Event-related synchronization (ERS) in the alpha band--an electrophysiological correlate of cortical idling: a review

Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment

Lack of situation awareness (SA) is the primary cause of human errors when operating forklifts, so determining the SA level of the forklift operator is crucial to the safety of forklift operations. An EEG recognition approach of forklift operator SA in actual settings was presented in order to address the issues with invasiveness, subjectivity, and intermittency of existing measuring methods. In this paper, we conducted a field experiment that mimicked a typical forklift operation scenario to verify the differences in EEG states of forklift operators with different SA levels and investigate the correlation of multi-band combination features of each brain region of forklift operators with SA. Based on the sensitive EEG combination indexes, Support Vector Mechanism was used to construct a forklift operator SA recognition model. The results revealed that there were differences between forklift operators with high and low SA in the θ, α, and β frequency bands in zones F, C, P, and O; combined EEG indicators θ/β, (α + θ)/(α + β), and θ/(α + β) in zones F, P, and C were significantly correlated with SA; the recognition accuracy of the model reached 88.64% in the case of combined EEG indicators of zones C & F & P as input. It could provide a reference for SA measurement, contributing to the improvement of SA.

High-Gamma Activity Is Coupled to Low-Gamma Oscillations in Precentral Cortices and Modulates with Movement and Speech

Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase-amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase-amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger-flexion or word-reading tasks. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

Sensory modality affects the spatiotemporal dynamics of alpha and theta oscillations associated with prospective memory

BACKGROUND

The maintenance of an intention in memory (Prospective Memory, PM) while performing a task is associated with a cost in terms of both performance (longer response times and lower accuracy) and neurophysiological modulations, which extent depends on several features of the stimuli.

AIM

This study explores the neural patterns associated with PM in different sensory modalities, to identify differences depending on this variable and discuss their functional meaning.

METHOD

Data were collected using a High-Density EEG during a baseline and a PM condition, proposed in a visual and an auditory version. Theta and alpha oscillations were compared between the two conditions within each modality using a cluster-based permutation approach.

RESULTS

PM conditions were associated with clusters of decreased alpha and theta activity in both modalities. However, different spatiotemporal dynamics were elicited as a function of sensory modality: alpha decreases displayed an overlapping onset between modalities, but different durations, lasting longer in the auditory modality. Conversely, the clusters of decreased theta activity presented similar durations between modalities, but different temporal and spatial onsets, appearing at different moments over the respective sensory areas.

CONCLUSIONS

The similar spatiotemporal properties of alpha suppression between modalities indicate that such oscillations may represent a supramodal, top-down process, presumably reflecting the external direction of attention to successfully detect the prospective cue (strategic monitoring). In theta, the clusters showed more modality-specific differences, which temporal and spatial properties correspond to the ones necessary to perform the ongoing task, suggesting a shift in resource allocation in favor of the PM task.

Neural correlates tracking different aspects of the emerging representation of novel visual categories

Current studies investigating electroencephalogram correlates associated with categorization of sensory stimuli (P300 event-related potential, alpha event-related desynchronization, theta event-related synchronization) typically use an oddball paradigm with few, familiar, highly distinct stimuli providing limited insight about the aspects of categorization (e.g. difficulty, membership, uncertainty) that the correlates are linked to. Using a more complex task, we investigated whether such more specific links could be established between correlates and learning and how these links change during the emergence of new categories. In our study, participants learned to categorize novel stimuli varying continuously on multiple integral feature dimensions, while electroencephalogram was recorded from the beginning of the learning process. While there was no significant P300 event-related potential modulation, both alpha event-related desynchronization and theta event-related synchronization followed a characteristic trajectory in proportion with the gradual acquisition of the two categories. Moreover, the two correlates were modulated by different aspects of categorization, alpha event-related desynchronization by the difficulty of the task, whereas the magnitude of theta -related synchronization by the identity and possibly the strength of category membership. Thus, neural signals commonly related to categorization are appropriate for tracking both the dynamic emergence of internal representation of categories, and different meaningful aspects of the categorization process.

Handwriting but not typewriting leads to widespread brain connectivity: a high-density EEG study with implications for the classroom

As traditional handwriting is progressively being replaced by digital devices, it is essential to investigate the implications for the human brain. Brain electrical activity was recorded in 36 university students as they were handwriting visually presented words using a digital pen and typewriting the words on a keyboard. Connectivity analyses were performed on EEG data recorded with a 256-channel sensor array. When writing by hand, brain connectivity patterns were far more elaborate than when typewriting on a keyboard, as shown by widespread theta/alpha connectivity coherence patterns between network hubs and nodes in parietal and central brain regions. Existing literature indicates that connectivity patterns in these brain areas and at such frequencies are crucial for memory formation and for encoding new information and, therefore, are beneficial for learning. Our findings suggest that the spatiotemporal pattern from visual and proprioceptive information obtained through the precisely controlled hand movements when using a pen, contribute extensively to the brain's connectivity patterns that promote learning. We urge that children, from an early age, must be exposed to handwriting activities in school to establish the neuronal connectivity patterns that provide the brain with optimal conditions for learning. Although it is vital to maintain handwriting practice at school, it is also important to keep up with continuously developing technological advances. Therefore, both teachers and students should be aware of which practice has the best learning effect in what context, for example when taking lecture notes or when writing an essay.

The emergence of the EEG dominant rhythm across the first year of life

The spectral composition of EEG provides important information on the function of the developing brain. For example, the frequency of the dominant rhythm, a salient features of EEG data, increases from infancy to adulthood. Changes of the dominant rhythm during infancy are yet to be fully characterized, in terms of their developmental trajectory and spectral characteristics. In this study, the development of dominant rhythm frequency was examined during a novel sustained attention task across 6-month-old (n = 39), 9-month-old (n = 30), and 12-month-old (n = 28) infants. During this task, computer-generated objects and faces floated down a computer screen for 10 s after a 5-second fixation cross. The peak frequency in the range between 5 and 9 Hz was calculated using center of gravity (CoG) and examined in response to faces and objects. Results indicated that peak frequency increased from 6 to 9 to 12 months of age in face and object conditions. We replicated the same result for the baseline. There was high reliability between the CoGs in the face, object, and baseline conditions across all channels. The developmental increase in CoG was more reliable than measures of mode frequency across different conditions. These findings suggest that CoG is a robust index of brain development across infancy.

Does 20 Hz Transcranial Alternating Current Stimulation over the Human Primary Motor Cortex Modulate Beta Rebound Following Voluntary Movement?

Beta frequency oscillations originating from the primary motor cortex increase in amplitude following the initiation of voluntary movement, a process termed beta rebound. The strength of beta rebound has been reported to predict the recovery of motor function following stroke, suggesting therapeutic applications of beta rebound modulation. The present study examined the effect of 20 Hz transcranial alternating current stimulation (tACS) on the beta rebound induced by self-paced voluntary movement. Electroencephalograms (EEGs) and electromyograms (EMGs) were recorded from 16 healthy adults during voluntary movements performed before and after active or sham tACS. There was no significant change in average beta rebound after active tACS. However, the beta rebound amplitude was significantly enhanced in a subset of participants, and the magnitude of the increase across all participants was negatively correlated with the difference between individual peak beta frequency and tACS frequency. Thus, matching the stimulus frequency of tACS with individual beta frequency may facilitate therapeutic enhancement for motor rehabilitation.

Electrophysiological effects of smartphone notifications on cognitive control following a brief mindfulness induction

Smartphone use is nearly ubiquitous, with 93% of adults among economically developed countries, including the United States, Canada, Israel, and South Korea owning a smartphone (Taylor & Silver, 2019). Multiple studies have demonstrated the distracting effects of smartphone notifications on behavioral measures of cognition. Fewer studies have examined the effects of notifications on neural activity underlying higher-level cognitive processes or behavioral inductions to reduce smartphone-related distraction. Using EEG spectral frequency power densities, we assessed the effects of smartphone notifications (vs. control trials) on engagement of attentional shifting processes involved in cognitive control during a Navon Letter visual oddball task. Participants were randomly assigned to a brief mindfulness induction (N = 44) or a neutral narration control condition (N = 43). Overall, participants had lower theta-band power, but higher alpha- and beta-band power densities on target letter trials preceded by smartphone notifications. Additionally, participants in the mindfulness (vs. control) condition had a larger attention shifting oddball assessed via theta power density and theta/beta ratio (TBR) values-reflecting increased engagement of cognitive control-particularly on smartphone notification (vs. control) trials. Altogether, these results provide evidence supporting the idea that smartphone notifications can decrease activity of neural correlates of cognitive control, and offer the promise of a brief mindfulness induction to buffer against the effects of smartphone notifications on cognitive control. The findings indicate a need for further research on mindfulness inductiosn as a means to reduce potential distraction caused by smartphones.

Translating Decades of Neuroscience Research into Diagnostic and Treatment Biomarkers for ADHD

In this chapter, we review scientific findings that form the basis for neuroimaging and neurophysiological biomarkers for ADHD diagnosis and treatment. We then highlight the different challenges in translating mechanistic findings into biomarkers for ADHD diagnosis and treatment. Population heterogeneity is a primary barrier for identifying biomarkers of ADHD diagnosis, which requires shifts toward dimensional approaches that identify clinically useful subgroups or prospective biomarkers that can identify trajectories of illness, function, or treatment response. Methodological limitations, including emphasis on group level analyses of treatment effects in small sample sizes, are the primary barriers to biomarker discovery in ADHD treatment. Modifications to clinical trials, including shifting towards testing biomarkers of a priori prediction of functionally related brain targets, treatment response, and side effects, are suggested. Finally, future directions for biomarker work are discussed.

Neural Oscillations and Functional Significances for Prioritizing Dual-Task Walking in Parkinson's Disease

Background

Task prioritization involves allocating brain resources in a dual-task scenario, but the mechanistic details of how prioritization strategies affect dual-task walking performance for Parkinson's disease (PD) are little understood.

Objective

We investigated the performance benefits and corresponding neural signatures for people with PD during dual-task walking, using gait-prioritization (GP) and manual-prioritization (MP) strategies.

Methods

Participants (N = 34) were asked to hold two inter-locking rings while walking and to prioritize either taking big steps (GP strategy) or separating the two rings (MP strategy). Gait parameters and ring-touch time were measured, and scalp electroencephalograph was performed.

Results

Compared with the MP strategy, the GP strategy yielded faster walking speed and longer step length, whereas ring-touch time did not significantly differ between the two strategies. The MP strategy led to higher alpha (8-12 Hz) power in the posterior cortex and beta (13-35 Hz) power in the left frontal-temporal area, but the GP strategy was associated with stronger network connectivity in the beta band. Changes in walking speed and step length because of prioritization negatively correlated with changes in alpha power. Prioritization-related changes in ring-touch time correlated negatively with changes in beta power but positively with changes in beta network connectivity.

Conclusions

A GP strategy in dual-task walking for PD can enhance walking speed and step length without compromising performance in a secondary manual task. This strategy augments attentional focus and facilitates compensatory reinforcement of inter-regional information exchange.

Neurophysiological Models in Individuals at Clinical High Risk for Psychosis: Using Translational EEG Paradigms to Forecast Psychosis Risk and Resilience

Over the last several decades, there have been major research efforts to improve the identification of youth and young adults at clinical high-risk for psychosis (CHR-P). Among individuals identified as CHR-P based on clinical criteria, approximately 20% progress to full-blown psychosis over 2-3 years and 30% achieve remission. In more recent years, neurophysiological measures with established sensitivity to schizophrenia have gained traction in the study of CHR-P and its range of clinical outcomes, with the goal of identifying specific biomarkers that precede psychosis onset that 7 chapter, we review studies examining several translational electroencephalography (EEG) and event-related potential (ERP) measures, which have known sensitivity to schizophrenia and reflect abnormal sensory, perceptual, and cognitive processing of task stimuli, as predictors of future clinical outcomes in CHR-P individuals. We discuss the promise of these EEG/ERP biomarkers of psychosis risk, including their potential to provide (a) translational bridges between human studies and animal models focused on drug development for early psychosis, (b) target engagement measures for clinical trials, and (c) prognostic indicators that could enhance personalized treatment planning.

Emotional Bias among Individuals at Risk for Seasonal Affective Disorder-An EEG Study during Remission in Summer

Emotional bias in attention and memory is well researched in depression. Patients with depression prioritize processing of negative information over positive input. While there is evidence that emotional bias exists in seasonal affective disorder (SAD) during winter, it is unclear whether such altered cognition exists also during summer. Moreover, it is unclear whether such bias affects attention, memory, or both. In this study, we investigated 110 individuals in summer, 34 of whom reported suffering from low mood during winter, according to the seasonal pattern assessment questionnaire. While the electroencephalogram was recorded, participants learned 60 emotional pictures and subsequently were asked to recognize them in an old/new task. There were no clear group differences in behavioral measures, and no brain response differences in frontal alpha power during learning. During recognition, at 100-300 ms post stimulus individuals with higher seasonality scores exhibited larger alpha power in response to negative as compared to neutral stimuli, while individuals with low seasonality scores exhibited larger alpha power in response to positive as compared to neutral stimuli. While we cannot draw conclusions whether this is an effect of attention or memory, the finding suggests that early cognitive processes are altered already during summer in individuals with increased likelihood to experience SAD during winter. Our data provide evidence for an all-year-round cognitive vulnerability in this population.

EEG microstates in early-to-middle childhood show associations with age, biological sex, and alpha power

Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large-scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early-to-middle childhood (4-12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A-E) across both eyes-closed and eyes-open resting-state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f-like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes-open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large-scale functional brain networks during early-to-middle childhood, extending understanding of neural dynamics across this important period for brain development.

Alpha oscillations encode Bayesian belief updating underlying attentional allocation in dynamic environments

In a dynamic environment, expectations of the future constantly change based on updated evidence and affect the dynamic allocation of attention. To further investigate the neural mechanisms underlying attentional expectancies, we employed a modified Central Cue Posner Paradigm in which the probability of cues being valid (that is, accurately indicated the upcoming target location) was manipulated. Attentional deployment to the cued location (α), which was governed by precision of predictions on previous trials, was estimated using a hierarchical Bayesian model and was included as a regressor in the analyses of electrophysiological (EEG) data. Our results revealed that before the target appeared, alpha oscillations (8∼13 Hz) for high-predictability cues (88 % valid) were significantly predicted by precision-dependent attention (α). This relationship was not observed under low-predictability conditions (69 % and 50 % valid cues). After the target appeared, precision-dependent attention (α) correlated with alpha band oscillations only in the valid cue condition and not in the invalid condition. Further analysis under conditions of significant attentional modulation by precision suggested a separate effect of cue orientation. These results provide new insights on how trial-by-trial Bayesian belief updating relates to alpha band encoding of environmentally-sensitive allocation of visual spatial attention.

Precision data-driven modeling of cortical dynamics reveals idiosyncratic mechanisms underlying canonical oscillations

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and is a strong marker of individual differences. In this work, we present an algorithmic optimization framework that makes it possible to directly invert and parameterize brain-wide dynamical-systems models involving hundreds of interacting brain areas, from single-subject time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG markers. Lastly, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitation-inhibition balance, highlighting the explanatory power of our framework in generating mechanistic insights.

Oscillatory Responses to Tactile Stimuli of Different Intensity

Tactile perception encompasses several submodalities that are realized with distinct sensory subsystems. The processing of those submodalities and their interactions remains understudied. We developed a paradigm consisting of three types of touch tuned in terms of their force and velocity for different submodalities: discriminative touch (haptics), affective touch (C-tactile touch), and knismesis (alerting tickle). Touch was delivered with a high-precision robotic rotary touch stimulation device. A total of 39 healthy individuals participated in the study. EEG cluster analysis revealed a decrease in alpha and beta range (mu-rhythm) as well as theta and delta increase most pronounced to the most salient and fastest type of stimulation. The participants confirmed that slower stimuli targeted to affective touch low-threshold receptors were the most pleasant ones, and less intense stimuli aimed at knismesis were indeed the most ticklish ones, but those sensations did not form an EEG cluster, probably implying their processing involves deeper brain structures that are less accessible with EEG.

Long-term exposure to high altitude reduces alpha and beta bands event-related desynchronization in a Go/NoGo task

More than 80 million people worldwide permanently live at high altitudes, and living in such a hypoxic environment can impair cognitive functions. However, it is largely unknown how long-term exposure to high altitude affects neural oscillations underlying these cognitive functions. The present study employed a Go/NoGo task to investigate the effects of long-term exposure to high altitude on neural oscillations during cognitive control. We compared event-related spectral perturbations between the low-altitude and high-altitude groups, and the results revealed increased theta event-related synchronization (ERS) and decreased alpha and beta event-related desynchronizations (ERDs) during the NoGo condition compared to the Go condition. Importantly, the high-altitude group showed reduced alpha and beta ERDs compared to the low-altitude group, while the theta ERS was not affected by altitude. We suggest that long-term exposure to high altitude has an impact on top-down inhibitory control and movement preparation and execution in the Go/NoGo task.

Alpha-frequency feedback to early visual cortex orchestrates coherent naturalistic vision

During naturalistic vision, the brain generates coherent percepts by integrating sensory inputs scattered across the visual field. Here, we asked whether this integration process is mediated by rhythmic cortical feedback. In electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) experiments, we experimentally manipulated integrative processing by changing the spatiotemporal coherence of naturalistic videos presented across visual hemifields. Our EEG data revealed that information about incoherent videos is coded in feedforward-related gamma activity while information about coherent videos is coded in feedback-related alpha activity, indicating that integration is indeed mediated by rhythmic activity. Our fMRI data identified scene-selective cortex and human middle temporal complex (hMT) as likely sources of this feedback. Analytically combining our EEG and fMRI data further revealed that feedback-related representations in the alpha band shape the earliest stages of visual processing in cortex. Together, our findings indicate that the construction of coherent visual experiences relies on cortical feedback rhythms that fully traverse the visual hierarchy.

Blink-related EEG activity measures cognitive load during proactive and reactive driving

Assessing drivers' cognitive load is crucial for driving safety in challenging situations. This research employed the occurrence of drivers' natural eye blinks as cues in continuously recorded EEG data to assess the cognitive workload while reactive or proactive driving. Twenty-eight participants performed either a lane-keeping task with varying levels of crosswind (reactive) or curve road (proactive). The blink event-related potentials (bERPs) and spectral perturbations (bERSPs) were analyzed to assess cognitive load variations. The study found that task load during reactive driving did not significantly impact bERPs or bERSPs, possibly due to enduring alertness for vehicle control. The proactive driving revealed significant differences in the occipital N1 component with task load, indicating the necessity to adapt the attentional resources allocation based on road demands. Also, increased steering complexity led to decreased frontal N2, parietal P3, occipital P2 amplitudes, and alpha power, requiring more cognitive resources for processing relevant information. Interestingly, the proactive and reactive driving scenarios demonstrated a significant interaction at the parietal P2 and occipital N1 for three difficulty levels. The study reveals that EEG measures related to natural eye blink behavior provide insights into the effect of cognitive load on different driving tasks, with implications for driver safety.

Pre-task Intrinsic Cortical Activity in Novice and Experienced Military Specialists: A Cross-sectional Study

INTRODUCTION

Neuroscience studies brain dynamics through the analysis of electrical signals. Cortical activity estimated by electroencephalography brings accurate information about perceptions of human behavior. The examination of resting states in relation to subsequent behaviors indicates that intrinsic cortical activity (ICA) has implications for decision-making processes, especially when inserted in the context of military activities and associated with stress. The objective of this study was to compare the absolute alpha power (AAP) in the ICA in the pre-task moment of novice specialized military (NG) with experienced (ExpG), associating with the level of stress.

MATERIALS AND METHODS

This was a cross-sectional, observational study with 19 military personnel (32.1 years old), divided into NG (10) and ExpG (9). The ICA was the outcome variable, with the level of stress and the time of specialization in military tasks as the exposure variables. ICA analysis were carried out based on the cortical areas to compare the ICA of the NG with that of the ExpG. The association of stress level with ICA was estimated by linear regression via linear models.

RESULTS

There was a significant difference in almost all cortical areas, and the averages were always higher in Exp. The high stress level was associated with greater AAP both for the NG and for the ExpG, and at the medium level, the AAP was obtained, varying according to each cortical area.

CONCLUSION

The AAP in ExpG was significantly higher than that in NG, indicating a lower level of cortical activity and greater efficiency in sensory, motor, and visual tasks.

Hierarchical network structure as the source of hierarchical dynamics (power-law frequency spectra) in living and non-living systems: How state-trait continua (body plans, personalities) emerge from first principles in biophysics

Living systems are hierarchical control systems that display a small world network structure. In such structures, many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a 'power-law' cluster size distribution (a mereology). Just like their structure, the dynamics of living systems shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or 'states' (treble) that are nested within lower frequencies or 'traits' (bass), producing a power-law frequency spectrum that is known as a 'state-trait continuum' in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms 'vertically encode' the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies exert a tonic regulatory pressure that produces morphological as well as behavioral traits (i.e., body plans and personalities). Nested-modular structure causes higher frequencies to be embedded within lower frequencies, producing a power-law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.q., earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g., during maturation and disease) should leave specific traces in system dynamics (shifts in lower frequencies, i.e. morphological and behavioral traits) that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine.

Cardiac activity impacts cortical motor excitability

Human cognition and action can be influenced by internal bodily processes such as heartbeats. For instance, somatosensory perception is impaired both during the systolic phase of the cardiac cycle and when heartbeats evoke stronger cortical responses. Here, we test whether these cardiac effects originate from overall changes in cortical excitability. Cortical and corticospinal excitability were assessed using electroencephalographic and electromyographic responses to transcranial magnetic stimulation while concurrently monitoring cardiac activity with electrocardiography. Cortical and corticospinal excitability were found to be highest during systole and following stronger neural responses to heartbeats. Furthermore, in a motor task, hand-muscle activity and the associated desynchronization of sensorimotor oscillations were stronger during systole. These results suggest that systolic cardiac signals have a facilitatory effect on motor excitability-in contrast to sensory attenuation that was previously reported for somatosensory perception. Thus, it is possible that distinct time windows exist across the cardiac cycle, optimizing either perception or action.

Beyond pulsed inhibition: Alpha oscillations modulate attenuation and amplification of neural activity in the awake resting state

Alpha oscillations are a distinctive feature of the awake resting state of the human brain. However, their functional role in resting-state neuronal dynamics remains poorly understood. Here we show that, during resting wakefulness, alpha oscillations drive an alternation of attenuation and amplification bouts in neural activity. Our analysis indicates that inhibition is activated in pulses that last for a single alpha cycle and gradually suppress neural activity, while excitation is successively enhanced over a few alpha cycles to amplify neural activity. Furthermore, we show that long-term alpha amplitude fluctuations-the "waxing and waning" phenomenon-are an attenuation-amplification mechanism described by a power-law decay of the activity rate in the "waning" phase. Importantly, we do not observe such dynamics during non-rapid eye movement (NREM) sleep with marginal alpha oscillations. The results suggest that alpha oscillations modulate neural activity not only through pulses of inhibition (pulsed inhibition hypothesis) but also by timely enhancement of excitation (or disinhibition).

Functional reorganization of the brain in distinct frequency bands during eyes-open meditation

Meditation is a self-regulatory process practiced primarily to reduce stress, manage emotions and mental health. The objective of this work is to study the information exchange between electrodes within and across the hemispheres during meditation using functional connectivity (FC) measures. We investigate the changes in the coherence between EEG electrode pairs during the meditation with open eyes practiced by long-term Brahmakumaris Rajyoga meditators and during listening to music by controls as the comparable task. FC derived from coherency, pairwise phase consistency (PPC) is used to study the changes in intra and interhemispheric coherence. Integrating connectivity (IC) derived from node degree strength has also been analyzed. Meditators show increased PPC in higher theta and alpha bands both within and across hemispheres. However, the control subjects with no knowledge of meditation show no change in theta band during the music session. Further, during baseline conditions, higher interhemispheric anterior to posterior IC is found in meditators in higher beta and slow gamma bands than controls. Distinct patterns of changes are observed with the PPC and IC measures in different frequency bands during meditation in the meditators and music-listening session in the control subjects indicating varied information processing between the right and left hemispheres. Increased IC is found between the frontal electrodes implying increased self-awareness in meditators. The PPC between the occipital electrodes in meditators is less than the controls in baseline condition indicating a possible modified visual information processing in Rajyoga meditators due to the long-term practice of meditation with open eyes. Overall, the changes in PPC and associated IC indicate increased functional integration during meditation supporting the hypothesis of communication through coherence and cortical integration theory during the non-ordinary state of consciousness induced by meditation.

The Experience of Meaningful Coincidences Is Associated with Stronger Alpha Power Increases during an Eyes-closed Resting Condition: A Bayesian Replication Approach

Recognizing and perceiving meaningful patterns in an ever-changing environment is fundamental to (human) beings. Apophenia, patternicity, and the propensity to perceive meaningful coincidences might result from the human brain working as a prediction machine that constantly matches sensory information to prior expectations. The propensity for Type I errors varies between people and, at its extreme, is associated with symptoms of schizophrenia. However, on a nonclinical level seeing meaning in randomness might be benevolent and was found to be associated with creativity and openness. However, hardly any neuroscientific investigation has examined EEG patterns of the propensity to experience meaningful coincidences in this manner. We hypothesized deviations in brain functions as one potential reason why some people experience more meaning in random arrangements than others. The gating by inhibition theory suggests that alpha power increases represent basic control mechanisms of sensory processes during varying task requirements. We found that people perceiving more meaningful coincidences had higher alpha power during an eyes-closed versus eyes-opened condition compared with people experiencing less meaningful coincidences. This indicates deviations in the sensory inhibition mechanism of the brain, which are critically relevant for higher cognitive functions. Applying Bayesian statistics, we replicated this finding in another independent sample.

EEG measurement for the effect of perceptual eye position and eye position training on comitant strabismus

One of the clinical features of comitant strabismus is that the deviation angles in the first and second eye positions are equal. However, there has been no report of consistency in the electroencephalography (EEG) signals between the 2 positions. In order to address this issue, we developed a new paradigm based on perceptual eye position. We collected steady-state visual evoked potentials (SSVEPs) signals and resting-state EEG data before and after the eye position training. We found that SSVEP signals could characterize the suppression effect and eye position effect of comitant strabismus, that is, the SSVEP response of the dominant eye was stronger than that of the strabismus eye in the first eye position but not in the second eye position. Perceptual eye position training could modulate the frequency band activities in the occipital and surrounding areas. The changes in the visual function of comitant strabismus after training could also be characterized by SSVEP. There was a correlation between intermodulation frequency, power of parietal electrodes, and perceptual eye position, indicating that EEG might be a potential indicator for evaluating strabismus visual function.

Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time-Frequency Representation, and Phase Dynamics

Characterizing the brain's dynamic pattern of response to an input in electroencephalography (EEG) is not a trivial task due to the entanglement of the complex spontaneous brain activity. In this context, the brain's response can be defined as (1) the additional neural activity components generated after the input or (2) the changes in the ongoing spontaneous activities induced by the input. Moreover, the response can be manifested in multiple features. Three commonly studied examples of features are (1) transient temporal waveform, (2) time-frequency representation, and (3) phase dynamics. The most extensively used method of average event-related potentials (ERPs) captures the first one, while the latter two and other more complex features are attracting increasing attention. However, there has not been much work providing a systematic illustration and guidance for how to effectively exploit multifaceted features in neural cognitive research. Based on a visual oddball ERPs dataset with 200 participants, this work demonstrates how the information from the above-mentioned features are complementary to each other and how they can be integrated based on stereotypical neural-network-based machine learning approaches to better exploit neural dynamic information in basic and applied cognitive research.

Frontal Alpha Asymmetry in Posttraumatic Stress Disorder: Group Differences Among Individuals With and Without PTSD During an Inhibitory Control Task

The current study examined frontal alpha asymmetry (FAA) as a marker of approach- and avoidance-related prefrontal activity in participants with and without trauma exposure and posttraumatic stress disorder (PTSD). We investigated FAA in an inhibitory control paradigm (threatening vs nonthreatening cues) under 2 levels of cognitive demand (baseline: images constant within a block of trials; vs filtering: images varied randomly within a block) in 3 groups of participants: individuals with PTSD (n = 16), exposed to trauma but without PTSD (n = 14), and a control group without PTSD or trauma exposure (n = 15). Under low demand (baseline), both PTSD and trauma-exposed participants exhibited significantly greater relative left than right frontal brain activity (approach) to threatening than to nonthreatening images. Under high demand (filtering), no FAA differences were found between threatening and nonthreatening images, but PTSD participants revealed more relative left than right FAA, whereas trauma-exposed participants showed reduced left relative right FAA. In all conditions, healthy controls exhibited reduced left relative to right FAA and no differences between threatening and nonthreatening images. Study findings suggest dysfunctional prefrontal mechanisms of emotion regulation in PTSD, but adaptive prefrontal regulation in trauma-exposed individuals without PTSD.

Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.

Neurophysiological time course of timbre-induced music-like perception

Traditionally, pitch variation in a sound stream has been integral to music identity. We attempt to expand music's definition, by demonstrating that the neural code for musicality is independent of pitch encoding. That is, pitchless sound streams can still induce music-like perception and a neurophysiological hierarchy similar to pitched melodies. Previous work reported that neural processing of sounds with no-pitch, fixed-pitch, and irregular-pitch (melodic) patterns, exhibits a right-lateralized hierarchical shift, with pitchless sounds favorably processed in Heschl's gyrus (HG), ascending laterally to nonprimary auditory areas for fixed-pitch and even more laterally for melodic patterns. The objective of this EEG study was to assess whether sound encoding maintains a similar hierarchical profile when musical perception is driven by timbre irregularities in the absence of pitch changes. Individuals listened to repetitions of three musical and three nonmusical sound-streams. The nonmusical streams were comprised of seven 200-ms segments of white, pink, or brown noise, separated by silent gaps. Musical streams were created similarly, but with all three noise types combined in a unique order within each stream to induce timbre variations and music-like perception. Subjects classified the sound streams as musical or nonmusical. Musical processing exhibited right dominant α power enhancement, followed by a lateralized increase in θ phase-locking and spectral power. The θ phase-locking was stronger in musicians than in nonmusicians. The lateralization of activity suggests higher-level auditory processing. Our findings validate the existence of a hierarchical shift, traditionally observed with pitched-melodic perception, underscoring that musicality can be achieved with timbre irregularities alone.NEW & NOTEWORTHY EEG induced by streams of pitchless noise segments varying in timbre were classified as music-like and exhibited a right-lateralized hierarchy in processing similar to pitched melodic processing. This study provides evidence that the neural-code of musicality is independent of pitch encoding. The results have implications for understanding music processing in individuals with degraded pitch perception, such as in cochlear-implant listeners, as well as the role of nonpitched sounds in the induction of music-like perceptual states.

Comparative analysis of multifaceted neural effects associated with varying endogenous cognitive load

Contemporary neuroscience has firmly established that mental state variation concurs with changes in neural dynamic activity in a complex way that a one-to-one mapping cannot describe. To explore the scenario of the multifaceted changes in neural dynamics associated with simple mental state variation, we took cognitive load - a common cognitive manipulation in psychology - as a venue to characterize how multiple neural dynamic features are simultaneously altered by the manipulation and how their sensitivity differs. Electroencephalogram was collected from 152 participants performing stimulus-free tasks with different demands. The results show that task demand alters wide-ranging neural dynamic features, including band-specific oscillations across broad frequency bands, scale-free dynamics, and cross-frequency phase-amplitude coupling. The scale-free dynamics outperformed others in indexing cognitive load variation. This study demonstrates a complex relationship between cognitive dynamics and neural dynamics, which points to a necessity to integrate multifaceted neural dynamic features when studying mind-brain relationship in the future.

A Dynamical Systems Approach to Characterizing Brain-Body Interactions during Movement: Challenges, Interpretations, and Recommendations

Brain-body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of 'brain' activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a fundamental challenge to the use of BBIs for answering basic and applied research questions in neuroimaging and neurorehabilitation. Thus, this review is written as a tutorial to address both limitations for those interested in studying BBIs through a dynamical systems lens. First, we outline current best practices for acquiring, interpreting, and cleaning scalp-measured electroencephalography (EEG) acquired during whole-body movement. Second, we discuss historical and current theories for modeling EEG and kinematic data as dynamical systems. Third, we provide worked examples from both canonical model systems and from empirical EEG and kinematic data collected from two subjects during an overground walking task.

Physical activity is associated with behavioral and neural changes across the lifespan

Physical activity is known to positively impact brain structure and function, but its effects on resting-state functional connectivity (rsFC) and its relationship with complex tasks as a function of age remain unclear. Here, we address these issues in a large population-based sample (N=540) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) repository. We relate levels of physical activity to rsFC patterns in magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) data, and to measures of executive function and visuomotor adaptation, across the lifespan. We show that higher self-reported daily physical activity is associated with lower alpha-band (8-12Hz) global coherence, indicating weaker synchrony of neural oscillations in this band. Physical activity affected between-network connectivity of resting-state functional networks, although its effects on individual networks did not survive correction for multiple comparisons. Furthermore, our results indicate that greater engagement in day-to-day physical activity is associated with better visuomotor adaptation, across the lifespan. Overall, our findings indicate that rsFC metrics indexed by MEG and fMRI are sensitive indicators of the brain's response to physical activity, and that a physically active lifestyle affects multiple aspects of neural function across the lifespan.

Dynamic networks differentiate the language ability of children with cochlear implants

Background

Cochlear implantation (CI) in prelingually deafened children has been shown to be an effective intervention for developing language and reading skill. However, there is a substantial proportion of the children receiving CI who struggle with language and reading. The current study-one of the first to implement electrical source imaging in CI population was designed to identify the neural underpinnings in two groups of CI children with good and poor language and reading skill.

Methods

Data using high density electroencephalography (EEG) under a resting state condition was obtained from 75 children, 50 with CIs having good (HL) or poor language skills (LL) and 25 normal hearing (NH) children. We identified coherent sources using dynamic imaging of coherent sources (DICS) and their effective connectivity computing time-frequency causality estimation based on temporal partial directed coherence (TPDC) in the two CI groups compared to a cohort of age and gender matched NH children.

Findings

Sources with higher coherence amplitude were observed in three frequency bands (alpha, beta and gamma) for the CI groups when compared to normal hearing children. The two groups of CI children with good (HL) and poor (LL) language ability exhibited not only different cortical and subcortical source profiles but also distinct effective connectivity between them. Additionally, a support vector machine (SVM) algorithm using these sources and their connectivity patterns for each CI group across the three frequency bands was able to predict the language and reading scores with high accuracy.

Interpretation

Increased coherence in the CI groups suggest overall that the oscillatory activity in some brain areas become more strongly coupled compared to the NH group. Moreover, the different sources and their connectivity patterns and their association to language and reading skill in both groups, suggest a compensatory adaptation that either facilitated or impeded language and reading development. The neural differences in the two groups of CI children may reflect potential biomarkers for predicting outcome success in CI children.

The Role of Stimuli-Driven and Goal-Driven Attention in Shopping Decision-Making Behaviors-An EEG and VR Study

The human attention system, similar to other networks in the brain, is of a complex nature. At any moment, our attention can shift between external and internal stimuli. In this study, we aimed to assess three EEG-based measures of attention (Power Spectral Density, Connectivity, and Spectral Entropy) in decision-making situations involving goal-directed and stimulus-driven attention using a Virtual Reality supermarket. We collected the EEG data of 29 participants in 2 shopping phases, planned and unplanned purchases. The three mentioned features were extracted and a statistical analysis was conducted. We evaluated the discriminatory power of these features using an SVM classifier. The results showed a significant (p-value < 0.001) increase in theta power over frontal, central, and temporal lobes for the planned purchase phase. There was also a significant decrease in alpha power over frontal and parietal lobes in the unplanned purchase phase. A significant increase in the frontoparietal connectivity during the planned purchase was observed. Additionally, an increase in spectral entropy was observed in the frontoparietal region for the unplanned purchase phase. The classification results showed that spectral entropy has the highest discriminatory power. This study can provide further insights into the attentional behaviors of consumers and how their type of attentional control can affect their decision-making processes.

Prestimulus α/β power in temporal-order judgments: individuals differ in direction of modulation but show consistency over auditory and visual tasks

The processing of incoming sensory information can be differentially affected by varying levels of α-power in the electroencephalogram (EEG). A prominent hypothesis is that relatively low prestimulus α-power is associated with improved perceptual performance. However, there are studies in the literature that do not fit easily into this picture, and the reasons for this are poorly understood and rarely discussed. To evaluate the robustness of previous findings and to better understand the overall mixed results, we used a spatial TOJ task in which we presented auditory and visual stimulus pairs in random order while recording EEG. For veridical and non-veridical TOJs, we calculated the power spectral density (PSD) for 3 frequencies (5 Hz steps: 10, 15, and 20 Hz). We found on the group level: (1) Veridical auditory TOJs, relative to non-veridical, were associated with higher β-band (20 Hz) power over central electrodes. (2) Veridical visual TOJs showed higher β-band (10, 15 Hz) power over parieto-occipital electrodes (3) Electrode site interacted with TOJ condition in the β-band: For auditory TOJs, PSD over central electrodes was higher for veridical than non-veridical and over parieto-occipital electrodes was lower for veridical than non-veridical trials, while the latter pattern was reversed for visual TOJs. While our group-level result showed a clear direction of prestimulus modulation, the individual-level modulation pattern was variable and included activations opposite to the group mean. Interestingly, our results at the individual-level mirror the situation in the literature, where reports of group-level prestimulus modulation were found in either direction. Because the direction of individual activation of electrodes over auditory brain regions and parieto-occipital electrodes was always negatively correlated in the respective TOJ conditions, this activation opposite to the group mean cannot be easily dismissed as noise. The consistency of the individual-level data cautions against premature generalization of group-effects and suggests different strategies that participants initially adopted and then consistently followed. We discuss our results in light of probabilistic information processing and complex system properties, and suggest that a general description of brain activity must account for variability in modulation directions at both the group and individual levels.

Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study

Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.

Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills

Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.

Spatial attention is not affected by alpha or beta transcranial alternating current stimulation: A registered report

Using Electroencephalography (EEG) an event-related change in alpha activity has been observed over primary sensory cortices during the allocation of spatial attention. This is most prominent during top-down, or endogenous, attention, and nearly absent in bottom-up, or exogenous orienting. These changes are highly lateralised, such that an increase in alpha power is seen ipsilateral to the attended region of space and a decrease is seen contralaterally. Whether these changes in alpha oscillatory activity are causally related to attentional resources, or to perceptual processes, or are simply epiphenomenal, is unknown. If alpha oscillations are indicative of a causal mechanism whereby attention is allocated to a region of space, it remains an open question as to whether this is driven by ipsilateral increases or contralateral decreases in alpha power. This preregistered report set out to test these questions. To do so, we used transcranial Alternating Current Stimulation (tACS) to modulate alpha activity in the somatosensory cortex whilst measuring performance on established tactile attention paradigms. All participants completed an endogenous and exogenous tactile attention task in three stimulation conditions; alpha, sham and beta. Sham and beta stimulation operated as controls so that any observed effects could be attributed to alpha stimulation specifically. We replicated previous behavioural findings in all stimulation conditions showing a facilitation of cued trials in the endogenous task, and inhibition of return in the exogenous task. However, these were not affected by stimulation manipulations. Using Bayes-factor analysis we show strong support for the null hypotheses - that the manipulation of Alpha by tACS does not cause changes in tactile spatial attention. This well-powered study, conducted over three separate days, is an important contribution to the current debate regarding the efficiency of brain stimulation.

Timing-specific patterns of cerebral activations during motor imagery: A case study of the expert brain signature

Brain activations elicited during motor imagery (MI) in experts are typically reduced compared to novices, which is interpreted as a neurophysiological correlate of increased neural efficiency. However, the modulatory effects of MI speed on expertise-related differences in brain activation remains largely unknown. In the present pilot study, we compared the magnetoencephalographic (MEG) correlates of MI in an Olympic medallist and an amateur athlete under conditions of slow, real-time and fast MI. Data revealed event-related changes in the time course of alpha (8-12 Hz) power of MEG oscillations, for all timing conditions. We found that slow MI was associated with a corollary increase in neural synchronization, in both participants. Sensor-level and source-level analyses however disclosed differences between the two expertise levels. The Olympic medallist achieved greater activation of cortical sensorimotor networks than the amateur athlete, particularly during fast MI. Fast MI elicited the strongest event-related desynchronization of alpha oscillations, which was generated from cortical sensorimotor sources in the Olympic medallist, but not in the amateur athlete. Taken together, data suggest that fast MI is a particularly demanding form of motor cognition, putting a specific emphasis on cortical sensorimotor networks to achieve the formation of accurate motor representations under demanding timing constraints.

The spectral profile of cortical activation during a visuospatial mental rotation task and its correlation with working memory

Introduction

The search for a cortical signature of intelligent behavior has been a longtime motivation in Neuroscience. One noticeable characteristic of intelligence is its association with visuospatial skills. This has led to a steady focus on the functional and structural characteristics of the frontoparietal network (FPN) of areas involved with higher cognition and spatial behavior in humans, including the question of whether intelligence is correlated with larger or smaller activity in this important cortical circuit. This question has broad significance, including speculations about the evolution of human cognition. One way to indirectly measure cortical activity with millisecond precision is to evaluate the event-related spectral perturbation (ERSP) of alpha power (alpha ERSP) during cognitive tasks. Mental rotation, or the ability to transform a mental representation of an object to accurately predict how the object would look from a different angle, is an important feature of everyday activities and has been shown in previous work by our group to be positively correlated with intelligence. In the present work, we evaluate whether alpha ERSP recorded over the parietal, frontal, temporal, and occipital regions of adolescents performing easy and difficult trials of the Shepard–Metzler’s mental rotation task, correlates or are predicted by intelligence measures of the Weschler’s intelligence scale.

Methods

We used a database obtained from a previous study of intellectually gifted (N = 15) and average intelligence (N = 15) adolescents.

Results

Our findings suggest that in challenging task conditions, there is a notable difference in the prominence of alpha event-related spectral perturbation (ERSP) activity between various cortical regions. Specifically, we found that alpha ERSP in the parietal region was less prominent relative to those in the frontal, temporal and occipital regions. Working memory scores predict alpha ERSP values in the frontal and parietal regions. In the frontal cortex, alpha ERSP of difficult trials was negatively correlated with working memory scores.

Discussion

Thus, our results suggest that even though the FPN is task-relevant during mental rotation tasks, only the frontal alpha ERSP is correlated with working memory score in mental rotation tasks.

A generic neural factor linking resting-state neural dynamics and the brain's response to unexpectedness in multilevel cognition

The brain's response to change is fundamental to learning and adaptation; this implies the presence of a universal neural mechanism under various contexts. We hypothesized that this mechanism manifests in neural activity patterns across low and high levels of cognition during task processing as well as in resting-state neural dynamics, because both these elements are different facets of the same dynamical system. We tested our hypothesis by (i) characterizing (a) the neural response to changes in low-level continuous information stream and unexpectedness at different cognitive levels and (b) the spontaneous neural dynamics in resting state, and (ii) examining the associations among the dynamics according to cross-individual variability (n = 200). Our results showed that the brain's response magnitude was monotonically correlated with the magnitude of information fluctuation in a low-level task, forming a simple psychophysical function; moreover, this effect was found to be associated with the brain's response to unexpectedness in high-level cognitive tasks (including language processing). These coherent multilevel neural effects in task processing were also shown to be strongly associated with resting-state neural dynamics characterized by the waxing and waning of Alpha oscillation. Taken together, our results revealed large-scale consistency between the neural dynamic system and multilevel cognition.

Frontocentral delta-beta amplitude coupling in endometriosis-related chronic pelvic pain

OBJECTIVE

Endometriosis is associated with neuroplastic changes in cognitive control and pain processing networks. This was the first study to assess eyes-closed resting electroencephalogram (EEG) oscillatory amplitudes in women with endometriosis compared to healthy controls, and explore the relationship with chronic pelvic pain.

METHODS

Women with endometriosis-related chronic pelvic pain and individually age-matched pain-free controls (N = 20 per group) documented pelvic pain for 28 days before having continuous EEG recorded during a 2 min eyes closed resting state. Natural frequency components were extracted for each group using frequency principal components analysis. Corresponding components were assessed for group differences and correlated with pain scores.

RESULTS

Relative to controls, the endometriosis group had greater component amplitudes in delta (0.5 Hz) and beta (∼28 Hz), and reduced alpha (∼10 Hz). Delta and beta amplitudes were positively associated with pain severity, but only beta maintained this association after delta-beta amplitude coupling was controlled.

CONCLUSIONS

Enhanced resting delta and beta amplitudes were seen in women with endometriosis experiencing chronic pelvic pain. This delta-beta coupling varied with pelvic pain severity, perhaps reflecting altered cholinergic tone and/or stress reactivity.

SIGNIFICANCE

Endometriosis-related changes in central pain processing demonstrate a distinct neuronal oscillatory signature detectable at rest.

Mu Desynchronisation in Autistic Individuals: What We Know and What We Need to Know

Autism spectrum disorder (ASD) is a neurodevelopmental condition that includes social-communication deficits and repetitive and stereotypical behaviours (APA 2022). Neurobiological methods of studying ASD are a promising methodology for identifying ASD biomarkers. Mu rhythms (Mu) have the potential to shed light on the socialisation deficits that characterise ASD; however, Mu/ASD studies thus far have yielded inconsistent results. This review examines the existing Mu/ASD studies to determine where this variability lies to elucidate potential factors that can be addressed in future studies.

Relating neural oscillations to laminar fMRI connectivity in visual cortex

Laminar functional magnetic resonance imaging (fMRI) holds the potential to study connectivity at the laminar level in humans. Here we analyze simultaneously recorded electroencephalography (EEG) and high-resolution fMRI data to investigate how EEG power modulations, induced by a task with an attentional component, relate to changes in fMRI laminar connectivity between and within brain regions in visual cortex. Our results indicate that our task-induced decrease in beta power relates to an increase in deep-to-deep layer coupling between regions and to an increase in deep/middle-to-superficial layer connectivity within brain regions. The attention-related alpha power decrease predominantly relates to reduced connectivity between deep and superficial layers within brain regions, since, unlike beta power, alpha power was found to be positively correlated to connectivity. We observed no strong relation between laminar connectivity and gamma band oscillations. These results indicate that especially beta band, and to a lesser extent, alpha band oscillations relate to laminar-specific fMRI connectivity. The differential effects for alpha and beta bands indicate that they relate to different feedback-related neural processes that are differentially expressed in intra-region laminar fMRI-based connectivity.

Mental strategies and resting state EEG: Effect on high alpha amplitude modulation by neurofeedback in healthy young adults

Neurofeedback (NFB) is a brain-computer interface which allows individuals to modulate their brain activity. Despite the self-regulatory nature of NFB, the effectiveness of strategies used during NFB training has been little investigated. In a single session of NFB training (6*3 min training blocks) with healthy young participants, we experimentally tested if providing a list of mental strategies (list group, N = 46), compared with a group receiving no strategies (no list group, N = 39), affected participants' neuromodulation ability of high alpha (10-12 Hz) amplitude. We additionally asked participants to verbally report the mental strategies used to enhance high alpha amplitude. The verbatim was then classified in pre-established categories in order to examine the effect of type of mental strategy on high alpha amplitude. First, we found that giving a list to the participants did not promote the ability to neuromodulate high alpha activity. However, our analysis of the specific strategies reported by learners during training blocks revealed that cognitive effort and recalling memories were associated with higher high alpha amplitude. Furthermore, the resting amplitude of trained high alpha frequency predicted an amplitude increase during training, a factor that may optimize inclusion in NFB protocols. The present results also corroborate the interrelation with other frequency bands during NFB training. Although these findings are based on a single NFB session, our study represents a further step towards developing effective protocols for high alpha neuromodulation by NFB.

High-gamma activity is coupled to low-gamma oscillations in precentral cortices and modulates with movement and speech

Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase-amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase-amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger movements or speaking words aloud. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.

Cortical-brainstem interplay during speech perception in older adults with and without hearing loss

Introduction

Real time modulation of brainstem frequency-following responses (FFRs) by online changes in cortical arousal state via the corticofugal (top-down) pathway has been demonstrated previously in young adults and is more prominent in the presence of background noise. FFRs during high cortical arousal states also have a stronger relationship with speech perception. Aging is associated with increased auditory brain responses, which might reflect degraded inhibitory processing within the peripheral and ascending pathways, or changes in attentional control regulation via descending auditory pathways. Here, we tested the hypothesis that online corticofugal interplay is impacted by age-related hearing loss.

Methods

We measured EEG in older adults with normal-hearing (NH) and mild to moderate hearing-loss (HL) while they performed speech identification tasks in different noise backgrounds. We measured α power to index online cortical arousal states during task engagement. Subsequently, we split brainstem speech-FFRs, on a trial-by-trial basis, according to fluctuations in concomitant cortical α power into low or high α FFRs to index cortical-brainstem modulation.

Results

We found cortical α power was smaller in the HL than the NH group. In NH listeners, α-FFRs modulation for clear speech (i.e., without noise) also resembled that previously observed in younger adults for speech in noise. Cortical-brainstem modulation was further diminished in HL older adults in the clear condition and by noise in NH older adults. Machine learning classification showed low α FFR frequency spectra yielded higher accuracy for classifying listeners' perceptual performance in both NH and HL participants. Moreover, low α FFRs decreased with increased hearing thresholds at 0.5-2 kHz for clear speech but noise generally reduced low α FFRs in the HL group.

Discussion

Collectively, our study reveals cortical arousal state actively shapes brainstem speech representations and provides a potential new mechanism for older listeners' difficulties perceiving speech in cocktail party-like listening situations in the form of a miss-coordination between cortical and subcortical levels of auditory processing.

Towards a systematization of brain oscillatory activity in actions

Information processing in the brain is governed by oscillatory activity. Activity oscillations in specific frequency bands (theta, alpha, beta and gamma) have been associated with various cognitive functions. A drawback of this is that the plethora of findings led to considerable uncertainty as to the functional relevance of activity in different frequency bands and their interrelation. Here, we use a novel cognitive-science theoretical framework to better understand and conceptually harmonize neurophysiological research on human action control. We outline how this validated starting point can systematize and probably reframe the functional relevance of oscillatory activity relevant for action control and beyond.