronger midfrontal theta activity than hits and this response was rather phase-unlocked. There was no difference between misses and hits in the FRN. The results raise the question whether the absence of certain ERP components like the FRN could be due to methodological reasons or to the fact that partially different neuronal processes may be activated in the laboratory as compared to more ecologically valid tasks. Overall, our results indicate that crucial neurocognitive processes of performance monitoring can be assessed in highly dynamic and ecologically valid settings by mobile EEG.

Inter-channel phase differences during sleep spindles are altered in Veterans with PTSD

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We assessed the spatiotemporal dynamics of slow and fast spindles in PTSD.

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Inter-channel phase differences during slow spindles were reduced in PTSD.

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This effect was reproducible across nights and subsamples of the study population.

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The spatiotemporal dynamics of fast spindles was not altered in PTSD.

Sleep disturbances are common complaints in patients with post-traumatic stress disorder (PTSD). To date, however, objective markers of PTSD during sleep remain elusive. Sleep spindles are distinctive bursts of brain oscillatory activity during non-rapid eye movement (NREM) sleep and have been implicated in sleep protection and sleep-dependent memory processes. In healthy sleep, spindles observed in electroencephalogram (EEG) data are highly synchronized across different regions of the scalp. Here, we aimed to investigate whether the spatiotemporal synchronization patterns between EEG channels during sleep spindles, as quantified by the phase-locking value (PLV) and the mean phase difference (MPD), are altered in PTSD. Using high-density (64-channel) EEG data recorded from 78 combat-exposed Veteran men (31 with PTSD and 47 without PTSD) during two consecutive nights of sleep, we examined group differences in the PLV and MPD for slow (10–13 Hz) and fast (13–16 Hz) spindles separately. To evaluate the reproducibility of our findings, we set apart the first 47 consecutive participants (18 with PTSD) for the initial discovery and reserved the remaining 31 participants (13 with PTSD) for replication analysis. In the discovery analysis, compared to the non-PTSD group, the PTSD group showed smaller MPDs during slow spindles between the frontal and centro-parietal channel pairs on both nights. We obtained reproducible results in the replication analysis in terms of statistical significance and effect size. The PLVs during slow or fast spindles did not significantly differ between groups. The reduced inter-channel phase difference during slow spindles in PTSD may reflect pathological changes in the underlying thalamocortical circuits. This novel finding, if independently validated, may prove useful in developing sleep-focused PTSD diagnostics and interventions.

Treatment of Neurodegeneration: Integrating Photobiomodulation and Neurofeedback in Alzheimer's Dementia and Parkinson's: A Review

Objective: A review of photobiomodulation (PBM) in Alzheimer's dementia is submitted. The addition of PBM in neurodegenerative diseases is a dual modality that is at present gaining traction as it is safe, antiviral, and anti-inflammatory for treating neurodegeneration with photons that stimulate mitochondria increasing adenosine triphosphate and proteasomes increasing misfolded protein removal. Neurofeedback provides neural plasticity with an increase in brain-derived nerve factor mRNA and an increase in dendrite production and density in the hippocampus coupled with overall growth in dendrites, density, and neuronal survival. Background: Alzheimer's disease pathophysiology is the accumulation of hyperphosphorylated tau protein neurofibrillary tangles and subsequently amyloid-beta (Aβ) plaques. PBM and neurobiofeedback (NBF)address the multiple gene expression and upregulation of multiple pathogenic pathway inflammation, reactive oxidative stress, mitochondrial disorders, insulin resistance, methylation defects, regulation of neuroprotective factors, and regional hypoperfusion of the brain. There is no human evidence to suggest a clinical therapeutic benefit from using consistent light sources while significantly increasing safety concerns. Methods: A PBM test with early- to mid-Alzheimer's was reported in 2017, consisting of a double-blind, placebo-controlled trial in a small pilot group of early- to mid-dementia subjects under Institutional Review Board (IRB)-approved Food and Drug Administration (FDA) Clinical Trial. Results: PBM-treated subjects showed that active treatment subjects tended to show greater improvement in the functioning of the executive: clock drawing, immediate recall, practical memory, and visual attention and task switching (Trails A&B). A larger study using the CerebroLite helmet in Temple Texas again of subjects in a double-blind, placebo-controlled IRB-approved FDA Clinical Trial demonstrated gain in memory and cognition by increased clock drawing. Conclusions: Next-generation trials with the Cognitolite for Parkinson's disease subjects will incorporate the insights regarding significant bilateral occipital hypocoherence deficits gained from the quantitative EEG analyses. Future applications will integrate noninvasive stimulation delivery, including full-body and transcranial and infrared light with pulsed electromagnetic frequencies.

Establishing Drug Effects on Electrocorticographic Activity in a Genetic Absence Epilepsy Model: Advances and Pitfalls

The genetic rat models such as rats of the WAG/Rij strain and GAERS were developed as models for generalized genetic epilepsy and in particular for childhood absence epilepsy. These animal models were described in the eighties of the previous century and both models have, among others, face, construct and predictive validity. Both models were and are currently used as models to predict the action of antiepileptic medication and other experimental treatments, to elucidate neurobiological mechanisms of spike-wave discharges and epileptogenesis. Although the electroencephalagram (EEG)/electrocorticogram (ECoG) is imperative for establishing absence seizures and to quantify the for absence epilepsy typical spike-wave discharges, monitoring the animals behavior is equally necessary. Here an overview is given regarding the design of drug evaluation studies, which animals to use, classical and new EEG variables, the monitoring and quantification of the behavior of the rats, some pitfalls regarding the interpretation of the data, and some developments in EEG technology.

Effect of suvorexant on event-related oscillations and EEG sleep in rats exposed to chronic intermittent ethanol vapor and protracted withdrawal

STUDY OBJECTIVES

Insomnia is a prominent complaint in patients with alcohol use disorders (AUD). However, despite the importance of sleep in the maintenance of sobriety, treatment options for sleep disturbance associated with a history of AUD are currently limited. Recent clinical trials have demonstrated that suvorexant, a dual Hct/OX receptor antagonist, normalizes sleep in patients with primary insomnia; yet, its potential for the treatment of sleep pathology associated with AUD has not been investigated in either preclinical or clinical studies.

METHODS

This study employed a model whereby ethanol vapor exposure or control conditions were administered for 8 weeks to adult rats. Waking event-related oscillations (EROs) and EEG sleep were evaluated at baseline before exposure and again following 24 hr of withdrawal from the exposure. Subsequently, the ability of vehicle (VEH) and two doses (10, 30 mg/kg IP) of suvorexant to modify EROs, sleep, and the sleep EEG was investigated.

RESULTS

After 24 hr following EtOH withdrawal, the ethanol-treated group had increases in waking ERO θ and β activity, more fragmented sleep (shorter duration and increased frequency of slow wave (SW) and rapid eye movement [REM] sleep episodes), and increased θ and β power in REM and SW sleep. Suvorexant induced a dose-dependent decrease in the latency to REM and SW sleep onsets but also produced REM and SW sleep fragmentation and increased β energy in waking EROs when compared with VEH.

CONCLUSIONS

Taken together, these studies suggest that suvorexant has overall sleep-promoting effects, but it may exacerbate some aspects of sleep and EEG pathology.

Exploration of Brain Connectivity during Human Inhibitory Control Using Inter-Trial Coherence

Inhibitory control is a cognitive process that inhibits a response. It is used in everyday activities, such as driving a motorcycle, driving a car and playing a game. The effect of this process can be compared to the red traffic light in the real world. In this study, we investigated brain connectivity under human inhibitory control using the phase lag index and inter-trial coherence (ITC). The human brain connectivity gives a more accurate representation of the functional neural network. Results of electroencephalography (EEG), the data sets were generated from twelve healthy subjects during left and right hand inhibitions using the auditory stop-signal task, showed that the inter-trial coherence in delta (1-4 Hz) and theta (4-7 Hz) band powers increased over the frontal and temporal lobe of the brain. These EEG delta and theta band activities neural markers have been related to human inhibition in the frontal lobe. In addition, inter-trial coherence in the delta-theta and alpha (8-12 Hz) band powers increased at the occipital lobe through visual stimulation. Moreover, the highest brain connectivity was observed under inhibitory control in the frontal lobe between F3-F4 channels compared to temporal and occipital lobes. The greater EEG coherence and phase lag index in the frontal lobe is associated with the human response inhibition. These findings revealed new insights to understand the neural network of brain connectivity and underlying mechanisms during human response inhibition.

Hyperscanning: A Valid Method to Study Neural Inter-brain Underpinnings of Social Interaction

Social interactions are a crucial part of human life. Understanding the neural underpinnings of social interactions is a challenging task that the hyperscanning method has been trying to tackle over the last two decades. Here, we review the existing literature and evaluate the current state of the hyperscanning method. We review the type of methods (fMRI, M/EEG, and fNIRS) that are used to measure brain activity from more than one participant simultaneously and weigh their pros and cons for hyperscanning. Further, we discuss different types of analyses that are used to estimate brain networks and synchronization. Lastly, we present results of hyperscanning studies in the context of different cognitive functions and their relations to social interactions. All in all, we aim to comprehensively present methods, analyses, and results from the last 20 years of hyperscanning research.

Modulation of the Visual to Auditory Human Inhibitory Brain Network: An EEG Dipole Source Localization Study

Auditory alarms are used to direct people's attention to critical events in complicated environments. The capacity for identifying the auditory alarms in order to take the right action in our daily life is critical. In this work, we investigate how auditory alarms affect the neural networks of human inhibition. We used a famous stop-signal or go/no-go task to measure the effect of visual stimuli and auditory alarms on the human brain. In this experiment, go-trials used visual stimulation, via a square or circle symbol, and stop trials used auditory stimulation, via an auditory alarm. Electroencephalography (EEG) signals from twelve subjects were acquired and analyzed using an advanced EEG dipole source localization method via independent component analysis (ICA) and EEG-coherence analysis. Behaviorally, the visual stimulus elicited a significantly higher accuracy rate (96.35%) than the auditory stimulus (57.07%) during inhibitory control. EEG theta and beta band power increases in the right middle frontal gyrus (rMFG) were associated with human inhibitory control. In addition, delta, theta, alpha, and beta band increases in the right cingulate gyrus (rCG) and delta band increases in both right superior temporal gyrus (rSTG) and left superior temporal gyrus (lSTG) were associated with the network changes induced by auditory alarms. We further observed that theta-alpha and beta bands between lSTG-rMFG and lSTG-rSTG pathways had higher connectivity magnitudes in the brain network when performing the visual tasks changed to receiving the auditory alarms. These findings could be useful for further understanding the human brain in realistic environments.

Neurodevelopment and asymmetry of auditory-related responses to repetitive syllabic stimuli in preterm neonates based on frequency-domain analysis

Sensory development of the human brain begins prenatally, allowing cortical auditory responses to be recorded at an early age in preterm infants. Despite several studies focusing on the temporal characteristics of preterm infants' cortical responses, few have been conducted on frequency analysis of these responses. In this study, we performed frequency and coherence analysis of preterm infants' auditory responses to series of syllables and also investigated the functional brain asymmetry of preterm infants for the detection of the regularity of auditory stimuli. Cortical auditory evoked potentials (CAEPs) were recorded in 16 preterm infants with a mean recording age of 31.48 weeks gestational age (29.57-34.14 wGA) in response to a repetitive syllabic stimulus. Peak amplitudes of the frequency response at the target frequency and the first harmonic, as well as the phase coherence (PC) at the target frequency were extracted as age-dependent variables. A functional asymmetry coefficient was defined as a lateralization index for the amplitude of the target frequency at each electrode site. While the findings revealed a significant positive correlation between the mean amplitude at the target frequency vs. age (R2 = 0.263, p = 0.042), no significant correlation was observed for age-related changes of the mean amplitude at the first harmonic. A significant correlation was also observed between the mean PC and age (R2 = 0.318, p = 0.023). A right hemisphere lateralization over many channels was also generally observed. The results demonstrate that rightward lateralization for slow rate modulation, previously observed in adults, children and newborns, appears to be in place at a very young age, even in preterm infants.

Functional brain connectivity in a rodent seizure model of autistic-like behavior

OBJECTIVE

There is increasing evidence that Autism Spectrum Disorder (ASD) is a disorder of functional connectivity with both human and rodent studies demonstrating alterations in connectivity. Here, we hypothesized that early-life seizures (ELS) in rats would interrupt normal brain connectivity and result in autistic-like behavior (ALB).

METHODS

Following 50 seizures, adult rats were tested in the social interaction and social novelty tests and then underwent qualitative and quantitative intracranial electroencephalography (EEG) monitoring in the medial prefrontal cortex (PFC) and the hippocampal subfields, CA3 and CA1.

RESULTS

Rats with ELS showed deficits in social interaction and novelty, and compared with control, rats had marked increases in coherence within the hippocampus (CA3-CA1) and between the hippocampus and PFC during the awake and sleep states indicating hyperconnectivity. In addition, sleep spindle density was significantly reduced in rats with ELS. There were no differences in voltage correlations and power spectral densities between the ELS and control rats in any bandwidths.

CONCLUSION

Taken together, these findings indicate that ELS can result in ALB and alter functional connectivity as measured by coherence and sleep spindle density. These findings implicate altered connectivity as a robust neural signature for ALB following ELS.

Graph Theoretical Characteristics of EEG-Based Functional Brain Networks in Patients With Epilepsy: The Effect of Reference Choice and Volume Conduction

It is well-established that both volume conduction and the choice of recording reference (montage) affect the correlation measures obtained from scalp EEG, both in the time and frequency domains. As a result, a number of correlation measures have been proposed aiming to reduce these effects. In our previous work, we have showed that scalp-EEG based functional brain networks in patients with epilepsy exhibit clear periodic patterns at different time scales and that these patterns are strongly correlated to seizure onset, particularly at shorter time scales (around 3 and 5 h), which has important clinical implications. In the present work, we use the same long-duration clinical scalp EEG data (multiple days) to investigate the extent to which the aforementioned results are affected by the choice of reference choice and correlation measure, by considering several widely used montages as well as correlation metrics that are differentially sensitive to the effects of volume conduction. Specifically, we compare two standard and commonly used linear correlation measures, cross-correlation in the time domain, and coherence in the frequency domain, with measures that account for zero-lag correlations: corrected cross-correlation, imaginary coherence, phase lag index, and weighted phase lag index. We show that the graphs constructed with corrected cross-correlation and WPLI are more stable across different choices of reference. Also, we demonstrate that all the examined correlation measures revealed similar periodic patterns in the obtained graph measures when the bipolar and common reference (Cz) montage were used. This includes circadian-related periodicities (e.g., a clear increase in connectivity during sleep periods as compared to awake periods), as well as periodicities at shorter time scales (around 3 and 5 h). On the other hand, these results were affected to a large degree when the average reference montage was used in combination with standard cross-correlation, coherence, imaginary coherence, and PLI, which is likely due to the low number of electrodes and inadequate electrode coverage of the scalp. Finally, we demonstrate that the correlation between seizure onset and the brain network periodicities is preserved when corrected cross-correlation and WPLI were used for all the examined montages. This suggests that, even in the standard clinical setting of EEG recording in epilepsy where only a limited number of scalp EEG measurements are available, graph-theoretic quantification of periodic patterns using appropriate montage, and correlation measures corrected for volume conduction provides useful insights into seizure onset.

Activity in Functional Cortical Networks Temporally Associated with Postural Instability

Human bipedal balance control is proposed to be the integrated activity of distributed neural areas. There is growing understanding about the cortical involvement in this highly automated behavior. While evidence exists for cortical activity temporally linked to reactive balance control, little is known about the functional interaction of potential cortical regions. Here, we used functional connectivity and graph theoretical analysis to derive functional cortical networks during reactive balance control from an event-related potential evoked by external perturbation known as the perturbation-evoked potential N1 (PEP N1). Fourteen healthy young adults were subjected to temporally unpredictable postural perturbations using a custom-made lean and release cable system. Electroencephalographic signals were recorded using a 64-channel electrode cap and segmented around perturbation onset. Functional connectivity was analyzed in source-space and sensor-space using coherence measures and functional cortical networks were characterized using graph measures. The results suggest that there might exist a balance control cortical network while standing and rapid, transient, and frequency-specific reorganization occurs in this network during reactive balance control events. This reorganization was characterized by an increased number of short-range connections between neighboring areas and increased strength between connections in delta, theta, alpha, and beta frequency bands during PEP N1 compared to baseline. To our knowledge, this is the first study to report the existence of functional cortical networks during reactive balance control with potential implications on assessing impaired balance associated with various neural diseases.

Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification

The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique “brain print”, which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.

Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation

Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artifacts at 7.6 Hz. Further, contrast maps of Levenshtein Distance highlight synchrony differences between ERP and no-ERP epochs, mainly at delta and theta bands. The framework, which is not limited to one synchrony measure, allows observing dynamics of phase changes and interactions among channels and can be applied to analyze other cognitive states rather than ERP versus no ERP.

Abnormal Resting-State Quantitative Electroencephalogram in Children With Central Auditory Processing Disorder: A Pilot Study

In this study, we showed an abnormal resting-state quantitative electroencephalogram (QEEG) pattern in children with central auditory processing disorder (CAPD). Twenty-seven children (16 male, 11 female; mean age = 10.7 years) with CAPD and no symptoms of other developmental disorders, as well as 23 age- and sex-matched, typically developing children (TDC, 11 male, 13 female; mean age = 11.8 years) underwent examination of central auditory processes (CAPs) and QEEG evaluation consisting of two randomly presented blocks of “Eyes Open” (EO) or “Eyes Closed” (EC) recordings. Significant correlations between individual frequency band powers and CAP tests performance were found. The QEEG studies revealed that in CAPD relative to TDC there was no effect of decreased delta absolute power (1.5–4 Hz) in EO compared to the EC condition. Furthermore, children with CAPD showed increased theta power (4–8 Hz) in the frontal area, a tendency toward elevated theta power in EO block, and reduced low-frequency beta power (12–15 Hz) in the bilateral occipital and the left temporo-occipital regions for both EO and EC conditions. Decreased middle-frequency beta power (15–18 Hz) in children with CAPD was observed only in the EC block. The findings of the present study suggest that QEEG could be an adequate tool to discriminate children with CAPD from normally developing children. Correlation analysis shows relationship between the individual EEG resting frequency bands and the CAPs. Increased power of slow waves and decreased power of fast rhythms could indicate abnormal functioning (hypoarousal of the cortex and/or an immaturity) of brain areas not specialized in auditory information processing.

The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow

Using the scalp time-varying network method, the present study is the first to investigate the temporal influence of the reference on N170, a negative event-related potential component (ERP) appeared about 170 ms that is elicited by facial recognition, in the network levels. Two kinds of scalp electroencephalogram (EEG) references, namely, AR (average of all recording channels) and reference electrode standardization technique (REST), were comparatively investigated via the time-varying processing of N170. Results showed that the latency and amplitude of N170 were significantly different between REST and AR, with the former being earlier and smaller. In particular, the information flow from right temporal-parietal P8 to left P7 in the time-varying network was earlier in REST than that in AR, and this phenomenon was reproduced by simulation, in which the performance of REST was closer to the true case at source level. These findings indicate that reference plays a crucial role in ERP data interpretation, and importantly, the newly developed approximate zero-reference REST would be a superior choice for precise evaluation of the scalp spatio-temporal changes relating to various cognitive events.

Time estimation and beta segregation: An EEG study and graph theoretical approach

Elucidation of the neural correlates of time perception constitutes an important research topic in cognitive neuroscience. The focus to date has been on durations in the millisecond to seconds range, but here we used electroencephalography (EEG) to examine brain functional connectivity during much longer durations (i.e., 15 min). For this purpose, we conducted an initial exploratory experiment followed by a confirmatory experiment. Our results showed that those participants who overestimated time exhibited lower activity of beta (18–30 Hz) at several electrode sites. Furthermore, graph theoretical analysis indicated significant differences in the beta range (15–30 Hz) between those that overestimated and underestimated time. Participants who underestimated time showed higher clustering coefficient compared to those that overestimated time. We discuss our results in terms of two aspects. FFT results, as a linear approach, are discussed within localized/dedicated models (i.e., scalar timing model). Second, non-localized properties of psychological interval timing (as emphasized by intrinsic models) are addressed and discussed based on results derived from graph theory. Results suggested that although beta amplitude in central regions (related to activity of BG-thalamocortical pathway as a dedicated module) is important in relation to timing mechanisms, the properties of functional activity of brain networks; such as the segregation of beta network, are also crucial for time perception. These results may suggest subjective time may be created by vector units instead of scalar ticks.

Changes in brain connectivity following exposure to bilateral eye movements

The present research assessed how engaging in bilateral eye movements influences brain activity. Participants had their resting-state brain activity recorded with electroencephalography (EEG) before and after they performed 30 s of bilateral eye movements or a center-control manipulation. We assessed differences in change scores for absolute power and coherence between the eye-movement and center-control conditions. A main effect for handedness was present for EEG power in the theta and beta frequency bands, with inconsistent-handed participants displaying a greater increase than consistent-handed participants in both frequency bands. For theta, the increase in power for inconsistent handers was specific to participants in the bilateral eye-movement condition, whose increase in theta power exceeded the increase in theta power for consistent-handed participants regardless of condition. In contrast, for coherence, a main effect for condition was present for the delta frequency band, with participants in the control condition exhibiting a significant drop in posterior delta coherence pre to post. We suggest that the maintenance of posterior delta coherence over time may be an important factor in sustaining attention. Further, the malleability of EEG power for inconsistent-handed participants reveals the importance of individual-differences variables in the potential for behavioral manipulations to change brain activity.

Relationship between speech-evoked neural responses and perception of speech in noise in older adults

Speech-in-noise (SPIN) perception involves neural encoding of temporal acoustic cues. Cues include temporal fine structure (TFS) and envelopes that modulate at syllable (Slow-rate ENV) and fundamental frequency (F₀-rate ENV) rates. Here the relationship between speech-evoked neural responses to these cues and SPIN perception was investigated in older adults. Theta-band phase-locking values (PLVs) that reflect cortical sensitivity to Slow-rate ENV and peripheral/brainstem frequency-following responses phase-locked to F₀-rate ENV (FFR_{ENV_F0}) and TFS (FFR_TFS) were measured from scalp-electroencephalography responses to a repeated speech syllable in steady-state speech-shaped noise (SpN) and 16-speaker babble noise (BbN). The results showed that (1) SPIN performance and PLVs were significantly higher under SpN than BbN, implying differential cortical encoding may serve as the neural mechanism of SPIN performance that varies as a function of noise types; (2) PLVs and FFR_TFS at resolved harmonics were significantly related to good SPIN performance, supporting the importance of phase-locked neural encoding of Slow-rate ENV and TFS of resolved harmonics during SPIN perception; (3) FFR_{ENV_F0} was not associated to SPIN performance until audiometric threshold was controlled for, indicating that hearing loss should be carefully controlled when studying the role of neural encoding of F₀-rate ENV. Implications are drawn with respect to fitting auditory prostheses.

Effect of Gabapentin on Sleep and Event-Related Oscillations (EROs) in Rats Exposed to Chronic Intermittent Ethanol Vapor and Protracted Withdrawal

BACKGROUND

Disturbances in sleep architecture, especially reductions in slow-wave sleep (SWS), are symptoms commonly observed in individuals with alcohol use disorders. Recent clinical trials have demonstrated that the anticonvulsant and analgesic drug gabapentin may have therapeutic value in normalizing sleep quality in recovering alcoholics. However, the brain mechanisms underlying this improvement in sleep following gabapentin treatment remain unknown.

METHODS

In this study, adult Wistar rats were exposed to 8 weeks of chronic intermittent ethanol [EtOH] vapor (blood EtOH concentrations averaged 128.2 ± 17.4 mg/dl) or control conditions and then withdrawn. Sleep electroencephalograms [EEGs] and event-related oscillations (EROs) were evaluated at baseline prior to EtOH exposure and 24 hours following EtOH withdrawal. Four weeks following EtOH withdrawal the effects of saline and 2 doses of gabapentin (30, 120 mg/kg), on EROs and sleep EEGs, were evaluated.

RESULTS

As compared to baseline, 24 hours following alcohol withdrawal SWS became fragmented as indexed by a significant increase in the number and a decrease in the duration of SWS episodes. Compared to controls, the EtOH-exposed group had more ERO energy in the beta frequency band in the parietal cortex. Gabapentin induced a dose-dependent decrease in the latency to the first SWS episode, and a reduction in sleep fragmentation. Gabapentin also produced a dose-dependent increase in ERO energy in the control group that was significantly attenuated in the EtOH-exposed group in the theta, and beta frequency bands.

CONCLUSIONS

Taken together, these studies suggest that gabapentin can reverse some of the alcohol-induced sleep and EEG deficits but does not eliminate all of the enduring brain effects of EtOH exposure.

Cortical high gamma network oscillations and connectivity: a translational index for antipsychotics to normalize aberrant neurophysiological activity

Oscillatory activity in the gamma frequency range is a critical mechanism, which integrates neural networks within and across brain structures during cognitive processes. In schizophrenia, abnormalities in high gamma oscillations are ubiquitous and most likely reflect dysfunction in neuronal networks. In conscious rats, disturbed network oscillations associated with positive symptoms and cognitive deficits were modeled in different cortical areas by the dopaminergic agonist (amphetamine) and the N-methyl-D-aspartate (NMDA) receptor antagonists (PCP and MK801). Subsequently, the efficacies of marketed atypical antipsychotics (olanzapine, risperidone, and clozapine) to normalize dysfunctional oscillations and network connectivity were examined. Acute NMDA antagonists elicited aberrant synchrony in the gamma frequency oscillations. In addition, coherent slow alpha network activity was observed with MK801 and amphetamine, both of whose oscillatory rhythms were correlated with pronounced locomotor activity. All antipsychotics commonly decreased slow alpha and high gamma network oscillations in different cortical regions as well as motion behavior. In the combined treatments, antipsychotics attenuated NMDA antagonist-induced abnormalities in functional network oscillations and connectivity, whose effects on motor behavior is mechanistically related. These results suggest that pharmacologically induced disruption of cortical gamma oscillations and network connectivity in rats is a candidate model to study dysfunctional oscillatory patterns described in positive and negative symptoms of schizophrenia. The efficacy of antipsychotics to rescue cortical network oscillatory patterns is in line with the idea that glutamatergic and dopaminergic systems play a role in maintaining the integrity of cortical circuits. Thus, gamma oscillations could provide a powerful translational index to assess the integrity of neural networks and to evaluate the efficacy of drugs with potential antipsychotic properties.

Resting state connectivity mediates the relationship between collectivism and social cognition

Humans are intrinsically social beings and it is natural that self-processing is associated with social cognition. The degree to which the self is perceived as a part of social environment is modulated by cultural stereotypes, such as collectivism and individualism. Here, we tested the hypothesis that individuals who endorse collectivist values would spontaneously think more about their relationships with other people and this association would be mediated by connectivity between the medial prefrontal cortex (MPFC) and the rest of the brain. Connectivity was evaluated based on resting state EEG data using the recently developed methods, which combine beamformer spatial filtering with seed based connectivity estimation. The formal mediation analysis revealed that collectivism is associated with an enhanced connectivity of MPFC with a set of cortical regions that are frequently co-activated in moral reasoning, empathy, and theory of mind tasks and with diminished connectivity with the precuneus\posterior cingulate cortex, which is involved in self-centered cognition. The relationship between collectivism and social cognition was mediated by MPFC connectivity with the left middle temporal gyrus implying that in participants with collectivistic attitude, thinking about relationships with other people may be associated with semantic memory retrieval and reasoning on moral issues and others' intentions.

The Impact of Age and Cognitive Reserve on Resting-State Brain Connectivity

Cognitive reserve (CR) is a protective mechanism that supports sustained cognitive function following damage to the physical brain associated with age, injury, or disease. The goal of the research was to identify relationships between age, CR, and brain connectivity. A sample of 90 cognitively normal adults, ages 45–64 years, had their resting-state brain activity recorded with electroencephalography (EEG) and completed a series of memory and executive function assessments. CR was estimated using years of education and verbal IQ scores. Participants were divided into younger and older age groups and low- and high-CR groups. We observed greater left- than right-hemisphere coherence in younger participants, and greater right- than left-hemisphere coherence in older participants. In addition, greater coherence was observed under eyes-closed than eyes-open recording conditions for both low-CR and high-CR participants, with a more substantial difference between recording conditions in individuals high in CR regardless of age. Finally, younger participants low in CR exhibited greater mean coherence than younger participants high in CR, whereas the opposite pattern was observed in older participants, with greater coherence in older participants high in CR. Together, these findings suggest the possibility of a shift in the relationship between CR and brain connectivity during aging.

Functional connectivity in the dorsal stream and between bilateral auditory-related cortical areas differentially contribute to speech decoding depending on spectro-temporal signal integrity and performance

Speech processing relies on the interdependence between auditory perception, sensorimotor integration, and verbal memory functions. Functional and structural connectivity between bilateral auditory-related cortical areas (ARCAs) facilitates spectro-temporal analyses, whereas the dynamic interplay between ARCAs and Broca's area (i.e., dorsal pathway) contributes to verbal memory functions, articulation, and sound-to-motor mapping. However, it remains unclear whether these two neural circuits are preferentially driven by spectral or temporal acoustic information, and whether their recruitment is predictive of speech perception performance and learning. Therefore, we evaluated EEG-based intracranial (eLORETA) functional connectivity (lagged coherence) in both pathways (i.e., between bilateral ARCAs and in the dorsal stream) while good- (GPs, N = 12) and poor performers (PPs, N = 13) learned to decode natural pseudowords (CLEAN) or comparable items (speech-noise chimeras) manipulated in the envelope (ENV) or in the fine-structure (FS). Learning to decode degraded speech was generally associated with increased functional connectivity in the theta, alpha, and beta frequency range in both circuits. Furthermore, GPs exhibited increased connectivity in the left dorsal stream compared to PPs, but only during the FS condition and in the theta frequency band. These results suggest that both pathways contribute to the decoding of spectro-temporal degraded speech by increasing the communication between brain regions involved in perceptual analyses and verbal memory functions. Otherwise, the left-hemispheric recruitment of the dorsal stream in GPs during the FS condition points to a contribution of this pathway to articulatory-based memory processes that are dependent on the temporal integrity of the speech signal. These results enable to better comprehend the neural circuits underlying word-learning as a function of temporal and spectral signal integrity and performance.

Increased functional connectivity in the ventral and dorsal streams during retrieval of novel words in professional musicians

Current models of speech and language processing postulate the involvement of two parallel processing streams (the dual stream model): a ventral stream involved in mapping sensory and phonological representations onto lexical and conceptual representations and a dorsal stream contributing to sound-to-motor mapping, articulation, and to how verbal information is encoded and manipulated in memory. Based on previous evidence showing that music training has an influence on language processing, cognitive functions, and word learning, we examined EEG-based intracranial functional connectivity in the ventral and dorsal streams while musicians and nonmusicians learned the meaning of novel words through picture-word associations. In accordance with the dual stream model, word learning was generally associated with increased beta functional connectivity in the ventral stream compared to the dorsal stream. In addition, in the linguistically most demanding "semantic task," musicians outperformed nonmusicians, and this behavioral advantage was accompanied by increased left-hemispheric theta connectivity in both streams. Moreover, theta coherence in the left dorsal pathway was positively correlated with the number of years of music training. These results provide evidence for a complex interplay within a network of brain regions involved in semantic processing and verbal memory functions, and suggest that intensive music training can modify its functional architecture leading to advantages in novel word learning.