Beta Oscillations in Working Memory, Executive Control of Movement and Thought, and Sensorimotor Function

How domain-general proactive control modulates the processing of English wh-dependencies: An EEG study

Language processing has been hypothesized to engage domain-general cognitive control processes. Studies supporting such an assumption have revealed that performing tasks that engage conflict resolution in an experiment could facilitate the disambiguation of garden-path sentences. While this modulation has typically been found between cognitive inhibition and disambiguation of garden-path sentences, it is unclear whether similar effects occur in other types of cognitive control or sentence structure. To address this, we conducted an EEG study to examine whether and how domain-general proactive control influences the processing of English wh-dependency sentences. We looked into both event-related brain potentials and time-frequency representations in the present study. During the experiment, each participant was asked to do one of the two versions of the AX-continuous performance task (AX-CPT), right after which they performed a sentence reading task containing English wh-dependency sentences. Participants were grouped based on the version of the AX-CPT they performed and different versions of the AX-CPT varied in the demand on proactive control. Seventy functionally monolingual and neurologically healthy native English speakers without any reading disorder participated in the study. Our results confirmed that the AX-CPT successfully induced different levels of proactive control across groups. Importantly, we found evidence for a modulatory effect between domain-general proactive control and language-specific processing under our experimental manipulations. This finding suggests that domain-general proactive control and language-specific processing may share overlapping neural mechanisms, and that changes in proactive control levels can influence language-specific processing.

Differential Beta and Gamma Activity Modulation during Unimanual and Bimanual Motor Learning

Movement-related dynamics in the beta and gamma bands have been studied in relation to motor execution and learning during unimanual movements, but their roles in complex bimanual tasks remain largely unexplored. This study aimed to investigate how beta and gamma activity differs between unimanual and bimanual movements and how these neural signatures evolve during the learning process. Our motor task incorporated varying levels of bimanual interaction: unimanual, bimanual-equal, and bimanual-unequal. Magnetoencephalography data were recorded in healthy participants (N = 43, 27 females) during task performance, and beta and gamma activity was quantified. As expected, increasing task complexity from unimanual to bimanual-equal and then to bimanual-unequal movements resulted in slower and less accurate performance. Across all conditions, significant beta event-related desynchronization (ERD) and gamma event-related synchronization (ERS) were observed during movement, as well as beta ERS after movement. Bimanual movements exhibited greater beta ERD, beta ERS, and gamma ERS compared with unimanual movements. With practice, participants demonstrated faster and more accurate movements, accompanied by enhanced beta ERS responses. Furthermore, learning-related reductions in errors correlated with increases in beta ERS. These findings suggest the distinct behavioral and neural demands of unimanual versus bimanual movements and highlight the important role of beta activity in motor performance and learning.

Unraveling the roles of spatial working memory sustained and selective neurons in prefrontal cortex

The heart of goal-directed behavior organization is working memory. Recent studies have emphasized the critical role of the prefrontal cortex (PFC) in working memory, highlighting elevated spiking levels in PFC neurons during working-memory delays. As a higher-order cortex, PFC contains various types of neurons with complex receptive fields, making it challenging to identify task-engaged neurons, particularly during the working memory periods when firing rates are lower compared to stimulus periods. While previous studies have primarily focused on neurons selective for sensory stimuli, there are also task-sustained neurons that are not selective for specific stimulus characteristics. In this study, we differentiate between working memory (WM)-sustained neurons, which show task-related activity without stimulus spatial selectivity, and working memory (WM)-selective neurons, which are selective for the location of the stimulus. To investigate their roles, we investigated the neural activities of the lateral PFC neurons in two macaque monkeys during a spatial working memory task. Fano factor analysis revealed that the neuronal variability of both WM-selective and WM-sustained neurons was similar and significantly higher than that of non-active neurons (neurons not modulated by the task). Moreover, the Fano factor of active neurons diminished during error trials compared to correct trials. The spike phase locking (SPL) value was measured to evaluate the coupling of local field potentials (LFPs) phases to spike times, considering neural network characteristics. SPL results indicated that both WM-selective neurons and WM-sustained neurons exhibited higher SPL in the alpha/beta-band compared to non-active neurons. Additionally, the alpha/beta-band SPL of working memory-active neurons decreased during error trials. In summary, despite the non-stimulus-specific activation of WM-sustained neurons, they may contribute to task performance alongside WM-selective neurons.

Predicting Placebo Responses Using EEG and Deep Convolutional Neural Networks: Correlations with Clinical Data Across Three Independent Datasets

Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, and enhancing the detection of true drug effects. In response to this need, we developed a deep convolutional neural network (DCNN) model using resting-state EEG data from the EMBARC study, achieving a balanced accuracy of 69% in predicting placebo responses in patients with major depressive disorder (MDD). We then applied this model to two additional datasets, LEMON and CAN-BIND-which did not include placebo groups-to investigate potential relationships between the model's predictions and various clinical features in independent samples. Notably, the model's predictions correlated with factors previously linked to placebo response in MDD, including age, extraversion, and cognitive processing speed. These findings highlight several factors associated with placebo susceptibility, offering insights that could guide more efficient clinical trial designs. Future research should explore the broader applicability of such predictive models across different medical conditions, and replicate the current EEG-based model of placebo response in independent samples.

Direct Comparison of EEG Resting State and Task Functional Connectivity Patterns for Predicting Working Memory Performance Using Connectome-Based Predictive Modeling

Background: The integration of machine learning with advanced neuroimaging has emerged as a powerful approach for uncovering the relationship between neuronal activity patterns and behavioral traits. While resting-state neuroimaging has significantly contributed to understanding the neural basis of cognition, recent fMRI studies suggest that task-based paradigms may offer superior predictive power for cognitive outcomes. However, this hypothesis has never been tested using electroencephalography (EEG) data. Methods: We conducted the first experimental comparison of predictive models built on high-density EEG data recorded during both resting-state and an auditory working memory task. Multiple data processing pipelines were employed to ensure robustness and reliability. Model performance was evaluated by computing the Pearson correlation coefficient between predicted and observed behavioral scores, supplemented by mean absolute error and root mean square error metrics for each model configuration. Results: Consistent with prior fMRI findings, task-based EEG data yielded slightly better modeling performance than resting-state data. Both conditions demonstrated high predictive accuracy, with peak correlations between observed and predicted values reaching r = 0.5. Alpha and beta band functional connectivity were the strongest predictors of working memory performance, followed by theta and gamma bands. Additionally, the choice of parcellation atlas and connectivity method significantly influenced results, highlighting the importance of methodological considerations. Conclusion: Our findings support the advantage of task-based EEG over resting-state data in predicting cognitive performance, aligning with. The study underscores the critical role of frequency-specific functional connectivity and methodological choices in model performance. These insights should guide future experimental designs in cognitive neuroscience. Impact Statement This study provides the first direct comparison of EEG-based functional connectivity during rest and task conditions for predicting working memory performance using connectome-based predictive modeling (CPM). It demonstrates that task-based EEG data slightly outperforms resting-state data, with alpha and beta bands being the most predictive. The findings highlight the critical influence of methodological choices, such as parcellation atlases and connectivity metrics, on model outcomes. By bridging gaps in EEG research and validating CPM's applicability, this work advances the optimization of neuroimaging protocols for cognitive assessment, offering insights for future studies in cognitive neuroscience.

Modulation of beta oscillatory dynamics in motor and frontal areas during physical fatigue

Beta-band oscillations have been suggested to promote the maintenance of the current motor (or cognitive) set, thus signaling the 'status quo' of the system. While this hypothesis has been reliably demonstrated in many studies, it fails to explain changes in beta-band activity due to the accumulation of physical fatigue. In the current study, we aimed to reconcile the functional role of beta oscillations during physical fatigue within the status quo theory. Using an innovative electroencephalography design, we identified two distinct beta-band power dynamics in the motor areas as fatigue rises: (i) an enhancement at rest, supposedly promoting the resting state, and (ii) a decrease during contraction, thought to reflect the increase in motor cortex activation necessary to cope with muscular fatigue. We then conducted effective connectivity analyses, which revealed that the modulations during contractions were driven by frontal areas. Finally, we implemented a biologically plausible model to replicate and characterize our results mechanistically. Together, our findings anchor the physical fatigue paradigm within the status quo theory, thus shedding light on the functional role of beta oscillations in physical fatigue. We further discuss a unified interpretation that might explain the conflicting evidence previously encountered in the physical fatigue literature.

Neurophysiological Markers of Reward Processing Can Inform Preclinical Neurorehabilitation Approaches for Cognitive Impairments Following Brain Injury

Brain stimulation therapies may be used to correct motor, social, emotional, and cognitive consequences of traumatic brain injury (TBI). Neuromodulation applied with anatomical specificity can ameliorate desired symptoms while leaving functional circuits intact. Before applying precision medicine approaches, preclinical animal studies are needed to explore potential neurophysiological signatures that could be modulated with neurostimulation. This review discusses potential neural signatures of cognition, particularly reward processing, which is chronically impaired after brain injury. Electrophysiology, compared to other types of biomarkers, can detect deficits missed by structural measures, holds translational potential between humans and animals, and directly informs neuromodulatory treatments. Disturbances in oscillatory activity underscore structural, molecular, and behavioral impairments seen following TBI. For instance, cortico-striatal beta frequency activity (15-30 Hz) during reward processing represents subjective value and is chronically disturbed after frontal TBI in rodents. We use the example of evoked beta oscillations in the cortico-striatal network as a putative marker of reward processing that could be targeted with electrical stimulation to improve decision making after TBI. This review highlights the necessity of collecting electrophysiological data in preclinical models to understand the underlying mechanisms of cognitive behavioral deficits after TBI and to develop targeted stimulation treatments in humans.

Monitoring pilots' mental workload in real flight conditions using multinomial logistic regression with a ridge estimator

Piloting an aircraft is a cognitive task that requires continuous verbal, visual, and auditory attentions (e.g., Air Traffic Control Communication). An increase or decrease in mental workload from a specific level can alter auditory and visual attention, resulting in pilot errors. The objective of this research is to monitor pilots' mental workload using advanced machine learning techniques to achieve improved accuracy compared to previous studies. Electroencephalogram (EEG) data were recorded from 22 pilots operating under visual flight rules (VFR) conditions using a six dry-electrode Enobio Neuroelectrics system, and the Riemannian artifact subspace reconstruction (rASR) filter was used for data cleaning. An information gain (IG) attribute evaluator was used to select 25 optimal features out of 72 power spectral and statistical extracted features. In this study, 15 classifiers were used for classification. Multinomial logistic regression with a ridge estimator was selected, achieving a significant mean accuracy of 84.6% on the dataset from 17 subjects. Data were initially collected from 22 subjects, but 5 were excluded due to data synchronization issues. This work has several limitations, such as the author did not counter balance the order of scenario, could not control all the variables such as wind conditions, and workload was not stationary in each leg of the flight pattern. This study demonstrates that multinomial logistic regression with a ridge estimator shows significant classification accuracy (p < 0.05) and effectively detects pilot mental workload in real flight scenarios.

Effects of Different Individuals and Verbal Tones on Neural Networks in the Brain of Children with Cerebral Palsy

Background/Objectives: Motivation is a key factor for improving motor function and cognitive control in patients. Motivation for rehabilitation is influenced by the relationship between the therapist and patient, wherein appropriate voice encouragement is necessary to increase motivation. Therefore, we examined the differences between mothers and other individuals, such as physical therapists (PTs), in their verbal interactions with children with cerebral palsy who have poor communication abilities, as well as the neurological and physiological effects of variations in the tone of their speech. Methods: The three participants were children with cerebral palsy (Participant A: boy, 3 years; Participant B: girl, 7 years; Participant C: girl, 9 years). Participants' mothers and the assigned PTs were asked to speak under three conditions. During this, the brain activity of the participants was measured using a 19-channel electroencephalogram. The results were further analyzed using Independent Component Analysis frequency analysis with exact Low-Resolution Brain Electromagnetic Tomography, allowing for the identification and visualization of neural activity in three-dimensional brain functional networks. Results: The results of the ICA frequency analysis for each participant revealed distinct patterns of brain activity in response to verbal encouragement from the mother and PT, with differences observed across the theta, alpha, and beta frequency bands. Conclusions: Our study suggests that the children were attentive to their mothers' inquiries and focused on their internal experiences. Furthermore, it was indicated that when addressed by the PT, the participants found it easier to grasp the meanings and intentions of the words.

Assessing the utility of Fronto-Parietal and Cingulo-Opercular networks in predicting the trial success of brain-machine interfaces for upper extremity stroke rehabilitation

For stroke participants undergoing motor rehabilitation, brain-machine/computer interfaces (BMI/BCI) can potentially improve the efficacy of robotic or exoskeleton-based therapies by ensuring patient engagement and active participation, through monitoring of motor intent. In such interventions, exploring the network-level understanding of the source space, in terms of various cognitive dimensions such as executive control versus reward processing is fruitful in both improving the existing therapy protocols as well as understanding the subject-level differences. This contrasts to traditional approaches that predominantly investigate rehabilitation from resting state data. Moreover, conventional BMIs used for stroke rehabilitation barely accommodate people suffering from moderate to severe cognitive impairments. In this first-of-the-kind study, we explore the cognitive dimensions of a BMI trial by probing the networks that are core to the BMI performance and propose a network connectivity-based measurement with the potential to characterize the cognitive impairments in patients for closed-loop intervention. Specifically, we tease apart the extent of cognitive evaluation versus executive control aspects of impairments in these patients, by measuring the activation power of a major cognitive evaluation network- the Cingulo-Opercular Network (CON) and a major executive control circuit- the Fronto-Parietal network (FPN), and the connectivity between FPN-CON. We test our hypothesis in a previously collected dataset of electroencephalography (EEG) and structural imaging performed on stroke patients with upper limb impairments, while they underwent an exoskeleton-based BMI intervention for about 12 sessions over 4 weeks. Our logistic regression modeling results suggest that the connectivity between FPN and CON networks and their source powers predict trial failure accurately to about 84.2%. In the future, we aim to integrate these observations into a closed-loop design to adaptively control the cognitive difficulty and passively increase the subject's motivation and attention factor for effective BMI learning.

Decoding the impact of negative physical self-perception on inhibitory control ability from theta and beta rhythms

Previous studies have found inhibitory control differences between obese individuals and those of normal weight. However, some normal-weight individuals with high negative physical self-perception on the fatness subscale show restrictive eating behaviors and attentional bias toward high-calorie food, potentially influencing these differences. We collected behavioral and electroencephalography data using a novel inhibitory control task. Results showed that individuals with high negative physical self-perception on the fatness subscale exhibited significantly greater restraint eating behavior compared to controls. Both theta and beta power differed between groups, with higher theta power in the high negative physical self-perception on the fatness subscale group than in the obese group and more negative beta power in the high negative physical self-perception on the fatness subscale group compared to both other groups. Theta power was greater in no-go than go conditions, while beta power was more negative in response to high-calorie versus low-calorie food stimuli. Importantly, theta power successfully decoded go/no-go conditions across all groups using multivariate pattern analysis, while beta power distinguished these conditions only in the negative physical self-perception on the fatness subscale and control groups. These findings suggest that theta and beta power, along with multivariate pattern analysis, can reliably distinguish inhibitory control ability among the three groups, highlighting the importance of considering negative physical self-perception on the fatness subscale when assessing inhibitory control differences between normal-weight and obese individuals.

Extreme ingroup and outgroup perspectives counter-intuitively impact intergroup polarisation at the level of neural oscillations

A powerful example of affective polarisation occurred between vaccine-supporters and -opposers when vaccinations were implemented to counter the recent global pandemic. In this social neuroscience study, we scanned 121 vaccine-supporters using magnetoencephalography to evaluate three levels of polarisation: explicit, implicit, and neural - and then to test whether exposing people to extreme ingroup perspectives (following the paradoxical thinking principles) or extreme outgroup perspectives can modulate those levels of affective polarisation between vaccinated and unvaccinated individuals. We show that a neural proxy for intergroup polarisation, expressed as late prefrontal beta rhythm suppression, can detect subtle changes in affective polarisation. More specifically, we find that exposing vaccine-supporters to extreme ingroup (i.e., pro-vaccination) viewpoints leads to a decrease in this neural proxy of affective polarisation. Conversely, exposure to extreme outgroup (i.e., anti-vaccination) narratives increases polarisation, which in turn predicts a decrease in positive affect towards vaccine opposers almost one year later. Altogether, the results show that although it may seem intuitive to expose people to counter-arguments (i.e., extreme outgroup perspectives) to change their opinions, such an approach can backlash and increase polarisation instead. However, using subtler methods such as the paradoxical thinking intervention (i.e., extreme ingroup perspectives) for attitude change can have the desired effects and reduce intergroup polarisation.

Subthalamic Nucleus Deep Brain Stimulation in the Beta Frequency Range Boosts Cortical Beta Oscillations and Slows Down Movement

Recordings from Parkinson's disease (PD) patients show strong beta-band oscillations (13-35 Hz), which can be modulated by deep brain stimulation (DBS). While high-frequency DBS (>100 Hz) ameliorates motor symptoms and reduces beta activity in the basal ganglia and motor cortex, the effects of low-frequency DBS (<30 Hz) are less clear. Clarifying these effects is relevant for the debate about the role of beta oscillations in motor slowing, which might be causal or epiphenomenal. Here, we investigated how subthalamic nucleus (STN) beta-band DBS affects cortical beta oscillations and motor performance. We recorded the magnetoencephalogram of 14 PD patients (nine males) with DBS implants while on their usual medication. Following a baseline recording (DBS OFF), we applied bipolar DBS at beta frequencies (10, 16, 20, 26, and 30 Hz) via the left electrode in a cyclic fashion, turning stimulation on (5 s) and off (3 s) repeatedly. Cyclic stimulation was applied at rest and during right-hand finger tapping. In the baseline recording, we observed a negative correlation between the strength of hemispheric beta power lateralization and the tap rate. Importantly, beta-band DBS accentuated the lateralization and reduced the tap rate proportionally. The change in lateralization was specific to the alpha/beta range (8-26 Hz), outlasted stimulation, and did not depend on the stimulation frequency, suggesting a remote-induced response rather than entrainment. Our study demonstrates that cortical beta oscillations can be manipulated by STN beta-band DBS. This manipulation has consequences for motor performance, supporting a causal role of beta oscillations.

Neural dynamics of proactive and reactive cognitive control in medial and lateral prefrontal cortex

Gol-directed behavior requires adjusting cognitive control to both react to and prepare for conflict. Previous work indicates theta oscillations and population activity in dorsomedial prefrontal cortex (dmPFC) and dorsolateral prefrontal cortex (dlPFC) are critical for reactive control. However, the neural mechanisms supporting proactive control are less clear. Here, we investigated the neural basis of behavioral adaptations when control is prepared in anticipation of conflict using intracranial EEG (iEEG) in dmPFC and dlPFC during a Stroop task where conflict frequency was manipulated across blocks. We observed canonical conflict-driven increases in dmPFC theta and in dmPFC and dlPFC local population activity, as indexed by high frequency activity (HFA). Conflict also suppressed theta power in both regions after the response, accentuated a pre-response beta desynchronization selectively in dlPFC, and increased a post-response beta rebound in both regions. Importantly, we identified a pre-trial marker of proactive control where dmPFC theta power increased before trials when conflict was expected, and theta, beta, and HFA conflict signals in both regions were enhanced when conflict was rare and diminished when conflict was common. These findings reveal shared HFA but dissociable oscillatory dynamics in dmPFC and dlPFC during reactive conflict processing, highlight pre-trial dmPFC theta as a potential substrate for proactive control, and refine the roles of dmPFC and dlPFC in control adaptations.

Emergent Aspects of the Integration of Sensory and Motor Functions

This article delves into the intricate mechanisms underlying sensory integration in the executive control of movement, encompassing ideomotor activity, predictive capabilities, and motor control systems. It examines the interplay between motor and sensory functions, highlighting the role of the cortical and subcortical regions of the central nervous system in enhancing environmental interaction. The acquisition of motor skills, procedural memory, and the representation of actions in the brain are discussed emphasizing the significance of mental imagery and training in motor function. The development of this aspect of sensorimotor integration control can help to advance our understanding of the interactions between executive motor control, cortical mechanisms, and consciousness. Bridging theoretical insights with practical applications, it sets the stage for future innovations in clinical rehabilitation, assistive technology, and education. The ongoing exploration of these domains promises to uncover new pathways for enhancing human capability and well-being.

Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling

Working memory, a fundamental cognitive function of the brain, necessitates the evaluation of cognitive load intensity due to limited cognitive resources. Optimizing cognitive load can enhance task performance efficiency by preventing resource waste and overload. Therefore, identifying working memory load is an essential area of research. Deep learning models have demonstrated remarkable potential in identifying the intensity of working memory load. However, existing neural networks based on electroencephalogram (EEG) decoding primarily focus on temporal and spatial characteristics while neglecting frequency characteristics. Drawing inspiration from the role of cross-frequency coupling in the hippocampal region, which plays a crucial role in advanced cognitive processes such as working memory, this study proposes a Multi-Band Multi-Scale Hybrid Sinc Convolutional Neural Network (MBSincNex). This model integrates multi-frequency and multi-scale Sinc convolution to facilitate time-frequency conversion and extract time-frequency information from multiple rhythms and regions of the EEG data with the aim of effectively model the cross-frequency coupling across different cognitive domains. Due to its unique structural design, the proposed model proficiently extracts features in temporal, frequency, and spatial domains while its feature extraction capability is validated through post-hoc interpretability techniques. On our collected three-class working memory dataset, the proposed model achieved higher classification accuracy compared to other state-of-the-art methods. Furthermore, by analyzing the model's classification performance during different stages of working memory processes, this study emphasizes the significance of the encoding phase and confirms that behavioral response does not accurately reflect cognitive load.

Characterizing psychological states in professional athletes through EEG: sex-based differences

The psychological states are essential to maintain a healthy balance and optimize performance, especially in athletes. Sex-related differences in elite athletes are one of the important factors, which are ambiguous. This study aims to explore sex differences in psychological states of 36 professional athletes (12M/24F, 21.6±3.9 y, 166.3±8.9 cm, 56.4±11.3 kg, mean±SD) in the lead-up to a major international competition using objective (quantitative electroencephalogram, QEEG) and subjective measures (Athletic Coping Skills Inventory, ASCI-28). Participants included fifteen gymnasts, eleven swimmers, and ten e-sports players. QEEG measurements were collected during 5 minutes at rest before athletes completed the ASCI-28 scale. Data were obtained during training camp within 3 months before the competition. QEEG data were analyzed using a generalized linear mixed modeling (GLMM) to report the linear predictor of QEEG in brain areas (random) and sex (fixed). A multivariate analysis of variance (MANOVA) was applied to ACSI-28 subscales data to compare sex groups. Female athletes exhibited higher EEG activity in the theta power in the parietal and occipital areas and the beta power in the pre-frontal and temporal areas. Furthermore, significant sex differences were found in the coping with adversity (p =0.038) and goal setting/mental preparation subscales of the ACSI-28. The correlations between the QEEG and questionnaire scores were low to moderate correlations for female athletes, and moderate correlations for male athletes. A significant correlation was observed in both male and female athletes between activity in the temporal region within theta and beta frequency bands (QEEG), and the coping with aversity subscale (questionnaire). These findings provide valuable insights for sports psychologists to design appropriate psychological interventions and for future studies examining the impact of differential psychological interventions for male and female athletes to optimize well-being and performance.

Excessive propagation of right frontal beta oscillations in patients with a history of major depressive disorder

Patients suffering from various neurological disorders, including major depressive disorder (MDD), often exhibit abnormal brain connectivity. In particular, patients with MDD show atypical brain oscillations propagation. This study aims to investigate an association between abnormal brain connectivity and atypical oscillatory propagation of electroencephalogram (EEG) signals in patients with a history of MDD. Previous findings of functional hyperconnectivity in beta oscillations (15-25 Hz) lead us to hypothesize that patients would experience abnormal beta oscillation propagation. Using the local phase gradient (LPG) method, we analyze a publicly available EEG dataset recorded during a probabilistic learning task. Our findings indicate that, upon receiving positive feedback during the learning task, patients with a history of MDD show more pronounced propagation directions of beta oscillations observed in the right frontal region compared to healthy controls. This directional pattern may help differentiate patients with a history of MDD from healthy controls. The observed abnormalities in brain oscillation propagation suggest that cognitive deficits in patients with a history of MDD might stem from excessive and negatively biased information transmission between brain regions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-024-00433-9.

Influences of speaking task demands on sensorimotor oscillations in adults who stutter: Implications for speech motor control

OBJECTIVE

Motivated by previous inconsistent findings, this study aims to improve understanding of sensorimotor beta (β; 15-30 Hz) and alpha (α; 8-14 Hz) speech-related power differences between stuttering and non-stuttering adults.

METHODS

Electroencephalography was recorded as adults who stutter (AWS) and matched fluent controls answered questions in Quiet and Informational Masked backgrounds. Bilateral sensorimotor β and α power during speech planning and execution were measured from mu (μ) rhythm components.

RESULTS

Compared to controls, AWS exhibited reduced left hemisphere β and α power in both speaking conditions during speech planning and execution. AWS displayed reduced left α power in the Informational Masking compared to Quiet. Within AWS β and α power, which were tightly coupled, oppositely predicted stuttering severity and β-α dissociation (β minus α) was the strongest predictor.

CONCLUSION

Neither β nor α power are reliable markers of speech motor stability due to their sensitivity to speech task automaticity. However, relationships between these two sensorimotor rhythms warrant further investigation for understanding motor control.

SIGNIFICANCE

Data help explain previous mixed findings in reference to extant models of speech motor control in stuttering and may have clinical implications for developing neurostimulation protocols targeting improved speech fluency.

Prefrontal cortex contribution in transitive inference task through the interplay of beta and gamma oscillations

Transitive inference allows people to infer new relations between previously experienced premises. It has been hypothesized that this logical thinking relies on a mental schema that spatially organizes elements, facilitating inferential insights. However, recent evidence challenges the need for these complex cognitive processes. To dig into the neural substrate driving TI cognitive processes, we examine the role of beta and gamma local field potential bands in the prefrontal cortex of 2 monkeys. During the inferential problem-solving period, we discover a tight link between beta and gamma bands modulation and TI complexity. This correlation diminishes its strength before initiating the motor response, indicating the chosen item. Notably, while the beta band maintains a constant relationship with TI performance throughout the trial, the gamma band shows a flexible relationship. This research highlights the role of beta and gamma interplay in cognitive computations when solving TI problems.

Physiological Entrainment: A Key Mind-Body Mechanism for Cognitive, Motor and Affective Functioning, and Well-Being

BACKGROUND

The human sensorimotor system can naturally synchronize with environmental rhythms, such as light pulses or sound beats. Several studies showed that different styles and tempos of music, or other rhythmic stimuli, have an impact on physiological rhythms, including electrocortical brain activity, heart rate, and motor coordination. Such synchronization, also known as the "entrainment effect", has been identified as a crucial mechanism impacting cognitive, motor, and affective functioning.

OBJECTIVES

This review examines theoretical and empirical contributions to the literature on entrainment, with a particular focus on the physiological mechanisms underlying this phenomenon and its role in cognitive, motor, and affective functions. We also address the inconsistent terminology used in the literature and evaluate the range of measurement approaches used to assess entrainment phenomena. Finally, we propose a definition of "physiological entrainment" that emphasizes its role as a fundamental mechanism that encompasses rhythmic interactions between the body and its environment, to support information processing across bodily systems and to sustain adaptive motor responses.

METHODS

We reviewed the recent literature through the lens of the "embodied cognition" framework, offering a unified perspective on the phenomenon of physiological entrainment.

RESULTS

Evidence from the current literature suggests that physiological entrainment produces measurable effects, especially on neural oscillations, heart rate variability, and motor synchronization. Eventually, such physiological changes can impact cognitive processing, affective functioning, and motor coordination.

CONCLUSIONS

Physiological entrainment emerges as a fundamental mechanism underlying the mind-body connection. Entrainment-based interventions may be used to promote well-being by enhancing cognitive, motor, and affective functions, suggesting potential rehabilitative approaches to enhancing mental health.

Aperiodic and oscillatory systems underpinning human domain-general cognition

Domain-general cognitive systems are essential for adaptive human behaviour, supporting various cognitive tasks through flexible neural mechanisms. While fMRI studies link frontoparietal network activation to increasing demands across various tasks, the electrophysiological mechanisms underlying this domain-general response to demand remain unclear. Here, we used MEG/EEG, and separated the aperiodic and oscillatory components of the signals to examine their roles in domain-general cognition across three cognitive tasks using multivariate analysis. We found that both aperiodic (broadband power, slope, and intercept) and oscillatory (theta, alpha, and beta power) components coded task demand and content across all subtasks. Aperiodic broadband power in particular strongly coded task demand, in a manner that generalised across all subtasks. Source estimation suggested that increasing cognitive demand decreased aperiodic broadband power across the brain, with the strongest modulations overlapping with the frontoparietal network. In contrast, oscillatory activity showed more localised patterns of modulation, primarily in frontal or occipital regions. These results provide insights into the electrophysiological underpinnings of human domain-general cognition, highlighting the critical role of aperiodic broadband power.

Cingulate and striatal hubs are linked to early skill learning

Early skill learning develops in the context of activity changes in distributed cortico-subcortical regions. Here, we investigated network hubs-centers of information integration and transmission-within the brain network supporting early skill learning. We recorded magnetoencephalographic (MEG) brain activity in healthy human subjects who learned a moderately difficult sequence skill with their non-dominant left hand. We then computed network hub strength by summing top 10% functional connectivity over 86 parcellated brain regions (AAL3 atlas) and five brain oscillatory frequency bands (alpha, low-, high-beta, low- and high-gamma). Virtually all skill gains developed during rest intervals of early learning (micro-offline gains). MEG hub strength in the alpha band (8-13Hz) in bilateral anterior cingulate (ACC) and caudate and in the low-beta band (13-16Hz) in bilateral caudate and right putamen correlated with micro-offline gains. These regions linked strongly with the hippocampus, parahippocampal cortex, and lingual and fusiform gyri. Thus, alpha and low-beta brain oscillatory activity in cingulate and striatal regions appear to contribute as hubs of information integration and transmission during early skill learning.

Subthalamic control of impulsive actions: insights from deep brain stimulation in Parkinson's disease

Control of actions allows adaptive, goal-directed behaviour. The basal ganglia, including the subthalamic nucleus, are thought to play a central role in dynamically controlling actions through recurrent negative feedback loops with the cerebral cortex. Here, we summarize recent translational studies that used deep brain stimulation to record neural activity from and apply electrical stimulation to the subthalamic nucleus in people with Parkinson's disease. These studies have elucidated spatial, spectral and temporal features of the neural mechanisms underlying the controlled delay of actions in cortico-subthalamic networks and demonstrated their causal effects on behaviour in distinct processing windows. While these mechanisms have been conceptualized as control signals for suppressing impulsive response tendencies in conflict tasks and as decision threshold adjustments in value-based and perceptual decisions, we propose a common framework linking decision-making, cognition and movement. Within this framework, subthalamic deep brain stimulation can lead to suboptimal choices by reducing the time that patients take for deliberation before committing to an action. However, clinical studies have consistently shown that the occurrence of impulse control disorders is reduced, not increased, after subthalamic deep brain stimulation surgery. This apparent contradiction can be reconciled when recognizing the multifaceted nature of impulsivity, its underlying mechanisms and modulation by treatment. While subthalamic deep brain stimulation renders patients susceptible to making decisions without proper forethought, this can be disentangled from effects related to dopamine comprising sensitivity to benefits versus costs, reward delay aversion and learning from outcomes. Alterations in these dopamine-mediated mechanisms are thought to underlie the development of impulse control disorders and can be relatively spared with reduced dopaminergic medication after subthalamic deep brain stimulation. Together, results from studies using deep brain stimulation as an experimental tool have improved our understanding of action control in the human brain and have important implications for treatment of patients with neurological disorders.

Oscillatory activity in bilateral prefrontal cortices is altered by distractor strength during working memory processing

Working memory (WM) enables the temporary storage of limited information and is a central component of higher order cognitive function. Irrelevant and/or distracting information can have a negative impact on WM processing and suppressing such incoming stimuli is critical to maintaining adequate performance. However, the neural mechanisms and dynamics underlying such distractor inhibition remain poorly understood. In the current study, we enrolled 46 healthy adults (Mage: 27.92, Nfemale: 28) who completed a Sternberg type WM task with high- and low-distractor conditions during magnetoencephalography (MEG). MEG data were transformed into the time-frequency domain and significant task-related oscillatory responses were imaged to identify the underlying anatomical areas. Whole-brain paired t-tests, with cluster-based permutation testing for multiple comparisons correction, were performed to assess differences between the low- and high-distractor conditions for each oscillatory response. Across conditions, we found strong alpha and beta oscillations (i.e., decreases relative to baseline) and increases in theta power throughout the encoding and maintenance periods. Whole-brain contrasts revealed significantly stronger alpha and beta oscillations in bilateral prefrontal regions during maintenance in high- compared to low-distractor trials, with the stronger beta oscillations being centered on the left dorsolateral prefrontal cortex and right inferior frontal gyrus, while those for alpha being within the right anterior prefrontal cortices and the right middle frontal gyrus. These findings suggest that alpha and beta oscillations in the bilateral prefrontal cortices play a major role in the inhibition of distracting information during WM maintenance. Our results also contribute to prior research on cognitive control and functional inhibition, in which prefrontal regions have been widely implicated.

Reduced resting-state periodic beta power in adults who stutter is related to sensorimotor control of speech execution

OBJECTIVE

The primary aim of the current study was to determine whether adults who stutter (AWS) present with anomalous periodic beta (β) rhythms when compared to typically fluent adults in the eyes-open resting state. A second aim was to determine whether lower β power in the RS is related to a measure of β event-related desynchronization (ERD) during syllable sequence execution.

METHODS

EEG data was collected from 128 channels in a 5 min, eyes-open resting state condition and from a syllable sequence repetition task. Temporal independent component analysis (ICA) was used to separate volume conducted EEG sources and to find a set of component weights common to the RS and syllable repetition task. Both traditional measures of power spectral density (PSD) and parameterized spectra were computed for components showing peaks in the β band (13-30 Hz). Parameterization was used to evaluate separable components adjusted for the 1/f part of the spectrum.

RESULTS

ICA revealed frontal-parietal midline and lateral sensorimotor (μ) components common to the RS and a syllable repetition task with peaks in the β band. The entire spectrum for each component was modeled using the FOOOF algorithm. Independent samples t-tests revealed significantly lower periodic β in midline central-parietal and lateral sensorimotor components in AWS. Regression analysis suggested a significant relationship between left periodic sensorimotor β power in the RS and ERD during syllable sequence execution.

CONCLUSIONS

Findings suggest that periodic β peaks in the spectrum are related to hypothesized underlying pathophysiological differences in stuttering, including midline rhythms associated the default mode network (DMN) and lateral sensorimotor rhythms associated with the control of movement.

The strength of anticipated distractors shapes EEG alpha and theta oscillations in a Working Memory task

Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding.

EEG β oscillations in aberrant data perception under cognitive load modulation

Data-driven decision making (DDDM) is becoming an indispensable component of work across various fields, and the perception of aberrant data (PAD) has emerged as an essential skill. Nonetheless, the neural processing mechanisms underpinning PAD remain incompletely elucidated. Direct evidence linking neural oscillations to PAD is currently lacking, and the impact of cognitive load remains ambiguous. We address this issue using EEG time-frequency analysis. Data were collected from 21 healthy participants. The experiment employed a 2 (low vs. high cognitive load) × 2 [PAD+ (aberrant data accurately identified as aberrant) vs. PAD- (non-aberrant data correctly recognized as normal)] within-subject laboratory design. Results indicate that upper β band oscillations (26-30 Hz) were significantly enhanced in the PAD + condition compared to PAD-, with consistent activity observed in the frontal (p < 0.001, [Formula: see text] = 0.41) and parietal lobes (p = 0.028, [Formula: see text] = 0.22) within the 300-350 ms time window. Additionally, as cognitive load increased, the time window of β oscillations for distinguishing PAD+ from PAD- shifted earlier. This study enriches our understanding of the PAD neural basis by exploring the distribution of neural oscillation frequencies, decision-making neural circuits, and the windowing effect induced by cognitive load. These findings have significant implications for elucidating the pathological mechanisms of neurodegenerative disorders, as well as in the initial screening, intervention, and treatment of diseases.

Low-Frequency Oscillations in Mid-rostral Dorsolateral Prefrontal Cortex Support Response Inhibition

Executive control of movement enables inhibiting impulsive responses critical for successful navigation of the environment. Circuits mediating stop commands involve prefrontal and basal ganglia structures with fMRI evidence demonstrating increased activity during response inhibition in the dorsolateral prefrontal cortex (dlPFC)-often ascribed to maintaining task attentional demands. Using direct intraoperative cortical recordings in male and female human subjects, we investigated oscillatory dynamics along the rostral-caudal axis of dlPFC during a modified Go/No-go task, probing components of both proactive and reactive motor control. We assessed whether cognitive control is topographically organized along this axis and observed that low-frequency power increased prominently in mid-rostral dlPFC when inhibiting and delaying responses. These findings provide evidence for a key role for mid-rostral dlPFC low-frequency oscillations in sculpting motor control.

Association of EEG and cognitive impairment in overweight and non-overweight patients with schizophrenia

OBJECTIVE

Schizophrenia (SCZ) is a globally prevalent, severe chronic mental disorder, with cognitive dysfunction being one of its core symptoms. Notably, overweight is exceedingly common among individuals with SCZ, and overweight can also impact cognitive function. Therefore, the relationship between overweight and cognition in SCZ is a clinical issue that is in need of research attention.

METHODS

This study enrolled 77 patients with SCZ, including 36 overweight and 41 non-overweight patients. The Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity, while cognitive functions were evaluated using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Electroencephalography (EEG) testing was performed, with power spectral analysis conducted across various frequency bands (δ, θ, α, β, low γ, and high γ).

RESULTS

Compared to non-overweight SCZ patients, those overweight exhibited significantly lower RBANS total and index scores in immediate memory, visuospatial/constructional abilities, and delayed memory. EEG spectral analysis revealed that overweight SCZ patients demonstrated significantly lower oscillation power ratios in the β, low γ, and high γ frequency bands compared to their non-overweight counterparts. Correlation analyses indicated a significant positive relationship between β wave activity and RBANS total scores among overweight SCZ patients, suggesting that reduced β power correlates with more severe cognitive dysfunction.

CONCLUSION

Our findings indicate that overweight SCZ patients experience more severe cognitive impairments in a resting state than those who are not overweight, with significant differences in EEG spectrum observed in the β and γ frequency bands. Additionally, our study establishes a correlation between various EEG spectrum dimensions and cognition. This research highlights the effects of overweight on cognition in individuals with SCZ. Additionally, employing EEG technology to study cognitive function in overweight SCZ patients can offer valuable electrophysiological insights.

Cortico-striatal beta oscillations as a reward-related signal

The value associated with reward is sensitive to external factors, such as the time between the choice and reward delivery as classically manipulated in temporal discounting tasks. Subjective preference for two reward options is dependent on objective variables of reward magnitude and reward delay. Single neuron correlates of reward value have been observed in regions, including ventral striatum, orbital, and medial prefrontal cortex. Brain imaging studies show cortico-striatal-limbic network activity related to subjective preferences. To explore how oscillatory dynamics represent reward processing across brain regions, we measured local field potentials of rats performing a temporal discounting task. Our goal was to use a data-driven approach to identify an electrophysiological marker that correlates with reward preference. We found that reward-locked oscillations at beta frequencies signaled the magnitude of reward and decayed with longer temporal delays. Electrodes in orbitofrontal/medial prefrontal cortex, anterior insula, ventral striatum, and amygdala individually increased power and were functionally connected at beta frequencies during reward outcome. Beta power during reward outcome correlated with subjective value as defined by a computational model fit to the discounting behavior. These data suggest that cortico-striatal beta oscillations are a reward signal correlated, which may represent subjective value and hold potential to serve as a biomarker and potential therapeutic target.

GABAergic dysfunction in postmortem dorsolateral prefrontal cortex: implications for cognitive deficits in schizophrenia and affective disorders

The microcircuitry within superficial layers of the dorsolateral prefrontal cortex (DLPFC), composed of excitatory pyramidal neurons and inhibitory GABAergic interneurons, has been suggested as the neural substrate of working memory performance. In schizophrenia, working memory impairments are thought to result from alterations of microcircuitry within the DLPFC. GABAergic interneurons, in particular, are crucially involved in synchronizing neural activity at gamma frequency, the power of which increases with working memory load. Alterations of GABAergic interneurons, particularly parvalbumin (PV) and somatostatin (SST) subtypes, are frequently observed in schizophrenia. Abnormalities of GABAergic neurotransmission, such as deficiencies in the 67 kDA isoform of GABA synthesis enzyme (GAD67), vesicular GABA transporter (vGAT), and GABA reuptake transporter 1 (GAT1) in presynaptic boutons, as well as postsynaptic alterations in GABA A receptor subunits further contribute to impaired inhibition. This review explores GABAergic abnormalities of the postmortem DLPFC in schizophrenia, with a focus on the roles of interneuron subtypes involved in cognition, and GABAergic neurotransmission within presynaptic boutons and postsynaptic alterations. Where available, comparisons between schizophrenia and affective disorders that share cognitive pathology such as bipolar disorder and major depressive disorder will be made. Challenges in directly measuring GABA levels are addressed, emphasizing the need for innovative techniques. Understanding GABAergic abnormalities and their implications for neural circuit dysfunction in schizophrenia is crucial for developing targeted therapies.

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.

Cortical modulations before lower limb motor blocks are associated with freezing of gait in Parkinson's disease: an EEG source localization study

BACKGROUND

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) characterized by paroxysmal episodes in which patients are unable to step forward. A research priority is identifying cortical changes before freezing in PD-FOG.

METHODS

We tested 19 patients with PD who had been assessed for FOG (n=14 with FOG and 5 without FOG). While seated, patients stepped bilaterally on pedals to progress forward through a virtual hallway while 64-channel EEG was recorded. We assessed cortical activities before and during lower limb motor blocks (LLMB), defined as a break in rhythmic pedaling, and stops, defined as movement cessation following an auditory stop cue. This task was selected because LLMB correlates with FOG severity in PD and allows recording of high-quality EEG. Patients were tested after overnight withdrawal from dopaminergic medications ("off" state) and in the "on" medications state. EEG source activities were evaluated using individual MRI and standardized low resolution brain electromagnetic tomography (sLORETA). Functional connectivity was evaluated by phase lag index between seeds and pre-defined cortical regions of interest.

RESULTS

EEG source activities for LLMB vs. cued stops localized to right posterior parietal area (Brodmann area 39), lateral premotor area (Brodmann area 6), and inferior frontal gyrus (Brodmann area 47). In these areas, PD-FOG (n=14) increased alpha rhythms (8-12 Hz) before LLMB vs. typical stepping, whereas PD without FOG (n=5) decreased alpha power. Alpha rhythms were linearly correlated with LLMB severity, and the relationship became an inverted U-shape when assessing alpha rhythms as a function of percent time in LLMB in the "off" medication state. Right inferior frontal gyrus and supplementary motor area connectivity was observed before LLMB in the beta band (13-30 Hz). This same pattern of connectivity was seen before stops. Dopaminergic medication improved FOG and led to less alpha synchronization and increased functional connections between frontal and parietal areas.

CONCLUSIONS

Right inferior parietofrontal structures are implicated in PD-FOG. The predominant changes were in the alpha rhythm, which increased before LLMB and with LLMB severity. Similar connectivity was observed for LLMB and stops between the right inferior frontal gyrus and supplementary motor area, suggesting that FOG may be a form of "unintended stopping." These findings may inform approaches to neurorehabilitation of PD-FOG.

Beta-band neural variability reveals age-related dissociations in human working memory maintenance and deletion

Maintaining and removing information in mind are 2 fundamental cognitive processes that decline sharply with age. Using a combination of beta-band neural oscillations, which have been implicated in the regulation of working memory contents, and cross-trial neural variability, an undervalued property of brain dynamics theorized to govern adaptive cognitive processes, we demonstrate an age-related dissociation between distinct working memory functions-information maintenance and post-response deletion. Load-dependent decreases in beta variability during maintenance predicted memory performance of younger, but not older adults. Surprisingly, the post-response phase emerged as the predictive locus of working memory performance for older adults, with post-response beta variability correlated with memory performance of older, but not younger adults. Single-trial analysis identified post-response beta power elevation as a frequency-specific signature indexing memory deletion. Our findings demonstrate the nuanced interplay between age, beta dynamics, and working memory, offering valuable insights into the neural mechanisms of cognitive decline in agreement with the inhibition deficit theory of aging.

Effects of Phytoncide Fragrance on Resting-State Brain Activity in Mild Cognitive Impairment: A Randomized Double-Blind Controlled Study

Introduction: The therapeutic potential of phytoncide fragrances may be optimal for patients with mild cognitive impairment (MCI) that display complex symptomatology. This study aimed to explore the clinical value of phytoncide by evaluating its electrophysiological effects in patients with MCI. Materials and Methods: This was a double-blind, randomized controlled trial. A total of 24 community-dwelling patients were randomly assigned to either a phytoncide or no-odor group. Participants wore a dental mask, for 30 min at rest that had either the fragrance stimulus or water added to it. The quantitative electroencephalography (EEG) during the resting state was recorded before and after a single intervention. Results: There were significant interaction effects in absolute EEG-power values in the occipital (F = 6.52, p = 0.018) and parietal (F = 5.41, p = 0.030) left hemisphere at β frequency. Phytoncide odor significantly decreased low and high β activity in the occipital (corrected p = 0.009) and parietal (corrected p = 0.047) left hemisphere, respectively. In source localization, phytoncide odor significantly decreased deep source activation in the left inferior and middle frontal gyri at β 2 frequency band compared with the no-odor group (threshold = 4.25, p < 0.05). Conclusions: Reductions in β, indicative of anxiety, depression, and stress, suggest relief from emotion-related symptoms that are common in patients with MCI. Trial Registration: Clinical Trials Registry Korea (registration: KCT0007317).

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

BACKGROUND

Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state functional magnetic resonance imaging (rs-fMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains.

METHODS

rs-fMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron emission tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients.

RESULTS

Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta-power. Exploratory analyses revealed a close statistical relationship between LEN and positive symptom severity in patients.

CONCLUSION

Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

The neuromechanical of Beta-band corticomuscular coupling within the human motor system

Beta-band activity in the sensorimotor cortex is considered a potential biomarker for evaluating motor functions. The intricate connection between the brain and muscle (corticomuscular coherence), especially in beta band, was found to be modulated by multiple motor demands. This coherence also showed abnormality in motion-related disorders. However, although there has been a substantial accumulation of experimental evidence, the neural mechanisms underlie corticomuscular coupling in beta band are not yet fully clear, and some are still a matter of controversy. In this review, we summarized the findings on the impact of Beta-band corticomuscular coherence to multiple conditions (sports, exercise training, injury recovery, human functional restoration, neurodegenerative diseases, age-related changes, cognitive functions, pain and fatigue, and clinical applications), and pointed out several future directions for the scientific questions currently unsolved. In conclusion, an in-depth study of Beta-band corticomuscular coupling not only elucidates the neural mechanisms of motor control but also offers new insights and methodologies for the diagnosis and treatment of motor rehabilitation and related disorders. Understanding these mechanisms can lead to personalized neuromodulation strategies and real-time neurofeedback systems, optimizing interventions based on individual neurophysiological profiles. This personalized approach has the potential to significantly improve therapeutic outcomes and athletic performance by addressing the unique needs of each individual.

Beta and theta oscillations track effort and previous reward in the human basal ganglia and prefrontal cortex during decision making

Choosing whether to exert effort to obtain rewards is fundamental to human motivated behavior. However, the neural dynamics underlying the evaluation of reward and effort in humans is poorly understood. Here, we report an exploratory investigation into this with chronic intracranial recordings from the prefrontal cortex (PFC) and basal ganglia (BG; subthalamic nuclei and globus pallidus) in people with Parkinson's disease performing a decision-making task with offers that varied in levels of reward and physical effort required. This revealed dissociable neural signatures of reward and effort, with BG beta (12 to 20 Hz) oscillations tracking effort on a single-trial basis and PFC theta (4 to 7 Hz) signaling previous trial reward, with no effects of net subjective value. Stimulation of PFC increased overall acceptance of offers and sensitivity to reward while decreasing the impact of effort on choices. This work uncovers oscillatory mechanisms that guide fundamental decisions to exert effort for reward across BG and PFC, supports a causal role of PFC for such choices, and seeds hypotheses for future studies.

Return of intracranial beta oscillations and traveling waves with recovery from traumatic brain injury

Traumatic brain injury (TBI) remains a pervasive clinical problem associated with significant morbidity and mortality. However, TBI remains clinically and biophysically ill-defined, and prognosis remains difficult even with the standardization of clinical guidelines and advent of multimodality monitoring. Here we leverage a unique data set from TBI patients implanted with either intracranial strip electrodes during craniotomy or quad-lumen intracranial bolts with depth electrodes as part of routine clinical practice. By extracting spectral profiles of this data, we found that the presence of narrow-band oscillatory activity in the beta band (12-30 Hz) closely corresponds with the neurological exam as quantified with the standard Glasgow Coma Scale (GCS). Further, beta oscillations were distributed over the cortical surface as traveling waves, and the evolution of these waves corresponded to recovery from coma, consistent with the putative role of waves in perception and cognitive activity. We consequently propose that beta oscillations and traveling waves are potential biomarkers of recovery from TBI. In a broader sense, our findings suggest that emergence from coma results from recovery of thalamo-cortical interactions that coordinate cortical beta rhythms.

Effects of head alignment devices on working memory and postural support during computer work

The most common risk factor of computer workers is poor head and neck posture. Therefore, upright seated posture has been recommended repeatedly. However, maintaining an upright seated posture is challenging during computer work and induces various complaints, such as fatigue and discomfort, which can interfere working performance. Therefore, it is necessary to maintain an upright posture without complaints or intentional efforts during long-term computer work. Alignment devices are an appropriate maneuver to support postural control for maintaining head-neck orientation and reduce head weight. This study aimed to demonstrate the effects of workstations combined with alignment device on head-neck alignment, muscle properties, comfort and working memory ability in computer workers. Computer workers (n = 37) participated in a total of three sessions (upright computer (CPT_U), upright support computer (CPT_US), traction computer (CPT_T) workstations). The craniovertebral angle, muscles tone and stiffness, visual analog discomfort scale score, 2-back working memory performance, and electroencephalogram signals were measured. All three workstations had a substantial effect on maintaining head-neck alignment (p< 0.001), but only CPT_US showed significant improvement on psychological comfort (p = 0.04) and working memory performance (p = 0.024), which is consistent with an increase in delta power. CPT_U showed the increased beta 2 activity, discomfort, and false rates compared to CPT_US. CPT_T showed increased alpha and beta 2 activity and decreased delta activity, which are not conductive to working memory performance. In conclusion, CPT_US can effectively induce efficient neural oscillations without causing any discomfort by increasing delta and decreasing beta 2 activity for working memory tasks.

Beta: bursts of cognition

Beta oscillations are linked to the control of goal-directed processing of sensory information and the timing of motor output. Recent evidence demonstrates they are not sustained but organized into intermittent high-power bursts mediating timely functional inhibition. This implies there is a considerable moment-to-moment variation in the neural dynamics supporting cognition. Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions. Recent method advances reveal diversity in beta bursts that provide deeper insights into their function and the underlying neural circuit activity motifs. We propose that brain-wide, spatiotemporal patterns of beta bursting reflect various cognitive operations and that their dynamics reveal nonlinear aspects of cortical processing.

Neuronal network activity and connectivity are impaired in a conditional knockout mouse model with PCDH19 mosaic expression

Mutations in PCDH19 gene, which encodes protocadherin-19 (PCDH19), cause Developmental and Epileptic Encephalopathy 9 (DEE9). Heterogeneous loss of PCDH19 expression in neurons is considered a key determinant of the disorder; however, how PCDH19 mosaic expression affects neuronal network activity and circuits is largely unclear. Here, we show that the hippocampus of Pcdh19 mosaic mice is characterized by structural and functional synaptic defects and by the presence of PCDH19-negative hyperexcitable neurons. Furthermore, global reduction of network firing rate and increased neuronal synchronization have been observed in different limbic system areas. Finally, network activity analysis in freely behaving mice revealed a decrease in excitatory/inhibitory ratio and functional hyperconnectivity within the limbic system of Pcdh19 mosaic mice. Altogether, these results indicate that altered PCDH19 expression profoundly affects circuit wiring and functioning, and provide new key to interpret DEE9 pathogenesis.

Low and high beta rhythms have different motor cortical sources and distinct roles in movement control and spatiotemporal attention

Low and high beta frequency rhythms were observed in the motor cortex, but their respective sources and behavioral correlates remain unknown. We studied local field potentials (LFPs) during pre-cued reaching behavior in macaques. They contained a low beta band (<20 Hz) dominant in primary motor cortex and a high beta band (>20 Hz) dominant in dorsal premotor cortex (PMd). Low beta correlated positively with reaction time (RT) from visual cue onset and negatively with uninstructed hand postural micro-movements throughout the trial. High beta reflected temporal task prediction, with selective modulations before and during cues, which were enhanced in moments of increased focal attention when the gaze was on the work area. This double-dissociation in sources and behavioral correlates of motor cortical low and high beta, with respect to both task-instructed and spontaneous behavior, reconciles the largely disparate roles proposed for the beta rhythm, by suggesting band-specific roles in both movement control and spatiotemporal attention.

Frontoparietal beta event characteristics are associated with early life stress and psychiatric symptoms in adults

Recent work has found that the presence of transient, oscillatory burst-like events, particularly within the beta band (15-29 Hz), is more closely tied to disease state and behavior across species than traditional electroencephalography (EEG) power metrics. This study sought to examine whether features of beta events over frontoparietal electrodes were associated with early life stress (ELS) and the related clinical presentation. Eighteen adults with documented ELS (n = 18; ELS + ) and eighteen adults without documented ELS (n = 18; ELS-) completed eyes-closed resting state EEG as part of their participation in a larger childhood stress study. The rate, power, duration, and frequency span of transient oscillatory events were calculated within the beta band at five frontoparietal electrodes. ELS variables were positively associated with beta event rate at Fp2 and beta event duration at Pz, in that greater ELS was associated with higher resting rates and longer durations. These beta event characteristics were used to successfully distinguish between ELS + and ELS- groups. In an independent clinical dataset (n = 25), beta event power at Pz was positively correlated with ELS. Beta events deserve ongoing investigation as a potential disease marker of ELS and subsequent psychiatric treatment outcomes.

Simultaneous oscillatory encoding of "hot" and "cold" information during social interactions in the monkey medial prefrontal cortex

Social interactions in primates require social cognition abilities such as anticipating the partner's future choices as well as pure cognitive skills involving processing task-relevant information. The medial prefrontal cortex (mPFC) has been implicated in these cognitive processes. Here, we investigated the neural oscillations underlying the complex social behaviors involving the interplay of social roles (Actor vs. Observer) and interaction types (whether working with a "Good" or "Bad" partner). We found opposite power modulations of the beta and gamma bands by social roles, indicating dedicated processing for task-related information. Concurrently, the interaction type was conveyed by lower frequencies, which are commonly associated with neural circuits linked to performance and reward monitoring. Thus, the mPFC exhibits parallel coding of both "cold" processes (purely cognitive) and "hot" processes (reward and social-related). This allocation of neural resources gives the mPFC a key neural node, flexibly integrating multiple sources of information during social interactions.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

Background

Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state fMRI (rfMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains.

Methods

rfMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron-Emission Tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients.

Results

Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta power. Exploratory analyses revealed a close statistical relationship between LEN and positive PSD symptoms.

Conclusion

Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

CRediT Authorship Contribution Statement

Fabian Hirsch: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization; Ângelo Bumanglag: Methodology, Software, Formal analysis, Writing - Review & Editing; Yifei Zhang: Methodology, Software, Formal analysis, Writing - Review & Editing; Afra Wohlschlaeger: Methodology, Writing - Review & Editing, Supervision, Project administration.

Brain oscillations during emotion regulation and the two-dimensional model of adult attachment

Emotion Regulation (ER) refers to the processes by which individuals influence their own emotions. It is a crucial aspect of human behavior, affecting everything from interpersonal relationships to mental health. The relationship between ER and Attachment Theory (AT) is pivotal. AT suggests that early bonds with primary caregivers influence future relationship expectations and behaviors. These initial experiences shape internal models of self and others, affecting how individuals regulate their emotions. Understanding the interplay between ER and AT is essential for comprehending the human affective system. In this study, we explored the neural underpinnings of ER, focusing on two distinct strategies: cognitive reappraisal and expressive suppression. Using electroencephalography (EEG), we examined changes in neural oscillations from 52 adults during an ER task. Specifically, we observed increased frontal theta activity (3-6 Hz) during reappraisal compared to suppression strategies. This frontal theta activity suggests enhanced cognitive control engagement. Conversely, during suppression, we noted a decrease in beta frequency (15-30 Hz) activity from central electrodes, indicative of differing neural processes. Further integrating psychological theories, we explored the relationship between these neural markers and dimensions of human attachment. Employing the Experiences in Close Relationships-12 scale (ECR-12), we identified a negative correlation between attachment anxiety and frontal theta activity. Lower levels of attachment anxiety were associated with increased theta activity, reflecting potentially more effective emotion regulation. Additionally, we found that higher theta activity corresponded with fewer difficulties in emotional control measured by the Difficulties in Emotion Regulation Scale (DERS). Regarding central beta activity, our findings revealed an interesting correlation with Emotional Inattention, a concept tied to Attachment Avoidance. This suggests that central beta activity may serve as a neural marker for specific attachment-related ER processing. These results highlight the distinct neural pathways involved in different ER strategies and their relationship with the AT and neural responses during emotional processing.

Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.