New insights into the relationship between dopamine, beta oscillations and motor function

Cortical Processing and Lower Limb Muscle Activity Increase During Bodyweight Supported Treadmill Locomotion Underwater Compared to On-Land

Body weight support (BWS) systems are commonly used during gait rehabilitation to assist individuals with motor impairments. Traditional approaches involve mechanical unloading through overhead harness systems or buoyancy-assisted underwater walking, each providing unique biomechanical and neuromuscular advantages. The effects of external loading conditions on neural and muscular dynamics are not well understood. We evaluated electrical brain and lower limb muscle activities during treadmill walking with mechanical BWS on-land and underwater. Here, we show that contrasting BWS mechanisms modulate frontoparietal electrocortical spectral power and lower limb myoelectric activity. Underwater walking reduced frontoparietal alpha (8-13 Hz) and beta band power (13-30 Hz) and increased rectus femoris, biceps femoris, tibialis anterior, and lateral gastrocnemius muscle activities compared to walking on-land treadmill, with and without mechanical unloading. Discernible changes in sensorimotor processing and muscle activations during bodyweight supported treadmill walking can provide objective biomarkers to help refine personalized rehabilitation strategies.

Temporal dynamics analysis reveals that concurrent working memory load eliminates the Stroop effect through disrupting stimulus-response mapping

Concurrent verbal working memory task can eliminate the color-word Stroop effect. Previous research, based on specific and limited resources, suggested that the disappearance of the conflict effect was due to the memory information preempting the resources for distractors. However, it remains unclear which particular stage of Stroop conflict processing is influenced by working memory loads. In this study, electroencephalography (EEG) recordings with event-related potential (ERP) analyses, time-frequency analyses, multivariate pattern analyses (MVPAs), and representational similarity analyses (RSAs) were applied to provide an in-depth investigation of the aforementioned issue. Subjects were required to complete the single task (the classical manual color-word Stroop task) and the dual task (the Sternberg working memory task combined with the Stroop task), respectively. Behaviorally, the results indicated that the Stroop effect was eliminated in the dual-task condition. The EEG results showed that the concurrent working memory task did not modulate the P1, N450, and alpha bands. However, it modulated the sustained potential (SP), late theta (740-820 ms), and beta (920-1040 ms) power, showing no difference between congruent and incongruent trials in the dual-task condition but significant difference in the single-task condition. Importantly, the RSA results revealed that the neural activation pattern of the late theta was similar to the response interaction pattern. Together, these findings implied that the concurrent working memory task eliminated the Stroop effect through disrupting stimulus-response mapping.

Beta-band power modulation in the human amygdala during a delayed reach task

INTRODUCTION

The amygdala is mostly known for its roles in emotional processing and social behavior. In recent years, it has been implicated in voluntary motor control due to its structural and functional connectivity with the motor cortex. By investigating whether the amygdala modulates during movement preparation, we can further examine its contributions to motor processing.

OBJECTIVE

We utilized a delayed reach task to measure beta-band (13-30 Hz) modulation in the amygdala during movement preparation. We hypothesized that we would see decreases in beta-band power during the Delay and Response phases of this task.

METHODS

Eleven subjects diagnosed with drug-resistant epilepsy (DRE), who were implanted with stereoelectroencephalographic (SEEG) electrodes, were recruited to this study. The beta-band power was recorded through a delayed reach task. We calculated the beta-band Power Spectral Density (PSD) using multi-taper spectral analysis and compared the trial-averaged PSD using a cluster-based permutation test to determine the significance of beta-band power differences between task phases.

RESULTS

100 % of participants and 44.8 % of gray matter contacts in the amygdala (n = 58) exhibited significantly decreased beta-band power during the Delay phase. During the Response phase, 90.9 % of participants and 58.6 % of gray matter contacts (n = 58) showed significantly decreased beta-band power. We also found a difference in the proportion of amygdala contacts showing beta-band modulation between those implanted in gray vs. white matter (p = 0.0035) but found no difference between contralateral vs. ipsilateral contacts (p = 0.17) and male vs. female participants (p = 0.34).

CONCLUSION

This study is the first to demonstrate beta-band power decreases in the amygdala during the Delay and Response phases of a delayed reach task. These findings demonstrate that the amygdala undergoes neural modulation prior to movement initiation and during movement execution.

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.

Cyclic Alternating Pattern of EEG Activities and Heart Rate Variability in Parkinson's Disease Patients during Deep Sleep

BACKGROUND

Sleep disturbance and autonomic dysfunction are often found in Parkinson's disease (PD) patients, but little is known about changes in cyclic alternating patterns (CAPs) of electroencephalographic (EEG) activities and heart rate variability (HRV) during deep sleep in PD patients.

OBJECTIVES

To investigate changes in EEG activities and HRV during CAPs and non-CAPs (NCAPs) of N3 sleep in PD patients.

METHODS

Polysomnographic (PSG) examinations were carried out on 18 PD patients and 18 healthy controls, and power spectral analysis of EEG activities and HRV during CAPs and NCAPs (the segment of sleep without CAPs for more than 60 seconds) of N3 sleep were carried out.

RESULTS

The percentages of N3 sleep with CAPs and CAP A1, as well as the CAP A1 index in the PD patients, were significantly smaller compared with the healthy controls. In addition, the power of α waves in NCAPs was significantly higher, while the powers of δ waves in Phase A and B of CAP A1 and A3, and NCAPs were significantly smaller. Furthermore, the durations of total δ waves and δ waves with an amplitude ≥75 μV were significantly shorter, and the low frequency (LF) power of HRV during CAPs and the LF/high frequency (HF) HRV ratio during both CAPs and NCAPs were significantly smaller.

CONCLUSIONS

The changes documented in EEG activities and HRV in PD patients during CAPs and NCAPs of N3 sleep compared with healthy controls suggest that N3 sleep quality and sympathetic function are compromised in PD patients.

Associations between neuromelanin depletion and cortical rhythmic activity in Parkinson's disease

Parkinson's disease (PD) is marked by the death of neuromelanin-rich dopaminergic and noradrenergic cells in the substantia nigra (SN) and the locus coeruleus (LC), respectively, resulting in motor and cognitive impairments. Although SN dopamine dysfunction has clear neurophysiological effects, the association of reduced LC norepinephrine signalling with brain activity in PD remains to be established. We used neuromelanin-sensitive T1-weighted MRI (PD, n = 58; healthy control, n = 27) and task-free magnetoencephalography (PD, n = 58; healthy control, n = 65) to identify neuropathophysiological factors related to the degeneration of the LC and SN in patients with PD. We found pathological increases in rhythmic alpha-band (8-12 Hz) activity in patients with decreased LC neuromelanin, which were more strongly associated in patients with worse attentional impairments. This negative alpha-band-LC neuromelanin relationship is strongest in fronto-motor cortices, where alpha-band activity is inversely related to attention scores. Using neurochemical co-localization analyses with normative atlases of neurotransmitter transporters, we also show that this effect is more pronounced in regions with high densities of norepinephrine transporters. These observations support a noradrenergic association between LC integrity and alpha-band activity. Our data also show that rhythmic beta-band (15-29 Hz) activity in the left somatomotor cortex decreases with lower levels of SN neuromelanin; the same regions where beta activity reflects axial motor symptoms. Together, our findings clarify the association of well-documented alterations of rhythmic neurophysiology in PD with cortical and subcortical neurochemical systems. Specifically, attention-related alpha-band activity is related to dysfunction of the noradrenergic system, and beta activity with relevance to motor impairments reflects dopaminergic dysfunction.

The Interplay of Spontaneous Pupil-Size Fluctuations and EEG Power in Near-Threshold Detection

Detection of near-threshold stimuli depends on the properties of the stimulus and the state of the observer. In visual detection tasks, improved accuracy is associated with larger prestimulus pupil size. However, it is still unclear whether this association is due to optical effects (more light entering the eye), correlations with arousal, correlations with cortical excitability (as reflected in alpha power), or a mix of these. To better understand this, we investigated the relative contributions of pupil size and power in the alpha, beta, and theta frequency bands on near-threshold detection. We found that larger prestimulus pupil size is associated with improved accuracy and more stimulus-present responses, and these effects were not mediated by spectral power in the EEG. Pupil size was also positively correlated with power in the beta and alpha bands. Taken together, our results show an independent effect of pupil size on detection performance that is not driven by cortical excitability but may be driven by optical effects, physiological arousal, or a mix of both.

Cortical beta oscillation in brain slices of hemi parkinsonian mice

Parkinson's disease (PD) is a neurodegenerative disorder characterized by progressive loss of dopaminergic neurons in the substantia nigra pars compacta, leading to significant motor and non-motor symptoms. Beta oscillations in cortical areas are a pathognomonic sign. Here we ask whether these oscillations can be recorded in in vitro cortical tissue despite severing the cortico-basal ganglia-thalamo-cortical loop. M1/M2 cortex of hemi parkinsonian mice (6-OHDA) was recorded with multielectrode arrays (MEAs). Spectral decomposition analysis shows a significantly augmented beta band power with respect to controls. The administration of L-DOPA diminished this exacerbated beta rhythm. This result suggests that plastic changes induced by dopamine (DA) depletion remain in isolated cortical tissue even when the complete circuit is no longer present. This finding brings the opportunity to test anti-parkinsonian drugs in vitro by quantifying cortical beta band power.

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.

Low-Frequency Deep Brain Stimulation in Non-Rapid Eye Movement Sleep Modifies Memory Retention in Parkinson's Disease

BACKGROUND AND OBJECTIVE

Memory impairment is a frequent and debilitating symptom in neurodegenerative disorders. The objective of this study was to provide proof-of-principle that deep brain stimulation during sleep can modify memory consolidation in people with Parkinson's disease depending on the stimulation frequency that is applied.

METHODS

Twenty-four patients with Parkinson's disease who were treated with deep brain stimulation of the subthalamic nucleus were included in this single-blind pilot study. Six patients had to be excluded because of insomnia on the night of testing. Patients were randomized (1:1 ratio) to receiving either low frequency deep brain stimulation (4 Hz) or clinically used high frequency deep brain stimulation (130 Hz) during early non-rapid eye movement (NREM) sleep. The main outcome measure was overnight memory retention as measured by a validated declarative memory task.

RESULTS

Patients receiving low frequency deep brain stimulation during early NREM sleep (n = 9, 4 females, mean age 61.1 ± 4.3 years) showed improved overnight memory retention (z = 2.549, P = 0.011). Patients receiving clinically used high frequency deep brain stimulation (n = 9, 2 females, mean age 62.2 ± 7.1) did not show any improvement (z = 1.023, P = 0.306) leading to a significant difference between groups (z = 2.214, P = 0.027). Stronger improvement in memory function was correlated with increased cortical low frequency activity after low frequency deep brain stimulation as measured by electroencephalography (ρ = 0.711, P = 0.037).

CONCLUSION

These results provide proof-of-principle that memory can be modulated by frequency-specific deep brain stimulation during sleep. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Electrophysiological approaches to informing therapeutic interventions with deep brain stimulation

Neuromodulation therapy comprises a range of non-destructive and adjustable methods for modulating neural activity using electrical stimulations, chemical agents, or mechanical interventions. Here, we discuss how electrophysiological brain recording and imaging at multiple scales, from cells to large-scale brain networks, contribute to defining the target location and stimulation parameters of neuromodulation, with an emphasis on deep brain stimulation (DBS).

The Mechanisms of Persisting Disability in Schizophrenia: Imprecise Predictive Coding via Corticostriatothalamic-Cortical Loop Dysfunction

Persisting symptoms and disability remain a problem for an appreciable proportion of people with schizophrenia despite treatment with antipsychotic medication. Improving outcomes requires an understanding of the nature and mechanisms of the pathological processes underlying persistence. Classical features of schizophrenia, which include disorganization and impoverishment of mental activity, are well-recognized early clinical features that predict poor long-term outcome. Substantial evidence indicates that these features reflect imprecise predictive coding. Predictive coding provides an overarching framework for understanding efficient functioning of the nervous system. Imprecise predictive coding also has the potential to precipitate acute psychosis characterized by reality distortion (delusions and hallucinations) at times of stress. On the other hand, substantial evidence indicates that persistent reality distortion itself gives rise to poor occupational and social function in the long term. Furthermore, abuse of psychotomimetic drugs, which exacerbate reality distortion, contributes to poor long-term outcome in schizophrenia. Neural circuits involved in modulating volitional acts are well understood to be implicated in addiction. Plastic changes in these circuits may account for the association between psychotomimetic drug abuse and poor outcomes in schizophrenia. We propose a mechanistic model according to which unbalanced inputs to the corpus striatum disturb the precision of subcortical modulation of cortical activity supporting volitional action. This model accounts for the evidence that early classical symptoms predict poor outcome, while in some circumstances, persistent reality distortion also predicts poor outcome. This model has implications for the development of novel treatments that address the risk of persisting symptoms and disabilities in schizophrenia.

Periodic and Aperiodic Components of Subthalamic Nucleus Activity Reflect Different Aspects of Motor Impairment in Parkinson's Disease

Excessive beta oscillations in the subthalamic nucleus are established as a primary electrophysiological biomarker for motor impairment in Parkinson's disease and are currently used as feedback signals in adaptive deep brain stimulation systems. However, there is still a need for optimization of stimulation parameters and the identification of optimal biomarkers that can accommodate varying patient conditions, such as ON and OFF levodopa medication. The precise boundaries of 'pathological' oscillatory ranges, associated with different aspects of motor impairment, are still not fully clarified. In this study, we hypothesized that analysing periodic and aperiodic components of subthalamic nucleus activity separately and identifying functionally distinct subranges within 8-35 Hz based on oscillatory properties may reveal robust biomarkers for specific aspects of motor impairment. We analysed subthalamic nucleus activity of 14 patients with Parkinson's disease. Local field potentials were recorded at rest from externalized electrodes postoperatively, both before and after levodopa administration. We showed that levodopa administration suppressed oscillations across a broad frequency range (11-32 Hz) and increased the slope of the aperiodic component. Changes in the aperiodic slope correlated with motor symptom alleviation. Periodic activity was linked to motor symptom severity: Peak amplitude within the 14- to 20-Hz range correlated with overall motor impairment in the OFF state, whereas the 7- to 11-Hz range was associated with bradykinesia in the ON state. Our findings suggest that, in addition to low beta, alpha oscillations and the aperiodic component may serve as promising biomarkers for motor impairment and potential feedback signals in adaptive DBS systems.

Increased visual alpha-band activity during self-paced finger tapping does not affect early visual stimulus processing

Alpha-band activity is thought to be involved in orchestrating neural processing within and across brain regions relevant to various functions such as perception, cognition, and motor activity. Across different studies, attenuated alpha-band activity has been linked to increased neural excitability. Yet, there have been conflicting results concerning the consequences of alpha-band modulations for early sensory processing. We here examined whether movement-related alterations in visual alpha-band activity affected the early sensory processing of visual stimuli. For this purpose, in an EEG experiment, participants were engaged in a voluntary finger-tapping task while passively viewing flickering dots. We found extensive and expected movement-related amplitude modulations of motor alpha- and beta-band activity with event-related-desynchronization (ERD) before and during, and event-related-synchronization (ERS) after single voluntary finger taps. Crucially, while a visual alpha-band ERS accompanied the motor alpha-ERD before and during each finger tap, flicker-evoked Steady-State-Visually-Evoked-Potentials (SSVEPs), as a marker of early visual sensory gain, were not modulated in amplitude. As early sensory stimulus processing was unaffected by amplitude-modulated visual alpha-band activity, this argues against the idea that alpha-band activity represents a mechanism by which early sensory gain modulation is implemented. The distinct neural dynamics of visual alpha-band activity and early sensory processing may point to distinct and multiplexed neural selection processes in visual processing.

Optimising children's movement assessment batteries through application of motivational and attentional manipulations

An external focus of attention, enhanced expectancies, and autonomy support (i.e., OPTIMAL factors) are key factors to optimise motor performance and uncover latent movement capabilities. However, research on the combination of OPTIMAL factors, particularly in children's dynamic movement settings is limited. Therefore, this study examined the combined effects of OPTIMAL factors on children's performance on a dynamic movement assessment battery, hypothesising higher performance scores in the optimised version of the assessment battery versus standardised version of the assessment. Forty-nine children (15 boys, 34 girls; mean age 10.61 ± 1.38 years) completed the Dragon Challenge (DC) dynamic movement assessment battery. Performance was measured via a summation of movement process (technique), outcome, and time-to-completion scores (max score N = 54) with higher scores representing better performance. Participants completed a standardised and an optimised version of the DC in a counterbalanced fashion. For the latter, DC protocols were optimised via the provision of choice (autonomy support); external focus instructions augmented by simple knowledge statement, positive feedback and promotion of a growth mindset (Enhanced expectancies). Results indicate that motor performance (DC score) was better in the optimised (M = 31.08 ± 6.66) vs. standardised (M = 29.04 ± 5.88). The findings indicate that the combination of OPTIMAL factors can improve children's motor performance in dynamic movement settings and that standardised motor assessment may not reveal children's true movement capabilities.

Effect of zonisamide on sleep and rapid eye movement sleep behavioral disorders in patients with Parkinson's disease: A randomized control trial

Introduction

Zonisamide is a medication developed in Japan that is effective for motor symptoms and wearing off in Parkinson's disease (PD). Zonisamide has properties that may improve sleep disorders. The aim of this study is to verify the safety and efficacy of zonisamide for sleep disorders and rapid eye movement (REM) sleep behavioral disorders (RBD) using a mobile two-channel electroencephalography /electrooculography recording system in patients with PD.

Methods

The present study is a single-blind randomized placebo-controlled trial. The subjects in the treatment group took zonisamide (25 mg per day) before bedtime. The primary outcome was sleep efficiency. The secondary endpoints were assessed as followed; objective outcomes of TST, WASO, SOL, REM sleep/non-REM sleep ratio, deep sleep (N3) time, ratio of RWA to total REM sleep epochs, and subjective outcomes of the PDSS-2, Pittsburgh sleep questionnaire, and RBDSQ.

Results

Between the zonisamide and placebo groups, no significant differences were found in the primary outcome and secondary outcomes.

Conclusions

The objective and subjective sleep metrics in this clinical trial did not significantly demonstrate zonisamide efficacy for sleep disorder in patients with PD. Although not significant, improvement in WASO and SOL was observed when zonisamide was compared with the placebo.

Early synaptic dysfunction of striatal parvalbumin interneurons in a mouse model of Parkinson's disease

In Parkinson's disease (PD), the loss of dopaminergic signaling remodels striatal circuits, causing abnormal network activity. The timing and impact on various striatal cell types during this reorganization are unclear. Here we demonstrate that dopamine depletion rapidly reduces parvalbumin (PV) expression. At the synaptic input level, PV interneurons shift toward inhibition in the excitation-inhibition balance early on, a week before a similar shift in spiny projection neurons (SPNs). At the cellular level, both PV interneurons and SPNs experience a significant decrease in their spiking and bursting rates, respectively, which corresponds to a reduction in gamma and beta (early beta) oscillations during the early stage of PD. Importantly, the pharmacogenetic activation of PV interneurons reverses gamma deficits and suppresses beta (late beta) oscillation in the striatum of parkinsonian mice. Collectively, our findings underscore the vulnerability of PV interneurons to dopamine depletion and their responsibility for the evolution of abnormal activities in parkinsonian striatum.

Modeling and Optimizing Deep Brain Stimulation to Enhance Gait in Parkinson's Disease: Personalized Treatment with Neurophysiological Insights

Although high-frequency deep brain stimulation (DBS) is effective at relieving many motor symptoms of Parkinson's disease (PD), its effects on gait can be variable and unpredictable. This is due to 1) a lack of standardized and robust metrics for gait assessment in PD patients, 2) the challenges of performing a thorough evaluation of all the stimulation parameters space that can alter gait, and 3) a lack of understanding for impacts of stimulation on the neurophysiological signatures of walking. In this study, our goal was to develop a data-driven approach to identify optimal, personalized DBS stimulation parameters to improve gait in PD patients and identify the neurophysiological signature of improved gait. Local field potentials from the globus pallidus and electrocorticography from the motor cortex of three PD patients were recorded using an implanted bidirectional neural stimulator during overground walking. A walking performance index (WPI) was developed to assess gait metrics with high reliability. DBS frequency, amplitude, and pulse width on the "clinically-optimized" stimulation contact were then systemically changed to study their impacts on gait metrics and underlying neural dynamics. We developed a Gaussian Process Regressor (GPR) model to map the relationship between DBS settings and the WPI. Using this model, we identified and validated personalized DBS settings that significantly improved gait metrics. Linear mixed models were employed to identify neural spectral features associated with enhanced walking performance. We demonstrated that improved walking performance was linked to the modulation of neural activity in specific frequency bands, with reduced beta band power in the pallidum and increased alpha band pallidal-motor cortex coherence synchronization during key moments of the gait cycle. Integrating WPI and GPR to optimize DBS parameters underscores the importance of developing and understanding personalized, data-driven interventions for gait improvement in PD.

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.

Post-Movement Beta Synchrony Inhibits Cortical Excitability

BACKGROUND/OBJECTIVES

This study investigates the relationship between movement-related beta synchrony and primary motor cortex (M1) excitability, focusing on the time-dependent inhibition of movement. Voluntary movement induces beta frequency (13-30 Hz) event-related desynchronisation (B-ERD) in M1, followed by post-movement beta rebound (PMBR). Although PMBR is linked to cortical inhibition, its temporal relationship with motor cortical excitability is unclear. This study aims to determine whether PMBR acts as a marker for post-movement inhibition by assessing motor-evoked potentials (MEPs) during distinct phases of the beta synchrony profile.

METHODS

Twenty-five right-handed participants (mean age: 24 years) were recruited. EMG data were recorded from the first dorsal interosseous muscle, and TMS was applied to the M1 motor hotspot to evoke MEPs. A reaction time task was used to elicit beta oscillations, with TMS delivered at participant-specific time points based on EEG-derived beta power envelopes. MEP amplitudes were compared across four phases: B-ERD, early PMBR, peak PMBR, and late PMBR.

RESULTS

Our findings demonstrate that MEP amplitude significantly increased during B-ERD compared to rest, indicating heightened cortical excitability. In contrast, MEPs recorded during peak PMBR were significantly reduced, suggesting cortical inhibition. While all three PMBR phases exhibited reduced cortical excitability, a trend toward amplitude-dependent inhibition was observed.

CONCLUSIONS

This study confirms that PMBR is linked to reduced cortical excitability, validating its role as a marker of motor cortical inhibition. These results enhance the understanding of beta oscillations in motor control and suggest that further research on altered PMBR could be crucial for understanding neurological and psychiatric disorders.

Exploring Embodied Cognition and Brain Dynamics Under Multi-Tasks Target Detection in Immerse Projector-Based Augmented Reality (IPAR) Scenarios

Embodied cognition explores the intricate interaction between the brain, body, and the surrounding environment. The advancement of mobile devices, such as immersive interactive computing and wireless electroencephalogram (EEG) devices, has presented new challenges and opportunities for studying embodied cognition. To address how mobile technology within immersive hybrid settings affects embodied cognition, we propose a target detection multitask incorporating mixed body movement interference and an environmental distraction light signal. We aim to investigate human embodied cognition in immersive projector-based augmented reality (IPAR) scenarios using wireless EEG technology. We recruited and engaged fifteen participants in four multitasking conditions: standing without distraction (SND), walking without distraction (WND), standing with distraction (SD), and walking with distraction (WD). We pre-processed the EEG data using Independent Component Analysis (ICA) to isolate brain sources and K-means clustering to categorize Independent Components (ICs). Following that, we conducted time-frequency and correlation analyses to identify neural dynamics changes associated with multitasking. Our findings reveal a decline in behavioral performance during multitasking activities. We also observed decreases in alpha and beta power in the frontal and motor cortex during standing target search tasks, decreases in theta power, and increases in alpha power in the occipital lobe during multitasking. We also noted perturbations in theta band power during distraction tasks. Notably, physical movement induced more significant fluctuations in the frontal and motor cortex than distractions from social environment light signals. Particularly in scenarios involving walking and multitasking, there was a noticeable reduction in beta suppression. Our study underscores the importance of brain-body collaboration in multitasking scenarios, where the simultaneous engagement of the body and brain in complex tasks highlights the dynamic nature of cognitive processes within the framework of embodied cognition. Furthermore, integrating immersive augmented reality technology into embodied cognition research enhances our understanding of the interplay between the body, environment, and cognitive functions, with profound implications for advancing human-computer interaction and elucidating cognitive dynamics in multitasking.

Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics

Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

The neural correlates of reinforcement sensitivity theory: A systematic review of the frontal asymmetry and spectral power literature

The original Reinforcement Sensitivity Theory (oRST) proposes two systems of approach (BAS) and avoidance (BIS) motivation to underpin personality and behavior. The revised-RST (rRST) model separates avoidance motivation into passive (BIS; anxiety) and active (FFFS; fear) systems. Prior research has attempted to map RST onto lateralized frontal asymmetry to provide a neurophysiological marker of RST. The main aim is to examine the relationships of the o/rRST scales with trait (baseline) and state (manipulated through experimental paradigms) frontal asymmetry. A systematic review was conducted, resulting in 158 studies designated to neuroimaging research. In total, 54 studies were included in this review using either frontal asymmetry or spectral power. The results were split into three main categories: resting frontal alpha asymmetry (N = 23), emotional induction and state-related frontal alpha asymmetry (N = 20), and spectral analysis (N = 16). Findings indicated that BAS was associated with enhanced left frontal asymmetry at baseline and during state-related paradigms. Findings for BIS were more inconsistent, especially at rest, suggesting that BIS, in particular, may require active engagement with the environment. Only 9 of the 54 papers included used the revised RST model, highlighting the need for more rRST research.

Neural indices of heritable impulsivity: Impact of the COMT Val158Met polymorphism on frontal beta power during early motor preparation

Studies of COMT Val158Met suggest that the neural circuitry subserving inhibitory control may be modulated by this functional polymorphism altering cortical dopamine availability, thus giving rise to heritable differences in behaviors. Using an anatomically-constrained magnetoencephalography method and stratifying the sample by COMT genotype, from a larger sample of 153 subjects, we examined the spatial and temporal dynamics of beta oscillations during motor execution and inhibition in 21 healthy Met158/Met158 (high dopamine) or 21 Val158/Val158 (low dopamine) genotype individuals during a Go/NoGo paradigm. While task performance was unaffected, Met158 homozygotes demonstrated an overall increase in beta power across regions essential for inhibitory control during early motor preparation (∼100 ms latency), suggestive of a global motor "pause" on behavior. This increase was especially evident on Go trials with slow response speed and was absent during inhibition failures. Such a pause could underlie the tendency of Met158 allele carriers to be more cautious and inhibited. In contrast, Val158 homozygotes exhibited a beta drop during early motor preparation, indicative of high response readiness. This decrease was associated with measures of behavioral disinhibition and consistent with greater extraversion and impulsivity observed in Val homozygotes. These results provide mechanistic insight into genetically-determined interindividual differences of inhibitory control with higher cortical dopamine associated with momentary response hesitation, and lower dopamine leading to motor impulsivity.

Cortical beta oscillations help synchronise muscles during static posture holding in healthy motor control

How cortical oscillations are involved in the coordination of functionally coupled muscles and how this is modulated by different movement contexts (static vs dynamic) remains unclear. Here, this is investigated by recording high-density electroencephalography (EEG) and electromyography (EMG) from different forearm muscles while healthy participants (n = 20) performed movement tasks (static and dynamic posture holding, and reaching) with their dominant hand. When dynamic perturbation was applied, beta band (15-35 Hz) activities in the motor cortex contralateral to the performing hand reduced during the holding phase, comparative to when there was no perturbation. During static posture holding, transient periods of increased cortical beta oscillations (beta bursts) were associated with greater corticomuscular coherence and increased phase synchrony between muscles (intermuscular coherence) in the beta frequency band compared to the no-burst period. This effect was not present when resisting dynamic perturbation. The results suggest that cortical beta bursts assist synchronisation of different muscles during static posture holding in healthy motor control, contributing to the maintenance and stabilisation of functional muscle groups. Theoretically, increased cortical beta oscillations could lead to exaggerated synchronisation in different muscles making the initialisation of movements more difficult, as observed in Parkinson's disease.

Investigating the Brain Mechanisms of Externally Cued Sit-to-Stand Movement in Parkinson's Disease

BACKGROUND

One of the more challenging daily-life actions for Parkinson's disease patients is starting to stand from a sitting position. Parkinson's disease patients are known to have difficulty with self-initiated movements and benefit from external cues. However, the brain processes underlying external cueing as an aid remain unknown. The advent of mobile electroencephalography (EEG) now enables the investigation of these processes in dynamic sit-to-stand movements.

OBJECTIVE

To identify cortical correlates of the mechanisms underlying auditory cued sit-to-stand movement in Parkinson's disease.

METHODS

Twenty-two Parkinson's disease patients and 24 healthy age-matched participants performed self-initiated and externally cued sit-to-stand movements while cortical activity was recorded through 32-channel mobile EEG.

RESULTS

Overall impaired integration of sensory and motor information can be seen in the Parkinson's disease patients exhibiting less modulation in the θ band during movement compared to healthy age-matched controls. How Parkinson's disease patients use external cueing of sit-to-stand movements can be seen in larger high β power over sensorimotor brain areas compared to healthy controls, signaling sensory integration supporting the maintenance of motor output. This appears to require changes in cognitive processing to update the motor plan, reflected in frontal θ power increases in Parkinson's disease patients when cued.

CONCLUSION

These findings provide the first neural evidence for why and how cueing improves motor function in sit-to-stand movement in Parkinson's disease. The Parkinson's disease patients' neural correlates indicate that cueing induces greater activation of motor cortical areas supporting the maintenance of a more stable motor output, but involves the use of cognitive resources to update the motor plan. © 2024 International Parkinson and Movement Disorder Society.

Age-Related Differences in Prestimulus EEG Affect ERPs and Behaviour in the Equiprobable Go/NoGo Task

Detailed studies of the equiprobable auditory Go/NoGo task have allowed for the development of a sequential-processing model of the perceptual and cognitive processes involved. These processes are reflected in various components differentiating the Go and NoGo event-related potentials (ERPs). It has long been established that electroencephalography (EEG) changes through normal lifespan development. It is also known that ERPs and behaviour in the equiprobable auditory Go/NoGo task change from children to young adults, and again in older adults. Here, we provide a novel examination of links between in-task prestimulus EEG, poststimulus ERPs, and behaviour in three gender-matched groups: children (8-12 years), young adults (18-24 years), and older adults (59-74 years). We used a frequency Principal Component Analysis (f-PCA) to estimate prestimulus EEG components and a temporal Principal Component Analysis (t-PCA) to separately estimate poststimulus ERP Go and NoGo components in each age group to avoid misallocation of variance. The links between EEG components, ERP components, and behavioural measures differed markedly between the groups. The young adults performed best and accomplished this with the simplest EEG-ERP-behaviour brain dynamics pattern. The children performed worst, and this was reflected in the most complex brain dynamics pattern. The older adults showed some reduction in performance, reflected in an EEG-ERP-behaviour pattern with intermediate complexity between those of the children and young adults. These novel brain dynamics patterns hold promise for future developmental research.

Comparison of beta peak detection algorithms for data-driven deep brain stimulation programming strategies in Parkinson's disease

Oscillatory activity within the beta frequency range (13-30 Hz) serves as a Parkinson's disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms against a benchmark established by expert consensus. The most accurate algorithms, all sharing similar underlying algebraic dynamic peak amplitude thresholding approaches, matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.

Why so slow? Models of parkinsonian bradykinesia

Bradykinesia, or slowness of movement, is a defining feature of Parkinson disease (PD) and a major contributor to the negative effects on quality of life associated with this disorder and related conditions. A dominant pathophysiological model of bradykinesia in PD has existed for approximately 30 years and has been the basis for the development of several therapeutic interventions, but accumulating evidence has made this model increasingly untenable. Although more recent models have been proposed, they also appear to be flawed. In this Perspective, I consider the leading prior models of bradykinesia in PD and argue that a more functionally related model is required, one that considers changes that disrupt the fundamental process of accurate information transmission. In doing so, I review emerging evidence of network level functional connectivity changes, information transfer dysfunction and potential motor code transmission error and present a novel model of bradykinesia in PD that incorporates this evidence. I hope that this model may reconcile inconsistencies in its predecessors and encourage further development of therapeutic interventions.

Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, we exploited a recently developed non-invasive deep brain stimulation technique called transcranial temporal interference stimulation (tTIS) during reinforcement motor learning with concurrent neuroimaging, in a randomized, sham-controlled, double-blind study. Striatal tTIS applied at 80 Hz, but not at 20 Hz, abolished the benefits of reinforcement on motor learning. This effect was related to a selective modulation of neural activity within the striatum. Moreover, 80 Hz, but not 20 Hz, tTIS increased the neuromodulatory influence of the striatum on frontal areas involved in reinforcement motor learning. These results show that tTIS can non-invasively and selectively modulate a striatal mechanism involved in reinforcement learning, expanding our tools for the study of causal relationships between deep brain structures and human behaviour.

Modulation of alpha oscillations by attention is predicted by hemispheric asymmetry of subcortical regions

Evidence suggests that subcortical structures play a role in high-level cognitive functions such as the allocation of spatial attention. While there is abundant evidence in humans for posterior alpha band oscillations being modulated by spatial attention, little is known about how subcortical regions contribute to these oscillatory modulations, particularly under varying conditions of cognitive challenge. In this study, we combined MEG and structural MRI data to investigate the role of subcortical structures in controlling the allocation of attentional resources by employing a cued spatial attention paradigm with varying levels of perceptual load. We asked whether hemispheric lateralization of volumetric measures of the thalamus and basal ganglia predicted the hemispheric modulation of alpha-band power. Lateral asymmetry of the globus pallidus, caudate nucleus, and thalamus predicted attention-related modulations of posterior alpha oscillations. When the perceptual load was applied to the target and the distractor was salient caudate nucleus asymmetry predicted alpha-band modulations. Globus pallidus was predictive of alpha-band modulations when either the target had a high load, or the distractor was salient, but not both. Finally, the asymmetry of the thalamus predicted alpha band modulation when neither component of the task was perceptually demanding. In addition to delivering new insight into the subcortical circuity controlling alpha oscillations with spatial attention, our finding might also have clinical applications. We provide a framework that could be followed for detecting how structural changes in subcortical regions that are associated with neurological disorders can be reflected in the modulation of oscillatory brain activity.

Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus

Objective.Can we classify movement execution and inhibition from hippocampal oscillations during arm-reaching tasks? Traditionally associated with memory encoding, spatial navigation, and motor sequence consolidation, the hippocampus has come under scrutiny for its potential role in movement processing. Stereotactic electroencephalography (SEEG) has provided a unique opportunity to study the neurophysiology of the human hippocampus during motor tasks. In this study, we assess the accuracy of discriminant functions, in combination with principal component analysis (PCA), in classifying between 'Go' and 'No-go' trials in a Go/No-go arm-reaching task.Approach.Our approach centers on capturing the modulation of beta-band (13-30 Hz) power from multiple SEEG contacts in the hippocampus and minimizing the dimensional complexity of channels and frequency bins. This study utilizes SEEG data from the human hippocampus of 10 participants diagnosed with epilepsy. Spectral power was computed during a 'center-out' Go/No-go arm-reaching task, where participants reached or withheld their hand based on a colored cue. PCA was used to reduce data dimension and isolate the highest-variance components within the beta band. The Silhouette score was employed to measure the quality of clustering between 'Go' and 'No-go' trials. The accuracy of five different discriminant functions was evaluated using cross-validation.Main results.The Diagonal-Quadratic model performed best of the 5 classification models, exhibiting the lowest error rate in all participants (median: 9.91%, average: 14.67%). PCA showed that the first two principal components collectively accounted for 54.83% of the total variance explained on average across all participants, ranging from 36.92% to 81.25% among participants.Significance.This study shows that PCA paired with a Diagonal-Quadratic model can be an effective method for classifying between Go/No-go trials from beta-band power in the hippocampus during arm-reaching responses. This emphasizes the significance of hippocampal beta-power modulation in motor control, unveiling its potential implications for brain-computer interface applications.

Premotor cortical beta synchronization and the network neuromodulation of externally paced finger tapping in Parkinson's disease

Parkinson's disease (PD) is characterized by the disruption of repetitive, concurrent and sequential motor actions due to compromised timing-functions principally located in cortex-basal ganglia (BG) circuits. Increasing evidence suggests that motor impairments in untreated PD patients are linked to an excessive synchronization of cortex-BG activity at beta frequencies (13-30 Hz). Levodopa and subthalamic nucleus deep brain stimulation (STN-DBS) suppress pathological beta-band reverberation and improve the motor symptoms in PD. Yet a dynamic tuning of beta oscillations in BG-cortical loops is fundamental for movement-timing and synchronization, and the impact of PD therapies on sensorimotor functions relying on neural transmission in the beta frequency-range remains controversial. Here, we set out to determine the differential effects of network neuromodulation through dopaminergic medication (ON and OFF levodopa) and STN-DBS (ON-DBS, OFF-DBS) on tapping synchronization and accompanying cortical activities. To this end, we conducted a rhythmic finger-tapping study with high-density EEG-recordings in 12 PD patients before and after surgery for STN-DBS and in 12 healthy controls. STN-DBS significantly ameliorated tapping parameters as frequency, amplitude and synchrony to the given auditory rhythms. Aberrant neurophysiologic signatures of sensorimotor feedback in the beta-range were found in PD patients: their neural modulation was weaker, temporally sluggish and less distributed over the right cortex in comparison to controls. Levodopa and STN-DBS boosted the dynamics of beta-band modulation over the right hemisphere, hinting to an improved timing of movements relying on tactile feedback. The strength of the post-event beta rebound over the supplementary motor area correlated significantly with the tapping asynchrony in patients, thus indexing the sensorimotor match between the external auditory pacing signals and the performed taps. PD patients showed an excessive interhemispheric coherence in the beta-frequency range during the finger-tapping task, while under DBS-ON the cortico-cortical connectivity in the beta-band was normalized. Ultimately, therapeutic DBS significantly ameliorated the auditory-motor coupling of PD patients, enhancing the electrophysiological processing of sensorimotor feedback-information related to beta-band activity, and thus allowing a more precise cued-tapping performance.

Computational models advance deep brain stimulation for Parkinson's disease

Deep brain stimulation(DBS) has become an effective intervention for advanced Parkinson's disease(PD), but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia (BG), the abnormal pathological changes of the BG in PD, and how computational models can help understand and advance DBS. We also describe two types of models: mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients' outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies.

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.

Multi-timescale neuromodulation strategy for closed-loop deep brain stimulation in Parkinson's disease

Objective.Beta triggered closed-loop deep brain stimulation (DBS) shows great potential for improving the efficacy while reducing side effect for Parkinson's disease. However, there remain great challenges due to the dynamics and stochasticity of neural activities. In this study, we aimed to tune the amplitude of beta oscillations with different time scales taking into account influence of inherent variations in the basal ganglia-thalamus-cortical circuit.Approach. A dynamic basal ganglia-thalamus-cortical mean-field model was established to emulate the medication rhythm. Then, a dynamic target model was designed to embody the multi-timescale dynamic of beta power with milliseconds, seconds and minutes. Moreover, we proposed a closed-loop DBS strategy based on a proportional-integral-differential (PID) controller with the dynamic control target. In addition, the bounds of stimulation amplitude increments and different parameters of the dynamic target were considered to meet the clinical constraints. The performance of the proposed closed-loop strategy, including beta power modulation accuracy, mean stimulation amplitude, and stimulation variation were calculated to determine the PID parameters and evaluate neuromodulation performance in the computational dynamic mean-field model.Main results. The Results show that the dynamic basal ganglia-thalamus-cortical mean-field model simulated the medication rhythm with the fasted and the slowest rate. The dynamic control target reflected the temporal variation in beta power from milliseconds to minutes. With the proposed closed-loop strategy, the beta power tracked the dynamic target with a smoother stimulation sequence compared with closed-loop DBS with the constant target. Furthermore, the beta power could be modulated to track the control target under different long-term targets, modulation strengths, and bounds of the stimulation increment.Significance. This work provides a new method of closed-loop DBS for multi-timescale beta power modulation with clinical constraints.

Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study

Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, β, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and β bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, β ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.

Sensorimotor Oscillations in Human Infants during an Innate Rhythmic Movement

The relationship between cerebral rhythms and early sensorimotor development is not clear. In recent decades, evidence revealed a rhythmic modulation involving sensorimotor processing. A widely corroborated functional role of oscillatory activity is to coordinate the information flow across sensorimotor networks. Their activity is coordinated by event-related synchronisation and desynchronisation in different sensorimotor rhythms, which indicate parallel processes may be occurring in the neuronal network during movement. To date, the dynamics of these brain oscillations and early sensorimotor development are unexplored. Our study investigates the relationship between the cerebral rhythms using EEG and a typical rhythmic movement of infants, the non-nutritive sucking (NNS) behaviour. NNS is an endogenous behaviour that originates from the suck central pattern generator in the brainstem. We find, in 17 infants, that sucking frequency correlates with beta synchronisation within the sensorimotor area in two phases: one strongly anticipating (~3 s) and the other encompassing the start of the motion. These findings suggest that a beta synchronisation of the sensorimotor cortex may influence the sensorimotor dynamics of NNS activity. Our results reveal the importance of rapid brain oscillations in infants and the role of beta synchronisation and their possible role in the communication between cortical and deep generators.

Modulation of subthalamic beta oscillations by movement, dopamine, and deep brain stimulation in Parkinson's disease

Subthalamic beta band activity (13-35 Hz) is known as a real-time correlate of motor symptom severity in Parkinson's disease (PD) and is currently explored as a feedback signal for closed-loop deep brain stimulation (DBS). Here, we investigate the interaction of movement, dopaminergic medication, and deep brain stimulation on subthalamic beta activity in PD patients implanted with sensing-enabled, implantable pulse generators. We recorded subthalamic activity from seven PD patients at rest and during repetitive movements in four conditions: after withdrawal of dopaminergic medication and DBS, with medication only, with DBS only, and with simultaneous medication and DBS. Medication and DBS showed additive effects in improving motor performance. Distinct effects of each therapy were seen in subthalamic recordings, with medication primarily suppressing low beta activity (13-20 Hz) and DBS being associated with a broad decrease in beta band activity (13-35 Hz). Movement suppressed beta band activity compared to rest. This suppression was most prominent when combining medication with DBS and correlated with motor improvement within patients. We conclude that DBS and medication have distinct effects on subthalamic beta activity during both rest and movement, which might explain their additive clinical effects as well as their difference in side-effect profiles. Importantly, subthalamic beta activity significantly correlated with motor symptoms across all conditions, highlighting its validity as a feedback signal for closed-loop DBS.

Pilot Study to Investigate the Use of In-Clinic Sensing to Identify Optimal Stimulation Parameters for Deep Brain Stimulation Therapy in Parkinson's Disease

BACKGROUND

Deep brain stimulation (DBS) programming is time intensive. Recent advances in sensing technology of local field potentials (LFPs) may enable improvements. Few studies have compared the use of this technology with standard of care.

OBJECTIVE/HYPOTHESIS

Sensing technology of subthalamic nucleus (STN) DBS leads in Parkinson's disease (PD) is reliable and predicts the optimal contacts and settings as predicted by clinical assessment.

MATERIALS AND METHODS

Five subjects with PD (n = 9 hemispheres) with bilateral STN DBS and sensing capable battery replacement were recruited. An LFP sensing review of all bipolar contact pairs was performed three times. Contact with the maximal beta peak power (MBP) was then clinically assessed in a double-blinded fashion, and five conditions were tested: 1) entry settings, 2) off stimulation, 3) MBP at 30 μs, 4) MBP at 60 μs, and 5) MBP at 90 μs.

RESULTS

Contact and frequency of the MBP power in all hemispheres did not differ across sessions. The entry settings matched with the contact with the MBP power in 5 of 9 hemispheres. No clinical difference was evident in the stimulation conditions. The clinician and subject preferred settings determined by MBP power in 7 of 9 and 5 of 7 hemispheres, respectively.

CONCLUSIONS

This study indicates that STN LFPs in PD recorded directly from contacts of the DBS lead provide consistent recordings across the frequency range and a reliably detected beta peak. Furthermore, programming based on the MBP power provides at least clinical equivalence to standard of care programming with STN DBS.

Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics

Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence and frequency of neural oscillations are determined by identifying peaks over 1/f noise within the power spectrum. However, this approach solely operates within the frequency domain and thus cannot adequately distinguish between the fundamental frequency of a non-sinusoidal oscillation and its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing the false-positive detection rate - a confounding factor in the analysis of neural oscillations. To overcome these limitations, we define the fundamental criteria that characterize a neural oscillation and introduce the Cyclic Homogeneous Oscillation (CHO) detection method that implements these criteria based on an auto-correlation approach that determines the oscillation's periodicity and fundamental frequency. We evaluated CHO by verifying its performance on simulated sinusoidal and non-sinusoidal oscillatory bursts convolved with 1/f noise. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. Specifically, we determined the sensitivity and specificity of CHO as a function of signal-to-noise ratio (SNR). We further assessed CHO by testing it on electrocorticographic (ECoG, 8 subjects) and electroencephalographic (EEG, 7 subjects) signals recorded during the pre-stimulus period of an auditory reaction time task and on electrocorticographic signals (6 SEEG subjects and 6 ECoG subjects) collected during resting state. In the reaction time task, the CHO method detected auditory alpha and pre-motor beta oscillations in ECoG signals and occipital alpha and pre-motor beta oscillations in EEG signals. Moreover, CHO determined the fundamental frequency of hippocampal oscillations in the human hippocampus during the resting state (6 SEEG subjects). In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

Training-related changes in neural beta oscillations associated with implicit and explicit motor sequence learning

Many motor actions we perform have a sequential nature while learning a motor sequence involves both implicit and explicit processes. In this work, we developed a task design where participants concurrently learn an implicit and an explicit motor sequence across five training sessions, with EEG recordings at sessions 1 and 5. This intra-subject approach allowed us to study training-induced behavioral and neural changes specific to the explicit and implicit components. Based on previous reports of beta power modulations in sensorimotor networks related to sequence learning, we focused our analysis on beta oscillations at motor-cortical sites. On a behavioral level, substantial performance gains were evident early in learning in the explicit condition, plus slower performance gains across training sessions in both explicit and implicit sequence learning. Consistent with the behavioral trends, we observed a training-related increase in beta power in both sequence learning conditions, while the explicit condition displayed stronger beta power suppression during early learning. The initially stronger beta suppression and subsequent increase in beta power specific to the explicit component, correlated with enhanced behavioral performance, possibly reflecting higher cortical excitability. Our study suggests an involvement of motor-cortical beta oscillations in the explicit component of motor sequence learning.

Amplitude Adaptive Modulation of Neural Oscillations Over Long-Term Dynamic Conditions: A Computational Study

Closed-loop deep brain stimulation (DBS) shows great potential for precise neuromodulation of various neurological disorders, particularly Parkinson's disease (PD). However, substantial challenges remain in clinical translation due to the complex programming procedure of closed-loop DBS parameters. In this study, we proposed an online optimized amplitude adaptive strategy based on the particle swarm optimization (PSO) and proportional-integral-differential (PID) controller for modulation of the beta oscillation in a PD mean field model over long-term dynamic conditions. The strategy aimed to calculate the stimulation amplitude adapting to the fluctuations caused by circadian rhythm, medication rhythm, and stochasticity in the basal ganglia-thalamus-cortical circuit. The PID gains were optimized online using PSO, based on modulation accuracy, mean stimulation amplitude, and stimulation variation. The results showed that the proposed strategy optimized the stimulation amplitude and achieved beta power modulation under the influence of circadian rhythm, medication rhythm, and stochasticity of beta oscillations. This work offers a novel approach for precise neuromodulation with the potential for clinical translation.

Oscillatory and non-oscillatory features of the magnetoencephalic sensorimotor rhythm in Parkinson's disease

Parkinson's disease (PD) is associated with changes in neural activity in the sensorimotor alpha and beta bands. Using magnetoencephalography (MEG), we investigated the role of spontaneous neuronal activity within the somatosensory cortex in a large cohort of early- to mid-stage PD patients (N = 78) on Parkinsonian medication and age- and sex-matched healthy controls (N = 60) using source reconstructed resting-state MEG. We quantified features of the time series data in terms of oscillatory alpha power and central alpha frequency, beta power and central beta frequency, and 1/f broadband characteristics using power spectral density. Furthermore, we characterised transient oscillatory burst events in the mu-beta band time-domain signals. We examined the relationship between these signal features and the patients' disease state, symptom severity, age, sex, and cortical thickness. PD patients and healthy controls differed on PSD broadband characteristics, with PD patients showing a steeper 1/f exponential slope and higher 1/f offset. PD patients further showed a steeper age-related decrease in the burst rate. Out of all the signal features of the sensorimotor activity, the burst rate was associated with increased severity of bradykinesia, whereas the burst duration was associated with axial symptoms. Our study shows that general non-oscillatory features (broadband 1/f exponent and offset) of the sensorimotor signals are related to disease state and oscillatory burst rate scales with symptom severity in PD.

Bilateral intracortical inhibition during unilateral motor preparation and sequence learning

Motor sequence learning gradually quickens reaction time, suggesting that sequence learning alters motor preparation processes. Interestingly, evidence has shown that preparing sequence movements decreases short intracortical inhibition (SICI) in the contralateral motor cortex (M1), but also that sequence learning alters motor preparation processes in both the contralateral and ipsilateral M1s. Therefore, one possibility is that sequence learning alters the SICI decreases occurring during motor preparation in bilateral M1s. To examine this, two novel hypotheses were tested: unilateral sequence preparation would decrease SICI in bilateral M1s, and sequence learning would alter such bilateral SICI responses. Paired-pulse transcranial magnetic stimulation was delivered over the contralateral and ipsilateral M1s to assess SICI in an index finger muscle during the preparation of sequences initiated by either the right index or little finger. In the absence of sequence learning, SICI decreased in both the contralateral and ipsilateral M1s during the preparation of sequences initiated by the right index finger, suggesting that SICI decreases in bilateral M1s during unilateral motor preparation. As sequence learning progressed, SICI decreased in the contralateral M1 whilst it increased in the ipsilateral M1. Moreover, these bilateral SICI responses were observed at the onset of motor preparation, suggesting that sequence learning altered baseline SICI levels rather than the SICI decreases occurring during motor preparation per se. Altogether, these results suggest that SICI responses in bilateral M1s reflect two motor processes: an acute decrease of inhibition during motor preparation, and a cooperative but bidirectional shift of baseline inhibition levels as sequence learning progresses.

Thalamic deep brain stimulation for tourette syndrome increases cortical beta activity

BACKGROUND

Deep brain stimulation (DBS) of the thalamus can effectively reduce tics in severely affected patients with Tourette syndrome (TS). Its effect on cortical oscillatory activity is currently unknown.

OBJECTIVE

We assessed whether DBS modulates beta activity at fronto-central electrodes. We explored concurrent EEG sources and probabilistic stimulation maps.

METHODS

Resting state EEG of TS patients treated with thalamic DBS was recorded in repeated DBS-on and DBS-off states. A mixed linear model was employed for statistical evaluation. EEG sources were estimated with eLORETA. Thalamic probabilistic stimulation maps were obtained by assigning beta power difference scores (DBS-on minus DBS-off) to stimulation sites.

RESULTS

We observed increased beta power in DBS-on compared to DBS-off states. Modulation of cortical beta activity was localized to the midcingulate cortex. Beta modulation was more pronounced when stimulating the thalamus posteriorly, peaking in the ventral posterior nucleus.

CONCLUSION

Thalamic DBS in TS patients modulates beta frequency oscillations presumably important for sensorimotor function and relevant to TS pathophysiology.

Effects of anodal transcranial direct current stimulation over motor cortex on resting-state brain activity in the early subacute stroke phase: A power spectral density analysis

INTRODUCTION

Despite promising results, the effects of transcranial direct current stimulation (tDCS) in the early stages of stroke and its impact on brain activity have been poorly studied. Therefore, this study aimed to investigate the effect of tDCS applied over the ipsilesional motor cortex on resting-state brain activity in the early subacute phase of stroke.

METHODS

This is a pilot, randomized, double-blind, proof-of-concept study. The patients with stroke were randomly assigned into two groups: anodal tDCS (A-tDCS) or sham tDCS (S-tDCS). For A-tDCS, the anode was placed over the ipsilesional motor cortex, while the cathode was placed over the left or right supraorbital area (Fp2 for left stroke or Fp1 for right stroke). For the real stimulation, a constant current of 1.0 mA was delivered for 20 min and then ramped down linearly for 30 s, maintaining a resistance below 10 kΩ. For the sham stimulation, the stimulator was turned on, and the current intensity was gradually increased for 30 s, tapered off over 30 s, and maintained for 30 min without stimulation. Each stimulation was performed for three consecutive sessions with an interval of 1 h between them. The primary outcome was spectral electroencephalography (EEG) analysis based on the Power Spectral Density (PSD) determined by EEG records of areas F3, F4, C3, C4, P3, and P4. Brain Vision Analyzer software processed the signals, EEG power spectral density (PSD) was calculated before and after stimulation, and alpha, beta, delta, and theta power were analyzed. The secondary outcomes included hemodynamic variables based on the difference between baseline (D0) and post-intervention session (D1) values of systolic (SBP) and diastolic (DBP) blood pressure, heart rate (HR), respiratory rate (RR) and peripheral oxygen saturation (SPO2). Mann-Whitney test was used to compare position measurements of two independent samples; Fisher's exact test was used to compare two proportions; paired Wilcoxon signed-rank test was used to compare the median differences in the within-group comparison, and Spearman correlations matrix among spectral power analysis between EEG bands was performed to verify consistency of occurrence of oscillations. Statistical significance was set at P < 0.05.

RESULTS

An increase in PSD in the alpha frequency in the P4 region was observed after the intervention in the A-tDCS group, as compared to the placebo group (before = 6.13; after = 10.45; p < 0.05). In the beta frequency, an increase in PSD was observed in P4 (before = 4.40; after = 6.79; p < 0.05) and C4 (before = 4.43; after = 6.94; p < 0.05) after intervention in the A-tDCS group. There was a reduction in PSD at delta frequency in C3 (before = 293.8; after = 58.6; p < 0.05) after intervention in the A-tDCS group. In addition, it was observed a strong relationship between alpha and theta power in the A-tDCS group before and after intervention. However, the sham group showed correlations between more power bands (alpha and theta, alpha and delta, and delta and theta) after intervention. There was no difference in hemodynamic variables between the intra- (before and after stimulation) and inter-groups (mean difference).

CONCLUSION

Anodal tDCS over the ipsilesional motor cortex had significant effects on the brain electrical activity in the early subacute stroke phase, increasing alpha and beta wave activities in sensorimotor regions while reducing slow delta wave activity in motor regions. These findings highlight the potential of anodal tDCS as a therapeutic intervention in the early stroke phase.

The temporal dynamics of how the brain structures natural scenes

Individuals organize the evolving stream of events in their environment by partitioning it into discrete units. Event segmentation theory (EST) provides a cognitive explanation for the process of this partitioning. Critically, the underlying time-resolved neural mechanisms are not understood, and thus a central conceptual aspect of how humans implement this central ability is missing. To gain better insight into the fundamental temporal dynamics of event segmentation, EEG oscillatory activity was measured while participants watched a narrative video and partitioned the movie into meaningful segments. Using EEG beamforming methods, we show that theta, alpha, and beta band activity in frontal, parietal, and occipital areas, as well as their interactions, reflect critical elements of the event segmentation process established by EST. In sum, we see a mechanistic temporal chain of processes that provides the neurophysiological basis for how the brain partitions and structures continuously evolving scenes and points to an integrated system that organizes the various subprocesses of event segmentation. This study thus integrates neurophysiology and cognitive theory to better understand how the human brain operates in rather variable and unpredictable situations. Therefore, it represents an important step toward studying neurophysiological dynamics in ecologically valid and naturalistic settings and, in doing so, addresses a critical gap in knowledge regarding the temporal dynamics of how the brain structures natural scenes.

The Swedish National Facility for Magnetoencephalography Parkinson's disease dataset

Parkinson's disease (PD) is characterised by a loss of dopamine and dopaminergic cells. The consequences hereof are widespread network disturbances in brain function. It is an ongoing topic of investigation how the disease-related changes in brain function manifest in PD relate to clinical symptoms. We present The Swedish National Facility for Magnetoencephalography Parkinson's Disease Dataset (NatMEG-PD) as an Open Science contribution to identify the functional neural signatures of Parkinson's disease and contribute to diagnosis and treatment. The dataset contains whole-head magnetoencephalographic (MEG) recordings from 66 well-characterised PD patients on their regular dose of dopamine replacement therapy and 68 age- and sex-matched healthy controls. NatMEG-PD contains three-minute eyes-closed resting-state MEG, MEG during an active movement task, and MEG during passive movements. The data includes anonymised MRI for source analysis and clinical scores. MEG data is rich in nature and can be used to explore numerous functional features. By sharing these data, we hope other researchers will contribute to advancing our understanding of the relationship between brain activity and disease state or symptoms.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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