Dynamic brain sources of visual evoked responses

Mobile neuroimaging: What we have learned about the neural control of human walking, with an emphasis on EEG-based research

Our understanding of the neural control of human walking has changed significantly over the last twenty years and mobile brain imaging methods have contributed substantially to current knowledge. High-density electroencephalography (EEG) has the advantages of being lightweight and mobile while providing temporal resolution of brain changes within a gait cycle. Advances in EEG hardware and processing methods have led to a proliferation of research on the neural control of locomotion in neurologically intact adults. We provide a narrative review of the advantages and disadvantages of different mobile brain imaging methods, then summarize findings from mobile EEG studies quantifying electrocortical activity during human walking. Contrary to historical views on the neural control of locomotion, recent studies highlight the widespread involvement of many areas, such as the anterior cingulate, posterior parietal, prefrontal, premotor, sensorimotor, supplementary motor, and occipital cortices, that show active fluctuations in electrical power during walking. The electrocortical activity changes with speed, stability, perturbations, and gait adaptation. We end with a discussion on the next steps in mobile EEG research.

Effect of Forward Head Posture on Resting State Brain Function

Forward head posture (FHP) is a common postural problem experienced by most people. However, its effect on brain activity is still unknown. Accordingly, we aimed to observe changes in brain waves at rest to determine the effect of FHP on the nervous systems. A total of 33 computer users (Male = 17; Female = 16; age = 22.18 ± 1.88) were examined in both FHP and neutral posture. For each session, brain waves were measured for 5 min, and then muscle mechanical properties and cranio-vertebral angle (CVA) were measured. Changes in brain waves between the neutral posture and FHP were prominent in gamma waves. A notable increase was confirmed in the frontal and parietal lobes. That is, eight channels in the frontal lobe and all channels in the parietal lobe showed a significant increase in FHP compared to neutral posture. Additionally, FHP changes were associated with a decrease in CVA (p < 0.001), an increase in levator scapulae tone (Right, p = 0.014; Left, p = 0.001), and an increase in right sternocleidomastoid stiffness (p = 0.002), and a decrease in platysma elasticity (Right, p = 0.039; Left, p = 0.017). The change in CVA was found to have a negative correlation with the gamma activity (P7, p = 0.044; P8, p = 0.004). Therefore, increased gamma wave activity in FHP appears to be related to CVA decrease due to external force that was applied to the nervous system and cervical spine.

Bayesian estimation of group event-related potential components (BEGEP): testing a model for synthetic and real datasets

Objective.The spatial resolution of event-related potentials (ERPs) recorded on the head surface is quite low, since the sensors located on the scalp register mixtures of signals from several cortical sources. Bayesian models for multi-channel ERPs obtained from a group of subjects under multiple task conditions can aid in recovering signals from these sources.Approach.This study introduces a novel model that captures several important characteristics of ERP, including person-to-person variability in the magnitude and latency of source signals. Furthermore, the model takes into account that ERP noise, the main source of which is the background electroencephalogram, has the following properties: it is spatially correlated, spatially heterogeneous, and varies over time and from person to person. Bayesian inference algorithms have been developed to estimate the parameters of this model, and their performance has been evaluated through extensive experiments using synthetic data and real ERPs records in a large number of subjects (N= 351).Main results.The signal estimates obtained using these algorithms were compared with the results of the analysis of ERPs by conventional methods. This comparison showed that the use of this model is suitable for the analysis of ERPs and helps to reveal some features of source signals that are difficult to observe in their mixture signals recorded on the scalp.Significance.This study shown that the proposed method is a potentially useful tool for analyzing ERPs collected from groups of subjects in various cognitive neuroscience experiments.

Events in context-The HED framework for the study of brain, experience and behavior

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between event processes themselves, that unfold through time, and event markers that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.

EEG Microstates as Markers for Cognitive Impairments in Fragile X Syndrome

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

Aligning Event-Related Potentials with Transcranial Alternating Current Stimulation for Modulation-a Review

This review aims to demonstrate the connections between event-related potentials (ERPs), event-related oscillations (EROs), and non-invasive brain stimulation (NIBS), with a specific focus on transcranial alternating current stimulation (tACS). We begin with a short examination and discussion of the relation between ERPs and EROs. Then, we investigate the diverse fields of NIBS, highlighting tACS as a potent tool for modulating neural oscillations and influencing cognitive performance. Emphasizing the impact of tACS on individual ERP components, this article offers insights into the potential of conventional tACS for targeted stimulation of single ERP components. Furthermore, we review recent articles that explore a novel approach of tACS: ERP-aligned tACS. This innovative technique exploits the temporal precision of ERP components, aligning tACS with specific neural events to optimize stimulation effects and target the desired neural response. In conclusion, this review combines current knowledge to explore how ERPs, EROs, and NIBS interact, particularly highlighting the modulatory possibilities offered by tACS. The incorporation of ERP-aligned tACS introduces new opportunities for future research, advancing our understanding of the complex connection between neural oscillations and cognitive processes.

Transcranial magnetic stimulation effects support an oscillatory model of ERP genesis

Whether prestimulus oscillatory brain activity contributes to the generation of post-stimulus-evoked neural responses has long been debated, but findings remain inconclusive. We first investigated the hypothesized relationship via EEG recordings during a perceptual task with this correlational evidence causally probed subsequently by means of online rhythmic transcranial magnetic stimulation. Both approaches revealed a close link between prestimulus individual alpha frequency (IAF) and P1 latency, with faster IAF being related to shorter latencies, best explained via phase-reset mechanisms. Moreover, prestimulus alpha amplitude predicted P3 size, best explained via additive (correlational and causal evidence) and baseline shift mechanisms (correlational evidence), each with distinct prestimulus alpha contributors. Finally, in terms of performance, faster prestimulus IAF and shorter P1 latencies were both associated with higher task accuracy, while lower prestimulus alpha amplitudes and higher P3 amplitudes were associated with higher confidence ratings. Our results are in favor of the oscillatory model of ERP genesis and modulation, shedding new light on the mechanistic relationship between prestimulus oscillations and functionally relevant evoked components.

Finding tau rhythms in EEG: An independent component analysis approach

Tau rhythms are largely defined by sound responsive alpha band (~8-13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low-pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8-13 Hz alpha band. These "tau ICs" showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high-pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high-pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.

Insights from simultaneous EEG-fMRI and patient data illuminate the role of the anterior medial temporal lobe in N400 generation

The N400, a negative event-related potential (ERP) peaking approximately 400 ms after stimulus onset, is known to reflect the processing of semantic information. While scalp recordings have contributed to understanding the psychological processes underlying the N400, they have been limited in identifying its neural basis. However, recent intracranial ERP recordings and fMRI studies have shed light on the crucial role of the anterior medial temporal lobe (AMTL) in semantic information processing. These findings suggest that the N400 partially represents activity in the AMTL structures. To investigate the neural underpinnings of the N400 effect, we simultaneously recorded ERPs and event-related fMRI during a semantic priming paradigm in a sample of 12 young, healthy subjects. Additionally, we collected ERPs and structural brain data from older healthy adults and patients with amnestic mild cognitive impairment (aMCI), a population characterized by neurodegenerative changes in the AMTL. In our fMRI results, we identified bilateral loci in the AMTL as the global maxima. Employing an EEG-informed fMRI analysis, we explored trial-to-trial fluctuations in semantic processing by linking single-trial N400 amplitudes to the Blood Oxygen Level Dependent (BOLD) signal. This approach provided the first direct evidence linking the N400 recorded at the scalp level to the corresponding BOLD signal in the AMTL. Consistent with these findings, patients with aMCI exhibited a diminished N400 effect compared to healthy older adults. Furthermore, voxel-based morphometry analysis revealed a correlation between the magnitude of the N400 effect and the integrity of the AMTL. By integrating data from simultaneous EEG-fMRI, and patient studies, our research advances our understanding of the neural substrate of the N400 and highlights the critical involvement of the AMTL in semantic processing.

A generative model of electrophysiological brain responses to stimulation

Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing, are fundamentally based on averages. Therefore, they can focus precisely on the features that are most stable across stimulus presentations. But which are these features exactly is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy that we observe in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding-and, therefore, likely reflect stable mechanisms of stimulus representation-have an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations.

Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG

Evoked responses and oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how most frequently studied EEG signals: the P300-evoked response and alpha oscillations (8-12 Hz) can be linked with the baseline-shift mechanism. This mechanism states that oscillations generate evoked responses if oscillations have a non-zero mean and their amplitude is modulated by the stimulus. Therefore, the following predictions should hold: (1) the temporal evolution of P300 and alpha amplitude is similar, (2) spatial localisations of the P300 and alpha amplitude modulation overlap, (3) oscillations are non-zero mean, (4) P300 and alpha amplitude correlate with cognitive scores in a similar fashion. To validate these predictions, we analysed the data set of elderly participants (N=2230, 60-82 years old), using (a) resting-state EEG recordings to quantify the mean of oscillations, (b) the event-related data, to extract parameters of P300 and alpha rhythm amplitude envelope. We showed that P300 is indeed linked to alpha rhythm, according to all four predictions. Our results provide an unifying view on the interdependency of evoked responses and neuronal oscillations and suggest that P300, at least partly, is generated by the modulation of alpha oscillations.

Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters

The present work investigates whether and how decisions in real-world online shopping scenarios can be predicted based on brain activation. Potential customers were asked to search through product pages on e-commerce platforms and decide, which products to buy, while their EEG signal was recorded. Machine learning algorithms were then trained to distinguish between EEG activation when viewing products that are later bought or put into the shopping card as opposed to products that are later discarded. We find that Hjorth parameters extracted from the raw EEG can be used to predict purchase choices to a high level of accuracy. Above-chance predictions based on Hjorth parameters are achieved via different standard machine learning methods with random forest models showing the best performance of above 80% prediction accuracy in both 2-class (bought or put into card vs. not bought) and 3-class (bought vs. put into card vs. not bought) classification. While conventional EEG signal analysis commonly employs frequency domain features such as alpha or theta power and phase, Hjorth parameters use time domain signals, which can be calculated rapidly with little computational cost. Given the presented evidence that Hjorth parameters are suitable for the prediction of complex behaviors, their potential and remaining challenges for implementation in real-time applications are discussed.

Is predictive coding falsifiable?

Predictive-coding has justifiably become a highly influential theory in Neuroscience. However, the possibility of its unfalsifiability has been raised. We argue that if predictive-coding were unfalsifiable, it would be a problem, but there are patterns of behavioural and neuroimaging data that would stand against predictive-coding. Contra (vanilla) predictive patterns are those in which the more expected stimulus generates the largest evoked-response. However, basic formulations of predictive-coding mandate that an expected stimulus should generate little, if any, prediction error and thus little, if any, evoked-response. It has, though, been argued that contra (vanilla) predictive patterns can be obtained if precision is higher for expected stimuli. Certainly, using precision, one can increase the amplitude of an evoked-response, turning a predictive into a contra (vanilla) predictive pattern. We demonstrate that, while this is true, it does not present an absolute barrier to falsification. This is because increasing precision also reduces latency and increases the frequency of the response. These properties can be used to determine whether precision-weighting in predictive-coding justifiably explains a contra (vanilla) predictive pattern, ensuring that predictive-coding is falsifiable.

ERP-aligned delta transcranial alternating current stimulation modulates the P3 amplitude

The underlying mechanisms of the event-related potential (ERP) generation are still under debate. One popular model considers the ERP as a superposition of phase-resets of ongoing endogenous oscillations of different frequencies. Brain oscillations have been shown to be modulated by transcranial alternating current stimulation (tACS). Thus, it seems feasible, that an ERP could be altered by modulating the contributing oscillations using tACS. One possible approach would be to target a frequency-matched stimulation signal to a specific ERP-component. One possible target for such an approach is the P3, which appears as delta/theta oscillations in the frequency-domain. Thus, an ERP-aligned stimulation in the delta/theta-range might be suitable to force synchronization in the stimulated frequency band and thus increase the amplitude of the P3 component. Building on an existing paradigm, in the present study 21 healthy participants received individualized ERP-aligned delta tACS and control stimulation while performing a visual task. The visual stimulation was matched to the continuous tACS in order to align the tACS peak with the P3 peak. Both the P3 amplitude and the evoked delta power were significantly increased after ERP-aligned tACS but not after control stimulation. The investigated behavioral parameter showed no stimulation dependent effect. Our results may provide new insights into the debate on the contribution of phase-reset mechanisms to the generation of ERPs and offer new opportunities for clinical trials.

Second Order Blind Identification of Event Related Potentials Sources

Event-related potentials (ERPs) recorded on the surface of the head are a mixture of signals from many sources in the brain due to volume conductions. As a result, the spatial resolution of the ERPs is quite low. Blind source separation can help to recover source signals from multichannel ERP records. In this study, we present a novel implementation of a method for decomposing multi-channel ERP into components, which is based on the modeling of second-order statistics of ERPs. We also report a new implementation of Bayesian Information Criteria (BIC), which is used to select the optimal number of hidden signals (components) in the original ERPs. We tested these methods using both synthetic datasets and real ERPs data arrays. Testing has shown that the ERP decomposition method can reconstruct the source signals from their mixture with acceptable accuracy even when these signals overlap significantly in time and the presence of noise. The use of BIC allows us to determine the correct number of source signals at the signal-to-noise ratio commonly observed in ERP studies. The proposed approach was compared with conventionally used methods for the analysis of ERPs. It turned out that the use of this new method makes it possible to observe such phenomena that are hidden by other signals in the original ERPs. The proposed method for decomposing a multichannel ERP into components can be useful for studying cognitive processes in laboratory settings, as well as in clinical studies.

Elevated EEG heartbeat-evoked potentials in adolescents with more ADHD symptoms

INTRODUCTION

Symptoms of attention deficit hyperactivity disorder (ADHD) are associated with a variety of mental abnormalities, but little is known about the perception and processing of internal signals, i.e., interoception, in individuals with ADHD symptoms. This study aimed to investigate the association between ADHD symptoms and the heartbeat-evoked potential (HEP), known as a neural correlate of automatic interoceptive processing of cardiac signals, in adolescents.

METHODS

HEPs of 47 healthy adolescent participants (53.2 % female) with a mean age of 14.29 years were measured during an emotional face recognition task. In addition, participants completed a self-report screening for ADHD symptoms.

RESULTS

ADHD symptoms were positively related to the HEP activity during the task in three of eight EEG sectors in the left hemisphere, as well as in all sectors in the right hemisphere.

DISCUSSION

This study is the first to demonstrate preliminary a relationship between the strength of HEP activity and ADHD symptoms in awake subjects. This finding of higher HEP amplitudes in subjects with more ADHD symptoms can be interpreted in terms of (i) increased arousal, (ii) altered neural processing of internal processes in an emotion-relevant task, and (iii) a misaligned precision-weighting process of task-irrelevant stimuli according to the predictive coding framework. These different interpretations could be reflected by previous studies showing heterogeneity of psychological deficits in individuals with ADHD symptoms. However, the generalizability to patients with diagnosed ADHD is limited due to the measurement tool for ADHD symptoms and the sample characteristics.

Observing memory encoding while it unfolds: Functional interpretation and current debates regarding ERP subsequent memory effects

Our ability to remember the past depends on neural processes set in train in the moment an event is experienced. These processes can be studied by segregating brain activity according to whether an event is later remembered or forgotten. The present review integrates a large number of studies examining this differential brain activity, labeled subsequent memory effect (SME), with the ERP technique, into a functional organization and discusses routes for further research. Based on the reviewed literature, we suggest that memory encoding is implemented by multiple processes, typically reflected in three functionally different subcomponents of the ERP SME elicited by study stimuli, which presumably interact with preparatory SME activity preceding the to be encoded event. We argue that ERPs are a valuable method in the SME paradigm because they have a sufficiently high temporal resolution to disclose the subcomponents of encoding-related brain activity. Implications of the proposed functional organization for future studies using the SME procedure in basic and applied settings will be discussed.

Removing the cardiac field artifact from the EEG using neural network regression

When EEG recordings are used to reveal interactions between central-nervous and cardiovascular processes, the cardiac field artifact (CFA) poses a major challenge. Because the electric field generated by cardiac activity is also captured by scalp electrodes, the CFA arises as a heavy contaminant whenever EEG data are analyzed time-locked to cardio-electric events. A typical example is measuring stimulus-evoked potentials elicited at different phases of the cardiac cycle. Here, we present a nonlinear regression method deploying neural networks that allows to remove the CFA from the EEG signal in such scenarios. We train neural network models to predict R-peak centered EEG episodes based on the ECG and additional CFA-related information. In a second step, these trained models are used to predict and consequently remove the CFA in EEG episodes containing visual stimulation occurring time-locked to the ECG. We show that removing these predictions from the signal effectively removes the CFA without affecting the intertrial phase coherence of stimulus-evoked activity. In addition, we provide the results of an extensive grid search suggesting a set of appropriate model hyperparameters. The proposed method offers a replicable way of removing the CFA on the single-trial level, without affecting stimulus-related variance occurring time-locked to cardiac events. Disentangling the cardiac field artifact (CFA) from the EEG signal is a major challenge when investigating the neurocognitive impact of cardioafferent traffic by means of the EEG. When stimuli are presented time-locked to the cardiac cycle, both sources of variance are systematically confounded. Here, we propose a regression-based approach deploying neural network models to remove the CFA from the EEG. This approach effectively removes the CFA on a single-trial level and is purely data-driven, providing replicable results.

Acute pain drives different effects on local and global cortical excitability in motor and prefrontal areas: insights into interregional and interpersonal differences in pain processing

Pain-related depression of corticomotor excitability has been explored using transcranial magnetic stimulation-elicited motor-evoked potentials. Transcranial magnetic stimulation-electroencephalography now enables non-motor area cortical excitability assessments, offering novel insights into cortical excitability changes during pain states. Here, pain-related cortical excitability changes were explored in the dorsolateral prefrontal cortex and primary motor cortex (M1). Cortical excitability was recorded in 24 healthy participants before (Baseline), during painful heat (Acute Pain), and non-noxious warm (Warm) stimulation at the right forearm in a randomized sequence, followed by a pain-free stimulation measurement. Local cortical excitability was assessed as the peak-to-peak amplitude of early transcranial magnetic stimulation evoked potential, whereas global-mean field power measured the global excitability. Relative to the Baseline, Acute Pain decreased the peak-to-peak amplitude in M1 and dorsolateral prefrontal cortex compared with Warm (both P < 0.05). A reduced global-mean field power was only found in M1 during Acute Pain compared with Warm (P = 0.003). Participants with the largest reduction in local cortical excitability under Acute Pain showed a negative correlation between dorsolateral prefrontal cortex and M1 local cortical excitability (P = 0.006). Acute experimental pain drove differential pain-related effects on local and global cortical excitability changes in motor and non-motor areas at a group level while also revealing different interindividual patterns of cortical excitability changes, which can be explored when designing personalized treatment plans.

A whole-task brain model of associative recognition that accounts for human behavior and neuroimaging data

Brain models typically focus either on low-level biological detail or on qualitative behavioral effects. In contrast, we present a biologically-plausible spiking-neuron model of associative learning and recognition that accounts for both human behavior and low-level brain activity across the whole task. Based on cognitive theories and insights from machine-learning analyses of M/EEG data, the model proceeds through five processing stages: stimulus encoding, familiarity judgement, associative retrieval, decision making, and motor response. The results matched human response times and source-localized MEG data in occipital, temporal, prefrontal, and precentral brain regions; as well as a classic fMRI effect in prefrontal cortex. This required two main conceptual advances: a basal-ganglia-thalamus action-selection system that relies on brief thalamic pulses to change the functional connectivity of the cortex, and a new unsupervised learning rule that causes very strong pattern separation in the hippocampus. The resulting model shows how low-level brain activity can result in goal-directed cognitive behavior in humans.

The Effect of the Peristimulus α Phase on Visual Perception through Real-Time Phase-Locked Stimulus Presentation

The α phase has been theorized to reflect fluctuations in cortical excitability and thereby impose a cyclic influence on visual perception. Despite its appeal, this notion is not fully substantiated, as both supporting and opposing evidence has been recently reported. In contrast to previous research, this study examined the effect of the peristimulus instead of prestimulus phase on visual detection through a real-time phase-locked stimulus presentation (PLSP) approach. Specifically, we monitored phase data from magnetoencephalography (MEG) recordings over time, with a newly developed algorithm based on adaptive Kalman filtering (AKF). This information guided online presentations of masked stimuli that were phased-locked to different stages of the α cycle while healthy humans concurrently performed detection tasks. Behavioral evidence showed that the overall detection rate did not significantly vary according to the four predetermined peristimulus α phases. Nevertheless, the follow-up analyses highlighted that the phase at 90° relative to 180° likely enhanced detection. Corroborating neural parietal activity showed that early interaction between α phases and incoming stimuli orchestrated the neural representation of the hits and misses of the stimuli. This neural representation varied according to the phase and in turn shaped the behavioral outcomes. In addition to directly investigating to what extent fluctuations in perception can be ascribed to the α phases, this study suggests that phase-dependent perception is not as robust as previously presumed, and might also depend on how the stimuli are differentially processed as a result of a stimulus-phase interaction, in addition to reflecting alternations of the perceptual states between phases.

The Objective Assessment of Event-Related Potentials: An Influence of Chronic Pain on ERP Parameters

The article presents an original method for the automatic assessment of the quality of event-related potentials (ERPs), based on the calculation of the coefficient ε, which describes the compliance of recorded ERPs with some statistically significant parameters. This method was used to analyze the neuropsychological EEG monitoring of patients suffering from migraines. The frequency of migraine attacks was correlated with the spatial distribution of the coefficients ε, calculated for EEG channels. More than 15 migraine attacks per month was accompanied by an increase in calculated values in the occipital region. Patients with infrequent migraines exhibited maximum quality in the frontal areas. The automatic analysis of spatial maps of the coefficient ε demonstrated a statistically significant difference between the two analyzed groups with different means of migraine attack numbers per month.

Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques

BACKGROUND

Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.

METHODS

EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.

RESULTS

The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers.

CONCLUSION

The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

Microsaccades transiently lateralise EEG alpha activity

The lateralisation of 8-12 Hz alpha activity is a canonical signature of human spatial cognition that is typically studied under strict fixation requirements. Yet, even during attempted fixation, the brain produces small involuntary eye movements known as microsaccades. Here we report how spontaneous microsaccades - made in the absence of incentives to look elsewhere - can themselves drive transient lateralisation of EEG alpha power according to microsaccade direction. This transient lateralisation of posterior alpha power occurs similarly following start and return microsaccades and is, at least for start microsaccades, driven by increased alpha power ipsilateral to microsaccade direction. This reveals new links between spontaneous microsaccades and human electrophysiological brain activity. It highlights how microsaccades are an important factor to consider in studies relating alpha activity - including spontaneous fluctuations in alpha activity - to spatial cognition, such as studies on visual attention, anticipation, and working memory.

Alpha Event-Related Desynchronization During Reward Processing in Schizophrenia

BACKGROUND

Alterations in the brain's reward system may underlie motivation and pleasure deficits in schizophrenia (SZ). Neuro-oscillatory desynchronization in the alpha band is thought to direct resource allocation away from the internal state, to prioritize processing salient environmental events, including reward feedback. We hypothesized reduced reward-related alpha event-related desynchronization (ERD) in SZ, consistent with less externally focused processing during reward feedback.

METHODS

Electroencephalography was recorded while participants with SZ (n = 54) and healthy control participants (n = 54) played a simple slot machine task. Total alpha band power (8-14 Hz), a measure of neural oscillation magnitude, was extracted via principal component analysis and compared between groups and reward outcomes. The clinical relevance of hypothesized alpha power alterations was examined by testing associations with negative symptoms within the SZ group and with trait rumination, dimensionally, across groups.

RESULTS

A group × reward outcome interaction (p = .018) was explained by healthy control participants showing significant posterior-occipital alpha power suppression to wins versus losses (p < .001), in contrast to participants with SZ (p > .1). Among participants with SZ, this alpha ERD was unrelated to negative symptoms (p > .1). Across all participants, less alpha ERD to reward outcomes covaried with greater trait rumination for both win (p = .005) and loss (p = .002) outcomes, with no group differences in slope.

CONCLUSIONS

These findings demonstrate alpha ERD alterations in SZ during reward outcome processing. Additionally, higher trait rumination was associated with less alpha ERD during reward feedback, suggesting that individual differences in rumination covary with external attention to reward processing, regardless of reward outcome valence or group membership.

Parietal alpha underlies slower cognitive responses during interference processing in adolescents

Adolescence is a critical period when cognitive control is rapidly maturing across several core dimensions. Here, we evaluated how healthy adolescents (13-17 years of age, n = 44) versus young adults (18-25 years of age, n = 49) differ across a series of cognitive assessments with simultaneous electroencephalography (EEG) recordings. Cognitive tasks included selective attention, inhibitory control, working memory, as well as both non-emotional and emotional interference processing. We found that adolescents displayed significantly slower responses than young adults specifically on the interference processing tasks. Evaluation of EEG event-related spectral perturbations (ERSPs) on the interference tasks showed that adolescents consistently had greater event-related desynchronization in alpha/beta frequencies in parietal regions. Midline frontal theta activity was also greater in the flanker interference task in adolescents, suggesting greater cognitive effort. Parietal alpha activity predicted age-related speed differences during non-emotional flanker interference processing, and frontoparietal connectivity, specifically midfrontal theta - parietal alpha functional connectivity predicted speed effects during emotional interference. Overall, our neuro-cognitive results illustrate developing cognitive control in adolescents particularly for interference processing, predicted by differential alpha band activity and connectivity in parietal brain regions.

Updating the relationship of the Ne/ERN to task-related behavior: A brief review and suggestions for future research

The error negativity/error-related negativity (Ne/ERN) is one of the most well-studied event-related potential (ERP) components in the electroencephalography (EEG) literature. Peaking about 50 ms after the commission of an error, the Ne/ERN is a negative deflection in the ERP waveform that is thought to reflect error processing in the brain. While its relationships to trait constructs such as anxiety are well-documented, there is still little known about how the Ne/ERN may subsequently influence task-related behavior. In other words, does the occurrence of the Ne/ERN trigger any sort of error corrective process, or any other behavioral adaptation to avoid errors? Several theories have emerged to explain how the Ne/ERN may implement or affect behavior on a task, but evidence supporting each has been mixed. In the following manuscript, we review these theories, and then systematically discuss the reasons that there may be discrepancies in the literature. We review both the inherent biological factors of the neural regions that underlie error-processing in the brain, and some of the researcher-induced factors in analytic and experimental choices that may be exacerbating these discrepancies. We end with a table of recommendations for future researchers who aim to understand the relationship between the Ne/ERN and behavior.

Oscillatory biomarkers of autism: evidence from the innate visual fear evoking paradigm

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with multiple associated deficits in both social and cognitive functioning. Diagnosing ASD usually relies on subjective clinical competencies, and research on objective criteria for diagnosing ASD in the early stage is still in its infancy. A recent animal study showed that the looming-evoked defensive response was impaired in mice with ASD, but whether the effect will be observed in human and contribute to finding a robust clinical neural biomarker remain unclear. Here, to investigate the looming-evoked defense response in humans, electroencephalogram responses toward looming and corresponding control stimuli (far and missing type) were recorded in children with ASD and typical developed (TD) children. Results revealed that alpha-band activity in the posterior brain region was strongly suppressed after looming stimuli in the TD group, but remained unchanged in the ASD group. This method could be a novel, objective way to detect ASD earlier. These findings suggest that further investigation of the neural mechanism underlying innate fear from the oscillatory view could be a helpful direction in the future.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09839-6.

Multiple Dipole Source Position and Orientation Estimation Using Non-Invasive EEG-like Signals

The problem of precisely estimating the position and orientation of multiple dipoles using synthetic EEG signals is considered in this paper. After determining a proper forward model, a nonlinear constrained optimization problem with regularization is solved, and the results are compared with a widely used research code, namely EEGLAB. A thorough sensitivity analysis of the estimation algorithm to the parameters (such as the number of samples and sensors) in the assumed signal measurement model is conducted. To confirm the efficacy of the proposed source identification algorithm on any category of data sets, three different kinds of data-synthetic model data, visually evoked clinical EEG data, and seizure clinical EEG data are used. Furthermore, the algorithm is tested on both the spherical head model and the realistic head model based on the MNI coordinates. The numerical results and comparisons with the EEGLAB show very good agreement, with little pre-processing required for the acquired data.

Gaze onsets during naturalistic infant-caregiver interaction associate with 'sender' but not 'receiver' neural responses, and do not lead to changes in inter-brain synchrony

Temporal coordination during infant-caregiver social interaction is thought to be crucial for supporting early language acquisition and cognitive development. Despite a growing prevalence of theories suggesting that increased inter-brain synchrony associates with many key aspects of social interactions such as mutual gaze, little is known about how this arises during development. Here, we investigated the role of mutual gaze onsets as a potential driver of inter-brain synchrony. We extracted dual EEG activity around naturally occurring gaze onsets during infant-caregiver social interactions in N = 55 dyads (mean age 12 months). We differentiated between two types of gaze onset, depending on each partners' role. 'Sender' gaze onsets were defined at a time when either the adult or the infant made a gaze shift towards their partner at a time when their partner was either already looking at them (mutual) or not looking at them (non-mutual). 'Receiver' gaze onsets were defined at a time when their partner made a gaze shift towards them at a time when either the adult or the infant was already looking at their partner (mutual) or not (non-mutual). Contrary to our hypothesis we found that, during a naturalistic interaction, both mutual and non-mutual gaze onsets were associated with changes in the sender, but not the receiver's brain activity and were not associated with increases in inter-brain synchrony above baseline. Further, we found that mutual, compared to non-mutual gaze onsets were not associated with increased inter brain synchrony. Overall, our results suggest that the effects of mutual gaze are strongest at the intra-brain level, in the 'sender' but not the 'receiver' of the mutual gaze.

A static paradigm based on illusion-induced VEP for brain-computer interfaces

OBJECTIVE

Visual evoked potentials (VEPs) have been commonly applied in brain-computer interfaces (BCIs) due to their satisfactory classification performance recently. However, most existing methods with flickering or oscillating stimuli will induce visual fatigue under long-term training, thus restricting the implementation of VEP-based BCIs. To address this issue, a novel paradigm adopting static motion illusion based on illusion-induced visual evoked potential (IVEP) is proposed for BCIs to enhance visual experience and practicality.

APPROACH

This study explored the responses to baseline and illusion tasks including the Rotating-Tilted-Lines (RTL) illusion and Rotating-Snakes (RS) illusion. The distinguishable features were examined between different illusions by analyzing the event-related potentials (ERPs) and amplitude modulation of evoked oscillatory responses.

MAIN RESULTS

The illusion stimuli elicited VEPs in an early time window encompassing a negative component (N1) from 110 to 200 ms and a positive component (P2) between 210 and 300 ms. Based on the feature analysis, a filter bank was designed to extract discriminative signals. The task-related component analysis (TRCA) was used to evaluate the binary classification task performance of the proposed method. Then the highest accuracy of 86.67% was achieved with a data length of 0.6 s.

SIGNIFICANCE

The results of this study demonstrate that the static motion illusion paradigm has the feasibility of implementation and is promising for VEP-based BCI applications.

Uncovering the Interplay of Oscillatory Processes During Creative Problem Solving: A Dynamic Modeling Approach

To solve a new problem, people spontaneously engage multiple cognitive processes. Previous work has identified a diverse set of oscillatory components critical at different stages of creative problem solving. In this project, we use hidden state modeling to untangle the roles of oscillation processes over time as people solve puzzles. Building on earlier work, we further developed analytical methods, such as incorporating source separating techniques and identifying the optimal number of states using cross-validation. We extracted brain states characterized by spatio-spectral topographies from time-resolved EEG spectral powers. The data driven approach allowed us to infer the dynamic, trial-by-trial, state sequences, and provided a comprehensive depiction of how various oscillation components interact recurrently throughout the trial. The properties of the states suggest their dissociable cognitive functions. For example, we identified three states with dominant activation in alpha bands but having distinct spatial distributions. People were differentially engaged in these states depending on the stages (e.g., onset or response) and outcomes of the trials (solved with insight or analysis). The current approach, applicable to many tasks requiring extended trial duration, can potentially reconcile findings from previous EEG studies and drive new hypotheses to further our understanding of the complex creative process.

Individual theta-band cortical entrainment to speech in quiet predicts word-in-noise comprehension

Speech elicits brain activity time-locked to its amplitude envelope. The resulting speech-brain synchrony (SBS) is thought to be crucial to speech parsing and comprehension. It has been shown that higher speech-brain coherence is associated with increased speech intelligibility. However, studies depending on the experimental manipulation of speech stimuli do not allow conclusion about the causality of the observed tracking. Here, we investigate whether individual differences in the intrinsic propensity to track the speech envelope when listening to speech-in-quiet is predictive of individual differences in speech-recognition-in-noise, in an independent task. We evaluated the cerebral tracking of speech in source-localized magnetoencephalography, at timescales corresponding to the phrases, words, syllables and phonemes. We found that individual differences in syllabic tracking in right superior temporal gyrus and in left middle temporal gyrus (MTG) were positively associated with recognition accuracy in an independent words-in-noise task. Furthermore, directed connectivity analysis showed that this relationship is partially mediated by top-down connectivity from premotor cortex-associated with speech processing and active sensing in the auditory domain-to left MTG. Thus, the extent of SBS-even during clear speech-reflects an active mechanism of the speech processing system that may confer resilience to noise.

Neural dynamics driving audio-visual integration in autism

Audio-visual (AV) integration plays a crucial role in supporting social functions and communication in autism spectrum disorder (ASD). However, behavioral findings remain mixed and, importantly, little is known about the underlying neurophysiological bases. Studies in neurotypical adults indicate that oscillatory brain activity in different frequencies subserves AV integration, pointing to a central role of (i) individual alpha frequency (IAF), which would determine the width of the cross-modal binding window; (ii) pre-/peri-stimulus theta oscillations, which would reflect the expectation of AV co-occurrence; (iii) post-stimulus oscillatory phase reset, which would temporally align the different unisensory signals. Here, we investigate the neural correlates of AV integration in children with ASD and typically developing (TD) peers, measuring electroencephalography during resting state and in an AV integration paradigm. As for neurotypical adults, AV integration dynamics in TD children could be predicted by the IAF measured at rest and by a modulation of anticipatory theta oscillations at single-trial level. Conversely, in ASD participants, AV integration/segregation was driven exclusively by the neural processing of the auditory stimulus and the consequent auditory-induced phase reset in visual regions, suggesting that a disproportionate elaboration of the auditory input could be the main factor characterizing atypical AV integration in autism.

Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

OBJECTIVE

The Temporal Response Function (TRF) is a linear model of neural activity time-locked to continuous stimuli, including continuous speech. TRFs based on speech envelopes typically have distinct components that have provided remarkable insights into the cortical processing of speech. However, current methods may lead to less than reliable estimates of single-subject TRF components. Here, we compare two established methods, in TRF component estimation, and also propose novel algorithms that utilize prior knowledge of these components, bypassing the full TRF estimation.

METHODS

We compared two established algorithms, ridge and boosting, and two novel algorithms based on Subspace Pursuit (SP) and Expectation Maximization (EM), which directly estimate TRF components given plausible assumptions regarding component characteristics. Single-channel, multi-channel, and source-localized TRFs were fit on simulations and real magnetoencephalographic data. Performance metrics included model fit and component estimation accuracy.

RESULTS

Boosting and ridge have comparable performance in component estimation. The novel algorithms outperformed the others in simulations, but not on real data, possibly due to the plausible assumptions not actually being met. Ridge had slightly better model fits on real data compared to boosting, but also more spurious TRF activity.

CONCLUSION

Results indicate that both smooth (ridge) and sparse (boosting) algorithms perform comparably at TRF component estimation. The SP and EM algorithms may be accurate, but rely on assumptions of component characteristics.

SIGNIFICANCE

This systematic comparison establishes the suitability of widely used and novel algorithms for estimating robust TRF components, which is essential for improved subject-specific investigations into the cortical processing of speech.

Attentional impairment in Parkinson's disease is modulated by side of onset: Neurophysiological evidence

OBJECTIVE

Neurophysiological studies exploring involuntary attention have reported that electroencephalographic (EEG) measures can indicate impaired neural processing from initial stages of Parkinson's disease (PD). Since involuntary attention is regulated by right hemisphere networks and PD generally initiates its motor symptomatology unilaterally, whether involuntary attention is impaired depending on the onset side of PD remains unknown.

METHODS

We compared the neurophysiological correlates of involuntary attention among a PD group with left-side onset (L-PD), a PD group with right-side onset (R-PD) symptomatology, and a healthy control group (HC). All participants performed an auditory involuntary attention task while a digital EEG was recorded.

RESULTS

Our main finding was a reduction both in the P3a amplitude and evoked delta-theta phase alignment in the L-PD group compared to the HC. Further, there was a significant correlation between P3a amplitude and disease duration in the R-PD, but not in the L-PD group. Behaviorally, both clinical groups, and in particular L-PD, showed reduced orientation towards novel stimuli, and no reduction of distraction effects during the experiment.

CONCLUSIONS

Our results indicate that involuntary attention is differentially impaired in patients with left side onset of symptoms. Involuntary attention impairment might be present from initial stages of left onset PD and become progressively compromised in patients with right onset PD.

SIGNIFICANCE

The onset side of symptomatology should be considered for attentional impairment in PD.

Frequency modulation of cortical rhythmicity governs behavioral variability, excitability and synchrony of neurons in the visual cortex

Research in cognitive neuroscience has renewed the idea that brain oscillations are a core organization implicated in fundamental brain functions. Growing evidence reveals that the characteristic features of these oscillations, including power, phase and frequency, are highly non-stationary, fluctuating alongside alternations in sensation, cognition and behavior. However, there is little consensus on the functional implications of the instantaneous frequency variation in cortical excitability and concomitant behavior. Here, we capitalized on intracortical electrophysiology in the macaque monkey's visual area MT performing a visuospatial discrimination task with visual cues. We observed that the instantaneous frequency of the theta-alpha oscillations (4-13 Hz) is modulated among specific neurons whose RFs overlap with the cued stimulus location. Interestingly, we found that such frequency modulation is causally correlated with MT excitability at both scales of individual and ensemble of neurons. Moreover, studying the functional relevance of frequency variations indicated that the average theta-alpha frequencies foreshadow the monkey's reaction time. Our results also revealed that the neural synchronization strength alters with the average frequency shift in theta-alpha oscillations, suggesting frequency modulation is critical for mutually adjusting MTs' rhythms. Overall, our findings propose that theta-alpha frequency variations modulate MT's excitability, regulate mutual neurons' rhythmicity and indicate variability in behavior.

Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model

Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network.

Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data

SIGNIFICANCE

Current techniques for data analysis in functional near-infrared spectroscopy (fNIRS), such as artifact correction, do not allow to integrate the information originating from both wavelengths, considering only temporal and spatial dimensions of the signal's structure. Parallel factor analysis (PARAFAC) has previously been validated as a multidimensional decomposition technique in other neuroimaging fields.

AIM

We aimed to introduce and validate the use of PARAFAC for the analysis of fNIRS data, which is inherently multidimensional (time, space, and wavelength).

APPROACH

We used data acquired in 17 healthy adults during a verbal fluency task to compare the efficacy of PARAFAC for motion artifact correction to traditional two-dimensional decomposition techniques, i.e., target principal (tPCA) and independent component analysis (ICA). Correction performance was further evaluated under controlled conditions with simulated artifacts and hemodynamic response functions.

RESULTS

PARAFAC achieved significantly higher improvement in data quality as compared to tPCA and ICA. Correction in several simulated signals further validated its use and promoted it as a robust method independent of the artifact's characteristics.

CONCLUSIONS

This study describes the first implementation of PARAFAC in fNIRS and provides validation for its use to correct artifacts. PARAFAC is a promising data-driven alternative for multidimensional data analyses in fNIRS and this study paves the way for further applications.

Compensatory mechanism of attention-deficit/hyperactivity disorder recovery in resting state alpha rhythms

Alpha rhythms in the human electroencephalogram (EEG), oscillating at 8-13 Hz, are located in parieto-occipital cortex and are strongest when awake people close their eyes. It has been suggested that alpha rhythms were related to attention-related functions and mental disorders (e.g., Attention-deficit/hyperactivity disorder (ADHD)). However, many studies have shown inconsistent results on the difference in alpha oscillation between ADHD and control groups. Hence it is essential to verify this difference. In this study, a dataset of EEG recording (128 channel EGI) from 87 healthy controls (HC) and 162 ADHD (141 persisters and 21 remitters) adults in a resting state with their eyes closed was used to address this question and a three-gauss model (summation of baseline and alpha components) was conducted to fit the data. To our surprise, the power of alpha components was not a significant difference among the three groups. Instead, the baseline power of remission and HC group in the alpha band is significantly stronger than that of persister groups. Our results suggest that ADHD recovery may have compensatory mechanisms and many abnormalities in EEG may be due to the influence of behavior rather than the difference in brain signals.

The degree of mu rhythm suppression in women is associated with presence of children as well as empathy and anxiety level

In experiments on observing and performing social gestures, the level of mu rhythm suppression is associated with the activity of the mirror neuron system (MNS), which is responsible for the perception and understanding of nonverbal signals in social communication. In turn, while MNS activity may be associated primarily with empathy, it is also associated with other psychological and demographic factors affecting the effectiveness of cortical neural networks.In this study, we verified the influence of empathy, state and trait anxiety levels, presence and number of children, age, and menstrual cycle phase on the mu-suppression level in 40 women. We used 32-channel EEG recorded during observation, and synchronous execution of various hand movements. The ICA infomax method was used for decomposing and selecting the left hemisphere component of the mu-rhythm.Mu-suppression was higher in women with one child, with higher levels of empathy, and with lower anxiety levels. It is possible that MNS activity is stronger in women during parental care.

Brain potential responses involved in decision-making in weightlessness

The brain is essential to human adaptation to any environment including space. We examined astronauts’ brain function through their electrical EEG brain potential responses related to their decision of executing a docking task in the same virtual scenario in Weightlessness and on Earth before and after the space stay of 6 months duration. Astronauts exhibited a P300 component in which amplitude decreased during, and recovered after, their microgravity stay. This effect is discussed as a post-value-based decision-making closing mechanism; The P300 amplitude decrease in weightlessness is suggested as an emotional stimuli valence reweighting during which orbitofrontal BA10 would play a major role. Additionally, when differentiating the bad and the good docks on Earth and in Weightlessness and keeping in mind that astronauts were instantaneously informed through a visual cue of their good or bad performance, it was observed that the good dockings resulted in earlier voltage redistribution over the scalp (in the 150–250 ms period after the docking) than the bad dockings (in the 250–400 ms) in Weightlessness. These results suggest that in Weightlessness the knowledge of positive or negative valence events is processed differently than on Earth.

Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions.

Decoding brain activities of literary metaphor comprehension: An event-related potential and EEG spectral analysis

Novel metaphors in literary texts (hereinafter referred to as literary metaphors) seem to be more creative and open-ended in meaning than metaphors in non-literary texts (non-literary metaphors). However, some disagreement still exists on how literary metaphors differ from non-literary metaphors. Therefore, this study explored the neural mechanisms of literary metaphors extracted from modern Chinese poetry by using the methods of Event-Related Potentials (ERPs) and Event-Related Spectral Perturbations (ERSPs), as compared with non-literary conventional metaphors and literal expressions outside literary texts. Forty-eight subjects were recruited to make the semantic relatedness judgment after reading the prime-target pairs in three linguistic conditions. According to the ERPs results, the earliest differences were presented during the time window of P200 component (170–260 ms) in the frontal and central areas, with the amplitude of P200 for literary metaphors more positive than the other two conditions, reflecting the early allocation of attention and the early conscious experience of the experimental stimuli. Meanwhile, significant differences were presented during the time window of N400 effect (430–530 ms), with the waveform of literary metaphors more negative than others in the frontal and central topography of scalp distributions, suggesting more efforts in retrieving conceptual knowledge for literary metaphors. The ERSPs analysis revealed that the frequency bands of delta and theta were both involved in the cognitive process of literary metaphor comprehension, with delta band distributed in the frontal and central scalp and theta band in parietal and occipital electrodes. Increases in the two power bands during different time windows provided extra evidences that the processing of literary metaphors required more attention and effort than non-literary metaphors and literal expressions in the semantic related tasks, suggesting that the cognitive process of literary metaphors was distinguished by different EEG spectral patterns.

A Combination of Green Tea, Rhodiola, Magnesium, and B Vitamins Increases Electroencephalogram Theta Activity During Attentional Task Performance Under Conditions of Induced Social Stress

Background

A combination of green tea, rhodiola and magnesium with B vitamins has previously been reported to significantly increase EEG resting state theta, attenuate subjective stress, anxiety and mood disturbance, and heighten subjective and autonomic arousal under acute psychosocial laboratory stress. Here we examine the capacity of green tea and rhodiola extract administered in combination or in isolation with magnesium and B vitamins to moderate spectral brain activity during attentional task performance under stress.

Materials and Methods

One-hundred moderately stressed adults received oral supplementation of (i) Mg + B vitamins + green tea + rhodiola; (ii) Mg + B vitamins + rhodiola; (iii) Mg + B vitamins + green tea; or (iv) placebo, in a double-blind, randomised, placebo-controlled, parallel-group design (Clinicaltrials.gov: NCT03262376; 25/0817). Participants completed an attention switching and emotionally threatening attentional bias task after stress induction (Trier Social Stress Test). Spectral alpha and theta brain activity and event related potentials (ERPs) were recorded during cognitive task performance by electroencephalogram (EEG; BioSemi ActiveTwo 64 channel).

Results

The combined treatment of Mg + B vitamins + green tea + rhodiola significantly increased frontal midline theta vs. placebo and rhodiola in isolation during the attention switching task, specifically in anticipation of a change in task performance parameter. The combined treatment also significantly increased contralateral theta activation in relation to viewing emotionally threatening images in the left (vs. placebo and rhodiola in isolation) and right parietal (vs. placebo) regions. Further, this treatment demonstrated significantly heightened ipsilateral left parietal theta activation in relation to viewing emotionally threatening images. The combined treatment attenuated a decrease in alpha power during the attentional bias task evident in comparator treatments, but this did not reach significance. No significant effects of treatments on behavioural performance or ERP were found.

Conclusion

The combination of Mg + B vitamins + green tea + rhodiola increased spectral theta brain activity during the execution of two attentional tasks suggestive of a potential to increase attentional capacity under conditions of stress. Further examination of these ingredients in relation to attentional performance under stress is warranted to ascertain if functional benefits suggested by theta activation can be shown behaviourally.

Physiological symmetry of transcranial magnetic stimulation-evoked EEG spectral features

Transcranial magnetic stimulation (TMS)-evoked EEG potentials (TEPs) have been used to study the excitability of different cortical areas (CAs) in humans. Characterising the interhemispheric symmetry of TMS-EEG may provide further understanding of structure-function association in physiological and pathological conditions. We hypothesise that, in keeping with the underlying cytoarchitectonics, TEPs in contralateral homologous CAs share similar, symmetric spectral features, whilst ipsilateral TEPs from different CAs diverge in their waveshape and frequency content. We performed single-pulse (<1 Hz) navigated monophasic TMS, combined with high-density EEG with active electrodes, in 10 healthy participants. We targeted two bilateral CAs: premotor and motor. We compared frequency power bands, computed Pearson correlation coefficient (R) and Correlated Component Analysis (CorrCA) to detect divergences, as well as common components across TEPs. The main frequency of TEPs was faster in premotor than in motor CAs (p < .05) across all participants. Frequencies were not different between contralateral homologous CAs, whilst, despite closer proximity, there was a significant difference between ipsilateral premotor and motor CAs (p > .5), with frequency decreasing from anterior to posterior CAs. Correlation was high between contralateral homologous CAs and low between ipsilateral CAs. When applying CorrCA, specific components were shared by contralateral homologous TEPs. We show physiological symmetry of TEP spectral features between contralateral homologous CAs, whilst ipsilateral premotor and motor TEPs differ despite lower geometrical distance. Our findings support the role of TEPs as biomarker of local cortical properties and provide a first reference dataset for TMS-EEG studies in asymmetric brain disorders.

Auditory driven gamma synchrony is associated with cortical thickness in widespread cortical areas

OBJECTIVE

Gamma synchrony is a fundamental functional property of the cerebral cortex, impaired in multiple neuropsychiatric conditions (i.e. schizophrenia, Alzheimer's disease, stroke etc.). Auditory stimulation in the gamma range allows to drive gamma synchrony of the entire cortical mantle and to estimate the efficiency of the mechanisms sustaining it. As gamma synchrony depends strongly on the interplay between parvalbumin-positive interneurons and pyramidal neurons, we hypothesize an association between cortical thickness and gamma synchrony. To test this hypothesis, we employed a combined magnetoencephalography (MEG) - Magnetic Resonance Imaging (MRI) study.

METHODS

Cortical thickness was estimated from anatomical MRI scans. MEG measurements related to exposure of 40 Hz amplitude modulated tones were projected onto the cortical surface. Two measures of cortical synchrony were considered: (a) inter-trial phase consistency at 40 Hz, providing a vertex-wise estimation of gamma synchronization, and (b) phase-locking values between primary auditory cortices and whole cortical mantle, providing a measure of long-range cortical synchrony. A correlation between cortical thickness and synchronization measures was then calculated for 72 MRI-MEG scans.

RESULTS

Both inter-trial phase consistency and phase locking values showed a significant positive correlation with cortical thickness. For inter-trial phase consistency, clusters of strong associations were found in the temporal and frontal lobes, especially in the bilateral auditory and pre-motor cortices. Higher phase-locking values corresponded to higher cortical thickness in the frontal, temporal, occipital and parietal lobes.

DISCUSSION AND CONCLUSIONS

In healthy subjects, a thicker cortex corresponds to higher gamma synchrony and connectivity in the primary auditory cortex and beyond, likely reflecting underlying cell density involved in gamma circuitries. This result hints towards an involvement of gamma synchrony together with underlying brain structure in brain areas for higher order cognitive functions. This study contributes to the understanding of inherent cortical functional and structural brain properties, which might in turn constitute the basis for the definition of useful biomarkers in patients showing aberrant gamma synchronization.

Non-zero mean alpha oscillations revealed with computational model and empirical data

Ongoing oscillations and evoked responses are two main types of neuronal activity obtained with diverse electrophysiological recordings (EEG/MEG/iEEG/LFP). Although typically studied separately, they might in fact be closely related. One possibility to unite them is to demonstrate that neuronal oscillations have non-zero mean which predicts that stimulus- or task-triggered amplitude modulation of oscillations can contribute to the generation of evoked responses. We validated this mechanism using computational modelling and analysis of a large EEG data set. With a biophysical model, we indeed demonstrated that intracellular currents in the neuron are asymmetric and, consequently, the mean of alpha oscillations is non-zero. To understand the effect that neuronal currents exert on oscillatory mean, we varied several biophysical and morphological properties of neurons in the network, such as voltage-gated channel densities, length of dendrites, and intensity of incoming stimuli. For a very large range of model parameters, we observed evidence for non-zero mean of oscillations. Complimentary, we analysed empirical rest EEG recordings of 90 participants (50 young, 40 elderly) and, with spatio-spectral decomposition, detected at least one spatially-filtred oscillatory component of non-zero mean alpha oscillations in 93% of participants. In order to explain a complex relationship between the dynamics of amplitude-envelope and corresponding baseline shifts, we performed additional simulations with simple oscillators coupled with different time delays. We demonstrated that the extent of spatial synchronisation may obscure macroscopic estimation of alpha rhythm modulation while leaving baseline shifts unchanged. Overall, our results predict that amplitude modulation of neural oscillations should at least partially explain the generation of evoked responses. Therefore, inference about changes in evoked responses with respect to cognitive conditions, age or neuropathologies should be constructed while taking into account oscillatory neuronal dynamics.

The power of rhythms: how steady-state evoked responses reveal early neurocognitive development

Electroencephalography (EEG) is a non-invasive and painless recording of cerebral activity, particularly well-suited for studying young infants, allowing the inspection of cerebral responses in a constellation of different ways. Of particular interest for developmental cognitive neuroscientists is the use of rhythmic stimulation, and the analysis of steady-state evoked potentials (SS-EPs) - an approach also known as frequency tagging. In this paper we rely on the existing SS-EP early developmental literature to illustrate the important advantages of SS-EPs for studying the developing brain. We argue that (1) the technique is both objective and predictive: the response is expected at the stimulation frequency (and/or higher harmonics), (2) its high spectral specificity makes the computed responses particularly robust to artifacts, and (3) the technique allows for short and efficient recordings, compatible with infants' limited attentional spans. We additionally provide an overview of some recent inspiring use of the SS-EP technique in adult research, in order to argue that (4) the SS-EP approach can be implemented creatively to target a wide range of cognitive and neural processes. For all these reasons, we expect SS-EPs to play an increasing role in the understanding of early cognitive processes. Finally, we provide practical guidelines for implementing and analyzing SS-EP studies.

Visual P300 as a neurophysiological correlate of symptomatic improvement by a virtual reality-based computer AT system in patients with auditory verbal hallucinations: A Pilot study

Previous comparative trials showed that virtual reality (VR) therapies achieved larger effects than gold-standard cognitive-behavioral therapy (CBT) on overall auditory verbal hallucinations (AVHs). However, no trial has examined the corresponding underlying electrophysiological mechanisms. We performed a pilot randomized comparative trial evaluating the efficacy of a virtual reality-based computer AT system (CATS) over CBT for schizophrenia (SCZ) patients with treatment-resistant AVHs and explored these potential electrophysiological changes via the visual P300 component. Patients (CATS, n = 32; CBT, n = 33) completed the clinical assessments pre- and post-interventions and at 12-week follow-up. The visual P300 were measured before and after both therapies. The analysis of changes in psychiatric symptoms used linear mixed-effects models, and the P300 response in temporal and time-frequency domains was analyzed with repeated-measures analysis of variance. There was no interaction effect between change in clinical symptoms and treatment group. However, several statistically significant within-group improvements were found for CATS and CBT over time. AVH improved significantly after both treatments, as measured with the Psychotic Symptom Rating Scales-Auditory Hallucinations (PSYRATS-AH) sub-scores. Especially for the CATS group, omnipotence beliefs, anxiety symptoms, self-esteem, and quality of life also remained improved at the 12-week follow-up. Moreover, P300 amplitude had a significant interaction effect and correlation with AVH response. Overall, our analysis did not demonstrate general clinical superiority of CATS over CBT, but CATS improved refractory AVH in SCZ patients, likely by increasing P300 amplitude. These findings support the continued development of CATS for persistent AVH and suggest further trials to clarify the neurological effects of CATS.