What does phase information of oscillatory brain activity tell us about cognitive processes?

Cross-Task Differences in Frontocentral Cortical Activations for Dynamic Balance in Neurotypical Adults

Although significant progress has been made in understanding the cortical correlates underlying balance control, these studies focused on a single task, limiting the ability to generalize the findings. Different balance tasks may elicit cortical activations in the same regions but show different levels of activation because of distinct underlying mechanisms. In this study, twenty young, neurotypical adults were instructed to maintain standing balance while the standing support surface was either translated or rotated. The differences in cortical activations in the frontocentral region between these two widely used tasks were examined using electroencephalography (EEG). Additionally, the study investigated whether transcranial magnetic stimulation could modulate these cortical activations during the platform translation task. Higher delta and lower alpha relative power were found over the frontocentral region during the platform translation task when compared to the platform rotation task, suggesting greater engagement of attentional and sensory integration resources for the former. Continuous theta burst stimulation over the supplementary motor area significantly reduced delta activity in the frontocentral region but did not alter alpha activity during the platform translation task. The results provide a direct comparison of neural activations between two commonly used balance tasks and are expected to lay a strong foundation for designing neurointerventions for balance improvements with effects generalizable across multiple balance scenarios.

Predicting the effectiveness of binaural beats on working memory

Working memory is vital for short-term information processing. Binaural beats can enhance working memory by improving attention and memory consolidation through neural synchronization. However, individual differences in cognitive and neuronal functioning affect effectiveness of binaural beats, necessitating personalized approaches. This study aimed to develop a machine learning model to predict binaural beats's effectiveness on working memory using electroencephalography. Sixty healthy participants underwent a 5-min electroencephalography recording, an initial working memory evaluation, 15 min of binaural beats stimulation, and a subsequent working memory evaluation using digit span tests of increasing difficulty. Recall accuracy and response times were measured. Differential scores from pre-evaluation and post-evaluation labeled participants as active or inactive to binaural beats stimulation. electroencephalography data, recorded using 14 electrodes, provided brain activity estimates across theta, alpha, beta, and gamma frequency bands, resulting in 56 features (14 channels × 4 bands) for the machine learning model. Several classifiers were tested to identify the most effective model. The weighted K-nearest neighbors model achieved the highest accuracy (90.0%) and area under the receiver operating characteristic curve (92.24%). Frontal and parietal electroencephalography channels in theta and alpha bands were crucial for classification. This study's findings offer significant clinical insights, enabling informed interventions and preventing resource inefficiency.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

The phase coherence of cortical oscillations predicts dynamic changes in perceived visibility

The phase synchronization of brain oscillations plays an important role in visual processing, perceptual awareness, and performance. Yet, the cortical mechanisms underlying modulatory effects of post-stimulus phase coherence and frequency-specific oscillations associated with different aspects of vision are still subject to debate. In this study, we aimed to identify the post-stimulus phase coherence of cortical oscillations associated with perceived visibility and contour discrimination. We analyzed electroencephalogram data from two masking experiments where target visibility was manipulated by the contrast ratio or polarity of the mask under various onset timing conditions (stimulus onset asynchronies, SOAs). The behavioral results indicated an SOA-dependent suppression of target visibility due to masking. The time-frequency analyses revealed significant modulations of phase coherence over occipital and parieto-occipital regions. We particularly identified modulations of phase coherence in the (i) 2-5 Hz frequency range, which may reflect feedforward-mediated contour detection and sustained visibility; and (ii) 10-25 Hz frequency range, which may be associated with suppressed visibility through inhibitory interactions between and within synchronized neural pathways. Taken together, our findings provide evidence that oscillatory phase alignments, not only in the pre-stimulus but also in the post-stimulus window, play a crucial role in shaping perceived visibility and dynamic vision.

Neurophenomenological Investigation of Mindfulness Meditation "Cessation" Experiences Using EEG Network Analysis in an Intensively Sampled Adept Meditator

Mindfulness meditation is a contemplative practice that is informed by Buddhism. It has been proven effective for improving mental and physical health in clinical and non-clinical contexts. To date, mainstream dialogue and scientific research on mindfulness has focused primarily on short-term mindfulness training and applications of mindfulness for reducing stress. Understanding advanced mindfulness practice has important implications for mental health and general wellbeing. According to Theravada Buddhist meditation, a "cessation" event is a dramatic experience of profound clarity and equanimity that involves a complete discontinuation in experience, and is evidence of mastery of mindfulness meditation. Thirty-seven cessation events were captured in a single intensively sampled advanced meditator (over 6,000 h of retreat mindfulness meditation training) while recording electroencephalography (EEG) in 29 sessions between November 12, 2019 and March 11, 2020. Functional connectivity and network integration were assessed from 40 s prior to cessations to 40 s after cessations. From 21 s prior to cessations there was a linear decrease in large-scale functional interactions at the whole-brain level in the alpha band. In the 40 s following cessations these interactions linearly returned to prior levels. No modulation of network integration was observed. The decrease in whole-brain functional connectivity was underlain by frontal to left temporal and to more posterior decreases in connectivity, while the increase was underlain by wide-spread increases in connectivity. These results provide neuroscientific evidence of large-scale modulation of brain activity related to cessation events that provides a foundation for future studies of advanced meditation.

Task-specific topology of brain networks supporting working memory and inhibition

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

Oscillatory mechanisms of intrinsic human brain networks

Non-invasive neuroimaging has revealed specific network-based resting-state dynamics in the human brain, yet the underlying neurophysiological mechanism remains unclear. We employed intracranial electroencephalography to characterize local field potentials within the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in 42 participants. We identified stronger within-network phase coherence at low frequencies (θ and α band) within the DMN, and at high frequencies (γ band) within the FPN. Hidden Markov modeling indicated that the DMN exhibited preferential low frequency phase coupling. Phase-amplitude coupling (PAC) analysis revealed that the low-frequency phase in the DMN modulated the high-frequency amplitude envelopes of the FPN, suggesting frequency-dependent characterizations of intrinsic brain networks at rest. These findings provide intracranial electrophysiological evidence in support of the network model for intrinsic organization of human brain and shed light on the way brain networks communicate at rest.

Does slow oscillation-spindle coupling contribute to sleep-dependent memory consolidation? A Bayesian meta-analysis

The active system consolidation theory suggests that information transfer between the hippocampus and cortex during sleep underlies memory consolidation. Neural oscillations during sleep, including the temporal coupling between slow oscillations (SO) and sleep spindles (SP), may play a mechanistic role in memory consolidation. However, differences in analytical approaches and the presence of physiological and behavioral moderators have led to inconsistent conclusions. This meta-analysis, comprising 23 studies and 297 effect sizes, focused on four standard phase-amplitude coupling measures including coupling phase, strength, percentage, and SP amplitude, and their relationship with memory retention. We developed a standardized approach to incorporate non-normal circular-linear correlations. We found strong evidence supporting that precise and strong SO-fast SP coupling in the frontal lobe predicts memory consolidation. The strength of this association is mediated by memory type, aging, and dynamic spatio-temporal features, including SP frequency and cortical topography. In conclusion, SO-SP coupling should be considered as a general physiological mechanism for memory consolidation.

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

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

Exploring functional connectivity in large-scale brain networks in obsessive-compulsive disorder: a systematic review of EEG and fMRI studies

Obsessive-compulsive disorder (OCD) is a debilitating psychiatric condition that is difficult to treat due to our limited understanding of its pathophysiology. Functional connectivity in brain networks, as evaluated through neuroimaging studies, plays a pivotal role in understanding OCD. While both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been extensively employed in OCD research, few have fully synthesized their findings. To bridge this gap, we reviewed 166 studies (10 EEG, 156 fMRI) published up to December 2023. In EEG studies, OCD exhibited lower connectivity in delta and alpha bands, with inconsistent findings in other frequency bands. Resting-state fMRI studies reported conflicting connectivity patterns within the default mode network (DMN) and sensorimotor cortico-striato-thalamo-cortical (CSTC) circuitry. Many studies observed decreased resting-state connectivity between the DMN and salience network (SN), implicating the 'triple network model' in OCD. Task-related hyperconnectivity within the DMN-SN and hypoconnectivity between the SN and frontoparietal network suggest OCD-related cognitive inflexibility, potentially due to triple network dysfunction. In conclusion, our review highlights diverse connectivity differences in OCD, revealing complex brain network interplay that contributes to symptom manifestation. However, the presence of conflicting findings underscores the necessity for targeted research to achieve a comprehensive understanding of the pathophysiology of OCD.

Examining the influence of self-care practices on brain activity in healthy older adults

Introduction

Studies on the aging brain often occur in active settings, but comparatively few investigate brain activity in resting states. However, exploring brain activity in a resting state offers valuable insights into spontaneous neural processes unaffected by task-specific influences. Objective: To investigate the relationship between self-care practices, cognitive function, and patterns of brain activity in healthy older adults, taking into account predictions from aging brain models.

Methodology

77 older adults aged 61 to 87 completing a self-care practices questionnaire, neuropsychological tests, and resting-state electroencephalogram (EEG) recordings. Participants were classified into two groups according to their self-care practices: traditional self-care (T-SC) and developmental self-care (D-SC).

Results

Although neuropsychological tests did not yield significant differences between the D-SC and T-SC groups, patterns of brain activity revealed distinct behaviors. The T-SC group demonstrated patterns more consistent with established aging brain models, contrasting with the D-SC group, which exhibited brain activity akin to that observed in younger adults. Specifically, the T-SC group displayed hyperactivation related to memory and executive function performance, alongside heightened alpha power in posterior regions. Furthermore, bilateral frontal activation in the beta band was evident.

Conclusions

The findings suggest a nuanced relationship between self-care practices and brain activity in older adults. While the T-SC group demonstrated brain activity patterns consistent with conservative aging, indicating the preservation of typical aging characteristics, the D-SC group displayed activity suggestive of a potential protective effect. This effect may be linked to self-care strategies that foster development and resilience in cognitive aging.

What does it mean for consciousness to be multidimensional? A narrative review

A recent development in the psychological and neuroscientific study of consciousness has been the tendency to conceptualize consciousness as a multidimensional phenomenon. This narrative review elucidates the notion of dimensionality of consciousness and outlines the key concepts and disagreements on this topic through the viewpoints of several theoretical proposals. The reviewed literature is critically evaluated, and the main issues to be resolved by future theoretical and empirical work are identified: the problems of dimension selection and dimension aggregation, as well as some ethical considerations. This narrative review is seemingly the first to comprehensively overview this specific aspect of consciousness science.

Rhythmic Attention and ADHD: A Narrative and Systematic Review

In recent decades, a growing body of evidence has confirmed the existence of rhythmic fluctuations in attention, but the effect of inter-individual variations in these attentional rhythms has yet to be investigated. The aim of this review is to identify trends in the attention deficit/hyperactivity disorder (ADHD) literature that could be indicative of between-subject differences in rhythmic attention. A narrative review of the rhythmic attention and electrophysiological ADHD research literature was conducted, and the commonly-reported difference in slow-wave power between ADHD subjects and controls was found to have the most relevance to an understanding of rhythmic attention. A systematic review of the literature examining electrophysiological power differences in ADHD was then conducted to identify studies with conditions similar to those utilised in the rhythmic attention research literature. Fifteen relevant studies were identified and reviewed. The most consistent finding in the studies reviewed was for no spectral power differences between ADHD subjects and controls. However, the strongest trend in the studies reporting power differences was for higher power in the delta and theta frequency bands and lower power in the alpha band. In the context of rhythmic attention, this trend is suggestive of a slowing in the frequency and/or increase in the amplitude of the attentional oscillation in a subgroup of ADHD subjects. It is suggested that this characteristic electrophysiological modulation could be indicative of a global slowing of the attentional rhythm and/or an increase in the rhythmic recruitment of neurons in frontal attention networks in individuals with ADHD.

OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation

The magnetoencephalogram (MEG) based on array optically pumped magnetometers (OPMs) has the potential of replacing conventional cryogenic superconducting quantum interference device. Phase synchronization is a common method for measuring brain oscillations and functional connectivity. Verifying the feasibility and fidelity of OPM-MEG in measuring phase synchronization will help its widespread application in the study of aforementioned neural mechanisms. The analysis method on source-level time series can weaken the influence of instantaneous field spread effect. In this paper, the OPM-MEG was used for measuring the evoked responses of 20Hz rhythmic and arrhythmic median nerve stimulation, and the inter-trial phase synchronization (ITPS) and inter-reginal phase synchronization (IRPS) of primary somatosensory cortex (SI) and secondary somatosensory cortex (SII) were analysed. The results find that under rhythmic condition, the evoked responses of SI and SII show continuous oscillations and the effect of resetting phase. The values of ITPS and IRPS significantly increase at the stimulation frequency of 20Hz and its harmonic of 40Hz, whereas the arrhythmic stimulation does not exhibit this phenomenon. Moreover, in the initial stage of stimulation, the ITPS and IRPS values are significantly higher at Mu rhythm in the rhythmic condition compared to arrhythmic. In conclusion, the results demonstrate the ability of OPM-MEG in measuring phase pattern and functional connectivity on source-level, and may also prove beneficial for the study on the mechanism of rhythmic stimulation therapy for rehabilitation.

Frontal theta oscillations during emotion regulation in people with borderline personality disorder

BACKGROUND

Borderline personality disorder (BPD) is a severe psychiatric disorder conceptualised as a disorder of emotion regulation. Emotion regulation has been linked to a frontolimbic network comprising the dorsolateral prefrontal cortex and the amygdala, which apparently synchronises its activity via oscillatory coupling in the theta frequency range.

AIMS

To analyse whether there are distinct differences in theta oscillatory coupling in frontal brain regions between individuals with BPD and matched controls during emotion regulation by cognitive reappraisal.

METHOD

Electroencephalogram (EEG) recordings were performed in 25 women diagnosed with BPD and 25 matched controls during a cognitive reappraisal task in which participants were instructed to downregulate negative emotions evoked by aversive visual stimuli. Between- and within-group time-frequency analyses were conducted to analyse regulation-associated theta activity (3.5-8.5 Hz).

RESULTS

Oscillatory theta activity differed between the participants with BPD and matched controls during cognitive reappraisal. Regulation-associated theta increases were lower in frontal regions in the BPD cohort compared with matched controls. Functional connectivity analysis for regulation-associated changes in the theta frequency band revealed a lower multivariate interaction measure (MIM) increase in frontal brain regions in persons with BPD compared with matched controls.

CONCLUSIONS

Our findings support the notion of alterations in a frontal theta network in BPD, which may be underlying core symptoms of the disorder such as deficits in emotion regulation. The results add to the growing body of evidence for altered oscillatory brain dynamics in psychiatric populations, which might be investigated as individualised treatment targets using non-invasive stimulation methods.

[Electroencephalography: features of the obtained data and its applicability in psychiatry]

Presently, there is an increased interest in expanding the range of diagnostic and scientific applications of electroencephalography (EEG). The method is attractive due to non-invasiveness, availability of equipment with a wide range of modifications for various purposes, and the ability to track the dynamics of brain electrical activity directly and with high temporal resolution. Spectral, coherency and other types of analysis provide volumetric information about its power, frequency distribution, spatial organization of signal and its self-similarity in dynamics or in different sections at a time. The development of computing technologies provides processing of volumetric data obtained using EEG and a qualitatively new level of their analysis using various mathematical models. This review discusses benefits and limitations of using the EEG in scientific research, currently known interpretation of the obtained data and its physiological and pathological correlates. It is expected to determine the complex relationship between the parameters of brain electrical activity and various functional and pathological conditions. The possibility of using EEG characteristics as biomarkers of various physiological and pathological conditions is being considered. Electronic databases, including MEDLINE (on PubMed), Google Scholar and Russian Scientific Citation Index (RSCI, on elibrary.ru), scientific journals and books were searched to find relevant studies.

Stability and synchronization in neural network with delayed synaptic connections

In this paper, we investigate the stability of the synchronous state in a complex network using the master stability function technique. We use the extended Hindmarsh-Rose neuronal model including time delayed electrical, chemical, and hybrid couplings. We find the corresponding master stability equation that describes the whole dynamics for each coupling mode. From the maximum Lyapunov exponent, we deduce the stability state for each coupling mode. We observe that for electrical coupling, there exists a mixing between stable and unstable states. For a good setting of some system parameters, the position and the size of unstable areas can be modified. For chemical coupling, we observe difficulties in having a stable area in the complex plane. For hybrid coupling, we observe a stable behavior in the whole system compared to the case where these couplings are considered separately. The obtained results for each coupling mode help to analyze the stability state of some network topologies by using the corresponding eigenvalues. We observe that using electrical coupling can involve a full or partial stability of the system. In the case of chemical coupling, unstable states are observed whereas in the case of hybrid interactions a full stability of the network is obtained. Temporal analysis of the global synchronization is also done for each coupling mode, and the results show that when the network is stable, the synchronization is globally observed, while in the case when it is unstable, its nodes are not globally synchronized.

Neural oscillatory activity and connectivity in children who stutter during a non-speech motor task

BACKGROUND

Neural motor control rests on the dynamic interaction of cortical and subcortical regions, which is reflected in the modulation of oscillatory activity and connectivity in multiple frequency bands. Motor control is thought to be compromised in developmental stuttering, particularly involving circuits in the left hemisphere that support speech, movement initiation, and timing control. However, to date, evidence comes from adult studies, with a limited understanding of motor processes in childhood, closer to the onset of stuttering.

METHODS

We investigated the neural control of movement initiation in children who stutter and children who do not stutter by evaluating transient changes in EEG oscillatory activity (power, phase locking to button press) and connectivity (phase synchronization) during a simple button press motor task. We compared temporal changes in these oscillatory dynamics between the left and right hemispheres and between children who stutter and children who do not stutter, using mixed-model analysis of variance.

RESULTS

We found reduced modulation of left hemisphere oscillatory power, phase locking to button press and phase connectivity in children who stutter compared to children who do not stutter, consistent with previous findings of dysfunction within the left sensorimotor circuits. Interhemispheric connectivity was weaker at lower frequencies (delta, theta) and stronger in the beta band in children who stutter than in children who do not stutter.

CONCLUSIONS

Taken together, these findings indicate weaker engagement of the contralateral left motor network in children who stutter even during low-demand non-speech tasks, and suggest that the right hemisphere might be recruited to support sensorimotor processing in childhood stuttering. Differences in oscillatory dynamics occurred despite comparable task performance between groups, indicating that an altered balance of cortical activity might be a core aspect of stuttering, observable during normal motor behavior.

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.

Repeated Transcranial Photobiomodulation with Light-Emitting Diodes Improves Psychomotor Vigilance and EEG Networks of the Human Brain

Transcranial photobiomodulation (tPBM) has been suggested as a non-invasive neuromodulation tool. The repetitive administration of light-emitting diode (LED)-based tPBM for several weeks significantly improves human cognition. To understand the electrophysiological effects of LED-tPBM on the human brain, we investigated alterations by repeated tPBM in vigilance performance and brain networks using electroencephalography (EEG) in healthy participants. Active and sham LED-based tPBM were administered to the right forehead of young participants twice a week for four weeks. The participants performed a psychomotor vigilance task (PVT) during each tPBM/sham experiment. A 64-electrode EEG system recorded electrophysiological signals from each participant during the first and last visits in a 4-week study. Topographical maps of the EEG power enhanced by tPBM were statistically compared for the repeated tPBM effect. A new data processing framework combining the group's singular value decomposition (gSVD) with eLORETA was implemented to identify EEG brain networks. The reaction time of the PVT in the tPBM-treated group was significantly improved over four weeks compared to that in the sham group. We observed acute increases in EEG delta and alpha powers during a 10 min LED-tPBM while the participants performed the PVT task. We also found that the theta, beta, and gamma EEG powers significantly increased overall after four weeks of LED-tPBM. Combining gSVD with eLORETA enabled us to identify EEG brain networks and the corresponding network power changes by repeated 4-week tPBM. This study clearly demonstrated that a 4-week prefrontal LED-tPBM can neuromodulate several key EEG networks, implying a possible causal effect between modulated brain networks and improved psychomotor vigilance outcomes.

Neurofilaments - Small proteins of physiological significance and predictive power for future neurodegeneration and cognitive decline across the life span

Neurofilaments (NFs) are not only important for axonal integrity and nerve conduction in large myelinated axons but they are also thought to be crucial for receptor and synaptic functioning. Therefore, NFs may play a critical role in cognitive functions, as cognitive processes are known to depend on synaptic integrity and are modulated by dopaminergic signaling. Here, we present a theory-driven interdisciplinary approach that NFs may link inflammation, neurodegeneration, and cognitive functions. We base our hypothesis on a wealth of evidence suggesting a causal link between inflammation and neurodegeneration and between these two and cognitive decline (see Fig. 1), also taking dopaminergic signaling into account. We conclude that NFs may not only serve as biomarkers for inflammatory, neurodegenerative, and cognitive processes but also represent a potential mechanical hinge between them, moreover, they may even have predictive power regarding future cognitive decline. In addition, we advocate the use of both NFs and MRI parameters, as their synthesis offers the opportunity to individualize medical treatment by providing a comprehensive view of underlying disease activity in neurological diseases. Since our society will become significantly older in the upcoming years and decades, maintaining cognitive functions and healthy aging will play an important role. Thanks to technological advances in recent decades, NFs could serve as a rapid, noninvasive, and relatively inexpensive early warning system to identify individuals at increased risk for cognitive decline and could facilitate the management of cognitive dysfunctions across the lifespan.

Energetic and informational masking place dissociable demands on listening effort: Evidence from simultaneous electroencephalography and pupillometrya)

The task of processing speech masked by concurrent speech/noise can pose a substantial challenge to listeners. However, performance on such tasks may not directly reflect the amount of listening effort they elicit. Changes in pupil size and neural oscillatory power in the alpha range (8-12 Hz) are prominent neurophysiological signals known to reflect listening effort; however, measurements obtained through these two approaches are rarely correlated, suggesting that they may respond differently depending on the specific cognitive demands (and, by extension, the specific type of effort) elicited by specific tasks. This study aimed to compare changes in pupil size and alpha power elicited by different types of auditory maskers (highly confusable intelligible speech maskers, speech-envelope-modulated speech-shaped noise, and unmodulated speech-shaped noise maskers) in young, normal-hearing listeners. Within each condition, the target-to-masker ratio was set at the participant's individually estimated 75% correct point on the psychometric function. The speech masking condition elicited a significantly greater increase in pupil size than either of the noise masking conditions, whereas the unmodulated noise masking condition elicited a significantly greater increase in alpha oscillatory power than the speech masking condition, suggesting that the effort needed to solve these respective tasks may have different neural origins.

Effortful listening produces both enhancement and suppression of alpha in the EEG

Introduction

Adverse listening conditions can drive increased mental effort during listening. Neuromagnetic alpha oscillations (8-13 Hz) may index this listening effort, but inconsistencies regarding the direction of the relationship are abundant. We performed source analyses on high-density EEG data collected during a speech-on-speech listening task to address the possibility that opposing alpha power relationships among alpha producing brain sources drive this inconsistency.

Methods

Listeners (N=20) heard two simultaneously presented sentences of the form: Ready go to now. They either reported the color/number pair of a "Baron" call sign sentence (active: high effort), or ignored the stimuli (passive: low effort). Independent component analysis (ICA) was used to segregate temporally distinct sources in the EEG.

Results

Analysis of independent components (ICs) revealed simultaneous alpha enhancements (e.g., for somatomotor mu ICs) and suppressions (e.g., for left temporal ICs) for different brain sources. The active condition exhibited stronger enhancement for left somatomotor mu rhythm ICs, but stronger suppression for central occipital ICs.

Discussion

This study shows both alpha enhancement and suppression to be associated with increases in listening effort. Literature inconsistencies could partially relate to some source activities overwhelming others in scalp recordings.

Event-related Oscillations to Emotional Faces are Related to a History of Internalizing Disorders

Event-related oscillations (EROs) may represent sensitive biomarkers or endophenotypes for disorders that underlie risk behaviors such as suicidal thoughts and actions. In this study, young adults of American Indian (AI) (n = 821) and Mexican American (MA) (n = 721) ancestry (age 18-30 yrs) were clinically assessed for internalizing and externalizing disorders, and an internalizing scale was generated by extracting core diagnostic items from 6 lifetime DSM5-compatible diagnoses (social phobia, panic disorder, agoraphobia, obsessive compulsive disorder, post-traumatic stress disorder, major depressive episode) and symptoms of suicidality. EROs were generated to sad, happy and neutral faces, and energy and phase locking of delta ERO oscillations were assessed in frontal areas. An increase in delta ERO energy was found in the frontal lead (FZ) following presentation of the sad facial expressions in those with a history of 10 or more internalizing symptoms compared to those with no symptoms. Increases in delta ERO energy in FZ were also associated with a diagnosis of major depressive disorder (MDD), but not with anxiety disorders or antisocial personality disorder/conduct disorders (ASP). Major depression was also associated with increases in cross-cortical phase-locking (FZ-PZ). A decrease in the percentage of correctly identified neutral faces also was seen among those with 10 or more internalizing symptoms compared to those without internalizing symptoms, and in those with anxiety disorders, but not in those with ASP or MDD as compared to their controls. These findings suggest ERO measures may represent important potential biomarkers of depressive disorders as well as risk indicators for suicidal behaviors.

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

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

Impact of interictal epileptiform discharges on brain network in self-limited epilepsy with centrotemporal spikes: A magnetoencephalography study

OBJECTIVE

This study aimed to investigate the differences on resting-state brain networks between the interictal epileptiform discharge (IED) group with self-limited epilepsy with centrotemporal spikes (SeLECTS), the non-IED group with SeLECTS, and the healthy control (HC) group.

METHODS

Patients were divided into the IED and non-IED group according to the presence or absence of IED during magnetoencephalography (MEG). We used Wechsler Intelligence Scale for Children, fourth edition (WISC-IV) to assess cognition in 30 children with SeLECTS and 15 HCs. Functional networks were constructed at the whole-brain level and graph theory (GT) analysis was used to quantify the topology of the brain network.

RESULTS

The IED group had the lowest cognitive function scores, followed by the non-IED group and then HCs. Our MEG results showed that the IED group had more dispersed functional connectivity (FC) in the 4-8 Hz frequency band, and more brain regions were involved compared to the other two groups. Furthermore, the IED group had fewer FC between the anterior and posterior brain regions in the 12-30 Hz frequency band. Both the IED group and the non-IED group had fewer FC between the anterior and posterior brain regions in the 80-250 Hz frequency band compared to the HC group. GT analysis showed that the IED group had a higher clustering coefficient compared to the HC group and a higher degree compared to the non-IED group in the 80-250 Hz frequency band. The non-IED group had a lower path length in the 30-80 Hz frequency band compared to the HC group.

CONCLUSIONS

The study data obtained in this study suggested that intrinsic neural activity was frequency-dependent and that FC networks of the IED group and the non-IED group underwent changes in different frequency bands. These network-related changes may contribute to cognitive dysfunction in children with SeLECTS.

Alpha and gamma EEG coherence during on-task and mind wandering states in schizophrenia

OBJECTIVE

Electroencephalographic (EEG) coherence is one of the most relevant physiological measures used to detect abnormalities in patients with schizophrenia. The present study applies a task-related EEG coherence approach to understand cognitive processing in patients with schizophrenia and healthy controls.

METHODS

EEG coherence for alpha and gamma frequency bands was analyzed in a group of patients with schizophrenia and a group of healthy controls during the performance of an ecological task of sustained attention. We compared EEG coherence when participants presented externally directed cognitive states (On-Task) and when they presented cognitive distraction episodes (Mind-Wandering).

RESULTS

Results reflect cortical differences between groups (higher coherence for schizophrenia in the frontocentral and fronto-temporal regions, and higher coherence for healthy-controls in the postero-central regions), especially in the On-Task condition for the alpha band, compared to Mind-Wandering episodes. Few individual differences in gamma coherence were found.

CONCLUSIONS

The current study provides evidence of neurophysiological differences underlying different cognitive states in schizophrenia and healthy controls.

SIGNIFICANCE

Differences between groups may reflect inhibitory processes necessary for the successful processing of information, especially in the alpha band, given its role in cortical inhibition processes. Patients may activate compensatory inhibitory mechanisms when performing the task, reflected in increased coherence in fronto-temporal regions.

Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication

Sleep inertia is the brief period of impaired alertness and performance experienced immediately after waking. Little is known about the neural mechanisms underlying this phenomenon. A better understanding of the neural processes during sleep inertia may offer insight into the awakening process. We observed brain activity every 15 min for 1 hr following abrupt awakening from slow wave sleep during the biological night. Using 32-channel electroencephalography, a network science approach, and a within-subject design, we evaluated power, clustering coefficient, and path length across frequency bands under both a control and a polychromatic short-wavelength-enriched light intervention condition. We found that under control conditions, the awakening brain is typified by an immediate reduction in global theta, alpha, and beta power. Simultaneously, we observed a decrease in the clustering coefficient and an increase in path length within the delta band. Exposure to light immediately after awakening ameliorated changes in clustering. Our results suggest that long-range network communication within the brain is crucial to the awakening process and that the brain may prioritize these long-range connections during this transitional state. Our study highlights a novel neurophysiological signature of the awakening brain and provides a potential mechanism by which light improves performance after waking.

Altered time-varying local spontaneous brain activity pattern in patients with high myopia: a dynamic amplitude of low-frequency fluctuations study

PURPOSE

To investigate the abnormal time-varying local spontaneous brain activity in patients with high myopia (HM) on the basis of the dynamic amplitude of low-frequency fluctuations (dALFF) approach.

METHODS

Age and gender matching were performed based on resting-state functional magnetic resonance imaging data from 86 HM patients and 87 healthy controls (HCs). Local spontaneous brain activities were evaluated using the time-varying dALFF method. Support vector machine combined with the radial basis function kernel was used for pattern classification analysis.

RESULTS

Inter-group comparison between HCs and HM patients has demonstrated that dALFF variability in the left inferior frontal gyrus (orbital part), left lingual gyrus, right anterior cingulate and paracingulate gyri, and right calcarine fissure and surrounding cortex was decreased in HM patients, while increased in the left thalamus, left paracentral lobule, and left inferior parietal (except supramarginal and angular gyri). Pattern classification between HM patients and HCs displayed a classification accuracy of 85.5%.

CONCLUSION

In this study, the findings mentioned above have suggested the association between local brain activities of HM patients and abnormal variability in brain regions performing visual sensorimotor and attentional control functions. Several useful information has been provided to elucidate the mechanism-related alterations of the myopic nervous system. In addition, the significant role of abnormal dALFF variability has been highlighted to achieve an in-depth comprehension of the pathological alterations and neuroimaging mechanisms in the field of HM.

The neural mechanism of non-phase-locked EEG activity in task switching

Flexible switching between different tasks is an important cognitive ability for humans and it is often studied using the task-switching paradigm. Although the neural mechanisms of task switching have been extensively explored in previous studies using event-related potentials techniques, the activity and process mechanisms of non-phase-locked electroencephalography (EEG) have rarely been revealed. For this reason, this paper discusses the processing of non-phase-locked EEG oscillations in task switching based on frequency-band delineation. First, the roles of each frequency band in local brain regions were summarized. In particular, during the proactive control process (the cue-stimulus interval), delta, theta, and alpha oscillations played more roles in the switch condition while beta played more roles in repeat task. In the reactive control process (post-target), delta, alpha, and beta are all related to sensorimotor function. Then, utilizing the functional connectivity (FC) method, delta connections in the frontotemporal regions and theta connections located in the parietal-to-occipital sites are involved in the preparatory period before task switching, while alpha connections located in the sensorimotor areas and beta connections located in the frontal-parietal cortex are involved in response inhibition. Finally, cross-frequency coupling (CFC) play an important role in working memory among different band oscillation. The present study shows that in addition to the processing mechanisms specific to each frequency band, there are some shared and interactive neural mechanism in task switching by using different analysis techniques.

Neurodynamical Computing at the Information Boundaries of Intelligent Systems

Artificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms. Cell assemblies-properly conceived as reentrant dynamical flows and not merely as identified groups of neurons-may fill that gap by providing a minimal supraneuronal level of organization that establishes a neurodynamical base layer for computation. By considering information streams from physical embodiment and situational embedding, we discuss this computational base layer in terms of conserved oscillatory and structural properties of cortical-hippocampal networks. Our synthesis of embodied cognition, based in dynamical systems and perceptual control, aims to bypass the neurosymbolic stalemates that have arisen in artificial intelligence, cognitive science, and computational neuroscience.

Resting state network dynamic reconfiguration and neuropsychological functioning in temporal lobe epilepsy: An HD-EEG investigation

Temporal lobe epilepsy (TLE) is nowadays considered a network disorder impacting several cognitive domains. In this work we investigated dynamic network reconfiguration differences in patients with unilateral TLE compared to a healthy control group, focusing on two connectivity indices: flexibility and integration. We apply these indices for the first time to high-density EEG source-based functional connectivity. We observed that patients with TLE exhibited significantly lower flexibility than healthy controls in the Control, Default Mode and Attentive Dorsal networks, expressed in the delta, theta and alpha bands. In addition, patients with TLE displayed greater integration values across the majority of the resting state networks, especially in the delta, theta and gamma bands. Relevantly, a higher integration index in the Control, Attentive Dorsal and Visual networks in the delta band was correlated with lower performance in visual attention and executive functions. Moreover, a greater integration index in the gamma band of the Control, Somatomotor and Temporoparietal networks was related to lower long-term memory performance. These results suggest that patients with TLE display dysregulated network reconfiguration, with lower flexibility in the brain areas related to cognitive control and attention, together with excessive inter-network communication (integration index). Finally, the correlation between network integration and the reduced cognitive performance suggests a potential mechanism underlying specific alterations in neuropsychological profile of patients with TLE.

The impact of background music on film audience's attentional processes: Electroencephalography alpha-rhythm and event-related potential analyses

Background music is an indispensable part of films and plays an important role in enhancing audiences' attention to scenes. However, few studies have examined the cognitive effect of background music at the neurophysiological level. Using electroencephalography (EEG), the present study examines the effect of background music tempo on the viewer's attentional processes. Participants' (N = 24) EEG responses were recorded while the participants watched segments of action films in three conditions with variations on the presence and tempo of background music (i.e., no background music vs. slow-tempo music vs. fast-tempo music). These responses were analyzed using the alpha-rhythm suppression and event-related potential (ERP) P300, a brainwave indicator of attentional processes. The results suggest that participants' attention levels increased when background music was present (compared to when background music was absent), but there was no difference in participants' attention levels based on tempo. The theoretical and practical implications of these findings are discussed.

Structural and functional network mechanisms of rescuing cognitive control in aging

Age-related declines in cognitive control, an ability critical in most daily tasks, threaten individual independence. We previously showed in both older and younger adults that transcranial alternating current stimulation (tACS) can improve cognitive control, with effects observed across neural regions distant from the stimulated site and frequencies outside the stimulated range. Here, we assess network-level changes in neural activity that extend beyond the stimulated site and evaluate anatomical pathways that subserve these effects. We investigated the potential to rescue cognitive control in aging using prefrontal (F3-F4) theta (6 Hz) or control (1 Hz) tACS while older adults engaged in a cognitive control video game intervention on three consecutive days. Functional connectivity was assessed with EEG by measuring daily changes in frontal-posterior phase-locking values (PLV) from the tACS-free baseline. Structural connectivity was measured using MRI diffusion tractography data collected at baseline. Theta tACS improved multitasking performance, and individual gains reflected a dissociation in daily PLV changes, where theta tACS strengthened PLV and control tACS reduced PLV. Strengthened alpha-beta PLV in the theta tACS group correlated positively with inferior longitudinal fasciculus and corpus callosum body integrity, and further explained multitasking gains. These results demonstrate that theta tACS can improve cognitive control in aging by strengthening functional connectivity, particularly in higher frequency bands. However, the extent of functional connectivity gains is limited by the integrity of structural white matter tracts. Given that advanced age is associated with decreased white matter integrity, results suggest that the deployment of tACS as a therapeutic is best prior to advanced age.

Association of resting-state theta–gamma coupling with selective visual attention in children with tic disorders

A tic disorder (TD) is a neurodevelopmental disorder characterized by tics, which are repetitive movements and/or vocalizations that occur due to aberrant sensory gating. Its pathophysiology involves dysfunction in multiple parts of the cortico-striato-thalamo-cortical circuits. Spontaneous brain activity during the resting state can be used to evaluate the baseline brain state, and it is associated with various aspects of behavior and cognitive processes. Theta–gamma coupling (TGC) is an emerging technique for examining how neural networks process information through interactions. However, the resting-state TGC of patients with TD and its correlation with cognitive function have not yet been studied. We investigated the resting-state TGC of 13 patients with TD and compared it with that of 13 age-matched healthy children. The participants underwent resting-state electroencephalography with their eyes closed. At the global level, patients with TD showed a significantly lower resting-state TGC than healthy children. Resting-state TGC with the eyes closed was significantly negatively correlated with the attention quotient calculated for omission errors in a selective visual attention test. These findings indicate that the resting-state brain network, which is important for the attentional processing of visual information, is dysfunctional in patients with TD. Additionally, these findings support the view that TGC reflects information processing and signal interactions at the global level. Patients with TD may have difficulty gating irrelevant sensory information in the resting state while their eyes are closed.

Electrophysiological measurements of holistic processing of Chinese characters

Holistic processing (HP) is a marker of perceptual expertise in facial recognition. In the present study, we examined neural responses to the HP of Chinese characters, adopting the composite paradigm. The behavioral results showed that the discrimination of congruent trials was significantly higher than that of incongruent trials, and participants responded faster. Moreover, the congruent trials elicited significantly larger N170 amplitude than the incongruent trials. The HP effect of the N170 component was observed for upright characters, as the configural information of inverted characters and misaligned characters were destroyed. Right-lateralization of processing Chinese characters was observed in the N170 amplitudes and delta-theta band oscillations. The results suggested that Chinese character recognition employed a strategy of HP, and the finding that neural indicators provide a better signal of the strength of HP in Chinese characters than behavioral indicators was also crucial.

The correlations between kinematic profiles and cerebral hemodynamics suggest changes of motor coordination in single and bilateral finger movement

Objective

The correlation between the performance of coordination movement and brain activity is still not fully understood. The current study aimed to identify activated brain regions and brain network connectivity changes for several coordinated finger movements with different difficulty levels and to correlate the brain hemodynamics and connectivity with kinematic performance.

Methods

Twenty-one right-dominant-handed subjects were recruited and asked to complete circular motions of single and bilateral fingers in the same direction (in-phase, IP) and in opposite directions (anti-phase, AP) on a plane. Kinematic data including radius and angular velocity at each task and synchronized blood oxygen concentration data using functional near-infrared spectroscopy (fNIRS) were recorded covering six brain regions including the prefrontal cortex, motor cortex, and occipital lobes. A general linear model was used to locate activated brain regions, and changes compared with baseline in blood oxygen concentration were used to evaluate the degree of brain region activation. Small-world properties, clustering coefficients, and efficiency were used to measure information interaction in brain activity during the movement.

Result

It was found that the radius error of the dominant hand was significantly lower than that of the non-dominant hand (p < 0.001) in both clockwise and counterclockwise movements. The fNIRS results confirmed that the contralateral brain region was activated during single finger movement and the dominant motor area was activated in IP movement, while both motor areas were activated simultaneously in AP movement. The Δhbo were weakly correlated with radius errors (p = 0.002). Brain information interaction in IP movement was significantly larger than that from AP movement in the brain network (p < 0.02) in the right prefrontal cortex. Brain activity in the right motor cortex reduces motor performance (p < 0.001), while the right prefrontal cortex region promotes it (p < 0.05).

Conclusion

Our results suggest there was a significant correlation between motion performance and brain activation level, as well as between motion deviation and brain functional connectivity. The findings may provide a basis for further exploration of the operation of complex brain networks.

Theta-phase connectivity between medial prefrontal and posterior areas underlies novel instructions implementation

Implementing novel instructions is a complex and uniquely human cognitive ability, that requires the rapid and flexible conversion of symbolic content into a format that enables the execution of the instructed behavior. Preparing to implement novel instructions, as opposed to their mere maintenance, involves the activation of the instructed motor plans, and the binding of the action information to the specific context in which this should be executed. Recent evidence and prominent computational models suggest that this efficient configuration of the system might involve a central role of frontal theta oscillations in establishing top-down long-range synchronization between distant and task-relevant brain areas. In the present EEG study (human subjects, 30 females, 4 males), we demonstrate that proactively preparing for the implementation of novels instructions, as opposed to their maintenance, involves a strengthened degree of connectivity in the theta frequency range between medial prefrontal and motor/visual areas. Moreover, we replicated previous results showing oscillatory features associated specifically with implementation demands, and extended on them demonstrating the role of theta oscillations in mediating the effect of task demands on behavioral performance. Taken together, these findings support our hypothesis that the modulation of connectivity patterns between frontal and task-relevant posterior brain areas is a core factor in the emergence of a behavior-guiding format from novel instructions.Significance statementEveryday life requires the use and manipulation of currently available information to guide behavior and reach specific goals. In the present study we investigate how the same instructed content elicits different neural activity depending on the task being performed. Crucially, connectivity between medial prefrontal cortex and posterior brain areas is strengthened when novel instructions have to be implemented, rather than simply maintained. This finding suggests that theta oscillations play a role in setting up a dynamic and flexible network of task-relevant regions optimized for the execution of the instructed behavior.

Time Is of the Essence: Neural Codes, Synchronies, Oscillations, Architectures

Time is of the essence in how neural codes, synchronies, and oscillations might function in encoding, representation, transmission, integration, storage, and retrieval of information in brains. This Hypothesis and Theory article examines observed and possible relations between codes, synchronies, oscillations, and types of neural networks they require. Toward reverse-engineering informational functions in brains, prospective, alternative neural architectures incorporating principles from radio modulation and demodulation, active reverberant circuits, distributed content-addressable memory, signal-signal time-domain correlation and convolution operations, spike-correlation-based holography, and self-organizing, autoencoding anticipatory systems are outlined. Synchronies and oscillations are thought to subserve many possible functions: sensation, perception, action, cognition, motivation, affect, memory, attention, anticipation, and imagination. These include direct involvement in coding attributes of events and objects through phase-locking as well as characteristic patterns of spike latency and oscillatory response. They are thought to be involved in segmentation and binding, working memory, attention, gating and routing of signals, temporal reset mechanisms, inter-regional coordination, time discretization, time-warping transformations, and support for temporal wave-interference based operations. A high level, partial taxonomy of neural codes consists of channel, temporal pattern, and spike latency codes. The functional roles of synchronies and oscillations in candidate neural codes, including oscillatory phase-offset codes, are outlined. Various forms of multiplexing neural signals are considered: time-division, frequency-division, code-division, oscillatory-phase, synchronized channels, oscillatory hierarchies, polychronous ensembles. An expandable, annotative neural spike train framework for encoding low- and high-level attributes of events and objects is proposed. Coding schemes require appropriate neural architectures for their interpretation. Time-delay, oscillatory, wave-interference, synfire chain, polychronous, and neural timing networks are discussed. Some novel concepts for formulating an alternative, more time-centric theory of brain function are discussed. As in radio communication systems, brains can be regarded as networks of dynamic, adaptive transceivers that broadcast and selectively receive multiplexed temporally-patterned pulse signals. These signals enable complex signal interactions that select, reinforce, and bind common subpatterns and create emergent lower dimensional signals that propagate through spreading activation interference networks. If memory traces share the same kind of temporal pattern forms as do active neuronal representations, then distributed, holograph-like content-addressable memories are made possible via temporal pattern resonances.

Language as the Working Model of Human Mind

The Human Mind, functional aspect of Human Brain, has been envisaged to be working on the tenets of Chaos, a seeming order within a disorder, the premise of Universe. The armamentarium of Human Mind makes use of distributed neuronal networks sub-serving Sensorial Mechanisms, Mirror Neurone System (MNS) and Motor Mechanisms etching a stochastic trajectory on the virtual phase-space of Human Mind, obeying the ethos of Chaos. The informational sensorial mechanisms recruit attentional mechanisms channelising through the window of chaotic neural dynamics onto MNS that providing algorithmic image information flow along virtual phase- space coordinates concluding onto motor mechanisms that generates and mirrors a stimulus- specific and stimulus-adequate response. The singularity of self-iterating fractal architectonics of Event-Related Synchrony (ERS), a Power Spectral Density (PSD) precept of electroencephalographic (EEG) time-series denotes preferential and categorical inhibition gateway and an Event-Related Desynchrony (ERD) represents event related and locked gateway to stimulatory/excitatory neuronal architectonics leading to stimulus-locked and adequate neural response. The contextual inference in relation to stochastic phase-space trajectory of self- iterating fractal of Off-Center α ERS (Central)-On-Surround α ERD-On Surround θ ERS document efficient neural dynamics of working memory., across patterned modulation and flow of the neurally coded information.

Treatment effects on event-related EEG potentials and oscillations in Alzheimer's disease

Alzheimer's disease dementia (ADD) is the most diffuse neurodegenerative disorder belonging to mild cognitive impairment (MCI) and dementia in old persons. This disease is provoked by an abnormal accumulation of amyloid-beta and tauopathy proteins in the brain. Very recently, the first disease-modifying drug has been licensed with reserve (i.e., Aducanumab). Therefore, there is a need to identify and use biomarkers probing the neurophysiological underpinnings of human cognitive functions to test the clinical efficacy of that drug. In this regard, event-related electroencephalographic potentials (ERPs) and oscillations (EROs) are promising candidates. Here, an Expert Panel from the Electrophysiology Professional Interest Area of the Alzheimer's Association and Global Brain Consortium reviewed the field literature on the effects of the most used symptomatic drug against ADD (i.e., Acetylcholinesterase inhibitors) on ERPs and EROs in ADD patients with MCI and dementia at the group level. The most convincing results were found in ADD patients. In those patients, Acetylcholinesterase inhibitors partially normalized ERP P300 peak latency and amplitude in oddball paradigms using visual stimuli. In these same paradigms, those drugs partially normalize ERO phase-locking at the theta band (4-7 Hz) and spectral coherence between electrode pairs at the gamma (around 40 Hz) band. These results are of great interest and may motivate multicentric, double-blind, randomized, and placebo-controlled clinical trials in MCI and ADD patients for final cross-validation.

Effective connectivity in cortical networks during deception: A lie detection study using EEG

Previous studies have identified activated regions associated with deceptive tasks and most of them utilized time, frequency, or temporal features to identify deceptive responses. However, when deception behaviors occur, the functional connectivity pattern and the communication between different brain areas remain largely unclear. In this study, we explored the most important information flows between different brain cortices during deception. First, we employed the guilty knowledge test protocol and recorded on 64 electrodes electroencephalogram (EEG) signals from 30 subjects (15 guilty and 15 innocent). EEG source estimation was then performed to compute the cortical activities on the 24 regions of interest (ROIs). Next, effective connectivity was calculated by partial directed coherence (PDC) analysis applied to the cortical signals. Furthermore, based on the graph-theoretical analysis, the network parameters with significant differences were extracted as features to identify two groups of subjects. In addition, the ROIs frequently involved in the above network parameters were selected, and based on the difference in the group mean of PDC values of all the edges connected with the selected ROIs, we presented the strongest information flows (MIIF) in the guilty group relative to the innocent group. Experimental results first show that the optimal classification features are mainly in-degree and out-degree measures of the ROI and the high classification accuracy for four bands demonstrated that the proposed method is suitable for lie detection. In addition, the frontoparietal network was found to be most prominent among all the MIIFs in four bands. Finally, combining the neurophysiology signification of four frequency bands, respectively, we analyzed the roles of all the important information flows to uncover the underlying cognitive processes and mechanisms used in deception.

Computer-aided Recognition Based on Decision-level Multimodal Fusion for Depression

Aiming at the problem of depression recognition, this paper proposes a computer-aided recognition framework based on decision-level multimodal fusion. In Song Dynasty of China, the idea of multimodal fusion was contained in "one gets different impressions of a mountain when viewing it from the front or sideways, at a close range or from afar" poetry. Objective and comprehensive analysis of depression can more accurately restore its essence, and multimodal can represent more information about depression compared to single modal. Linear electroencephalography (EEG) features based on adaptive auto regression (AR) model and typical nonlinear EEG features are extracted. EEG features related to depression and graph metric features in depression related brain regions are selected as the data basis of multimodal fusion to ensure data diversity. Based on the theory of multi-agent cooperation, the computer-aided depression recognition model of decision-level is realized. The experimental data comes from 24 depressed patients and 29 healthy controls (HC). The results of multi-group controlled trials show that compared with single modal or independent classifiers, the decision-level multimodal fusion method has a stronger ability to recognize depression, and the highest accuracy rate 92.13% was obtained. In addition, our results suggest that improving the brain region associated with information processing can help alleviate and treat depression. In the field of classification and recognition, our results clarify that there is no universal classifier suitable for any condition.

Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep

Auditory stimulation of EEG slow waves (SW) during non-rapid eye movement (NREM) sleep has shown to improve cognitive function when it is delivered at the up-phase of SW. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration such as Parkinson disease (PD). However, existing algorithms to estimate the up-phase suffer from a poor phase accuracy at low EEG amplitudes and when SW frequencies are not constant. We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices. The algorithms were based on a phase-locked loop (PLL) and, for the first time, a phase vocoder (PV). We compared these phase tracking algorithms with a simple amplitude threshold approach. The optimized algorithms were benchmarked for phase accuracy, the capacity to estimate phase at SW amplitudes between 20 and 60 V, and SW frequencies above 1 Hz on 324 recordings from healthy older adults and PD patients. Furthermore, the algorithms were implemented on a wearable device and the computational efficiency and the performance was evaluated on simulated sleep EEG, as well as prospectively during a recording with a PD patient. All three algorithms delivered more than 70% of the stimulation triggers during the SW up-phase. The PV showed the highest capacity on targeting low-amplitude SW and SW with frequencies above 1 Hz. The testing on real-time hardware revealed that both PV and PLL have marginal impact on microcontroller load, while the efficiency of the PV was 4% lower than the PLL. Active auditory stimulation did not influence the phase tracking. This work demonstrated that phase-accurate auditory stimulation can be delivered during home-based sleep interventions with a wearable device also in populations with low-amplitude SW.

Altered Neuromagnetic Activity in Persistent Postural-Perceptual Dizziness: A Multifrequency Magnetoencephalography Study

Objective

The aim of our study was to investigate abnormal changes in brain activity in patients with persistent postural-perceptual dizziness (PPPD) using magnetoencephalography (MEG).

Methods

Magnetoencephalography recordings from 18 PPPD patients and 18 healthy controls were analyzed to determine the source of brain activity in seven frequency ranges using accumulated source imaging (ASI).

Results

Our study showed that significant changes in the patterns of localization in the temporal-parietal junction (TPJ) were observed at 1–4, 4–8, and 12–30 Hz in PPPD patients compared with healthy controls, and changes in the frontal cortex were found at 1–4, 80–250, and 250–500 Hz in PPPD patients compared with controls. The neuromagnetic activity in TPJ was observed increased significantly in 1–4 and 4–8 Hz, while the neuromagnetic activity in frontal cortex was found increased significantly in 1–4 Hz. In addition, the localized source strength in TPJ in 1–4 Hz was positively correlated with DHI score (r = 0.7085, p < 0.05), while the localized source strength in frontal cortex in 1–4 Hz was positively correlated with HAMA score (r = 0.5542, p < 0.05).

Conclusion

Our results demonstrated that alterations in the TPJ and frontal cortex may play a critical role in the pathophysiological mechanism of PPPD. The neuromagnetic activity in TPJ may be related to dizziness symptom of PPPD patients, while the neuromagnetic activity in frontal lobe may be related to emotional symptoms of PPPD patients. In addition, frequency-dependent changes in neuromagnetic activity, especially neuromagnetic activity in low frequency bands, were involved in the pathophysiology of PPPD.

Brain Dynamics of Action Monitoring in Higher-Order Motor Control Disorders: The Case of Apraxia

Limb apraxia (LA) refers to a high-order motor disorder characterized by the inability to reproduce transitive actions on commands or after observation. Studies demonstrate that action observation and action execution activate the same networks in the human brain, and provides an onlooker’s motor system with appropriate cognitive, motor and sensory-motor cues to flexibly implementing action-sequences and gestures. Tellingly, the temporal dynamics of action monitoring has never been explored in people suffering from LA. To fill this gap, we studied the electro-cortical signatures of error observation in human participants suffering from acquired left-brain lesions with (LA+) and without (LA–) LA, and in a group of healthy controls (H). EEG was acquired while participants observed from a first-person perspective (1PP) an avatar performing correct or incorrect reach-to-grasp a glass action in an immersive-virtual environment. Alterations of typical EEG signatures of error observation in time (early error positivity; Pe) and time-frequency domain (theta band-power) were found reduced in LA+ compared with H. Connectivity analyses showed that LA+ exhibited a decreased theta phase synchronization of both the frontoparietal and frontofrontal network, compared with H and LA–. Moreover, linear regression analysis revealed that the severity of LA [test of upper LA (TULIA) scores] was predicted by mid-frontal error-related theta activity, suggesting a link between error monitoring capacity and apraxic phenotypes. These results provide novel neurophysiological evidence of altered neurophysiological dynamics of action monitoring in individuals with LA and shed light on the performance monitoring changes occurring in this disorder.

Neural mechanisms associated with semantic and basic self-oriented memory processes interact moderating self-esteem

Individuals constantly encounter feedback from others and process this feedback in various ways to maintain positive situational state self-esteem in relation to semantic-based or trait self-esteem. Individuals may utilize episodic or semantic-driven processes that modulate feedback in two different ways to maintain general self-esteem levels. To date, it is unclear how these processes work while individuals receive social feedback to modulate state self-esteem. Utilizing neural regions associated with semantic self-oriented and basic encoding processes (medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), respectively), in addition to time-frequency and Granger causality analyses to assess mPFC and PCC interactions, this study examined how the encoding of social feedback modulated individuals' (N = 45) post-task state self-esteem in relation to their trait self-esteem. Findings highlight the dynamic interplay between mPFC and PCC that modulate state self-esteem in relation to trait self-esteem, to maintain high self-esteem in general in the moment and over time.

The Functional Interactions between Cortical Regions through Theta-Gamma Coupling during Resting-State and a Visual Working Memory Task

Theta phase-gamma amplitude coupling (TGC) plays an important role in several different cognitive processes. Although spontaneous brain activity at the resting state is crucial in preparing for cognitive performance, the functional role of resting-state TGC remains unclear. To investigate the role of resting-state TGC, electroencephalogram recordings were obtained for 56 healthy volunteers while they were in the resting state, with their eyes closed, and then when they were engaged in a retention interval period in the visual memory task. The TGCs of the two different conditions were calculated and compared. The results indicated that the modulation index of TGC during the retention interval of the visual working memory (VWM) task was not higher than that during the resting state; however, the topographical distribution of TGC during the resting state was negatively correlated with TGC during VWM task at the local level. The topographical distribution of TGC during the resting state was negatively correlated with TGC coordinates’ engagement of brain areas in local and large-scale networks and during task performance at the local level. These findings support the view that TGC reflects information-processing and signal interaction across distant brain areas. These results demonstrate that TGC could explain the efficiency of competing brain networks.