Oscillations In Frequency Bands Research Articles

Effects of Mental Workload Manipulation on Electroencephalography Spectrum Oscillation and Microstates in Multitasking Environments.

Multitasking during flights leads to a high mental workload, which is detrimental for maintaining task performance. Electroencephalography (EEG) power spectral analysis based on frequency-band oscillations and microstate analysis based on global brain network activation can be used to evaluate mental workload. This study explored the effects of a high mental workload during simulated flight multitasking on EEG frequency-band power and microstate parameters. Thirty-six participants performed multitasking with low and high mental workloads after 4 consecutive days of training. Two levels of mental workload were set by varying the number of subtasks. EEG signals were acquired during the task. Power spectral and microstate analyses were performed on the EEG. The indices of four frequency bands (delta, theta, alpha, and beta) and four microstate classes (A-D) were calculated, changes in the frequency-band power and microstate parameters under different mental workloads were compared, and the relationships between the two types of EEG indices were analyzed. The theta-, alpha-, and beta-band powers were higher under the high than under the low mental workload condition. Compared with the low mental workload condition, the high mental workload condition had a lower global explained variance and time parameters of microstate B but higher time parameters of microstate D. Less frequent transitions between microstates A and B and more frequent transitions between microstates C and D were observed during high mental workload conditions. The time parameters of microstate B were positively correlated with the delta-, theta-, and beta-band powers, whereas the duration of microstate C was negatively correlated with the beta-band power. EEG frequency-band power and microstate parameters can be used to detect a high mental workload. Power spectral analyses based on frequency-band oscillations and microstate analyses based on global brain network activation were not completely isolated during multitasking.

Differential modulations of theta and beta oscillations by audiovisual congruency in letter-speech sound integration.

The integration of visual letters and speech sounds is a crucial part of learning to read. Previous studies investigating this integration have revealed a modulation by audiovisual (AV) congruency, commonly known as the congruency effect. To investigate the cortical oscillations of the congruency effects across different oscillatory frequency bands, we conducted a Japanese priming task in which a visual letter was followed by a speech sound. We analyzed the power and phase properties of oscillatory activities in the theta and beta bands between congruent and incongruent letter-speech sound (L-SS) pairs. Our results revealed stronger theta-band (5-7Hz) power in the congruent condition and cross-modal phase resetting within the auditory cortex, accompanied by enhanced inter-trial phase coherence (ITPC) in the auditory-related areas in response to the congruent condition. The observed congruency effect of theta-band power may reflect increased neural activities in the left auditory region during L-SS integration. Additionally, theta ITPC findings suggest that visual letters amplify neuronal responses to the following corresponding auditory stimulus, which may reflect the differential cross-modal influences in the primary auditory cortex. In contrast, decreased beta-band (20-35 Hz) oscillatory power was observed in the right centroparietal regions for the congruent condition. The reduced beta power seems to be unrelated to the processing of AV integration, but may be interpreted as the brain response to predicting auditory sounds during language processing. Our data provide valuable insights by indicating that oscillations in different frequency bands contribute to the disparate aspects of L-SS integration.

Alpha coherence is a network signature of cognitive recovery from disorders of consciousness.

Alpha (8-12 Hz) frequency band oscillations are among the most informative features in electroencephalographic (EEG) assessment of patients with disorders of consciousness (DoC). Because interareal alpha synchrony is thought to facilitate long-range communication in healthy brains, coherence measures of resting-state alpha oscillations may provide insights into a patients capacity for higher-order cognition beyond channel-wise estimates of alpha power. In multi-channel EEG, global coherence methods may be used to augment standard spectral analysis methods by both estimating the strength and identifying the structure of coherent oscillatory networks. We performed global coherence analysis in 95 separate clinical EEG recordings (28 healthy controls and 33 patients with acute or chronic DoC, 25 of whom returned for follow-up) collected between two academic medical centers. We found that posterior alpha coherence is associated with recovery of higher-level cognition. We developed a measure of network organization, based on the distance between eigenvectors of the alpha cross-spectral matrix, that detects recovery of posterior alpha networks. In patients who have emerged from a minimally conscious state, we showed that coherence-based alpha networks are reconfigured prior to restoration of alpha power to resemble those seen in healthy controls. This alpha network measure performs well in classifying recovery from DoC (AUC = 0.78) compared to common representations of functional connectivity using the weighted phase lag index (AUC = 0.50 - 0.57). Lastly, we observed that activity within these alpha networks is suppressed during positive responses to task-based EEG command-following paradigms, supporting the potential utility of this biomarker to detect covert cognition. Our findings suggest that restored alpha networks may represent a sensitive early signature of cognitive recovery in patients with DoC. Therefore, network detection methods may augment the utility of EEG assessments for DoC.

Non-rapid eye movement sleep slow-wave activity features are associated with amyloid accumulation in older adults with obstructive sleep apnoea.

Obstructive sleep apnoea (OSA) is associated with an increased risk for cognitive impairment and dementia, which likely involves Alzheimer's disease pathology. Non-rapid eye movement slow-wave activity (SWA) has been implicated in amyloid clearance, but it has not been studied in the context of longitudinal amyloid accumulation in OSA. This longitudinal retrospective study aims to investigate the relationship between polysomnographic and electrophysiological SWA features and amyloid accumulation. From the Mayo Clinic Study of Aging cohort, we identified 71 participants ≥60 years old with OSA (mean baseline age = 72.9 ± 7.5 years, 60.6% male, 93% cognitively unimpaired) who had at least 2 consecutive Amyloid Pittsburgh Compound B (PiB)-PET scans and a polysomnographic study within 5 years of the baseline scan and before the second scan. Annualized PiB-PET accumulation [global ΔPiB(log)/year] was estimated by the difference between the second and first log-transformed global PiB-PET uptake estimations divided by the interval between scans (years). Sixty-four participants were included in SWA analysis. SWA was characterized by the mean relative spectral power density (%) in slow oscillation (SO: 0.5-0.9 Hz) and delta (1-3.9 Hz) frequency bands and by their downslopes (SO-slope and delta-slope, respectively) during the diagnostic portion of polysomnography. We fit linear regression models to test for associations among global ΔPiB(log)/year, SWA features (mean SO% and delta% or mean SO-slope and delta-slope), and OSA severity markers, after adjusting for age at baseline PiB-PET, APOE ɛ4 and baseline amyloid positivity. For 1 SD increase in SO% and SO-slope, global ΔPiB(log)/year increased by 0.0033 (95% CI: 0.0001; 0.0064, P = 0.042) and 0.0069 (95% CI: 0.0009; 0.0129, P = 0.026), which were comparable to 32% and 59% of the effect size associated with baseline amyloid positivity, respectively. Delta-slope was associated with a reduction in global ΔPiB(log)/year by -0.0082 (95% CI: -0.0143; -0.0021, P = 0.009). Sleep apnoea severity was not associated with amyloid accumulation. Regional associations were stronger in the pre-frontal region. Both slow-wave slopes had more significant and widespread regional associations. Annualized PiB-PET accumulation was positively associated with SO and SO-slope, which may reflect altered sleep homeostasis due to increased homeostatic pressure in the setting of unmet sleep needs, increased synaptic strength, and/or hyper-excitability in OSA. Delta-slope was inversely associated with PiB-PET accumulation, suggesting it may represent residual physiological activity. Further investigation of SWA dynamics in the presence of sleep disorders before and after treatment is necessary for understanding the relationship between amyloid accumulation and SWA physiology.

A Novel Time-Frequency Parameterization Method for Oscillations in Specific Frequency Bands and Its Application on OPM-MEG.

Time-frequency parameterization for oscillations in specific frequency bands reflects the dynamic changes in the brain. It is related to cognitive behavior and diseases and has received significant attention in neuroscience. However, many studies do not consider the impact of the aperiodic noise and neural activity, including their time-varying fluctuations. Some studies are limited by the low resolution of the time-frequency spectrum and parameter-solved operation. Therefore, this paper proposes super-resolution time-frequency periodic parameterization of (transient) oscillation (STPPTO). STPPTO obtains a super-resolution time-frequency spectrum with Superlet transform. Then, the time-frequency representation of oscillations is obtained by removing the aperiodic component fitted in a time-resolved way. Finally, the definition of transient events is used to parameterize oscillations. The performance of this method is validated on simulated data and its reliability is demonstrated on magnetoencephalography. We show how it can be used to explore and analyze oscillatory activity under rhythmic stimulation.

Cue-based modulation of pain stimulus expectation: do ongoing oscillations reflect changes in pain perception? A registered report.

A promising stream of investigations is targeting ongoing neural oscillations and whether their modulation could be related to the perception of pain. Using an electroencephalography (EEG) frequency-tagging approach, sustained periodic thermonociceptive stimuli perceived as painful have been shown to modulate ongoing oscillations in the theta, alpha and beta bands at the frequency of stimulation. Nonetheless, it remains uncertain whether these modulations are indeed linked to pain perception. To test this relationship, we modulated pain perception using a cue-based expectation modulation paradigm and investigated whether ongoing oscillations in different frequency bands mirror the changes in stimulus perception. Forty healthy participants were instructed that a visual cue can precede either a high- or low-intensity stimulation. These cues were paired with three different levels of sustained periodic thermonociceptive stimuli (low, medium and high). Despite a strong effect of expectation on perceived stimulus intensity, this effect was not reflected in the modulation of the ongoing oscillations, suggesting a potential dissociation of pain perception and these oscillatory activities. Rather, it seems that the intensity of stimulation is the primary generator of the frequency-tagged EEG responses. Importantly, these results need to be confirmed by further investigations that could allow the detection of smaller effects than originally estimated.

A Review of the Research on the Wide-Band Oscillation Analysis and Suppression of Renewable Energy Grid-Connected Systems

With the continuous expansion of the scale of power generated by grid-connected renewable energy, the form and operation characteristics of power grids have undergone significant changes, and the power electronic characteristics of power systems are prominent, resulting in the frequent occurrence of wide-band oscillation problems when renewable energy power generation equipment is connected to a power grid. Oscillation has the characteristics of nonlinearity and has an oscillation frequency ranging from a few hertz to several thousand hertz or more, which seriously threatens the stable operation of power grids. This paper summarizes the wide-band oscillation events that occur worldwide under the background of renewable energy access to power grids, classifies the wide-band oscillation events according to the distribution of oscillation frequency bands, and sorts out the characteristics of various oscillations. From the perspectives of the source side and network side, the existing oscillation suppression measures are classified and prospected.

Crossmodal Associations and Working Memory in the Brain.

Crossmodal associations between stimuli from different sensory modalities could emerge in non-synesthetic people and be stored in working memory to guide goal-directed behaviors. This chapter reviews a plethora of studies in this field to summarize where, when, and how crossmodal associations and working memory are processed. It has been found that in those brain regions that are traditionally considered as unimodal primary sensory areas, neural activity could be influenced by crossmodal sensory signals at temporally very early stage of information processing. This phenomenon could not be due to feedback projections from higher level associative areas. Sequentially, neural processes would then occur in associative cortical areas including the posterior parietal cortex and prefrontal cortex. Neural oscillations in multiple frequency bands may reflect brain activity in crossmodal associations, and it is likely that neural synchrony is related to potential neural mechanisms underlying these processes. Primary sensory areas and associative areas coordinate together through neural synchrony to fulfil crossmodal associations and to guide working memory performance.

Probing signals of atmospheric gravity waves excited by the July 29, 2021 MW8.2 Alaska earthquake

It is commonly believed that the atmosphere is decoupled from the solid Earth. Thus, it is difficult for the seismic wave energy inside the Earth to propagate into the atmosphere, and atmospheric pressure wave signals excited by earthquakes are unlikely to exist in atmospheric observations. An increasing number of studies have shown that earthquakes, volcanoes, and tsunamis can perturb the Earth's atmosphere due to various coupling effects. However, the observations mainly focus on acoustic waves with periods of less than 10 min and inertial gravity waves with periods of greater than 1 h. There are almost no clear observations of gravity waves that coincide with observations of low-frequency signals of the Earth's free oscillation frequency band within 1 h. This paper investigates atmospheric gravity wave signals within 1 h of surface-atmosphere observations using the periodogram method based on seismometer and microbarometer observations from the global seismic network before and after the July 29, 2021 MW8.2 Alaska earthquake in the United States. The numerical results show that the atmospheric gravity wave signals with frequencies similar to those of the Earth's free oscillations 0S2 and 0T2 can be detected in the microbarometer observations. The results confirm the existence of atmospheric gravity waves, indicating that the atmosphere and the solid Earth are not decoupled within this frequency band and that seismic wave energy excited by earthquakes can propagate from the interior of the Earth to the atmosphere and enhance the atmospheric gravity wave signals within 1 h.

Neural oscillations modulation during working memory in pre-manifest and early Huntington’s disease

IntroductionWe recently demonstrated specific spectral signatures associated with updating of memory information, working memory (WM) maintenance and readout, with relatively high spatial resolution by means of high-density electroencephalography (hdEEG). WM is impaired already in early symptomatic HD (early-HD) and in pre-manifest HD (pre-HD). The aim of this study was to test whether hdEEG coupled to source localization allows for the identification of neuronal oscillations in specific frequency bands in 16 pre-HD and early-HD during different phases of a WM task. MethodsWe examined modulation of neural oscillations by event-related synchronization and desynchronization (ERS/ERD) of θ, β, gamma low, γLOW and γHIGH EEG bands in a-priori selected large fronto-parietal network, including the insula and the cerebellum. ResultsWe found: (i) Reduced θ oscillations in HD with respect to controls in almost all the areas of the WM network during the update and readout phases; (ii) Modulation of β oscillations, which increased during the maintenance phase of the WM task in both groups; (iii) correlation of γHIGH oscillations during WM task with disease burden score in HD patients. ConclusionsOur data show reduced phase-specific modulation of oscillations in pre-HD and early-HD, even in the presence of preserved dynamic of modulation. Particularly, reduced synchronization in the θ band in the areas of the WM network, consistent with abnormal long-range coordination of neuronal activity within this network, was found in update and readout phases in HD groups.

Alpha and Theta Oscillations Are Causally Linked to Interference Inhibition: Evidence from High-Definition Transcranial Alternating Current Stimulation.

(1) Background: The Go/NoGo task and color-word Stroop task were used to investigate the effect of applying different frequency bands of neural oscillations to the lDLPFC on inhibitory control modulation. (2) Methods: Participants were randomly categorized into four groups and received HD-tACS at 6, 10, and 20 Hz or sham stimulation at 1.5 mA for 20 min. All participants performed a color-word Stroop task and Go/NoGo task before and immediately after the stimulation; closed-eye resting-state EEG signals were acquired for 3 min before and after the tasks. (3) Results: There were no significant differences in the Go/NoGo behavioral indices task across the four groups. In the color-word Stroop task, the Stroop effect of response time was significantly reduced by 6 and 10 Hz stimulations compared to sham stimulation, and the Stroop effect of accuracy was significantly reduced by 10 Hz stimulation. There were no significant differences in the frequency range-specific (delta, theta, alpha, beta, or gamma) resting EEG power before and after stimulation. (4) Conclusions: HD-tACS at 6 and 10 Hz effectively improved participants' performance on the color-word Stroop task, demonstrating the importance of the lDLPFC in interference inhibition and supporting a causal relationship between theta and alpha oscillations in interference inhibition.

Stability analysis of two-terminal HVDC transmission systems using SISO open-loop gains

The ports of a three-phase voltage source converter have multiple frequency electrical parameters, which makes HVDC systems with these converters present multiple input multiple output (MIMO) characteristics. The generalized Nyquist criterion of the MIMO model is often used to analyze the system stability. However, this model is more difficult to obtain than the single-input single-output (SISO) model, and the stability margin and the oscillation frequency band are difficult to capture. To solve these problems, this paper took the two-terminal HVDC transmission system as the research object to derive a SISO model that can be measured by a port. Moreover, this study also proposed a method for analyzing the system stability through envelope SISO open-loop gains. Finally, the key port and parameters affecting the system stability were analyzed according to the stability margin, which provided theoretical support for the design of the system parameters. The established model and the proposed analysis method were verified in a MATLAB/Simulink simulation model.

Transmission of delta band (0.5-4 Hz) oscillations from the globus pallidus to the substantia nigra pars reticulata in dopamine depletion.

Parkinson's disease (PD) and animal models of PD feature enhanced oscillations in several frequency bands in the basal ganglia (BG). Past research has emphasized the enhancement of 13-30 Hz beta oscillations. Recently, however, oscillations in the delta band (0.5-4 Hz) have been identified as a robust predictor of dopamine loss and motor dysfunction in several BG regions in mouse models of PD. In particular, delta oscillations in the substantia nigra pars reticulata (SNr) were shown to lead oscillations in motor cortex (M1) and persist under M1 lesion, but it is not clear where these oscillations are initially generated. In this paper, we use a computational model to study how delta oscillations may arise in the SNr due to projections from the globus pallidus externa (GPe). We propose a network architecture that incorporates inhibition in SNr from oscillating GPe neurons and other SNr neurons. In our simulations, this configuration yields firing patterns in model SNr neurons that match those measured in vivo. In particular, we see the spontaneous emergence of near-antiphase active-predicting and inactive-predicting neural populations in the SNr, which persist under the inclusion of STN inputs based on experimental recordings. These results demonstrate how delta oscillations can propagate through BG nuclei despite imperfect oscillatory synchrony in the source site, narrowing down potential targets for the source of delta oscillations in PD models and giving new insight into the dynamics of SNr oscillations.

Performance analysis of modeling scale on multiband oscillations in grid-connected wind farm

Grid-connected permanent magnet synchronous generator (PMSG) wind farms may be susceptible to oscillation when connected to weak grids. This paper explores the root causes of the oscillations from two perspectives: the model and the oscillation frequency band. First, the small signal model of PMSG wind farm grid-side system is established, and the small disturbance comparison with the PMSGA full-order electromagnetic transient model in MATLAB/Simulink is carried out. Then, we calculatue the eigenvalues of the small signal model and use model analysis techniques such as participation factors calculation and root locus method to identify the critical factors that cause the oscillation modes to be dominant. Finally, time-domain simulation is used to verify the theoretical analysis. Two dominant modes are identified: the subsynchronous oscillation mode and the low-frequency oscillation mode. The subsynchronous oscillation mode is closely related to the direct current (DC) voltage control and the dynamics of DC link. The low-frequency oscillation is significantly weakened with the decrease in grid strength, and it is closely related to the phase-locked loop (PLL) control. The conclusions can provide a reference for tuning the control parameters of PMSG converter.

Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis.

Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase-amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.

Targeted rhythmic visual stimulation at individual participants’ intrinsic alpha frequency causes selective increase of occipitoparietal BOLD-fMRI and EEG functional connectivity

Neural oscillations in distinct frequency bands are ubiquitous in the brain and play a role in many cognitive processes. The "communication by coherence" hypothesis, poses that the synchronization through phase coupling of frequency-specific neural oscillations regulate information flow across distribute brain regions. Specifically, the posterior alpha frequency band (7-12Hz) is thought to gate bottom-up visual information flow by inhibition during visual processing. Evidence shows that increased alpha phase coherency positively correlates with functional connectivity in resting state connectivity networks, supporting alpha mediates neural communication through coherency. However, these findings have mainly been derived from spontaneous changes in the ongoing alpha rhythm. In this study, we experimentally modulate the alpha rhythm by targeting individuals' intrinsic alpha frequency with sustained rhythmic light to investigate alpha-mediated synchronous cortical activity in both EEG and fMRI. We hypothesize increased alpha coherency and fMRI connectivity should arise from modulation of the intrinsic alpha frequency (IAF) as opposed to control frequencies in the alpha range. Sustained rhythmic and arrhythmic stimulation at the IAF and at neighboring frequencies within the alpha band range (7-12Hz) was implemented and assessed in a separate EEG and fMRI study. We observed increased cortical alpha phase coherency in the visual cortex during rhythmic stimulation at the IAF as in comparison to rhythmic stimulation of control frequencies. In the fMRI, we found increased functional connectivity for stimulation at the IAF in visual and parietal areas as compared to other rhythmic control frequencies by correlating time courses from a set of regions of interest for the different stimulation conditions and applying network-based statistics. This suggests that rhythmic stimulation at the IAF frequency induces a higher degree of synchronicity of neural activity across the occipital and parietal cortex, which supports the role of the alpha oscillation in gating information flow during visual processing.

Neuromodulation of Neural Oscillations in Health and Disease.

Using EEG and local field potentials (LFPs) as an index of large-scale neural activities, research has been able to associate neural oscillations in different frequency bands with markers of cognitive functions, goal-directed behavior, and various neurological disorders. While this gives us a glimpse into how neurons communicate throughout the brain, the causality of these synchronized network activities remains poorly understood. Moreover, the effect of the major neuromodulatory systems (e.g., noradrenergic, cholinergic, and dopaminergic) on brain oscillations has drawn much attention. More recent studies have suggested that cross-frequency coupling (CFC) is heavily responsible for mediating network-wide communication across subcortical and cortical brain structures, implicating the importance of neurotransmitters in shaping coordinated actions. By bringing to light the role each neuromodulatory system plays in regulating brain-wide neural oscillations, we hope to paint a clearer picture of the pivotal role neural oscillations play in a variety of cognitive functions and neurological disorders, and how neuromodulation techniques can be optimized as a means of controlling neural network dynamics. The aim of this review is to showcase the important role that neuromodulatory systems play in large-scale neural network dynamics, informing future studies to pay close attention to their involvement in specific features of neural oscillations and associated behaviors.

Hopf bifurcation mechanism analysis in an improved cortex-basal ganglia network with distributed delays: An application to Parkinson’s disease

In this paper, we introduce general distributed delays (in pathways between different neurons) and self-feedback distributed delays (in self-feedback pathways) into an improved cortex-basal ganglia (BG) network to study possible Hopf bifurcation mechanisms for Parkinson’s oscillations. Hopf bifurcation critical conditions for discrete delays are obtained, which compare well to numerical simulations. General distributed delays inhibit oscillations in BG, but self-feedback distributed delays promote oscillations. The different effects of strong and weak kernels on Hopf bifurcations mainly depend on self-feedback distributed delays, strong kernels are more conducive to the generation of oscillations when self-feedback distributed delays exceed a certain value. The excitatory feedback and inhibitory feedback from cortex to BG have different effects on oscillation. We define four different states (absolutely stable, conditional stable, conditional oscillation and absolutely oscillation) in this model, which can explain different mechanisms of oscillation origin. An increase in distributed delays can induce the transition between supercritical (SPH) and subcritical (SBH) Hopf bifurcations, which in turn may cause the transition of oscillations in different frequency bands, such as beta oscillations and alpha oscillations. Near the SBH, frequencies increase with an increase in discrete delays; and the trend is opposite at the SPH. In general, the amplitude of oscillation increases with the increase of firing activation level. Discrete delays can improve the firing activation level of the BG and suppress oscillations in its adjacent circuits. With the increase of distributed delays, oscillatory regions evolve regularly and the roles of general distributed delays and self-feedback distributed delays are contrary. Different types of delay have different effects on oscillation frequency. Discrete delay and self-feedback distributed delay have similar effects on oscillation frequency, but are different from general distributed delay. Whether the frequency will change with an increase in delay depends on coupling weights.

Amplitude and frequency modulation of subthalamic beta oscillations jointly encode the dopaminergic state in Parkinson’s disease

Brain states in health and disease are classically defined by the power or the spontaneous amplitude modulation (AM) of neuronal oscillations in specific frequency bands. Conversely, the possible role of the spontaneous frequency modulation (FM) in defining pathophysiological brain states remains unclear. As a paradigmatic example of pathophysiological resting states, here we assessed the spontaneous AM and FM dynamics of subthalamic beta oscillations recorded in patients with Parkinson’s disease before and after levodopa administration. Even though AM and FM are mathematically independent, they displayed negatively correlated dynamics. First, AM decreased while FM increased with levodopa. Second, instantaneous amplitude and instantaneous frequency were negatively cross-correlated within dopaminergic states, with FM following AM by approximately one beta cycle. Third, AM and FM changes were also negatively correlated between dopaminergic states. Both the slow component of the FM and the fast component (i.e. the phase slips) increased after levodopa, but they differently contributed to the AM-FM correlations within and between states. Finally, AM and FM provided information about whether the patients were OFF vs. ON levodopa, with partial redundancy and with FM being more informative than AM. AM and FM of spontaneous beta oscillations can thus both separately and jointly encode the dopaminergic state in patients with Parkinson’s disease. These results suggest that resting brain states are defined not only by AM dynamics but also, and possibly more prominently, by FM dynamics of neuronal oscillations.

EEG Oscillations in Specific Frequency Bands Are Differently Coupled with Angular Joint Angle Kinematics during Rhythmic Passive Elbow Movement.

Rhythmic passive movements are often used during rehabilitation to improve physical functions. Previous studies have explored oscillatory activities in the sensorimotor cortex during active movements; however, the relationship between movement rhythms and oscillatory activities during passive movements has not been substantially tested. Therefore, we aimed to quantitatively identify changes in cortical oscillations during rhythmic passive movements. Twenty healthy young adults participated in our study. We placed electroencephalography electrodes over a nine-position grid; the center was oriented on the transcranial magnetic stimulation hotspot of the biceps brachii muscle. Passive movements included elbow flexion and extension; the participants were instructed to perform rhythmic elbow flexion and extension in response to the blinking of 0.67 Hz light-emitting diode lamps. The coherence between high-beta and low-gamma oscillations near the hotspot of the biceps brachii muscle and passive movement rhythms was higher than that between alpha oscillation and passive movement rhythm. These results imply that alpha, beta, and gamma oscillations of the primary motor cortex are differently related to passive movement rhythm.