Fast and slow oscillations in human primary motor cortex predict oncoming behaviorally relevant cues

Altered theta band and theta/beta ratio in mismatch negativity associate with treatment effect in schizophrenia with auditory hallucinations

Evidence suggests that attenuated mismatch negative (MMN) waves have a close link to auditory verbal hallucinations (AVH) and their clinical outcomes, especially impaired neural oscillations such as θ, β representing attentional control. In current study, thirty patients with schizophrenia and AVH (SZ) and twenty-nine healthy controls (HC) underwent multi-feature MMN paradigm measurements including frequency and duration deviant stimuli (fMMN and dMMN). Clinical symptoms and MMN paradigm were followed up among SZ group after 8-week treatment. Results demonstrated that hallucinating patients exhibited attenuated dMMN amplitudes across Fz (p = 0.010), F1 (p = 0.020) and F2 (p = 0.014) electrodes, which were trendily recovered after treatment. Meanwhile, θ band and TBR at frontal fMMN and right temporal dMMN were significantly reduced in SZs. After treatment, SZs showed reduced scores of Hoffman's Auditory Hallucinations Rating Scale (AHRS), with a remarkable recovery in right temporal TBR of dMMN (p = 0.042) and a trending change in frontal TBR of fMMN (p = 0.090). The β band was decreased in dMMN (p = 0.035) by time. Additionally, P3 scores of Positive and Negative Syndrome Scale (PANSS) were negatively correlated with θ band of fMMN at baseline. Baseline scores of AHRS negatively predicted changes of dMMN amplitude after treatment, and changes of β band in left temporal dMMN predicted the reduction in scores of PANSS negative scale. These findings supported that deficits in θ oscillation and TBR during auditory attention process were crucial to clinical progression of schizophrenia with AVH.

Motor learning leverages coordinated low-frequency cortico-basal ganglia activity to optimize motor preparation in humans with Parkinson's disease

Learning dexterous motor sequences is crucial to autonomy and quality of life but can be altered in Parkinson's disease (PD). Learning involves optimizing pre-movement planning (preplanning) of multiple sequence elements to reduce computational overhead during active movement. However, it is unclear which brain regions mediate preplanning or how this process evolves with learning. Recording cortico-basal ganglia field potentials during a multi-day typing task in four individuals with PD, we found evidence for network-wide multi-element preplanning that improved with learning, facilitated by functional connectivity. In both cortex and basal ganglia, pre-movement gamma (γ, 30-250 Hz) activity, historically linked to population spiking, distinguished between future action sequences and became increasingly predictive with learning. For motor cortex γ, this increase was tied to learning-related cross-frequency coupling led by cortically-driven network delta (δ, 0.5-4 Hz) synchrony. More generally, coordinated network δ supported a complex pattern of learning-driven cross-frequency couplings within and between cortex and basal ganglia, including striatal lead of cortical beta (β, 12-30 Hz) activity, reflecting the specialized roles of these brain regions in motor preparation. In contrast, impaired learning was characterized by practice-driven decreases in γ's predictive value, limited cross-frequency coupling and absent network δ synchrony, with network dynamics possibly altered by pathologically high inter-basal ganglia δ synchrony. These results suggest that cortically-led δ phase coordination optimized cortico-basal ganglia multi-element preplanning through enhanced recruitment of higher-frequency neural activity. Neurostimulation that enhances cortico-basal ganglia δ synchrony may thus hold potential for improving skilled fine motor control in PD.

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.

Moving rhythmically can facilitate naturalistic speech perception in a noisy environment

The motor system is known to process temporal information, and moving rhythmically while listening to a melody can improve auditory processing. In three interrelated behavioural experiments, we demonstrate that this effect translates to speech processing. Motor priming improves the efficiency of subsequent naturalistic speech-in-noise processing under specific conditions. (i) Moving rhythmically at the lexical rate (~1.8 Hz) significantly improves subsequent speech processing compared to moving at other rates, such as the phrasal or syllabic rates. (ii) The impact of such rhythmic motor priming is not influenced by whether it is self-generated or triggered by an auditory beat. (iii) Overt lexical vocalization, regardless of its semantic content, also enhances the efficiency of subsequent speech processing. These findings provide evidence for the functional role of the motor system in processing the temporal dynamics of naturalistic speech.

Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features

Introduction

Phase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear.

Methods

This study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes.

Results

The PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands.

Discussion

These preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.

Beta oscillations predict the envelope sharpness in a rhythmic beat sequence

Periodic sensory inputs entrain oscillatory brain activity, reflecting a neural mechanism that might be fundamental to temporal prediction and perception. Most environmental rhythms and patterns in human behavior, such as walking, dancing, and speech do not, however, display strict isochrony but are instead quasi-periodic. Research has shown that neural tracking of speech is driven by modulations of the amplitude envelope, especially via sharp acoustic edges, which serve as prominent temporal landmarks. In the same vein, research on rhythm processing in music supports the notion that perceptual timing precision varies systematically with the sharpness of acoustic onset edges, conceptualized in the beat bin hypothesis. Increased envelope sharpness induces increased precision in localizing a sound in time. Despite this tight relationship between envelope shape and temporal processing, it is currently unknown how the brain uses predictive information about envelope features to optimize temporal perception. With the current EEG study, we show that the predicted sharpness of the amplitude envelope is encoded by pre-target neural activity in the beta band (15-25 Hz), and has an impact on the temporal perception of target sounds. We used probabilistic sound cues in a timing judgment task to inform participants about the sharpness of the amplitude envelope of an upcoming target sound embedded in a beat sequence. The predictive information about the envelope shape modulated task performance and pre-target beta power. Interestingly, these conditional beta-power modulations correlated positively with behavioral performance in the timing judgment task and with perceptual temporal precision in a click-alignment task. This study provides new insight into the neural processes underlying prediction of the sharpness of the amplitude envelope during beat perception, which modulate the temporal perception of sounds. This finding could reflect a process that is involved in temporal prediction, exerting top-down control on neural entrainment via the prediction of acoustic edges in the auditory stream.

Neural encoding of melodic expectations in music across EEG frequency bands

The human brain tracks regularities in the environment and extrapolates these to predict future events. Prior work on music cognition suggests that low-frequency (1-8 Hz) brain activity encodes melodic predictions beyond the stimulus acoustics. Building on this work, we aimed to disentangle the frequency-specific neural dynamics linked to melodic prediction uncertainty (modelled as entropy) and prediction error (modelled as surprisal) for temporal (note onset) and content (note pitch) information. By using multivariate temporal response function (TRF) models, we re-analysed the electroencephalogram (EEG) from 20 subjects (10 musicians) who listened to Western tonal music. Our results show that melodic expectation metrics improve the EEG reconstruction accuracy in all frequency bands below the gamma range (< 30 Hz). Crucially, we found that entropy contributed more strongly to the reconstruction accuracy enhancement compared to surprisal in all frequency bands. Additionally, we found that the encoding of temporal, but not content, information metrics was not limited to low frequencies, rather it extended to higher frequencies (> 8 Hz). An analysis of the TRF weights revealed that the temporal predictability of a note (entropy of note onset) may be encoded in the delta- (1-4 Hz) and beta-band (12-30 Hz) brain activity prior to the stimulus, suggesting that these frequency bands associate with temporal predictions. Strikingly, we also revealed that melodic expectations selectively enhanced EEG reconstruction accuracy in the beta band for musicians, and in the alpha band (8-12 Hz) for non-musicians, suggesting that musical expertise influences the neural dynamics underlying predictive processing in music cognition.

Sensory Entrained TMS (seTMS) enhances motor cortex excitability

Transcranial magnetic stimulation (TMS) applied to the motor cortex has revolutionized the study of motor physiology in humans. Despite this, TMS-evoked electrophysiological responses show significant variability, due in part to inconsistencies between TMS pulse timing and ongoing brain oscillations. Variable responses to TMS limit mechanistic insights and clinical efficacy, necessitating the development of methods to precisely coordinate the timing of TMS pulses to the phase of relevant oscillatory activity. We introduce Sensory Entrained TMS (seTMS), a novel approach that uses musical rhythms to synchronize brain oscillations and time TMS pulses to enhance cortical excitability. Focusing on the sensorimotor alpha rhythm, a neural oscillation associated with motor cortical inhibition, we examine whether rhythm-evoked sensorimotor alpha phase alignment affects primary motor cortical (M1) excitability in healthy young adults (n=33). We first confirmed using electroencephalography (EEG) that passive listening to musical rhythms desynchronizes inhibitory sensorimotor brain rhythms (mu oscillations) around 200 ms before auditory rhythmic events (27 participants). We then targeted this optimal time window by delivering single TMS pulses over M1 200 ms before rhythmic auditory events while recording motor-evoked potentials (MEPs; 19 participants), which resulted in significantly larger MEPs compared to standard single pulse TMS and an auditory control condition. Neither EEG measures during passive listening nor seTMS-induced MEP enhancement showed dependence on musical experience or training. These findings demonstrate that seTMS effectively enhances corticomotor excitability and establishes a practical, cost-effective method for optimizing non-invasive brain stimulation outcomes.

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.

Variations in Clustering of Multielectrode Local Field Potentials in the Motor Cortex of Macaque Monkeys during a Reach-and-Grasp Task

There is experimental evidence of varying correlation among the elements of the neuromuscular system over the course of the reach-and-grasp task. The aim of this study was to investigate if modifications in correlations and clustering can be detected in the local field potential (LFP) recordings of the motor cortex during the task. To this end, we analyzed the LFP recordings from a previously published study on monkeys that performed a reach-and-grasp task for targets with a vertical or horizontal orientation. LFP signals were recorded from the motor and premotor cortex of macaque monkeys as they performed the task. We found very robust changes in the correlations of the multielectrode LFP recordings that corresponded to task epochs. Mean LFP correlation increased significantly during reach and then decreased during grasp. This pattern was very robust for both left and right arm reaches irrespective of target orientation. A hierarchical cluster analysis also demonstrated similar changes. In focusing on correlations, our study has contributed new insights to the understanding of LFP signals and their relationship to movement. A sliding window computation of the number of clusters was performed to probe the capacities of the LFP clusters for detecting upcoming task events. For a very high percentage of trials (97.89%), there was a downturn in cluster number following the Pellet Drop (GO signal) that reached a minimum preceding the Start of grasp, hence indicating that cluster analyses of LFPs could contribute to signaling an increased probability of the Start of grasp.

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

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

Speech and music recruit frequency-specific distributed and overlapping cortical networks

To what extent does speech and music processing rely on domain-specific and domain-general neural networks? Using whole-brain intracranial EEG recordings in 18 epilepsy patients listening to natural, continuous speech or music, we investigated the presence of frequency-specific and network-level brain activity. We combined it with a statistical approach in which a clear operational distinction is made between shared, preferred, and domain-selective neural responses. We show that the majority of focal and network-level neural activity is shared between speech and music processing. Our data also reveal an absence of anatomical regional selectivity. Instead, domain-selective neural responses are restricted to distributed and frequency-specific coherent oscillations, typical of spectral fingerprints. Our work highlights the importance of considering natural stimuli and brain dynamics in their full complexity to map cognitive and brain functions.

Neurophysiological Oscillatory Mechanisms Underlying the Effect of Mirror Visual Feedback-Induced Illusion of Hand Movements on Nociception and Cortical Activation

Mirror Visual Feedback (MVF)-induced illusion of hand movements produces beneficial effects in patients with chronic pain. However, neurophysiological mechanisms underlying these effects are poorly known. In this preliminary study, we test the novel hypothesis that such an MVF-induced movement illusion may exert its effects by changing the activity in midline cortical areas associated with pain processing. Electrical stimuli with individually fixed intensity were applied to the left hand of healthy adults to produce painful and non-painful sensations during unilateral right-hand movements with such an MVF illusion and right and bilateral hand movements without MVF. During these events, electroencephalographic (EEG) activity was recorded from 64 scalp electrodes. Event-related desynchronization (ERD) of EEG alpha rhythms (8-12 Hz) indexed the neurophysiological oscillatory mechanisms inducing cortical activation. Compared to the painful sensations, the non-painful sensations were specifically characterized by (1) lower alpha ERD estimated in the cortical midline, angular gyrus, and lateral parietal regions during the experimental condition with MVF and (2) higher alpha ERD estimated in the lateral prefrontal and parietal regions during the control conditions without MVF. These preliminary results suggest that the MVF-induced movement illusion may affect nociception and neurophysiological oscillatory mechanisms, reducing the activation in cortical limbic and default mode regions.

Neural oscillations guiding action during effects imagery

Goal-directed acting requires the integration of sensory information but can also be performed without direct sensory input. Examples of this can be found in sports and can be conceptualized by feedforward processes. There is, however, still a lack of understanding of the temporal neural dynamics and neuroanatomical structures involved in such processes. In the current study, we used EEG beamforming methods and examined 37 healthy participants in two well-controlled experiments varying the necessity of anticipatory processes during goal-directed action. We found that alpha and beta activity in the medial and posterior cingulate cortex enabled feedforward predictions about the position of an object based on the latest sensorimotor state. On this basis, theta band activity seems more related to sensorimotor representations, while beta band activity would be more involved in setting up the structure of the neural representations themselves. Alpha band activity in sensory cortices reflects an intensified gating of the anticipated perceptual consequences of the to-be-executed action. Together, the findings indicate that goal-directed acting through the anticipation of the predicted state of an effector is based on accompanying processes in multiple frequency bands in midcingulate and sensory brain regions.

Cross-Frequency Coupling as a Biomarker for Early Stroke Recovery

BACKGROUND

The application of neuroimaging-based biomarkers in stroke has enriched our understanding of post-stroke recovery mechanisms, including alterations in functional connectivity based on synchronous oscillatory activity across various cortical regions. Phase-amplitude coupling, a type of cross-frequency coupling, may provide additional mechanistic insight.

OBJECTIVE

To determine how the phase of prefrontal cortex delta (1-3 Hz) oscillatory activity mediates the amplitude of motor cortex beta (13-20 Hz) oscillations in individual's early post-stroke.

METHODS

Participants admitted to an inpatient rehabilitation facility completed resting and task-based EEG recordings and motor assessments around the time of admission and discharge along with structural neuroimaging. Unimpaired controls completed EEG procedures during a single visit. Mixed-effects linear models were performed to assess within- and between-group differences in delta-beta prefrontomotor coupling. Associations between coupling and motor status and injury were also determined.

RESULTS

Thirty individuals with stroke and 17 unimpaired controls participated. Coupling was greater during task versus rest conditions for all participants. Though coupling during affected extremity task performance decreased during hospitalization, coupling remained elevated at discharge compared to controls. Greater baseline coupling was associated with better motor status at admission and discharge and positively related to motor recovery. Coupling demonstrated both positive and negative associations with injury involving measures of lesion volume and overlap injury to anterior thalamic radiation, respectively.

CONCLUSIONS

This work highlights the utility of prefrontomotor cross-frequency coupling as a potential motor status and recovery biomarker in stroke. The frequency- and region-specific neurocircuitry featured in this work may also facilitate novel treatment strategies in stroke.

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.

Beta oscillation is an indicator for two patterns of sensorimotor synchronization

Previous study indicates that there are two distinct behavioral patterns in the sensory-motor synchronization task with short stimulus onset asynchrony (SOA; 2-3 s) or long SOA (beyond 4 s). However, the underlying neural indicators and mechanisms have not been elucidated. The present study applied magnetoencephalography (MEG) technology to examine the functional role of several oscillations (beta, gamma, and mu) in sensorimotor synchronization with different SOAs to identify a reliable neural indicator. During MEG recording, participants underwent a listening task without motor response, a sound-motor synchronization task, and a motor-only continuation task. These tasks were used to explore whether and how the activity of oscillations changes across different behavioral patterns with different tempos. Results showed that during both the listening and the synchronization task, the beta oscillation changes with the tempo. Moreover, the event-related synchronization of beta oscillations was significantly correlated with motor timing during synchronization. In contrast, mu activity only changes with the tempo in the synchronization task, while the gamma activity remains unchanged. In summary, the current study indicates that beta oscillation could be an indicator of behavioral patterns between fast tempo and slow tempo in sensorimotor synchronization. Also, it is likely to be the potential mechanism of maintaining rhythmic continuous movements with short SOA, which is embedded within the 3 s time window.

Neural dynamics of predictive timing and motor engagement in music listening

Why do humans spontaneously dance to music? To test the hypothesis that motor dynamics reflect predictive timing during music listening, we created melodies with varying degrees of rhythmic predictability (syncopation) and asked participants to rate their wanting-to-move (groove) experience. Degree of syncopation and groove ratings are quadratically correlated. Magnetoencephalography data showed that, while auditory regions track the rhythm of melodies, beat-related 2-hertz activity and neural dynamics at delta (1.4 hertz) and beta (20 to 30 hertz) rates in the dorsal auditory pathway code for the experience of groove. Critically, the left sensorimotor cortex coordinates these groove-related delta and beta activities. These findings align with the predictions of a neurodynamic model, suggesting that oscillatory motor engagement during music listening reflects predictive timing and is effected by interaction of neural dynamics along the dorsal auditory pathway.

The Motor of Time: Coupling Action to Temporally Predictable Events Heightens Perception

Timing and motor function share neural circuits and dynamics, which underpin their close and synergistic relationship. For instance, the temporal predictability of a sensory event optimizes motor responses to that event. Knowing when an event is likely to occur lowers response thresholds, leading to faster and more efficient motor behavior though in situations of response conflict can induce impulsive and inappropriate responding. In turn, through a process of active sensing, coupling action to temporally predictable sensory input enhances perceptual processing. Action not only hones perception of the event's onset or duration, but also boosts sensory processing of its non-temporal features such as pitch or shape. The effects of temporal predictability on motor behavior and sensory processing involve motor and left parietal cortices and are mediated by changes in delta and beta oscillations in motor areas of the brain.

Neural Oscillations and Multisensory Processing

Neural oscillations play a role in sensory processing by coordinating synchronized neuronal activity. Synchronization of gamma oscillations is engaged in local computation of feedforward signals and synchronization of alpha-beta oscillations is engaged in feedback processing over long-range areas. These spatially and spectrally segregated bi-directional signals may be integrated by a mechanism of cross-frequency coupling. Synchronization of neural oscillations has also been proposed as a mechanism for information integration across multiple sensory modalities. A transient stimulus or rhythmic stimulus from one modality may lead to phase alignment of ongoing neural oscillations in multiple sensory cortices, through a mechanism of cross-modal phase reset or cross-modal neural entrainment. Synchronized activities in multiple sensory cortices are more likely to boost stronger activities in downstream areas. Compared to synchronized oscillations, asynchronized oscillations may impede signal processing, and may contribute to sensory selection by setting the oscillations in the target-related cortex and the oscillations in the distractor-related cortex to opposite phases.

On natural attunement: Shared rhythms between the brain and the environment

Rhythms exist both in the embodied brain and the built environment. Becoming attuned to the rhythms of the environment, such as repetitive columns, can greatly affect perception. Here, we explore how the built environment affects human cognition and behavior through the concept of natural attunement, often resulting from the coordination of a person's sensory and motor systems with the rhythmic elements of the environment. We argue that the built environment should not be reduced to mere states, representations, and single variables but instead be considered a bundle of highly related continuous signals with which we can resonate. Resonance and entrainment are dynamic processes observed when intrinsic frequencies of the oscillatory brain are influenced by the oscillations of an external signal. This allows visual rhythmic stimulations of the environment to affect the brain and body through neural entrainment, cross-frequency coupling, and phase resetting. We review how real-world architectural settings can affect neural dynamics, cognitive processes, and behavior in people, suggesting the crucial role of everyday rhythms in the brain-body-environment relationship.

Exploratory study of the acute and mid-term effects of using a novel dynamic meeting environment (Aeris®) on cognitive performance and neurophysiological responses

The purpose of the present study was to assess the acute and mid-term effects of the dynamic aeris®-meeting- environment on brain activity, cognitive performance, heart rate variability (HRV), sleepiness, mental workload (EEG-MWI), as well as local experienced discomfort (LED) in healthy adults. Twenty-four healthy adults (16 females, age: 25.2 ± 3.1 years old) were randomly assigned to either the control (i.e., conventional meeting environment, CG) or experimental (Aeris® dynamic meeting-environment, DG) group with a 1:1 allocation. Participants reported to the laboratory on two test sessions separated by a 2-week intervention period (5 meetings of 90 min each week). Spontaneous resting EEG and HRV activities, as well as attentional (D2-R test) and vigilance (PVT) cognitive performances, sleepiness perceptions, and EEG-MWI, were recorded at the beginning of each test session and immediately following the 90-min meeting. The LED was measured pre- and post-intervention. The changes (Δ) from pre- to post-90 min meeting and from pre- to post- intervention were computed to further examine the acute and mid-term effects, respectively. Compared to the CG, the DG showed higher Δ (pre-post 90 min-meeting) in fronto-central beta (z = -2.41, p = 0.016, d = 1.10) and gamma (z = -2.34, p = 0.019, d = 0.94) frequencies at post-intervention. From pre- to post-intervention, only the DG group showed a significant increase in fronto-central gamma response (Δ) to the meeting session (z = -2.09, p = 0.04, d = 1.08). The acute use of the Aeris®-meeting-environment during the 90-min meeting session seems to be supportive for (i) maintaining vigilance performance, as evidenced by the significant increase in N-lapses from pre- to post-90 min session only in the CG (p = 0.04, d = 0.99, Δ = 2.5 ± 3 lapses), and (ii) improving alertness, as evidenced by the lower sleepiness score (p = 0.05, d = -0.84) in DG compared to CG. The mid-term use of such an environment showed to blind the higher baseline values of EEG-MWI recorded in DG compared to CG (p = 0.01, d = 1.05) and may prevent lower-back discomfort (i.e., a significant increase only in CG with p = 0.05 and d = 0.78), suggesting a less mentally and physically exhausting meeting in this environment. There were no acute and/or mid-term effects of the dynamic meeting environment on any of the HRV parameters. These findings are of relevance in the field of neuroergonomics, as they give preliminary support to the advantages of meeting in a dynamic office compared to a static office environment.

Local field potentials and single unit dynamics in motor cortex of unconstrained macaques during different behavioral states

Different sleep stages have been shown to be vital for a variety of brain functions, including learning, memory, and skill consolidation. However, our understanding of neural dynamics during sleep and the role of prominent LFP frequency bands remain incomplete. To elucidate such dynamics and differences between behavioral states we collected multichannel LFP and spike data in primary motor cortex of unconstrained macaques for up to 24 h using a head-fixed brain-computer interface (Neurochip3). Each 8-s bin of time was classified into awake-moving (Move), awake-resting (Rest), REM sleep (REM), or non-REM sleep (NREM) by using dimensionality reduction and clustering on the average spectral density and the acceleration of the head. LFP power showed high delta during NREM, high theta during REM, and high beta when the animal was awake. Cross-frequency phase-amplitude coupling typically showed higher coupling during NREM between all pairs of frequency bands. Two notable exceptions were high delta-high gamma and theta-high gamma coupling during Move, and high theta-beta coupling during REM. Single units showed decreased firing rate during NREM, though with increased short ISIs compared to other states. Spike-LFP synchrony showed high delta synchrony during Move, and higher coupling with all other frequency bands during NREM. These results altogether reveal potential roles and functions of different LFP bands that have previously been unexplored.

Investigating Cerebellar Modulation of Premovement Beta-Band Activity during Motor Adaptation

Enhancing cerebellar activity influences motor cortical activity and contributes to motor adaptation, though it is unclear which neurophysiological mechanisms contributing to adaptation are influenced by the cerebellum. Pre-movement beta event-related desynchronization (β-ERD), which reflects a release of inhibitory control in the premotor cortex during movement planning, is one mechanism that may be modulated by the cerebellum through cerebellar-premotor connections. We hypothesized that enhancing cerebellar activity with intermittent theta burst stimulation (iTBS) would improve adaptation rates and increase β-ERD during motor adaptation. Thirty-four participants were randomly assigned to an active (A-iTBS) or sham cerebellar iTBS (S-iTBS) group. Participants performed a visuomotor task, using a joystick to move a cursor to targets, prior to receiving A-iTBS or S-iTBS, following which they completed training with a 45° rotation to the cursor movement. Behavioural adaptation was assessed using the angular error of the cursor path relative to the ideal trajectory. The results showed a greater adaptation rate following A-iTBS and an increase in β-ERD, specific to the high β range (20-30 Hz) during motor planning, compared to S-iTBS, indicative of cerebellar modulation of the motor cortical inhibitory control network. The enhanced release of inhibitory activity persisted throughout training, which suggests that the cerebellar influence over the premotor cortex extends beyond adaptation to other stages of motor learning. The results from this study further understanding of cerebellum-motor connections as they relate to acquiring motor skills and may inform future skill training and rehabilitation protocols.

[A study on the application of cross-frequency coupling characteristics of neural oscillation in the diagnosis of mild cognitive impairment]

In order to fully explore the neural oscillatory coupling characteristics of patients with mild cognitive impairment (MCI), this paper analyzed and compared the strength of the coupling characteristics for 28 MCI patients and 21 normal subjects under six different-frequency combinations. The results showed that the difference in the global phase synchronization index of cross-frequency coupling under δ-θ rhythm combination was statistically significant in the MCI group compared with the normal control group ( P = 0.025, d = 0.398). To further validate this coupling feature, this paper proposed an optimized convolutional neural network model that incorporated a time-frequency data enhancement module and batch normalization layers to prevent overfitting while enhancing the robustness of the model. Based on this optimized model, with the phase locking value matrix of δ-θ rhythm combination as the single input feature, the diagnostic accuracy of MCI patients was (95.49 ± 4.15)%, sensitivity and specificity were (93.71 ± 7.21)% and (97.50 ± 5.34)%, respectively. The results showed that the characteristics of the phase locking value matrix under the combination of δ-θ rhythms can adequately reflect the cognitive status of MCI patients, which is helpful to assist the diagnosis of MCI.

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodents has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguishes control types at the go cue and target acquisition, respectively, while M1 best predicts target-direction at both task events. We also find effective connectivity from DLPFC → M1 throughout both control types and Cd → M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

Macaque monkeys and humans sample temporal regularities in the acoustic environment

Many animal species show comparable abilities to detect basic rhythms and produce rhythmic behavior. Yet, the capacities to process complex rhythms and synchronize rhythmic behavior appear to be species-specific: vocal learning animals can, but some primates might not. This discrepancy is of high interest as there is a putative link between rhythm processing and the development of sophisticated sensorimotor behavior in humans. Do our closest ancestors show comparable endogenous dispositions to sample the acoustic environment in the absence of task instructions and training? We recorded EEG from macaque monkeys and humans while they passively listened to isochronous equitone sequences. Individual- and trial-level analyses showed that macaque monkeys' and humans' delta-band neural oscillations encoded and tracked the timing of auditory events. Further, mu- (8-15 Hz) and beta-band (12-20 Hz) oscillations revealed the superimposition of varied accentuation patterns on a subset of trials. These observations suggest convergence in the encoding and dynamic attending of temporal regularities in the acoustic environment, bridging a gap in the phylogenesis of rhythm cognition.

On relaxation times of heteroclinic dynamics

Heteroclinic dynamics provide a suitable framework for describing transient dynamics such as cognitive processes in the brain. It is appreciated for being well reproducible and at the same time highly sensitive to external input. It is supposed to capture features of switching statistics between metastable states in the brain. Beyond the high sensitivity, a further desirable feature of these dynamics is to enable a fast adaptation to new external input. In view of this, we analyze relaxation times of heteroclinic motion toward a new resting state, when oscillations in heteroclinic networks are arrested by a quench of a bifurcation parameter from a parameter regime of oscillations to a regime of equilibrium states. As it turns out, the relaxation is underdamped and depends on the nesting of the attractor space, the size of the attractor's basin of attraction, the depth of the quench, and the level of noise. In the case of coupled heteroclinic units, it depends on the coupling strength, the coupling type, and synchronization between different units. Depending on how these factors are combined, finite relaxation times may support or impede a fast switching to new external input. Our results also shed some light on the discussion of how the stability of a system changes with its complexity.

Characterizing endogenous delta oscillations in human MEG

Rhythmic activity in the delta frequency range (0.5-3 Hz) is a prominent feature of brain dynamics. Here, we examined whether spontaneous delta oscillations, as found in invasive recordings in awake animals, can be observed in non-invasive recordings performed in humans with magnetoencephalography (MEG). In humans, delta activity is commonly reported when processing rhythmic sensory inputs, with direct relationships to behaviour. However, rhythmic brain dynamics observed during rhythmic sensory stimulation cannot be interpreted as an endogenous oscillation. To test for endogenous delta oscillations we analysed human MEG data during rest. For comparison, we additionally analysed two conditions in which participants engaged in spontaneous finger tapping and silent counting, arguing that internally rhythmic behaviours could incite an otherwise silent neural oscillator. A novel set of analysis steps allowed us to show narrow spectral peaks in the delta frequency range in rest, and during overt and covert rhythmic activity. Additional analyses in the time domain revealed that only the resting state condition warranted an interpretation of these peaks as endogenously periodic neural dynamics. In sum, this work shows that using advanced signal processing techniques, it is possible to observe endogenous delta oscillations in non-invasive recordings of human brain dynamics.

Tensor Decomposition Analysis of Longitudinal EEG Signals Reveals Differential Oscillatory Dynamics in Eyes-Closed and Eyes-Open Motor Imagery BCI: A Case Report

Functional dissociation of brain neural activity induced by opening or closing the eyes has been well established. However, how the temporal dynamics of the underlying neuronal modulations differ between these eye conditions during movement-related behaviours is less known. Using a robotic-assisted motor imagery brain-computer interface (MI BCI), we measured neural activity over the motor regions with electroencephalography (EEG) in a stroke survivor during his longitudinal rehabilitation training. We investigated lateralized oscillatory sensorimotor rhythm modulations while the patient imagined moving his hemiplegic hand with closed and open eyes to control an external robotic splint. In order to precisely identify the main profiles of neural activation affected by MI with eyes-open (MIEO) and eyes-closed (MIEC), a data-driven approach based on parallel factor analysis (PARAFAC) tensor decomposition was employed. Using the proposed framework, a set of narrow-band, subject-specific sensorimotor rhythms was identified; each of them had its own spatial and time signature. When MIEC trials were compared with MIEO trials, three key narrow-band rhythms whose peak frequencies centred at ∼8.0 Hz, ∼11.5 Hz, and ∼15.5 Hz, were identified with differently modulated oscillatory dynamics during movement preparation, initiation, and completion time frames. Furthermore, we observed that lower and higher sensorimotor oscillations represent different functional mechanisms within the MI paradigm, reinforcing the hypothesis that rhythmic activity in the human sensorimotor system is dissociated. Leveraging PARAFAC, this study achieves remarkable precision in estimating latent sensorimotor neural substrates, aiding the investigation of the specific functional mechanisms involved in the MI process.

Striatal cholinergic interneuron membrane voltage tracks locomotor rhythms in mice

Rhythmic neural network activity has been broadly linked to behavior. However, it is unclear how membrane potentials of individual neurons track behavioral rhythms, even though many neurons exhibit pace-making properties in isolated brain circuits. To examine whether single-cell voltage rhythmicity is coupled to behavioral rhythms, we focused on delta-frequencies (1-4 Hz) that are known to occur at both the neural network and behavioral levels. We performed membrane voltage imaging of individual striatal neurons simultaneously with network-level local field potential recordings in mice during voluntary movement. We report sustained delta oscillations in the membrane potentials of many striatal neurons, particularly cholinergic interneurons, which organize spikes and network oscillations at beta-frequencies (20-40 Hz) associated with locomotion. Furthermore, the delta-frequency patterned cellular dynamics are coupled to animals' stepping cycles. Thus, delta-rhythmic cellular dynamics in cholinergic interneurons, known for their autonomous pace-making capabilities, play an important role in regulating network rhythmicity and movement patterning.

Evaluation of abstract rule-based associations in the human premotor cortex during passive observation

Decision-making often manifests in behavior, typically yielding overt motor actions. This complex process requires the registration of sensory information with one's internal representation of the current context, before a categorical judgment of the most appropriate motor behavior can be issued. The construct concept of embodied decision-making encapsulates this sequence of complex processes, whereby behaviorally salient information from the environment is represented in an abstracted space of potential motor actions rather than only in an abstract cognitive "decision" space. Theoretical foundations and some empirical evidence account for support the involvement of premotor cortical circuits in embodied cognitive functions. Animal models show that premotor circuits participate in the registration and evaluation of actions performed by peers in social situations, that is, prior to controlling one's voluntary movements guided by arbitrary stimulus-response rules. However, such evidence from human data is currently limited. Here we used time-resolved magnetoencephalography imaging to characterize activations of the premotor cortex as human participants observed arbitrary, non-biological visual stimuli that either respected or violated a simple stimulus-response association rule. The participants had learned this rule previously, either actively, by performing a motor task (active learning), or passively, by observing a computer perform the same task (passive learning). We discovered that the human premotor cortex is activated during the passive observation of the correct execution of a sequence of events according to a rule learned previously. Premotor activation also differs when the subjects observe incorrect stimulus sequences. These premotor effects are present even when the observed events are of a non-motor, abstract nature, and even when the stimulus-response association rule was learned via passive observations of a computer agent performing the task, without requiring overt motor actions from the human participant. We found evidence of these phenomena by tracking cortical beta-band signaling in temporal alignment with the observation of task events and behavior. We conclude that premotor cortical circuits that are typically engaged during voluntary motor behavior are also involved in the interpretation of events of a non-ecological, unfamiliar nature but related to a learned abstract rule. As such, the present study provides the first evidence of neurophysiological processes of embodied decision-making in human premotor circuits when the observed events do not involve motor actions of a third party.

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodent BMI has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguish between control types at the go cue and target acquisition, respectively. We also found effective connectivity from DLPFC→M1 throughout trials across both control types and Cd→M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

Rhythmic auditory stimulation as a potential neuromodulator for Parkinson's disease

Auditory rhythm-based therapeutic interventions such as rhythmic auditory stimulation (RAS) are effective in improving gait and balance and preventing falls in idiopathic Parkinson's disease (PD). Research showing associated neuromodulatory effects of RAS on brain oscillations is also emerging. The neuromodulation may be induced by neural entrainment and cross-frequency oscillatory coupling. Auditory rhythm and RAS based interventions are potentially effective in improving other PD symptoms and can be extended to atypical parkinsonism.

A dissociable functional relevance of theta- and beta-band activities during complex sensorimotor integration

Sensorimotor integration processes play a central role in daily life and require that different sources of sensory information become integrated: i.e. the information related to the object being under control of the agent (i.e. indicator) and the information about the goal of acting. Yet, how this is accomplished on a neurophysiological level is contentious. We focus on the role of theta- and beta-band activities and examine which neuroanatomical structures are involved. Healthy participants (n = 41) performed 3 consecutive pursuit-tracking EEG experiments in which the source of visual information available for tracking was varied (i.e. that of the indicator and the goal of acting). The initial specification of indicator dynamics is determined through beta-band activity in parietal cortices. When information about the goal was not accessible, but operating the indicator was required nevertheless, this incurred increased theta-band activity in the superior frontal cortex, signaling a higher need for control. Later, theta- and beta-band activities encode distinct information within the ventral processing stream: Theta-band activity is affected by the indicator information, while beta-band activity is affected by the information about the action goal. Complex sensorimotor integration is realized through a cascade of theta- and beta-band activities in a ventral-stream-parieto-frontal network.

How the conception of control influences our understanding of actions

Wilful movement requires neural control. Commonly, neural computations are thought to generate motor commands that bring the musculoskeletal system - that is, the plant - from its current physical state into a desired physical state. The current state can be estimated from past motor commands and from sensory information. Modelling movement on the basis of this concept of plant control strives to explain behaviour by identifying the computational principles for control signals that can reproduce the observed features of movements. From an alternative perspective, movements emerge in a dynamically coupled agent-environment system from the pursuit of subjective perceptual goals. Modelling movement on the basis of this concept of perceptual control aims to identify the controlled percepts and their coupling rules that can give rise to the observed characteristics of behaviour. In this Perspective, we discuss a broad spectrum of approaches to modelling human motor control and their notions of control signals, internal models, handling of sensory feedback delays and learning. We focus on the influence that the plant control and the perceptual control perspective may have on decisions when modelling empirical data, which may in turn shape our understanding of actions.

Voluntary self-initiation of the stimuli onset improves working memory and accelerates visual and attentional processing

The ability of an organism to voluntarily control the stimuli onset modulates perceptual and attentional functions. Since stimulus encoding is an essential component of working memory (WM), we conjectured that controlling the initiation of the perceptual process would positively modulate WM. To corroborate this proposition, we tested twenty-five healthy subjects in a modified-Sternberg WM task under three stimuli presentation conditions: an automatic presentation of the stimuli, a self-initiated presentation of the stimuli (through a button press), and a self-initiated presentation with random-delay stimuli onset. Concurrently, we recorded the subjects' electroencephalographic signals during WM encoding. We found that the self-initiated condition was associated with better WM accuracy, and earlier latencies of N1, P2 and P3 evoked potential components representing visual, attentional and mental review of the stimuli processes, respectively. Our work demonstrates that self-initiated stimuli enhance WM performance and accelerate early visual and attentional processes deployed during WM encoding. We also found that self-initiated stimuli correlate with an increased attentional state compared to the other two conditions, suggesting a role for temporal stimuli predictability. Our study remarks on the relevance of self-control of the stimuli onset in sensory, attentional and memory updating processing for WM.

The Effects of Different Theta and Beta Neurofeedback Training Protocols on Cognitive Control in ADHD

Neurofeedback (NF) is an important treatment for attention deficit/hyperactivity disorder (ADHD). In ADHD, cognitive control deficits pose considerable problems to patients. However, NF protocols are not yet optimized to enhance cognitive control alongside with clinical symptoms, partly because they are not driven by basic cognitive neuroscience. In this study, we evaluated different EEG theta and/or beta frequency band NF protocols designed to enhance cognitive control. Participants were n = 157 children and adolescents, n = 129 of them were patients with ADHD (n = 28 typically developing (TD) controls). Patients with ADHD were divided into five groups in the order of referral, with four of them taking part in different NF protocols systematically varying theta and beta power. The fifth ADHD group and the TD group did not undergo NF. All NF protocols resulted in reductions of ADHD symptoms. Importantly, only when beta frequencies were enhanced during NF (without any theta regulation or in combination with theta upregulation), consistent enhancing effects in both response inhibition and conflict control were achieved. The theta/beta NF protocol most widely used in clinical settings revealed comparatively limited effects. Enhancements in beta band activity are key when aiming to improve cognitive control functions in ADHD. This calls for a change in the use of theta/beta NF protocols and shows that protocols differing from the current clinical standard are effective in enhancing important facets of cognitive control in ADHD. Further studies need to examine regulation data within the neurofeedback sessions to provide more information about the mechanisms underlying the observed effects.

Universal Lifespan Trajectories of Source-Space Information Flow Extracted from Resting-State MEG Data

Source activity was extracted from resting-state magnetoencephalography data of 103 subjects aged 18-60 years. The directionality of information flow was computed from the regional time courses using delay symbolic transfer entropy and phase entropy. The analysis yielded a dynamic source connectivity profile, disentangling the direction, strength, and time delay of the underlying causal interactions, producing independent time delays for cross-frequency amplitude-to-amplitude and phase-to-phase coupling. The computation of the dominant intrinsic coupling mode (DoCM) allowed me to estimate the probability distribution of the DoCM independently of phase and amplitude. The results support earlier observations of a posterior-to-anterior information flow for phase dynamics in {α1, α2, β, γ} and an opposite flow (anterior to posterior) in θ. Amplitude dynamics reveal posterior-to-anterior information flow in {α1, α2, γ}, a sensory-motor β-oriented pattern, and an anterior-to-posterior pattern in {δ, θ}. The DoCM between intra- and cross-frequency couplings (CFC) are reported here for the first time and independently for amplitude and phase; in both domains {δ, θ, α1}, frequencies are the main contributors to DoCM. Finally, a novel brain age index (BAI) is introduced, defined as the ratio of the probability distribution of inter- over intra-frequency couplings. This ratio shows a universal age trajectory: a rapid rise from the end of adolescence, reaching a peak in adulthood, and declining slowly thereafter. The universal pattern is seen in the BAI of each frequency studied and for both amplitude and phase domains. No such universal age dependence was previously reported.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

The phase of sensorimotor mu and beta oscillations has the opposite effect on corticospinal excitability

BACKGROUND

Neural oscillations in the primary motor cortex (M1) shape corticospinal excitability. Power and phase of ongoing mu (8-13 Hz) and beta (14-30 Hz) activity may mediate motor cortical output. However, the functional dynamics of both mu and beta phase and power relationships and their interaction, are largely unknown.

OBJECTIVE

Here, we employ recently developed real-time targeting of the mu and beta rhythm, to apply phase-specific brain stimulation and probe motor corticospinal excitability non-invasively. For this, we used instantaneous read-out and analysis of ongoing oscillations, targeting four different phases (0°, 90°, 180°, and 270°) of mu and beta rhythms with suprathreshold single-pulse transcranial magnetic stimulation (TMS) to M1. Ensuing motor evoked potentials (MEPs) in the right first dorsal interossei muscle were recorded. Twenty healthy adults took part in this double-blind randomized crossover study.

RESULTS

Mixed model regression analyses showed significant phase-dependent modulation of corticospinal output by both mu and beta rhythm. Strikingly, these modulations exhibit a double dissociation. MEPs are larger at the mu trough and rising phase and smaller at the peak and falling phase. For the beta rhythm we found the opposite behavior. Also, mu power, but not beta power, was positively correlated with corticospinal output. Power and phase effects did not interact for either rhythm, suggesting independence between these aspects of oscillations.

CONCLUSION

Our results provide insights into real-time motor cortical oscillation dynamics, which offers the opportunity to improve the effectiveness of TMS by specifically targeting different frequency bands.

Processing of embedded response plans is modulated by an interplay of fronto-parietal theta and beta activity

Even simple actions like opening a door require integration/binding and flexible re-activation of different motor elements. Yet, the neural mechanisms underlying the processing of such 'embedded response plans' are largely elusive, despite theoretical frameworks, such as the Theory of Event Coding, describing the involved cognitive processes. In a sample of N = 40 healthy participants we combine time-frequency decomposition and various beamforming methods to examine neurophysiological dynamics of such action plans - with special emphasis on the interplay of theta and beta frequency activity during the processing of these plans. We show that the integration and rule-guided reactivation of embedded response plans is modulated by a complex interplay of theta and beta activity. Pre-trial BBA is related to different functional neuroanatomical structures which are activated in a consecutive fashion. Enhanced preparatory activity is positively associated with higher binding-related BBA in the precuneus/parietal areas, indicating that activity in the precuneus/parietal cortex facilitates the execution of an embedded action sequence. Increased preparation subsequently leads to reduced working memory retrieval demands. A cascading pattern of interactions between pre-trial and within-trial activity indicates the importance of preparatory brain activity. The study shows that there are multiple roles of beta and theta oscillations associated with different functional neuroanatomical structures during the integration and reactivation of motor elements during actions.

Time Perception for Musical Rhythms: Sensorimotor Perspectives on Entrainment, Simulation, and Prediction

Neural mechanisms supporting time perception in continuously changing sensory environments may be relevant to a broader understanding of how the human brain utilizes time in cognition and action. In this review, we describe current theories of sensorimotor engagement in the support of subsecond timing. We focus on musical timing due to the extensive literature surrounding movement with and perception of musical rhythms. First, we define commonly used but ambiguous concepts including neural entrainment, simulation, and prediction in the context of musical timing. Next, we summarize the literature on sensorimotor timing during perception and performance and describe current theories of sensorimotor engagement in the support of subsecond timing. We review the evidence supporting that sensorimotor engagement is critical in accurate time perception. Finally, potential clinical implications for a sensorimotor perspective of timing are highlighted.

Mutual beta power modulation in dyadic entrainment

Across a broad spectrum of interactions, humans exhibit a prominent tendency to synchronize their movements with one another. Traditionally, this phenomenon has been explained from the perspectives of predictive coding or dynamical systems theory. While these theories diverge with respect to whether individuals hold internal models of each other, they both assume a predictive or anticipatory mechanism enabling rhythmic interactions. However, the neural bases underpinning interpersonal synchronization are still a subject under active investigation. Here we provide evidence that the brain relies on a common oscillatory mechanism to pace self-generated rhythmic movements and to track the movements produced by a partner. By performing dual-electroencephalography recordings during a joint finger-tapping task, we identified an oscillatory component in the beta range (∼ 20 Hz), which was significantly modulated by both self-generated and other-generated movement. In conditions where the partners perceived each other, we observed periodic fluctuations of beta power as a function of the reciprocal movement cycles. Crucially, this modulation occurred both in visually and in auditorily coupled conditions, and was accompanied by recurrent periods of dyadic synchronized behavior. Our results show that periodic beta power modulations may be a critical mechanism underlying interpersonal synchronization, possibly enabling mutual predictions between coupled individuals, leading to co-regulation of timing and overt mutual adaptation. Our findings thus provide a potential bridge between influential theories attempting to explain interpersonal coordination, and a concrete connection to its neurophysiological bases.

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.

Dynamic corticomuscular multi-regional modulations during finger movement revealed by time-varying network analysis

OBJECTIVE

A body movement involves the complicated information exchange between the central and peripheral systems, which is characterized by the dynamical coupling patterns between the multiple brain areas and multiple muscle units. How the central and peripheral nerves coordinate multiple internal brain regions and muscle groups is very important when accomplishing the action.

APPROACH

In this study, we extend the adaptive directed transfer function to construct the time-varying networks between multiple corticomuscular regions and divide the movement duration into different stages by the time-varying corticomuscular network patterns.

MAIN RESULTS

The inter dynamical corticomuscular network demonstrated the different interaction patterns between the central and peripheral systems during the different hand movement stages. The muscles transmit bottom-up movement information in the preparation stage, but the brain issues top-down control commands and dominates in the execution stage, and finally, the brain's dominant advantage gradually weakens in the relaxation stage. When classifying the different movement stages based on time-varying corticomuscular network indicators, an average accuracy above 74% could be reliably achieved.

SIGNIFICANCE

The findings of this study help deepen our knowledge of central-peripheral nerve pathways and coordination mechanisms, and also provide opportunities for monitoring and regulating movement disorders.

New methods for oscillation analyses push new theories of discrete cognition

Classical ways of analyzing neural time series data has led to static views on cognition, in which the cognitive processes are linked to sustained neural activity and interpreted as stationary states. The core analytical focus was on slow power modulations of neural oscillations averaged across many experimental trials. Whereas this custom analytical approach reduces the complexity and increases the signal-to-noise ratio, it may disregard or even remove important aspects of the underlying neural dynamics. Novel analysis methods investigate the instantaneous frequency and phase of neural oscillations and relate them to the precisely controlled timing of brief successive sensory stimuli. This enables to capture how cognitive processes unfold in discrete windows within and across oscillatory cycles. Moreover, several recent studies analyze the oscillatory power modulations on single experimental trials. They suggest that the power modulations are packed into discrete bursts of activity, which occur at different rates and times, and with different durations from trial-to-trial. Here, we review the current work that made use of these methodological advances for neural oscillations. These novel analysis perspectives emphasize that cognitive processes occur in discrete time windows, instead of sustained, stationary states. Evidence for discretization was observed for the entire range of cognitive functions from perception and attention to working memory, goal-directed thought and motor actions, as well as throughout the entire cortical hierarchy and in subcortical regions. These empirical observations create demand for new psychological theories and computational models of cognition in the brain, which integrate its discrete temporal dynamics.

Lower resting brain entropy is associated with stronger task activation and deactivation

Brain entropy (BEN) calculated from resting state fMRI has been the subject of increasing research interest in recent years. Previous studies have shown the correlations between rest BEN and neurocognition and task performance, but how this relates to task-evoked brain activations and deactivations remains unknown. The purpose of this study is to address this open question using large data (n = 862). Voxel wise correlations were calculated between rest BEN and task activations/deactivations of five different tasks. For most of the assessed tasks, lower rest BEN was found to be associated with stronger activations (negative correlations) and stronger deactivations (positive correlations) only in brain regions activated or deactivated by the tasks. Higher workload evoked spatially more extended negative correlations between rest BEN and task activations. These results not only confirm that resting brain activity can predict brain activity during task performance but also for the first time show that resting brain activity may facilitate both task activations and deactivations. In addition, the results provide a clue to understanding the individual differences of task performance and brain activations.

The sound of silence: Predictive error responses to unexpected sound omission in adults

The human auditory system excels at detecting patterns needed for processing speech and music. According to predictive coding, the brain predicts incoming sounds, compares predictions to sensory input, and generates a prediction error whenever a mismatch between the prediction and sensory input occurs. Predictive coding can be indexed in EEG with the mismatch negativity (MMN) and P3a components, two ERP components that are elicited by infrequent deviant sounds (e.g., differing in pitch, duration, loudness) in a stream of frequent sounds. If these components reflect prediction error, they should also be elicited by omitting an expected sound, but few studies have examined this. We compared ERPs elicited by infrequent randomly occurring omissions (unexpected silences) in tone sequences presented at 2 tones/sec to ERPs elicited by frequent, regularly occurring omissions (expected silences) within a sequence of tones and resting state EEG (a constant silence). We found that unexpected silences elicited significant MMN and P3a, although the magnitude of these components was quite small and variable. These results provide evidence for hierarchical predictive coding, indicating that the brain predicts silences as well as sounds.