The rate of transient beta frequency events predicts behavior across tasks and species

Short-delay neurofeedback facilitates training of the parietal alpha rhythm

OBJECTIVE

Feedback latency was shown to be a critical parameter in a range of applications that imply learning. The therapeutic effects of neurofeedback (NFB) remain controversial. We hypothesized that often encountered unreliable results of NFB intervention could be associated with large feedback latency values that are often uncontrolled and may preclude the efficient learning.

APPROACH

We engaged our subjects into a parietal alpha power unpregulating paradigm faciliated by visual neurofeedback based on the invidually extracted envelope of the alpha-rhythm at P4 electrode. NFB was displayed either as soon as EEG envelope was processed, or with an extra 250 or 500-ms delay. The feedback training consisted of 15 two-minute long blocks interleaved with 15s pauses. We have also recorded two minute long baselines immediately before and after the training.

MAIN RESULTS

The time course of NFB-induced changes in the alpha rhythm power clearly depended on NFB latency, as shown with the adaptive Neyman test. NFB had a strong effect on the alpha-spindle incidence rate, but not on their duration or amplitude. The sustained changes in alpha activity measured after the completion of NFB training were negatively correlated to latency, with the maximum change for the shortest tested latency and no change for the longest.

SIGNIFICANCE

Here we for the first time show that visual NFB of parietal electroencephalographic (EEG) alpha-activity is efficient only when delivered to human subjects at short latency, which guarantees that NFB arrives when an alpha spindle is still ongoing. Such a considerable effect of NFB latency on the alpha-activity temporal structure could explain some of the previous inconsistent results, where latency was neither controlled nor documented. Clinical practitioners and manufacturers of NFB equipment should add latency to their specifications while enabling latency monitoring and supporting short-latency operations.

Dissociation of Medial Frontal β-Bursts and Executive Control

The neural mechanisms of executive and motor control concern both basic researchers and clinicians. In human studies, preparation and cancellation of movements are accompanied by changes in the β-frequency band (15-29 Hz) of electroencephalogram (EEG). Previous studies with human participants performing stop signal (countermanding) tasks have described reduced frequency of transient β-bursts over sensorimotor cortical areas before movement initiation and increased β-bursting over medial frontal areas with movement cancellation. This modulation has been interpreted as contributing to the trial-by-trial control of behavior. We performed identical analyses of EEG recorded over the frontal lobe of macaque monkeys (one male, one female) performing a saccade countermanding task. While we replicate the occurrence and modulation of β-bursts associated with initiation and cancellation of saccades, we found that β-bursts occur too infrequently to account for the observed stopping behavior. We also found β-bursts were more common after errors, but their incidence was unrelated to response time (RT) adaptation. These results demonstrate the homology of this EEG signature between humans and macaques but raise questions about the current interpretation of β band functional significance.SIGNIFICANCE STATEMENT The finding of increased β-bursting over medial frontal cortex with movement cancellation in humans is difficult to reconcile with the finding of modulation too late to contribute to movement cancellation in medial frontal cortex of macaque monkeys. To obtain comparable measurement scales, we recorded electroencephalogram (EEG) over medial frontal cortex of macaques performing a stop signal (countermanding) task. We replicated the occurrence and modulation of β-bursts associated with the cancellation of movements, but we found that β-bursts occur too infrequently to account for observed stopping behavior. Unfortunately, this finding raises doubts whether β-bursts can be a causal mechanism of response inhibition, which impacts future applications in devices such as brain-machine interfaces.

Parkinson's disease uncovers an underlying sensitivity of subthalamic nucleus neurons to beta-frequency cortical input in vivo

Abnormally sustained beta-frequency synchronisation between the motor cortex and subthalamic nucleus (STN) is associated with motor symptoms in Parkinson's disease (PD). It is currently unclear whether STN neurons have a preference for beta-frequency input (12-35 Hz), rather than cortical input at other frequencies, and how such a preference would arise following dopamine depletion. To address this question, we combined analysis of cortical and STN recordings from awake human PD patients undergoing deep brain stimulation surgery with recordings of identified STN neurons in anaesthetised rats. In these patients, we demonstrate that a subset of putative STN neurons is strongly and selectively sensitive to magnitude fluctuations of cortical beta oscillations over time, linearly increasing their phase-locking strength with respect to the full range of instantaneous amplitude in the beta-frequency range. In rats, we probed the frequency response of STN neurons in the cortico-basal-ganglia-network more precisely, by recording spikes evoked by short bursts of cortical stimulation with variable frequency (4-40 Hz) and constant amplitude. In both healthy and dopamine-depleted rats, only beta-frequency stimulation led to a progressive reduction in the variability of spike timing through the stimulation train. This suggests, that the interval of beta-frequency input provides an optimal window for eliciting the next spike with high fidelity. We hypothesize, that abnormal activation of the indirect pathway, via dopamine depletion and/or cortical stimulation, could trigger an underlying sensitivity of the STN microcircuit to beta-frequency input.

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STN-neurons are selectively entrained to cortical beta oscillations in PD patients.

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Phase-locking of STN-neurons is linearly dependent on oscillation magnitude.

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Beta bursts in LFP/EEG are accompanied by transient synchronisation of STN spiking.

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STN neurons are selectively entrained to cortical beta stimulation in rats.

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Beta-selectivity of STN neurons is present in control and dopamine-depleted rats.

Beta Rebound as an Index of Temporal Integration of Somatosensory and Motor Signals

Modulation of cortical beta rhythm (15–30 Hz) is present during preparation for and execution of voluntary movements as well as during somatosensory stimulation. A rebound in beta synchronization is observed after the end of voluntary movements as well as after somatosensory stimulation and is believed to describe the return to baseline of sensorimotor networks. However, the contribution of efferent and afferent signals to the beta rebound remains poorly understood. Here, we applied electrical median nerve stimulation (MNS) to the right side followed by transcranial magnetic stimulation (TMS) on the left primary motor cortex after either 15 or 25 ms. Because the afferent volley reaches the somatosensory cortex after about 20 ms, TMS on the motor cortex was either anticipating or following the cortical arrival of the peripheral stimulus. We show modulations in different beta sub-bands and in both hemispheres, following a pattern of greater resynchronization when motor signals are paired with a peripheral one. The beta rebound in the left hemisphere (stimulated) is modulated in its lower frequency range when TMS precedes the cortical arrival of the afferent volley. In the right hemisphere (unstimulated), instead, the increase is limited to higher beta frequencies when TMS is delivered after the arrival of the afferent signal. In general, we demonstrate that the temporal integration of afferent and efferent signals plays a key role in the genesis of the beta rebound and that these signals may be carried in parallel by different beta sub-bands.

Phase of firing coding of learning variables across the fronto-striatal network during feature-based learning

The prefrontal cortex and striatum form a recurrent network whose spiking activity encodes multiple types of learning-relevant information. This spike-encoded information is evident in average firing rates, but finer temporal coding might allow multiplexing and enhanced readout across the connected network. We tested this hypothesis in the fronto-striatal network of nonhuman primates during reversal learning of feature values. We found that populations of neurons encoding choice outcomes, outcome prediction errors, and outcome history in their firing rates also carry significant information in their phase-of-firing at a 10–25 Hz band-limited beta frequency at which they synchronize across lateral prefrontal cortex, anterior cingulate cortex and anterior striatum when outcomes were processed. The phase-of-firing code exceeds information that can be obtained from firing rates alone and is evident for inter-areal connections between anterior cingulate cortex, lateral prefrontal cortex and anterior striatum. For the majority of connections, the phase-of-firing information gain is maximal at phases of the beta cycle that were offset from the preferred spiking phase of neurons. Taken together, these findings document enhanced information of three important learning variables at specific phases of firing in the beta cycle at an inter-areally shared beta oscillation frequency during goal-directed behavior.

The average spiking frequency in the fronto-striatal network encodes multiple types of learning-relevant information. Here, the authors show that populations of neurons in non-human primates also carry significant information in their phase-of-firing when learning-relevant outcomes are processed.

Oscillatory Bursting as a Mechanism for Temporal Coupling and Information Coding

Even the simplest cognitive processes involve interactions between cortical regions. To study these processes, we usually rely on averaging across several repetitions of a task or across long segments of data to reach a statistically valid conclusion. Neuronal oscillations reflect synchronized excitability fluctuations in ensembles of neurons and can be observed in electrophysiological recordings in the presence or absence of an external stimulus. Oscillatory brain activity has been viewed as sustained increase in power at specific frequency bands. However, this perspective has been challenged in recent years by the notion that oscillations may occur as transient burst-like events that occur in individual trials and may only appear as sustained activity when multiple trials are averaged together. In this review, we examine the idea that oscillatory activity can manifest as a transient burst as well as a sustained increase in power. We discuss the technical challenges involved in the detection and characterization of transient events at the single trial level, the mechanisms that might generate them and the features that can be extracted from these events to study single-trial dynamics of neuronal ensemble activity.

Pain phenotypes classified by machine learning using electroencephalography features

Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.

Temporal codes of visual working memory in the human cerebral cortex: Brain rhythms associated with high memory capacity

Visual working memory (vWM) is an important ability required for various cognitive tasks although its neural underpinnings remain unclear. While many studies have focused on theta (4-7 Hz) and gamma (> 30 Hz) rhythms as a substrate of vWM, here we show that temporal signals embedded in alpha (8-12 Hz) and beta (13-30 Hz) bands can be a good predictor of vWM capacity. Neural activity of healthy human participants was recorded with magnetoencephalography when they performed a classical vWM task (change detection). We analyzed changes in inter-peak intervals (IPIs) of oscillatory signals along with an increase in WM load (a number of to-be-memorized items, 1-6). Results showed a load-dependent reduction of IPIs in the parietal and frontal regions, indicating that alpha/beta rhythms became faster when multiple items were stored in vWM. Furthermore, this reduction in IPIs was positively correlated with individual vWM capacity, especially in the frontal cortex. Those results indicate that vWM is represented as a change in oscillation frequency in the human cerebral cortex.

Dissecting Transient Burst Events

Increasing efforts are being made to understand the role of intermittent, transient, high-power burst events of neural activity. These events have a temporal, spectral, and spatial domain. Here, we argue that considering all three domains is crucial to fully reveal the functional relevance of these events in health and disease.

Age-related trends in neuromagnetic transient beta burst characteristics during a sensorimotor task and rest in the Cam-CAN open-access dataset

Non-invasive neurophysiological recordings, such as those measured by magnetoencelography (MEG), provide insight into the behaviour of neural networks and how these networks change with factors such as task performance, disease state, and age. Recently, there has been a trend in describing neurophysiological recordings as a series of transient bursts of neural activity rather than averaged sustained oscillations as burst characteristics may be more directly correlated with the neurological generators of brain activity. In this work, we investigate how beta burst characteristics change with age in a large open access dataset. The objectives are (1) to detect and characterize transient beta bursts over the ipsilateral and contralateral primary sensorimotor cortices during a unilateral motor task performance and during wakeful resting, and (2) to identify age-related changes in beta burst characteristics, in the context of earlier reports of age-related changes in beta suppression and the post-movement beta rebound. MEG data, acquired at the Cambridge Centre for Ageing and Neuroscience, of roughly 600 participants with a nearly uniform distribution of ages between 18 and 88 years old was used for analysis. We found that burst rate is the predominant factor related to age-related changes in the amplitude of the induced beta rhythm responses associated with a button press task. Furthermore, we present a cross-validation of burst parameters detected at the sensor- (peak sensor and sensor ROI) and source-level (beamformer spatial filter). This work is as an important step in characterizing transient bursts in neuromagnetic signals in the temporal domain, towards a better understanding of the healthy aging human brain.

Interactions Between Motor Thalamic Field Potentials and Single-Unit Spiking Are Correlated With Behavior in Rats

Field potential (FP) oscillations are believed to coordinate brain activity over large spatiotemporal scales, with specific features (e.g., phase and power) in discrete frequency bands correlated with motor output. Furthermore, complex correlations between oscillations in distinct frequency bands (phase-amplitude, amplitude-amplitude, and phase-phase coupling) are commonly observed. However, the mechanisms underlying FP-behavior correlations and cross-frequency coupling remain unknown. The thalamus plays a central role in generating many circuit-level neural oscillations, and single-unit activity in motor thalamus (Mthal) is correlated with behavioral output. We, therefore, hypothesized that motor thalamic spiking coordinates motor system FPs and underlies FP-behavior correlations. To investigate this possibility, we recorded wideband motor thalamic (Mthal) electrophysiology as healthy rats performed a two-alternative forced-choice task. Delta (1–4 Hz), beta (13–30 Hz), low gamma (30–70 Hz), and high gamma (70–200 Hz) power were strongly modulated by task performance. As in the cortex, the delta phase was correlated with beta/low gamma power and reaction time. Most interestingly, subpopulations of Mthal neurons defined by their relationship to the behavior exhibited distinct relationships with FP features. Specifically, neurons whose activity was correlated with action selection and movement speed were entrained to delta oscillations. Furthermore, changes in their activity anticipated power fluctuations in beta/low gamma bands. These complex relationships suggest mechanisms for commonly observed FP-FP and spike-FP correlations, as well as subcortical influences on motor output.

Temporally-precise disruption of prefrontal cortex informed by the timing of beta bursts impairs human action-stopping

Human action-stopping is thought to rely on a prefronto-basal ganglia-thalamocortical network, with right inferior frontal cortex (rIFC) posited to play a critical role in the early stage of implementation. Here we sought causal evidence for this idea in experiments involving healthy human participants. We first show that action-stopping is preceded by bursts of electroencephalographic activity in the beta band over prefrontal electrodes, putatively rIFC, and that the timing of these bursts correlates with the latency of stopping at a single-trial level: earlier bursts are associated with faster stopping. From this we reasoned that the integrity of rIFC at the time of beta bursts might be critical to successful stopping. We then used fMRI-guided transcranial magnetic stimulation (TMS) to disrupt rIFC at the approximate time of beta bursting. Stimulation prolonged stopping latencies and, moreover, the prolongation was most pronounced in individuals for whom the pulse appeared closer to the presumed time of beta bursting. These results help validate a model of the neural architecture and temporal dynamics of action-stopping. They also highlight the usefulness of prefrontal beta bursts to index an apparently important sub-process of stopping, the timing of which might help explain within- and between-individual variation in impulse control.

Oscillatory Bursts in Parietal Cortex Reflect Dynamic Attention between Multiple Objects and Ensembles

The visual system uses two complimentary strategies to process multiple objects simultaneously within a scene and update their spatial positions in real time. It either uses selective attention to individuate a complex, dynamic scene into a few focal objects (i.e., object individuation), or it represents multiple objects as an ensemble by distributing attention more globally across the scene (i.e., ensemble grouping). Neural oscillations may be a key signature for focal object individuation versus distributed ensemble grouping, because they are thought to regulate neural excitability over visual areas through inhibitory control mechanisms. We recorded whole-head MEG data during a multiple-object tracking paradigm, in which human participants (13 female, 11 male) switched between different instructions for object individuation and ensemble grouping on different trials. The stimuli, responses, and the demand to keep track of multiple spatial locations over time were held constant between the two conditions. We observed increased α-band power (9-13 Hz) packed into oscillatory bursts in bilateral inferior parietal cortex during multiple-object processing. Single-trial analysis revealed greater burst occurrences on object individuation versus ensemble grouping trials. By contrast, we found no differences using standard analyses on across-trials averaged α-band power. Moreover, the bursting effects occurred only below/at, but not above, the typical capacity limits for multiple-object processing (at ∼4 objects). Our findings reveal the real-time neural correlates underlying the dynamic processing of multiple-object scenarios, which are modulated by grouping strategies and capacity. They support a rhythmic, α-pulsed organization of dynamic attention to multiple objects and ensembles.SIGNIFICANCE STATEMENT Dynamic multiple-object scenarios are an important problem in real-world and computer vision. They require keeping track of multiple objects as they move through space and time. Such problems can be solved in two ways: One can individuate a scene object by object, or alternatively group objects into ensembles. We observed greater occurrences of α-oscillatory burst events in parietal cortex for processing objects versus ensembles and below/at versus above processing capacity. These results demonstrate a unique top-down mechanism by which the brain dynamically adjusts its computational level between objects and ensembles. They help to explain how the brain copes with its capacity limitations in real-time environments and may lead the way to technological innovations for time-critical video analysis in computer vision.

Digital filters for low-latency quantification of brain rhythms in real time

OBJECTIVE

The rapidly developing paradigm of closed-loop neuroscience has extensively employed brain rhythms as the signal forming real-time neurofeedback, triggering brain stimulation, or governing stimulus selection. However, the efficacy of brain rhythm contingent paradigms suffers from significant delays related to the process of extraction of oscillatory parameters from broad-band neural signals with conventional methods. To this end, real-time algorithms are needed that would shorten the delay while maintaining an acceptable speed-accuracy trade-off.

APPROACH

Here we evaluated a family of techniques based on the application of the least-squares complex-valued filter (LSCF) design to real-time quantification of brain rhythms. These techniques allow for explicit optimization of the speed-accuracy trade-off when quantifying oscillatory patterns. We used EEG data collected from 10 human participants to systematically compare LSCF approach to the other commonly used algorithms. Each method being evaluated was optimized by scanning through the grid of its hyperparameters using independent data samples.

MAIN RESULTS

When applied to the task of estimating oscillatory envelope and phase, the LSCF techniques outperformed in speed and accuracy both conventional Fourier transform and rectification based methods as well as more advanced techniques such as those that exploit autoregressive extrapolation of narrow-band filtered signals. When operating at zero latency, the weighted LSCF approach yielded 75% accuracy when detecting alpha-activity episodes, as defined by the amplitude crossing of the 95th-percentile threshold.

SIGNIFICANCE

The LSCF approaches are easily applicable to low-delay quantification of brain rhythms. As such, these methods are useful in a variety of neurofeedback, brain-computer-interface and other experimental paradigms that require rapid monitoring of brain rhythms.

Waveform changes with the evolution of beta bursts in the human subthalamic nucleus

OBJECTIVE

Phasic bursts of beta band synchronisation have been linked to motor impairment in Parkinson's disease (PD). However, little is known about what terminates bursts.

METHODS

We used the Hilbert-Huang transform to investigate beta bursts in the local field potential recorded from the subthalamic nucleus in nine patients with PD on and off levodopa.

RESULTS

The sharpness of the beta waveform extrema fell as burst amplitude dropped. Conversely, an index of phase slips between waveform extrema, and the power of concurrent theta activity increased as burst amplitude fell. Theta activity was also increased on levodopa when beta bursts were attenuated. These phenomena were associated with reduction in coupling between beta phase and high gamma activity amplitude. We discuss how these findings may suggest that beta burst termination is associated with relative desynchronization of the beta drive, increase in competing theta activity and increased phase slips in the beta activity.

CONCLUSIONS

We characterise the dynamical nature of beta bursts, thereby permitting inferences about underlying activities and, in particular, about why bursts terminate.

SIGNIFICANCE

Understanding the dynamical nature of beta bursts may help point to interventions that can cause their termination and potentially treat motor impairment in PD.

Prefrontal-Subthalamic Hyperdirect Pathway Modulates Movement Inhibition in Humans

The ability to dynamically change motor outputs, such as stopping an initiated response, is an important aspect of human behavior. A hyperdirect pathway between the inferior frontal gyrus and subthalamic nucleus is hypothesized to mediate movement inhibition, but there is limited evidence for this in humans. We recorded high spatial and temporal resolution field potentials from both the inferior frontal gyrus and subthalamic nucleus in 21 subjects. Cortical potentials evoked by subthalamic stimulation revealed short latency events indicative of monosynaptic connectivity between the inferior frontal gyrus and ventral subthalamic nucleus. During a stop signal task, stopping-related potentials in the cortex preceded stopping-related activity in the subthalamic nucleus, and synchronization between these task-evoked potentials predicted the stop signal reaction time. Thus, we show that a prefrontal-subthalamic hyperdirect pathway is present in humans and mediates rapid stopping. These findings may inform therapies to treat disorders featuring perturbed movement inhibition.

Older Age Increases the Amplitude of Muscle Stretch-Induced Cortical Beta-Band Suppression But Does not Affect Rebound Strength

Healthy aging is associated with deterioration of the sensorimotor system, which impairs balance and somatosensation. However, the exact age-related changes in the cortical processing of sensorimotor integration are unclear. This study investigated primary sensorimotor cortex (SM1) oscillations in the 15-30 Hz beta band at rest and following (involuntary) rapid stretches to the triceps surae muscles (i.e., proprioceptive stimulation) of young and older adults. A custom-built, magnetoencephalography (MEG)-compatible device was used to deliver rapid (190°·s^-1) ankle rotations as subjects sat passively in a magnetically-shielded room while MEG recorded their cortical signals. Eleven young (age 25 ± 3 years) and 12 older (age 70 ± 3 years) adults matched for physical activity level demonstrated clear 15-30 Hz beta band suppression and rebound in response to the stretches. A sub-sample (10 young and nine older) were tested for dynamic balance control on a sliding platform. Older adults had greater cortical beta power pre-stretch (e.g., right leg: 4.0 ± 1.6 fT vs. 5.6 ± 1.7 fT, P = 0.044) and, subsequently, greater normalized movement-related cortical beta suppression post-proprioceptive stimulation (e.g., right leg: -5.8 ± 1.3 vs. -7.6 ± 1.7, P = 0.01) than young adults. Furthermore, poorer balance was associated with stronger cortical beta suppression following proprioceptive stimulation (r = -0.478, P = 0.038, n = 19). These results provide further support that cortical processing of proprioception is hindered in older adults, potentially (adversely) influencing sensorimotor integration. This was demonstrated by the impairment of prompt motor action control, i.e., regaining perturbed balance. Finally, SM1 cortex beta suppression to a proprioceptive stimulus seems to indicate poorer sensorimotor functioning in older adults.

Freezing of gait in Parkinson's disease reflects a sudden derangement of locomotor network dynamics

Freezing of gait is a disabling symptom of Parkinson's disease that causes a paroxysmal inability to generate effective stepping. The underlying pathophysiology has recently migrated towards a dysfunctional supraspinal locomotor network, but the actual network derangements during ongoing gait freezing are unknown. We investigated the communication between the cortex and the subthalamic nucleus, two main nodes of the locomotor network, in seven freely-moving subjects with Parkinson's disease with a novel deep brain stimulation device, which allows on-demand recording of subthalamic neural activity from the chronically-implanted electrodes months after the surgical procedure. Multisite neurophysiological recordings during (effective) walking and ongoing gait freezing were combined with kinematic measurements and individual molecular brain imaging studies. Patients walked in a supervised environment closely resembling everyday life challenges. We found that during (effective) walking, the cortex and subthalamic nucleus were synchronized in a low frequency band (4-13 Hz). In contrast, gait freezing was characterized in every patient by low frequency cortical-subthalamic decoupling in the hemisphere with less striatal dopaminergic innervation. Of relevance, this decoupling was already evident at the transition from normal (effective) walking into gait freezing, was maintained during the freezing episode, and resolved with recovery of the effective walking pattern. This is the first evidence for a decoding of the networked processing of locomotion in Parkinson's disease and suggests that freezing of gait is a 'circuitopathy' related to a dysfunctional cortical-subcortical communication. A successful therapeutic approach for gait freezing in Parkinson's disease should aim at directly targeting derangements of neural network dynamics.

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it?

Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via "similarity bounds" biases coarse MSE scales-that are thought to reflect slow dynamics-by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales-presumed to indicate fast dynamics-is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest.

Reduction of spontaneous cortical beta bursts in Parkinson's disease is linked to symptom severity

Parkinson's disease is characterized by a gradual loss of dopaminergic neurons, which is associated with altered neuronal activity in the beta-band (13-30 Hz). Assessing beta-band activity typically involves transforming the time-series to get the power of the signal in the frequency domain. Such transformation assumes that the time-series can be reduced to a combination of steady-state sine- and cosine waves. However, recent studies have suggested that this approach masks relevant biophysical features in the beta-band-for example, that the beta-band exhibits transient bursts of high-amplitude activity. In an exploratory study, we used magnetoencephalography to record beta-band activity from the sensorimotor cortex, to characterize how spontaneous cortical beta bursts manifest in Parkinson's patients on and off dopaminergic medication, and compare this to matched healthy controls. We extracted the time-course of beta-band activity from the sensorimotor cortex and characterized bursts in the signal. We then compared the burst rate, duration, inter-burst interval and peak amplitude between the Parkinson's patients and healthy controls. Our results show that Parkinson's patients off medication had a 5-17% lower beta bursts rate compared to healthy controls, while both the duration and the amplitude of the bursts were the same for healthy controls and medicated state of the Parkinson's patients. These data thus support the view that beta bursts are fundamental underlying features of beta-band activity, and show that changes in cortical beta-band power in Parkinson's disease can be explained-primarily by changes in the underlying burst rate. Importantly, our results also revealed a relationship between beta burst rate and motor symptom severity in Parkinson's disease: a lower burst rate scaled with increased severity of bradykinesia and postural/kinetic tremor. Beta burst rate might thus serve as a neuromarker for Parkinson's disease that can help in the assessment of symptom severity in Parkinson's disease or in the evaluation of treatment effectiveness.

Neurofeedback-Linked Suppression of Cortical β Bursts Speeds Up Movement Initiation in Healthy Motor Control: A Double-Blind Sham-Controlled Study

Abnormally increased β bursts in cortical-basal ganglia-thalamic circuits are associated with rigidity and bradykinesia in patients with Parkinson's disease. Increased β bursts detected in the motor cortex have also been associated with longer reaction times (RTs) in healthy participants. Here we further hypothesize that suppressing β bursts through neurofeedback training can improve motor performance in healthy subjects.

Abnormally increased β bursts in cortical-basal ganglia-thalamic circuits are associated with rigidity and bradykinesia in patients with Parkinson's disease. Increased β bursts detected in the motor cortex have also been associated with longer reaction times (RTs) in healthy participants. Here we further hypothesize that suppressing β bursts through neurofeedback training can improve motor performance in healthy subjects. We conducted a double-blind sham-controlled study on 20 human volunteers (10 females) using a sequential neurofeedback-behavior task with the neurofeedback reflecting the occurrence of β bursts over sensorimotor cortex quantified in real time. The results show that neurofeedback training helps healthy participants learn to volitionally suppress β bursts in the sensorimotor cortex, with training being accompanied by reduced RT in subsequent cued movements. These changes were only significant in the real feedback group but not in the sham group, confirming the effect of neurofeedback training over simple motor imagery. In addition, RTs correlated with the rate and accumulated duration of β bursts in the contralateral motor cortex before the go-cue, but not with averaged β power. The reduced RTs induced by neurofeedback training positively correlated with reduced β bursts across all tested hemispheres. These results strengthen the link between the occurrence of β bursts in the sensorimotor cortex before the go-cue and slowed movement initiation in healthy motor control. The results also highlight the potential benefit of neurofeedback training in facilitating voluntary suppression of β bursts to speed up movement initiation.

SIGNIFICANCE STATEMENT This double-blind sham-controlled study suggested that neurofeedback training can facilitate volitional suppression of β bursts in sensorimotor cortex in healthy motor control better than sham feedback. The training was accompanied by reduced reaction time (RT) in subsequent cued movements, and the reduced RT positively correlated with the level of reduction in cortical β bursts before the go-cue, but not with average β power. These results provide further evidence of a causal link between sensorimotor β bursts and movement initiation and suggest that neurofeedback training could potentially be used to train participants to speed up movement initiation.

Transient spectral events in resting state MEG predict individual task responses

Even in response to simple tasks such as hand movement, human brain activity shows remarkable inter-subject variability. Recently, it has been shown that individual spatial variability in fMRI task responses can be predicted from measurements collected at rest; suggesting that the spatial variability is a stable feature, inherent to the individual's brain. However, it is not clear if this is also true for individual variability in the spatio-spectral content of oscillatory brain activity. Here, we show using MEG (N ​= ​89) that we can predict the spatial and spectral content of an individual's task response using features estimated from the individual's resting MEG data. This works by learning when transient spectral 'bursts' or events in the resting state tend to reoccur in the task responses. We applied our method to motor, working memory and language comprehension tasks. All task conditions were predicted significantly above chance. Finally, we found a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. Our approach can predict individual differences in brain activity and suggests a link between transient spectral events in task and rest that can be captured at the level of individuals.

Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations

The excess of 15-30 Hz (β-band) oscillations in the basal ganglia is one of the key signatures of Parkinson's disease (PD). The STN-GPe network is integral to generation and modulation of β band oscillations in basal ganglia. However, the role of changes in the firing rates and spike bursting of STN and GPe neurons in shaping these oscillations has remained unclear. In order to uncouple their effects, we studied the dynamics of STN-GPe network using numerical simulations. In particular, we used a neuron model, in which firing rates and spike bursting can be independently controlled. Using this model, we found that while STN firing rate is predictive of oscillations, GPe firing rate is not. The effect of spike bursting in STN and GPe neurons was state-dependent. That is, only when the network was operating in a state close to the border of oscillatory and non-oscillatory regimes, spike bursting had a qualitative effect on the β band oscillations. In these network states, an increase in GPe bursting enhanced the oscillations whereas an equivalent proportion of spike bursting in STN suppressed the oscillations. These results provide new insights into the mechanisms underlying the transient β bursts and how duration and power of β band oscillations may be controlled by an interplay of GPe and STN firing rates and spike bursts.

Is Alpha Asymmetry a Byproduct or Cause of Spatial Attention? New Evidence Alpha Neurofeedback Controls Measures of Spatial Attention

Cued spatial attention differentially modulates alpha power in attended relative to non-attended brain representations, termed the alpha asymmetry. Yet a causal role for alpha in attention is debated. In this issue of Neuron, Bagherzadeh et al., (2019) utilize neurofeedback to train alpha asymmetry and causally impact measures of spatial attention.

Temporal cascade of frontal, motor and muscle processes underlying human action-stopping

Action-stopping is a canonical executive function thought to involve top-down control over the motor system. Here we aimed to validate this stopping system using high temporal resolution methods in humans. We show that, following the requirement to stop, there was an increase of right frontal beta (~13 to 30 Hz) at ~120 ms, likely a proxy of right inferior frontal gyrus; then, at 140 ms, there was a broad skeletomotor suppression, likely reflecting the impact of the subthalamic nucleus on basal ganglia output; then, at ~160 ms, suppression was detected in the muscle, and, finally, the behavioral time of stopping was ~220 ms. This temporal cascade supports a physiological model of action-stopping, and partitions it into subprocesses that are isolable to different nodes and are more precise than the behavioral latency of stopping. Variation in these subprocesses, including at the single-trial level, could better explain individual differences in impulse control.

Continuous deep brain stimulation of the subthalamic nucleus may not modulate beta bursts in patients with Parkinson's disease

BACKGROUND

Neural oscillations represent synchronous neuronal activation and are ubiquitous throughout the brain. Oscillatory activity often includes brief high-amplitude bursts in addition to background oscillations, and burst activity may predict performance on working memory, motor, and comprehension tasks.

OBJECTIVE

We evaluated beta burst activity as a possible biomarker for motor symptoms in Parkinson's disease (PD). The relationship between beta amplitude dynamics and motor symptoms is critical for adaptive DBS for treatment of PD.

METHODS

We applied threshold-based and support vector machine (SVM) analyses of burst parameters to a defined on/off oscillator and to intraoperative recordings of local field potentials from the subthalamic nucleus of 16 awake patients with PD.

RESULTS

Filtering and time-frequency analysis techniques critically influenced the accuracy of identifying burst activity. Threshold-based analysis lead to biased results in the presence of changes in long-term beta amplitude and accurate quantification of bursts with thresholds required unknowable a priori knowledge of the time in bursts. We therefore implemented an SVM analysis, and we did not observe changes in burst fraction, rate, or duration with the application of cDBS in the participant data, even though SVM analysis was able to correctly identify bursts of the defined on/off oscillator.

CONCLUSION

Our results suggest that cDBS of the STN may not change beta burst activity. Additionally, threshold-based analysis can bias the fraction of time spent in bursts. Improved analysis strategies for continuous and adaptive DBS may achieve improved symptom control and reduce side-effects.

Parkinsonian Beta Dynamics during Rest and Movement in the Dorsal Pallidum and Subthalamic Nucleus

In Parkinson's disease (PD), pathologically high levels of beta activity (12-30 Hz) reflect specific symptomatology and normalize with pharmacological or surgical intervention. Although beta characterization in the subthalamic nucleus (STN) of PD patients undergoing deep brain stimulation (DBS) has now been translated into adaptive DBS paradigms, a limited number of studies have characterized beta power in the globus pallidus internus (GPi), an equally effective DBS target. Our objective was to compare beta power in the STN and GPi during rest and movement in people with PD undergoing DBS. Thirty-seven human female and male participants completed a simple behavioral experiment consisting of periods of rest and button presses, leading to local field potential recordings from 19 (15 participants) STN and 26 (22 participants) GPi nuclei. We examined overall beta power as well as beta time-domain dynamics (i.e., beta bursts). We found higher beta power during rest and movement in the GPi, which also had more beta desynchronization during movement. Beta power was positively associated with bradykinesia and rigidity severity; however, these clinical associations were present only in the GPi cohort. With regards to beta dynamics, bursts were similar in duration and frequency in the GPi and STN, but GPi bursts were stronger and correlated to bradykinesia-rigidity severity. Beta dynamics therefore differ across basal ganglia nuclei. Relative to the STN, beta power in the GPi may be readily detected, modulates more with movement, and relates more to clinical impairment. Together, this could point to the GPi as a potentially effective target for beta-based adaptive DBS.SIGNIFICANCE STATEMENT It is known that subthalamic nucleus (STN) beta activity is linked to symptom severity in Parkinson's disease (PD), but few studies have characterized beta activity in the globus pallidus internus (GPi), another effective target for deep brain stimulation (DBS). We compared beta power in the STN and GPi during rest and movement in 37 people with PD undergoing DBS. We found that beta dynamics differed across basal ganglia nuclei. Our results show that, relative to the STN, beta power in the GPi may be readily detected, modulates more with movement, and relates more to clinical impairment. Together, this could point to the GPi as a potentially effective target for beta-based adaptive DBS.

Real-time detection of neural oscillation bursts allows behaviourally relevant neurofeedback

Neural oscillations as important information carrier in the brain, are increasingly interpreted as transient bursts rather than as sustained oscillations. Short (<150 ms) bursts of beta-waves (15-30 Hz) have been documented in humans, monkeys and mice. These events were correlated with memory, movement and perception, and were even suggested as the primary ingredient of all beta-band activity. However, a method to measure these short-lived events in real-time and to investigate their impact on behaviour is missing. Here we present a real-time data analysis system, capable to detect short narrowband bursts, and demonstrate its usefulness to increase the beta-band burst-rate in rats. This neurofeedback training induced changes in overall oscillatory power, and bursts could be decoded from the movement of the rats, thus enabling future investigation of the role of oscillatory bursts.

Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.

Induced oscillatory signaling in the beta frequency of top-down pain modulation

Supplemental Digital Content is Available in the Text.

Background:

Induced synchronized brain activity, particularly in the beta-frequency range, has rarely been investigated in human electrophysiological studies of attentional modulation of the perception of nociceptive stimuli.

Methods:

We measured time-resolved brain responses to nociceptive stimuli in healthy subjects (final data set: n = 17) using magnetoencephalography (MEG). In addition to investigating evoked responses as previous studies, we tested whether synchronized beta activity induced by nociceptive stimuli differs between 2 attentional conditions. Subjects were presented simultaneously with 2 stimulus modalities (pain-producing intraepidermal electrical stimuli and visual stimuli) in 2 different experimental conditions, ie, “attention to pain” and “attention to color.” Pain ratings between conditions were compared using a 2-sided paired-sample t test; MEG data were analyzed with Brainstorm.

Results:

Pain ratings were significantly higher in the “attention to pain” compared with the “attention to color” condition. Peak amplitudes of the evoked responses were significantly larger in the “attention to pain” condition bilaterally in the insula and secondary somatosensory cortex, and in the primary somatosensory cortex (SI) contralateral to stimulation. Induced responses to painful stimuli were significantly stronger in contralateral SI in the beta-frequency range in the “attention to pain” condition.

Conclusions:

This study replicates previous reports w.r.t. the attentional modulation of evoked responses and suggests a functional role of induced oscillatory activity in the beta frequency in top-down modulation of nociceptive stimuli.

The role of transient spectral 'bursts' in functional connectivity: A magnetoencephalography study

Neural oscillations dominate electrophysiological measures of macroscopic brain activity and fluctuations in these rhythms offer an insightful window on cortical excitation, inhibition, and connectivity. However, in recent years the 'classical' picture of smoothly varying oscillations has been challenged by the idea that many 'oscillations' may actually be formed from the recurrence of punctate high-amplitude bursts in activity, whose spectral composition intersects the traditionally defined frequency ranges (e.g. alpha/beta band). This finding offers a new interpretation of measurable brain activity, however neither the methodological means to detect bursts, nor their link to other findings (e.g. connectivity) have been settled. Here, we use a new approach to detect bursts in magnetoencephalography (MEG) data. We show that a time-delay embedded Hidden Markov Model (HMM) can be used to delineate single-region bursts which are in agreement with existing techniques. However, unlike existing techniques, the HMM looks for specific spectral patterns in timecourse data. We characterise the distribution of burst duration, frequency of occurrence and amplitude across the cortex in resting state MEG data. During a motor task we show how the movement related beta decrease and post movement beta rebound are driven by changes in burst occurrence. Finally, we show that the beta band functional connectome can be derived using a simple measure of burst overlap, and that coincident bursts in separate regions correspond to a period of heightened coherence. In summary, this paper offers a new methodology for burst identification and connectivity analysis which will be important for future investigations of neural oscillations.

The Cumulative Effect of Transient Synchrony States on Motor Performance in Parkinson's Disease

Bursts of beta frequency band activity in the basal ganglia of patients with Parkinson's disease (PD) are associated with impaired motor performance. Here we test in human adults whether small variations in the timing of movement relative to beta bursts have a critical effect on movement velocity and whether the cumulative effects of multiple beta bursts, both locally and across networks, matter.

Bursts of beta frequency band activity in the basal ganglia of patients with Parkinson's disease (PD) are associated with impaired motor performance. Here we test in human adults whether small variations in the timing of movement relative to beta bursts have a critical effect on movement velocity and whether the cumulative effects of multiple beta bursts, both locally and across networks, matter. We recorded local field potentials from the subthalamic nucleus (STN) in 15 PD patients of both genders OFF-medication, during temporary lead externalization after deep brain stimulation surgery. Beta bursts were defined as periods exceeding the 75th percentile amplitude threshold. Subjects performed a visual cued joystick reaching task, with the visual cue being triggered in real time with different temporal relationships to bursts of STN beta activity. The velocity of actions made in response to cues prospectively triggered by STN beta bursts was slower than when responses were not time-locked to recent beta bursts. Importantly, slow movements were those that followed multiple bursts close to each other within a trial. In contrast, small differences in the delay between the last burst and movement onset had no significant impact on velocity. Moreover, when the overlap of bursts between the two STN was high, slowing was more pronounced. Our findings suggest that the cumulative, but recent, history of beta bursting, both locally and across basal ganglia networks, may impact on motor performance.

SIGNIFICANCE STATEMENT Bursts of beta frequency band activity in the basal ganglia are associated with slowing of voluntary movement in patients with Parkinson's disease. We show that slow movements are those that follow multiple bursts close to each other and bursts that are coupled across regions. These results suggest that the cumulative, but recent, history of beta bursting, both locally and across basal ganglia networks, impacts on motor performance in this condition. The manipulation of burst dynamics may be a means of selectively improving motor impairment.

Under the Mind's Hood: What We Have Learned by Watching the Brain at Work

Imagine you were asked to investigate the workings of an engine, but to do so without ever opening the hood. Now imagine the engine fueled the human mind. This is the challenge faced by cognitive neuroscientists worldwide aiming to understand the neural bases of our psychological functions. Luckily, human ingenuity comes to the rescue. Around the same time as the Society for Neuroscience was being established in the 1960s, the first tools for measuring the human brain at work were becoming available. Noninvasive human brain imaging and neurophysiology have continued developing at a relentless pace ever since. In this 50 year anniversary, we reflect on how these methods have been changing our understanding of how brain supports mind.

Beta rhythm events predict corticospinal motor output

The beta rhythm (15-30 Hz) is a prominent signal of sensorimotor cortical activity. This rhythm is not sustained but occurs non-rhythmically as brief events of a few (1-2) oscillatory cycles. Recent work on the relationship between these events and sensorimotor performance suggests that they are the biologically relevant elements of the beta rhythm. However, the influence of these events on corticospinal excitability, a mechanism through which the primary motor cortex controls motor output, is unknown. Here, we addressed this question by evaluating relationships between beta event characteristics and corticospinal excitability in healthy adults. Results show that the number, amplitude, and timing of beta events preceding transcranial magnetic stimulation (TMS) each significantly predicted motor-evoked potential (MEP) amplitudes. However, beta event characteristics did not explain additional MEP amplitude variance beyond that explained by mean beta power alone, suggesting that conventional beta power measures and beta event characteristics similarly captured natural variation in human corticospinal excitability. Despite this lack of additional explained variance, these results provide first evidence that endogenous beta oscillatory events shape human corticospinal excitability.

Dissecting beta-state changes during timed movement preparation in Parkinson's disease

An emerging perspective describes beta-band (15-28 Hz) activity as consisting of short-lived high-amplitude events that only appear sustained in conventional measures of trial-average power. This has important implications for characterising abnormalities observed in beta-band activity in disorders like Parkinson's disease. Measuring parameters associated with beta-event dynamics may yield more sensitive measures, provide more selective diagnostic neural markers, and provide greater mechanistic insight into the breakdown of brain dynamics in this disease. Here, we used magnetoencephalography in eighteen Parkinson's disease participants off dopaminergic medication and eighteen healthy control participants to investigate beta-event dynamics during timed movement preparation. We used the Hidden Markov Model to classify event dynamics in a data-driven manner and derived three parameters of beta events: (1) beta-state amplitude, (2) beta-state lifetime, and (3) beta-state interval time. Of these, changes in beta-state interval time explained the overall decreases in beta power during timed movement preparation and uniquely captured the impairment in such preparation in patients with Parkinson's disease. Thus, the increased granularity of the Hidden Markov Model analysis (compared with conventional analysis of power) provides increased sensitivity and suggests a possible reason for impairments of timed movement preparation in Parkinson's disease.

Unpacking Transient Event Dynamics in Electrophysiological Power Spectra

Electrophysiological recordings of neuronal activity show spontaneous and task-dependent changes in their frequency-domain power spectra. These changes are conventionally interpreted as modulations in the amplitude of underlying oscillations. However, this overlooks the possibility of underlying transient spectral ‘bursts’ or events whose dynamics can map to changes in trial-average spectral power in numerous ways. Under this emerging perspective, a key challenge is to perform burst detection, i.e. to characterise single-trial transient spectral events, in a principled manner. Here, we describe how transient spectral events can be operationalised and estimated using Hidden Markov Models (HMMs). The HMM overcomes a number of the limitations of the standard amplitude-thresholding approach to burst detection; in that it is able to concurrently detect different types of bursts, each with distinct spectral content, without the need to predefine frequency bands of interest, and does so with less dependence on a priori threshold specification. We describe how the HMM can be used for burst detection and illustrate its benefits on simulated data. Finally, we apply this method to empirical data to detect multiple burst types in a task-MEG dataset, and illustrate how we can compute burst metrics, such as the task-evoked timecourse of burst duration.

β-Bursts Reveal the Trial-to-Trial Dynamics of Movement Initiation and Cancellation

The neurophysiological basis of motor control is of substantial interest to basic researchers and clinicians alike. Motor processes are accompanied by prominent field potential changes in the β-frequency band (15-29 Hz): in trial-averages, movement initiation is accompanied by β-band desynchronization over sensorimotor areas, whereas movement cancellation is accompanied by β-power increases over (pre)frontal areas. However, averaging misrepresents the true nature of the β-signal. Unaveraged β-band activity is characterized by short-lasting, burst-like events, rather than by steady modulations. Therefore, averaging-based quantifications may miss important brain-behavior relationships. To investigate how β-bursts relate to movement in male and female humans (N = 234), we investigated scalp-recorded β-band activity during the stop-signal task, which operationalizes both movement initiation and cancellation. Both processes were indexed by systematic spatiotemporal changes in β-burst rates. Before movement initiation, β-bursting was prominent at bilateral sensorimotor sites. These burst-rates predicted reaction time (a relationship that was absent in trial-average data), suggesting that sensorimotor β-bursting signifies an inhibited motor system, which has to be overcome to initiate movements. Indeed, during movement initiation, sensorimotor burst-rates steadily decreased, lateralizing just before movement execution. In contrast, successful movement cancellation was signified by increased phasic β-bursting over fronto-central sites. Such β-bursts were followed by short-latency increases of bilateral sensorimotor β-burst rates, suggesting that motor inhibition can be rapidly re-instantiated by frontal areas when movements have to be rapidly cancelled. Together, these findings suggest that β-bursting is a fundamental signature of the motor system, used by both sensorimotor and frontal areas involved in the trial-by-trial control of behavior.SIGNIFICANCE STATEMENT Movement-related β-frequency (15-29 Hz) changes are among the most prominent features of neural recordings across species, scales, and methods. However, standard averaging-based methods obscure the true dynamics of β-band activity, which is dominated by short-lived, burst-like events. Here, we demonstrate that both movement-initiation and cancellation in humans are characterized by unique trial-to-trial patterns of β-bursting. Movement initiation is characterized by steady reductions of β-bursting over bilateral sensorimotor sites. In contrast, during rapid movement cancellation, β-bursts first emerge over fronto-central sites typically associated with motor control, after which sensorimotor β-bursting re-initiates. These findings suggest a fundamentally novel, non-invasive measure of the neural interaction underlying movement-initiation and -cancellation, opening new avenues for the study of motor control in health and disease.

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

Beta oscillations (∼13 to 30 Hz) have been observed during many perceptual, cognitive, and motor processes in a plethora of brain recording studies. Although the function of beta oscillations (hereafter "beta" for short) is unlikely to be explained by any single monolithic description, we here discuss several convergent findings. In prefrontal cortex (PFC), increased beta appears at the end of a trial when working memory information needs to be erased. A similar "clear-out" function might apply during the stopping of action and the stopping of long-term memory retrieval (stopping thoughts), where increased prefrontal beta is also observed. A different apparent role for beta in PFC occurs during the delay period of working memory tasks: it might serve to maintain the current contents and/or to prevent interference from distraction. We confront the challenge of relating these observations to the large literature on beta recorded from sensorimotor cortex. Potentially, the clear-out of working memory in PFC has its counterpart in the postmovement clear-out of the motor plan in sensorimotor cortex. However, recent studies support alternative interpretations. In addition, we flag emerging research on different frequencies of beta and the relationship between beta and single-neuron spiking. We also discuss where beta might be generated: basal ganglia, cortex, or both. We end by considering the clinical implications for adaptive deep-brain stimulation.

Human motor cortical beta bursts relate to movement planning and response errors

Motor cortical beta activity (13-30 Hz) is a hallmark signature of healthy and pathological movement, but its behavioural relevance remains unclear. Using high-precision magnetoencephalography (MEG), we show that during the classical event-related desynchronisation (ERD) and event-related synchronisation (ERS) periods, motor cortical beta activity in individual trials (n > 12,000) is dominated by high amplitude, transient, and infrequent bursts. Beta burst probability closely matched the trial-averaged beta amplitude in both the pre- and post-movement periods, but individual bursts were spatially more focal than the classical ERS peak. Furthermore, prior to movement (ERD period), beta burst timing was related to the degree of motor preparation, with later bursts resulting in delayed response times. Following movement (ERS period), the first beta burst was delayed by approximately 100 milliseconds when an incorrect response was made. Overall, beta burst timing was a stronger predictor of single trial behaviour than beta burst rate or single trial beta amplitude. This transient nature of motor cortical beta provides new constraints for theories of its role in information processing within and across cortical circuits, and its functional relevance for behaviour in both healthy and pathological movement.

Persistent Gamma Spiking in SI Nonsensory Fast Spiking Cells Predicts Perceptual Success

Gamma oscillations (30-55 Hz) are hypothesized to temporally coordinate sensory encoding, enabling perception. However, fast spiking interneurons (FS), key gamma generators, can be highly sensory responsive, as is the gamma band local field potential (LFP). How can FS-mediated gamma act as an impartial temporal reference for sensory encoding, when the sensory drive itself presumably perturbs the pre-established rhythm? Combining tetrode recording in SI barrel cortex with controlled psychophysics, we found a unique FS subtype that was not sensory responsive and spiked regularly at gamma range intervals (gamma regular nonsensory FS [grnsFS]). Successful detection was predicted by a further increase in gamma regular spiking of grnsFS, persisting from before to after sensory onset. In contrast, broadband LFP power, including gamma, negatively predicted detection and did not cohere with gamma band spiking by grnsFS. These results suggest that a distinct FS subtype mediates perceptually relevant oscillations, independent of the LFP and sensory drive.

Empirical analysis of phase-amplitude coupling approaches

Analysis of the coupling between the phases and amplitudes of oscillations within the same continuously sampled signal has provided interesting insights into the physiology of memory and other brain process, and, more recently, the pathophysiology of parkinsonism and other movement disorders. Technical aspects of the analysis have a significant impact on the results. We present an empirical exploration of a variety of analysis design choices that need to be considered when measuring phase-amplitude coupling (PAC). We studied three alternative filtering approaches to the commonly used Kullback-Leibler distance-based method of PAC analysis, including one method that uses wavelets, one that uses constant filter settings, and one in which filtering of the data is optimized for individual frequency bands. Additionally, we introduce a time-dependent PAC analysis technique that takes advantage of the inherent temporality of wavelets. We examined how the duration of the sampled data, the stability of oscillations, or the presence of artifacts affect the value of the "modulation index", a commonly used parameter describing the degree of PAC. We also studied the computational costs associated with calculating modulation indices by the three techniques. We found that wavelet-based PAC performs better with similar or less computational cost than the two other methods while also allowing to examine temporal changes of PAC. We also show that the reliability of PAC measurements strongly depends on the duration and stability of PAC, and the presence (or absence) of artifacts. The best parameters to be used for PAC analyses of long samples of data may differ, depending on data characteristics and analysis objectives. Prior to settling on a specific PAC analysis approach for a given set of data, it may be useful to conduct an initial analysis of the time-dependence of PAC using our time-resolved PAC analysis.

Monkey EEG links neuronal color and motion information across species and scales

It remains challenging to relate EEG and MEG to underlying circuit processes and comparable experiments on both spatial scales are rare. To close this gap between invasive and non-invasive electrophysiology we developed and recorded human-comparable EEG in macaque monkeys during visual stimulation with colored dynamic random dot patterns. Furthermore, we performed simultaneous microelectrode recordings from 6 areas of macaque cortex and human MEG. Motion direction and color information were accessible in all signals. Tuning of the non-invasive signals was similar to V4 and IT, but not to dorsal and frontal areas. Thus, MEG and EEG were dominated by early visual and ventral stream sources. Source level analysis revealed corresponding information and latency gradients across cortex. We show how information-based methods and monkey EEG can identify analogous properties of visual processing in signals spanning spatial scales from single units to MEG - a valuable framework for relating human and animal studies.

Beta bursts during continuous movements accompany the velocity decrement in Parkinson's disease patients

Bradykinesia is reported to correlate with subthalamic beta power (13-35 Hz) recorded at rest in Parkinson's disease (PD). Pilot studies suggest adaptive deep brain stimulation triggered by amplitude threshold crossings of beta activity defined at rest is effective. This is puzzling, given that beta is suppressed during repetitive movements when bradykinesia becomes apparent. Recently, increased beta power in PD has been linked to beta bursts. Here we investigate whether beta bursts also occur during repetitive movements and relate to progressive decrement in movement velocity. Therefore, subthalamic local field potentials were recorded in 12 PD patients off medication while performing 30s blocks of rotatory movements alternating with rest periods. Bursts were defined separately for the low (13-20 Hz) and high (20-35 Hz) beta band using thresholds defined at rest. As expected, velocity significantly decreased within movement blocks. Despite the sustained suppression of both beta sub-bands, bursts could still be detected during movement. Beta bursts were reduced in amplitude, duration and rate during movement with beta rate correlating best with beta power. A mixed-effects linear model revealed that percentage time spent in beta bursts predicted velocity decreases better than averaged power. This correlation was specific for the low beta band. Our results link beta bursts during movement to bradykinesia. This helps explain how beta activity may contribute to bradykinetic movement decrement even though mean beta power is reduced during movement. Moreover, our findings help explain the effectiveness of adaptive DBS triggered off beta bursts, even though these may be defined with respect to beta levels at rest.

Multiple mechanisms link prestimulus neural oscillations to sensory responses

Spontaneous fluctuations of neural activity may explain why sensory responses vary across repeated presentations of the same physical stimulus. To test this hypothesis, we recorded electroencephalography in humans during stimulation with identical visual stimuli and analyzed how prestimulus neural oscillations modulate different stages of sensory processing reflected by distinct components of the event-related potential (ERP). We found that strong prestimulus alpha- and beta-band power resulted in a suppression of early ERP components (C1 and N150) and in an amplification of late components (after 0.4 s), even after controlling for fluctuations in 1/f aperiodic signal and sleepiness. Whereas functional inhibition of sensory processing underlies the reduction of early ERP responses, we found that the modulation of non-zero-mean oscillations (baseline shift) accounted for the amplification of late responses. Distinguishing between these two mechanisms is crucial for understanding how internal brain states modulate the processing of incoming sensory information.

Effects of Electroacupuncture on Pain Memory-Related Behaviors and Synchronous Neural Oscillations in the Rostral Anterior Cingulate Cortex in Freely Moving Rats

Our previous studies have confirmed that electroacupuncture (EA) can effectively intervene in pain memory, but the neural mechanism involved remains unclear. In this study, we observed the effects of EA in regulating pain memory-related behaviors and synchronous neural oscillations in the rostral anterior cingulate cortex (rACC). During nociceptive behavioral testing, pain memory induced a nonpain stimulus that spurred a neural oscillatory reaction similar to that caused by pain stimuli in the rACC. After EA, nonpain stimuli did not induce decreased neural oscillatory activity in the rACC until the presentation of pain stimuli. During aversive behavioral testing, EA, through the downregulation of theta power, inhibited the retrieval of aversive memory and relieved pain memory-induced aversive behaviors. These changes of oscillatory activity may be the hallmarks of EA therapy for pain memory.

Desynchronizing to be faster? Perceptual- and attentional-modulation of brain rhythms at the sub-millisecond scale

Neural oscillatory signals has been associated with many high-level functions (e.g. attention and working memory), because they reflect correlated behaviors of neural population that would facilitate the information transfer in the brain. On the other hand, a decreased power of oscillation (event-related desynchronization, ERD) has been associated with an irregular state in which many neurons behave in an uncorrelated manner. In contrast to this view, here we show that the human ERD is linked to the increased regularity of oscillatory signals. Using magnetoencephalography, we found that presenting a visual stimulus not only induced a decrease in power of alpha (8-12 Hz) to beta (13-30 Hz) rhythms in the contralateral visual cortex but also reduced the mean and variance of their inter-peak intervals (IPIs). This indicates that the suppressed alpha/beta rhythms became faster (reduced mean) and more regular (reduced variance) during visual stimulation. The same changes in IPIs, especially those of beta rhythm, were observed when subjects allocated their attention to a contralateral visual field. Those results revealed a new role of the event-related decrease in alpha/beta power and further suggested that our brain regulates and accelerates a clock for neural computations by actively suppressing the oscillation amplitude in task-relevant regions.

Imaging human cortical responses to intraneural microstimulation using magnetoencephalography

The sensation of touch in the glabrous skin of the human hand is conveyed by thousands of fast-conducting mechanoreceptive afferents, which can be categorised into four distinct types. The spiking properties of these afferents in the periphery in response to varied tactile stimuli are well-characterised, but relatively little is known about the spatiotemporal properties of the neural representations of these different receptor types in the human cortex. Here, we use the novel methodological combination of single-unit intraneural microstimulation (INMS) with magnetoencephalography (MEG) to localise cortical representations of individual touch afferents in humans, by measuring the extracranial magnetic fields from neural currents. We found that by assessing the modulation of the beta (13-30 Hz) rhythm during single-unit INMS, significant changes in oscillatory amplitude occur in the contralateral primary somatosensory cortex within and across a group of fast adapting type I mechanoreceptive afferents, which corresponded well to the induced response from matched vibrotactile stimulation. Combining the spatiotemporal specificity of MEG with the selective single-unit stimulation of INMS enables the interrogation of the central representations of different aspects of tactile afferent signalling within the human cortices. The fundamental finding that single-unit INMS ERD responses are robust and consistent with natural somatosensory stimuli will permit us to more dynamically probe the central nervous system responses in humans, to address questions about the processing of touch from the different classes of mechanoreceptive afferents and the effects of varying the stimulus frequency and patterning.