Bursts of beta oscillation differentiate postperformance activity in the striatum and motor cortex of monkeys performing movement tasks

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

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

Beta-frequency sensory stimulation enhances gait rhythmicity through strengthened coupling between striatal networks and stepping movement

Stepping movement is delta (1-4 Hz) rhythmic and depends on sensory inputs. In addition to delta rhythms, beta (10-30 Hz) frequency dynamics are also prominent in the motor circuits and are coupled to neuronal delta rhythms both at the network and the cellular levels. Since beta rhythms are broadly supported by cortical and subcortical sensorimotor circuits, we explore how beta-frequency sensory stimulation influences delta-rhythmic stepping movement, and dorsal striatal circuit regulation of stepping. We delivered audiovisual stimulation at 10 Hz or 145 Hz to mice voluntarily locomoting, while simultaneously recording stepping movement, striatal cellular calcium dynamics and local field potentials (LFPs). We found that 10 Hz, but not 145 Hz stimulation prominently entrained striatal LFPs. Even though sensory stimulation at both frequencies promoted locomotion and desynchronized striatal network, only 10 Hz stimulation enhanced the delta rhythmicity of stepping movement and strengthened the coupling between stepping and striatal LFP delta and beta oscillations. These results demonstrate that higher frequency sensory stimulation can modulate lower frequency dorsal striatal neural dynamics and improve stepping rhythmicity, highlighting the translational potential of non-invasive beta-frequency sensory stimulation for improving gait.

Dysfunction of motor cortices in Parkinson's disease

The cerebral cortex has long been thought to be involved in the pathophysiology of motor symptoms of Parkinson's disease. The impaired cortical function is believed to be a direct and immediate effect of pathologically patterned basal ganglia output, mediated to the cerebral cortex by way of the ventral motor thalamus. However, recent studies in humans with Parkinson's disease and in animal models of the disease have provided strong evidence suggesting that the involvement of the cerebral cortex is much broader than merely serving as a passive conduit for subcortical disturbances. In the present review, we discuss Parkinson's disease-related changes in frontal cortical motor regions, focusing on neuropathology, plasticity, changes in neurotransmission, and altered network interactions. We will also examine recent studies exploring the cortical circuits as potential targets for neuromodulation to treat Parkinson's disease.

Beta: bursts of cognition

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

Human sensorimotor resting state beta events and aperiodic activity show good test-retest reliability

OBJECTIVE

Diseases affecting sensorimotor function impair physical independence. Reliable functional clinical biomarkers allowing early diagnosis or targeting treatment and rehabilitation could reduce this burden. Magnetoencephalography (MEG) non-invasively measures brain rhythms such as the somatomotor 'rolandic' rhythm which shows intermittent high-amplitude beta (14-30 Hz) 'events' that predict behavior across tasks and species and are altered by sensorimotor neurological diseases.

METHODS

We assessed test-retest stability, a prerequisite for biomarkers, of spontaneous sensorimotor aperiodic (1/f) signal and beta events in 50 healthy human controls across two MEG sessions using the intraclass correlation coefficient (ICC). Beta events were determined using an amplitude-thresholding approach on a narrow-band filtered amplitude envelope obtained using Morlet wavelet decomposition.

RESULTS

Resting sensorimotor characteristics showed good to excellent test-retest stability. Aperiodic component (ICC 0.77-0.88) and beta event amplitude (ICC 0.74-0.82) were very stable, whereas beta event duration was more variable (ICC 0.55-0.7). 2-3 minute recordings were sufficient to obtain stable results. Analysis automatization was successful in 86%.

CONCLUSIONS

Sensorimotor beta phenotype is a stable feature of an individual's resting brain activity even for short recordings easily measured in patients.

SIGNIFICANCE

Spontaneous sensorimotor beta phenotype has potential as a clinical biomarker of sensorimotor system integrity.

Decreased cellular excitability of pyramidal tract neurons in primary motor cortex leads to paradoxically increased network activity in simulated parkinsonian motor cortex

Decreased excitability of pyramidal tract neurons in layer 5B (PT5B) of primary motor cortex (M1) has recently been shown in a dopamine-depleted mouse model of parkinsonism. We hypothesized that decreased PT5B neuron excitability would substantially disrupt oscillatory and non-oscillatory firing patterns of neurons in layer 5 (L5) of primary motor cortex (M1). To test this hypothesis, we performed computer simulations using a previously validated computer model of mouse M1. Inclusion of the experimentally identified parkinsonism-associated decrease of PT5B excitability into our computational model produced a paradoxical increase in rest-state PT5B firing rate, as well as an increase in beta-band oscillatory power in local field potential (LFP). In the movement-state, PT5B population firing and LFP showed reduced beta and increased high-beta, low-gamma activity of 20-35 Hz in the parkinsonian, but not in control condition. The appearance of beta-band oscillations in parkinsonism would be expected to disrupt normal M1 motor output and contribute to motor activity deficits seen in patients with Parkinson's disease (PD).

Asymmetries of the subthalamic activity in Parkinson's disease: phase-amplitude coupling among local field potentials

The role of brain asymmetries of dopaminergic neurons in motor symptoms of Parkinson's disease is still undefined. Local field recordings from the subthalamic nucleus revealed some neurophysiological biomarkers of the disease: increased beta activity, increased low-frequency activity and high-frequency oscillations. Phase-amplitude coupling coordinates the timing of neuronal activity and allows determining the mechanism for communication within distinct regions of the brain. In this study, we discuss the use of phase-amplitude coupling to assess the differences between the two hemispheres in a cohort of 24 patients with Parkinson's disease before and after levodopa administration. Subthalamic low- (12-20 Hz) and high-beta (20-30 Hz) oscillations were compared with low- (30-45 Hz), medium- (70-100 Hz) and high-frequency (260-360 Hz) bands. We found a significant beta-phase-amplitude coupling asymmetry between left and right and an opposite-side-dependent effect of the pharmacological treatment, which is associated with the reduction of motor symptoms. In particular, high coupling between high frequencies and high-beta oscillations was found during the OFF condition (P < 0.01) and a low coupling during the ON state (P < 0.0001) when the right subthalamus was assessed; exactly the opposite happened when the left subthalamus was considered in the analysis, showing a lower coupling between high frequencies and high-beta oscillations during the OFF condition (P < 0.01), followed by a higher one during the ON state (P < 0.01). Interestingly, these asymmetries are independent of the motor onset side, either left or right. These findings have important implications for neural signals that may be used to trigger adaptive deep brain stimulation in Parkinson's and could provide more exhaustive insights into subthalamic dynamics.

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

Objectives

An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods.

Materials

Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC).

Methods

EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups.

Results

The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ.

Discussion

FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes.

Conclusion

Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.

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.

The dual role of striatal interneurons: circuit modulation and trophic support for the basal ganglia

ABSTRACT

Striatal interneurons play a key role in modulating striatal-dependent behaviors, including motor activity and reward and emotional processing. Interneurons not only provide modulation to the basal ganglia circuitry under homeostasis but are also involved in changes to plasticity and adaptation during disease conditions such as Parkinson's or Huntington's disease. This review aims to summarize recent findings regarding the role of striatal cholinergic and GABAergic interneurons in providing circuit modulation to the basal ganglia in both homeostatic and disease conditions. In addition to direct circuit modulation, striatal interneurons have also been shown to provide trophic support to maintain neuron populations in adulthood. We discuss this interesting and novel role of striatal interneurons, with a focus on the maintenance of adult dopaminergic neurons from interneuron-derived sonic-hedgehog.

The dynamic state of a prefrontal-hypothalamic-midbrain circuit commands behavioral transitions

Innate behaviors meet multiple needs adaptively and in a serial order, suggesting the existence of a hitherto elusive brain dynamics that brings together representations of upcoming behaviors during their selection. Here we show that during behavioral transitions, possible upcoming behaviors are encoded by specific signatures of neuronal populations in the lateral hypothalamus (LH) that are active near beta oscillation peaks. Optogenetic recruitment of intrahypothalamic inhibition at this phase eliminates behavioral transitions. We show that transitions are elicited by beta-rhythmic inputs from the prefrontal cortex that spontaneously synchronize with LH 'transition cells' encoding multiple behaviors. Downstream of the LH, dopamine neurons increase firing during beta oscillations and also encode behavioral transitions. Thus, a hypothalamic transition state signals alternative future behaviors, encodes the one most likely to be selected and enables rapid coordination with cognitive and reward-processing circuitries, commanding adaptive social contact and eating behaviors.

Amygdala-hippocampus somatostatin interneuron beta-synchrony underlies a cross-species biomarker of emotional state

Emotional responses arise from limbic circuits including the hippocampus and amygdala. In the human brain, beta-frequency communication between these structures correlates with self-reported mood and anxiety. However, both the mechanism and significance of this biomarker as a readout vs. driver of emotional state remain unknown. Here, we show that beta-frequency communication between ventral hippocampus and basolateral amygdala also predicts anxiety-related behavior in mice, both on long timescales (∼30 min) and immediately preceding behavioral choices. Genetically encoded voltage indicators reveal that this biomarker reflects synchronization between somatostatin interneurons across both structures. Indeed, synchrony between these neurons dynamically predicts approach-avoidance decisions, and optogenetically shifting the phase of synchronization by just 25 ms is sufficient to bidirectionally modulate anxiety-related behaviors. Thus, back-translation establishes a human biomarker as a causal determinant (not just predictor) of emotional state, revealing a novel mechanism whereby interregional synchronization that is frequency, phase, and cell type specific controls emotional processing.

A Role for Bottom-Up Alpha Oscillations in Temporal Integration

Neural oscillations in the 8-12 Hz alpha band are thought to represent top-down inhibitory control and to influence temporal resolution: Individuals with faster peak frequencies segregate stimuli appearing closer in time. Recently, this theory has been challenged. Here, we investigate a special case in which alpha does not correlate with temporal resolution: when stimuli are presented amidst strong visual drive. Based on findings regarding alpha rhythmogenesis and wave spatial propagation, we suggest that stimulus-induced, bottom-up alpha oscillations play a role in temporal integration. We propose a theoretical model, informed by visual persistence, lateral inhibition, and network refractory periods, and simulate physiologically plausible scenarios of the interaction between bottom-up alpha and the temporal segregation. Our simulations reveal that different features of oscillations, including frequency, phase, and power, can influence temporal perception and provide a theoretically informed starting point for future empirical studies.

Towards therapeutic electrophysiological neurofeedback in Parkinson's disease

Neurofeedback (NF) techniques support individuals to self-regulate specific features of brain activity, which has been shown to impact behavior and potentially ameliorate clinical symptoms. Electrophysiological NF (epNF) may be particularly impactful for patients with Parkinson's disease (PD), as evidence mounts to suggest a central role of pathological neural oscillations underlying symptoms in PD. Exaggerated beta oscillations (12-30 Hz) in the basal ganglia-cortical network are linked to motor symptoms (e.g., bradykinesia, rigidity), and beta is reduced by successful therapy with dopaminergic medication and Deep Brain Stimulation (DBS). PD patients also experience non-motor symptoms related to sleep, mood, motivation, and cognitive control. Although less is known about the mechanisms of non-motor symptoms in PD and how to successfully treat them, low frequency neural oscillations (1-12 Hz) in the basal ganglia-cortical network are particularly implicated in non-motor symptoms. Here, we review how cortical and subcortical epNF could be used to target motor and non-motor specific oscillations, and potentially serve as an adjunct therapy that enables PD patients to endogenously control their own pathological neural activities. Recent studies have demonstrated that epNF protocols can successfully support volitional control of cortical and subcortical beta rhythms. Importantly, this endogenous control of beta has been linked to changes in motor behavior. epNF for PD, as a casual intervention on neural signals, has the potential to increase understanding of the neurophysiology of movement, mood, and cognition and to identify new therapeutic approaches for motor and non-motor symptoms.

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

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

Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on?

Deep brain stimulation has been in clinical use for 30 years and during that time it has changed markedly from a small-scale treatment employed by only a few highly specialized centers into a widespread keystone approach to the management of disorders such as Parkinson's disease. In the intervening decades, many of the broad principles of deep brain stimulation have remained unchanged, that of electrode insertion into stereotactically targeted brain nuclei, however the underlying technology and understanding around the approach have progressed markedly. Some of the most significant advances have taken place over the last decade with the advent of artificial intelligence, directional electrodes, stimulation/recording implantable pulse generators and the potential for remote programming among many other innovations. New therapeutic targets are being assessed for their potential benefits and a surge in the number of deep brain stimulation implantations has given birth to a flourishing scientific literature surrounding the pathophysiology of brain disorders such as Parkinson's disease. Here we outline the developments of the last decade and look to the future of deep brain stimulation to attempt to discern some of the most promising lines of inquiry in this fast-paced and rapidly evolving field.

Dopaminergic reinforcement in the motor system: Implications for Parkinson's disease and deep brain stimulation

Millions of people suffer from dopamine-related disorders spanning disturbances in movement, cognition and emotion. These changes are often attributed to changes in striatal dopamine function. Thus, understanding how dopamine signalling in the striatum and basal ganglia shapes human behaviour is fundamental to advancing the treatment of affected patients. Dopaminergic neurons innervate large-scale brain networks, and accordingly, many different roles for dopamine signals have been proposed, such as invigoration of movement and tracking of reward contingencies. The canonical circuit architecture of cortico-striatal loops sparks the question, of whether dopamine signals in the basal ganglia serve an overarching computational principle. Such a holistic understanding of dopamine functioning could provide new insights into symptom generation in psychiatry to neurology. Here, we review the perspective that dopamine could bidirectionally control neural population dynamics, increasing or decreasing their strength and likelihood to reoccur in the future, a process previously termed neural reinforcement. We outline how the basal ganglia pathways could drive strengthening and weakening of circuit dynamics and discuss the implication of this hypothesis on the understanding of motor signs of Parkinson's disease (PD), the most frequent dopaminergic disorder. We propose that loss of dopamine in PD may lead to a pathological brain state where repetition of neural activity leads to weakening and instability, possibly explanatory for the fact that movement in PD deteriorates with repetition. Finally, we speculate on how therapeutic interventions such as deep brain stimulation may be able to reinstate reinforcement signals and thereby improve treatment strategies for PD in the future.

Human Sensorimotor Beta Event Characteristics and Aperiodic Signal Are Highly Heritable

Individuals' phenotypes, including the brain's structure and function, are largely determined by genes and their interplay. The resting brain generates salient rhythmic patterns that can be characterized noninvasively using functional neuroimaging such as magnetoencephalography (MEG). One of these rhythms, the somatomotor (rolandic) beta rhythm, shows intermittent high amplitude "events" that predict behavior across tasks and species. Beta rhythm is altered in neurological disease. The aperiodic (1/f) signal present in electrophysiological recordings is also modulated by some neurological conditions and aging. Both sensorimotor beta and aperiodic signal could thus serve as biomarkers of sensorimotor function. Knowledge about the extent to which these brain functional measures are heritable could shed light on the mechanisms underlying their generation. We investigated the heritability and variability of human spontaneous sensorimotor beta rhythm events and aperiodic activity in 210 healthy male and female adult siblings' spontaneous MEG activity. The most heritable trait was the aperiodic 1/f signal, with a heritability of 0.87 in the right hemisphere. Time-resolved beta event amplitude parameters were also highly heritable, whereas the heritabilities for overall beta power, peak frequency, and measures of event duration remained nonsignificant. Human sensorimotor neural activity can thus be dissected into different components with variable heritability. We postulate that these differences partially reflect different underlying signal-generating mechanisms. The 1/f signal and beta event amplitude measures may depend more on fixed, anatomical parameters, whereas beta event duration and its modulation reflect dynamic characteristics, guiding their use as potential disease biomarkers.

Prefrontal signals precede striatal signals for biased credit assignment in motivational learning biases

Actions are biased by the outcomes they can produce: Humans are more likely to show action under reward prospect, but hold back under punishment prospect. Such motivational biases derive not only from biased response selection, but also from biased learning: humans tend to attribute rewards to their own actions, but are reluctant to attribute punishments to having held back. The neural origin of these biases is unclear. Specifically, it remains open whether motivational biases arise primarily from the architecture of subcortical regions or also reflect cortical influences, the latter being typically associated with increased behavioral flexibility and control beyond stereotyped behaviors. Simultaneous EEG-fMRI allowed us to track which regions encoded biased prediction errors in which order. Biased prediction errors occurred in cortical regions (dorsal anterior and posterior cingulate cortices) before subcortical regions (striatum). These results highlight that biased learning is not a mere feature of the basal ganglia, but arises through prefrontal cortical contributions, revealing motivational biases to be a potentially flexible, sophisticated mechanism.

Beta oscillations in vision: a (preconscious) neural mechanism for the dorsal visual stream?

Neural oscillations in alpha (8-12 Hz) and beta (13-30 Hz) frequency bands are thought to reflect feedback/reentrant loops and large-scale cortical interactions. In the last decades a main effort has been made in linking perception with alpha-band oscillations, with converging evidence showing that alpha oscillations have a key role in the temporal and featural binding of visual input, configuring the alpha rhythm a key determinant of conscious visual experience. Less attention has been historically dedicated to link beta oscillations and visual processing. Nonetheless, increasing studies report that task conditions that require to segregate/integrate stimuli in space, to disentangle local/global shapes, to spatially reorganize visual inputs, and to achieve motion perception or form-motion integration, rely on the activity of beta oscillations, with a main hub in parietal areas. In the present review, we summarize the evidence linking oscillations within the beta band and visual perception. We propose that beta oscillations represent a neural code that supports the functionality of the magnocellular-dorsal (M-D) visual pathway, serving as a fast primary neural code to exert top-down influences on the slower parvocellular-ventral visual pathway activity. Such M-D-related beta activity is proposed to act mainly pre-consciously, providing the spatial coordinates of vision and guiding the conscious extraction of objects identity that are achieved with slower alpha rhythms in ventral areas. Finally, within this new theoretical framework, we discuss the potential role of M-D-related beta oscillations in visuo-spatial attention, oculo-motor behavior and reading (dis)abilities.

Bursting with Potential: How Sensorimotor Beta Bursts Develop from Infancy to Adulthood

Beta activity is thought to play a critical role in sensorimotor processes. However, little is known about how activity in this frequency band develops. Here, we investigated the developmental trajectory of sensorimotor beta activity from infancy to adulthood. We recorded EEG from 9-month-old, 12-month-old, and adult humans (male and female) while they observed and executed grasping movements. We analyzed "beta burst" activity using a novel method that combines time-frequency decomposition and principal component analysis. We then examined the changes in burst rate and waveform motifs along the selected principal components. Our results reveal systematic changes in beta activity during action execution across development. We found a decrease in beta burst rate during movement execution in all age groups, with the greatest decrease observed in adults. Additionally, we identified three principal components that defined waveform motifs that systematically changed throughout the trial. We found that bursts with waveform shapes closer to the median waveform were not rate-modulated, whereas those with waveform shapes further from the median were differentially rate-modulated. Interestingly, the decrease in the rate of certain burst motifs occurred earlier during movement and was more lateralized in adults than in infants, suggesting that the rate modulation of specific types of beta bursts becomes increasingly refined with age.SIGNIFICANCE STATEMENT We demonstrate that, like in adults, sensorimotor beta activity in infants during reaching and grasping movements occurs in bursts, not oscillations like thought traditionally. Furthermore, different beta waveform shapes were differentially modulated with age, including more lateralization in adults. Aberrant beta activity characterizes various developmental disorders and motor difficulties linked to early brain injury, so looking at burst waveform shape could provide more sensitivity for early identification and treatment of affected individuals before any behavioral symptoms emerge. More generally, comparison of beta burst activity in typical versus atypical motor development will also be instrumental in teasing apart the mechanistic functional roles of different types of beta bursts.

Beta-triggered adaptive deep brain stimulation during reaching movement in Parkinson's disease

Subthalamic nucleus (STN) beta-triggered adaptive deep brain stimulation (ADBS) has been shown to provide clinical improvement comparable to conventional continuous DBS (CDBS) with less energy delivered to the brain and less stimulation induced side effects. However, several questions remain unanswered. First, there is a normal physiological reduction of STN beta band power just prior to and during voluntary movement. ADBS systems will therefore reduce or cease stimulation during movement in people with Parkinson's disease and could therefore compromise motor performance compared to CDBS. Second, beta power was smoothed and estimated over a time period of 400 ms in most previous ADBS studies, but a shorter smoothing period could have the advantage of being more sensitive to changes in beta power, which could enhance motor performance. In this study, we addressed these two questions by evaluating the effectiveness of STN beta-triggered ADBS using a standard 400 ms and a shorter 200 ms smoothing window during reaching movements. Results from 13 people with Parkinson's disease showed that reducing the smoothing window for quantifying beta did lead to shortened beta burst durations by increasing the number of beta bursts shorter than 200 ms and more frequent switching on/off of the stimulator but had no behavioural effects. Both ADBS and CDBS improved motor performance to an equivalent extent compared to no DBS. Secondary analysis revealed that there were independent effects of a decrease in beta power and an increase in gamma power in predicting faster movement speed, while a decrease in beta event related desynchronization (ERD) predicted quicker movement initiation. CDBS suppressed both beta and gamma more than ADBS, whereas beta ERD was reduced to a similar level during CDBS and ADBS compared with no DBS, which together explained the achieved similar performance improvement in reaching movements during CDBS and ADBS. In addition, ADBS significantly improved tremor compared with no DBS but was not as effective as CDBS. These results suggest that STN beta-triggered ADBS is effective in improving motor performance during reaching movements in people with Parkinson's disease, and that shortening of the smoothing window does not result in any additional behavioural benefit. When developing ADBS systems for Parkinson's disease, it might not be necessary to track very fast beta dynamics; combining beta, gamma, and information from motor decoding might be more beneficial with additional biomarkers needed for optimal treatment of tremor.

Transient oscillations as computations for cognition: Analysis, modeling and function

Our view of neural oscillations is currently changing. The dominant picture of sustained oscillations is now often replaced by transient oscillations occurring in bursts. This phenomenon seems to be quite comprehensive, as it has been reported for different oscillation frequencies, including the theta, beta, and gamma bands, as well as cortical and subcortical regions in a variety of cognitive tasks and species. Here we review recent developments in their analysis, computational modeling, and functional roles. For the analysis of transient oscillations methods using lagged coherence and Hidden Markov Models have been developed and applied in recent studies to ascertain their transient nature and study their contribution to cognitive functions. Furthermore, computational models have been developed that account for their stochastic nature, which poses interesting functional constraints. Finally, as transient oscillations have been observed across many species, they are likely of functional significance and we consider challenges in characterizing their function.

Wavelet-Based Bracketing, Time-Frequency Beta Burst Detection: New Insights in Parkinson's Disease

Studies have shown that beta band activity is not tonically elevated but comprises exaggerated phasic bursts of varying durations and magnitudes, for Parkinson's disease (PD) patients. Current methods for detecting beta bursts target a single frequency peak in beta band, potentially ignoring bursts in the wider beta band. In this study, we propose a new robust framework for beta burst identification across wide frequency ranges. Chronic local field potential at-rest recordings were obtained from seven PD patients implanted with Medtronic SenSight™ deep brain stimulation (DBS) electrodes. The proposed method uses wavelet decomposition to compute the time-frequency spectrum and identifies bursts spanning multiple frequency bins by thresholding, offering an additional burst measure, ∆f, that captures the width of a burst in the frequency domain. Analysis included calculating burst duration, magnitude, and ∆f and evaluating the distribution and likelihood of bursts between the low beta (13-20 Hz) and high beta (21-35 Hz). Finally, the results of the analysis were correlated to motor impairment (MDS-UPDRS III) med off scores. We found that low beta bursts with longer durations and larger width in the frequency domain (∆f) were positively correlated, while high beta bursts with longer durations and larger ∆f were negatively correlated with motor impairment. The proposed method, finding clear differences between bursting behavior in high and low beta bands, has clearly demonstrated the importance of considering wide frequency bands for beta burst behavior with implications for closed-loop DBS paradigms.

Routing states transition during oscillatory bursts and attentional selection

Brain-wide information routing relies on the spatio-temporal dynamics of neural activity, but it remains unclear how routing states emerge at fast spiking timescales and relate to slower activity dynamics during cognitive processes. Here, we show that localized spiking events participate in directional routing states with spiking activity in distant brain areas that dynamically switch or amplify states during oscillatory bursts, attentional selection, and decision-making. Modeling and neural recordings from lateral prefrontal cortex (LPFC), anterior cingulate cortex (ACC), and striatum of nonhuman primates revealed that cross-regional routing states arise within 20 ms following individual neuron spikes, with LPFC spikes leading the activity in ACC and striatum. The baseline routing state amplified during LPFC beta bursts between LPFC and striatum and switched direction during ACC theta/alpha bursts between ACC and LPFC. Selective attention amplified theta-/alpha-band-specific lead ensembles in ACC, while decision-making increased the lead of ACC and LPFC spikes to the striatum.

Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants

Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.

Dynamic switching of neural oscillations in the prefrontal-amygdala circuit for naturalistic freeze-or-flight

The medial prefrontal cortex (mPFC) and basolateral amygdala (BLA) are involved in the regulation of defensive behavior under threat, but their engagement in flexible behavior shifts remains unclear. Here, we report the oscillatory activities of mPFC-BLA circuit in reaction to a naturalistic threat, created by a predatory robot in mice. Specifically, we found dynamic frequency tuning among two different theta rhythms (~5 or ~10 Hz) was accompanied by agile changes of two different defensive behaviors (freeze-or-flight). By analyzing flight trajectories, we also found that high beta (~30 Hz) is engaged in the top-down process for goal-directed flights and accompanied by a reduction in fast gamma (60 to 120 Hz, peak near 70 Hz). The elevated beta nested the fast gamma activity by its phase more strongly. Our results suggest that the mPFC-BLA circuit has a potential role in oscillatory gear shifting allowing flexible information routing for behavior switches.

Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.

A global pause generates nonselective response inhibition during selective stopping

Selective response inhibition may be required when stopping a part of a multicomponent action. A persistent response delay (stopping-interference effect) indicates nonselective response inhibition during selective stopping. This study aimed to elucidate whether nonselective response inhibition is the consequence of a global pause process during attentional capture or specific to a nonselective cancel process during selective stopping. Twenty healthy human participants performed a bimanual anticipatory response inhibition paradigm with selective stop and ignore signals. Frontocentral and sensorimotor beta-bursts were recorded with electroencephalography. Corticomotor excitability and short-interval intracortical inhibition in primary motor cortex were recorded with transcranial magnetic stimulation. Behaviorally, responses in the non-signaled hand were delayed during selective ignore and stop trials. The response delay was largest during selective stop trials and indicated that stopping-interference could not be attributed entirely to attentional capture. A stimulus-nonselective increase in frontocentral beta-bursts occurred during stop and ignore trials. Sensorimotor response inhibition was reflected in maintenance of beta-bursts and short-interval intracortical inhibition relative to disinhibition observed during go trials. Response inhibition signatures were not associated with the magnitude of stopping-interference. Therefore, nonselective response inhibition during selective stopping results primarily from a nonselective pause process but does not entirely account for the stopping-interference effect.

Using occipital ⍺-bursts to modulate behavior in real-time

Pre-stimulus endogenous neural activity can influence the processing of upcoming sensory input and subsequent behavioral reactions. Despite it is known that spontaneous oscillatory activity mostly appears in stochastic bursts, typical approaches based on trial averaging fail to capture this. We aimed at relating spontaneous oscillatory bursts in the alpha band (8-13 Hz) to visual detection behavior, via an electroencephalography-based brain-computer interface (BCI) that allowed for burst-triggered stimulus presentation in real-time. According to alpha theories, we hypothesized that visual targets presented during alpha-bursts should lead to slower responses and higher miss rates, whereas targets presented in the absence of bursts (low alpha activity) should lead to faster responses and higher false alarm rates. Our findings support the role of bursts of alpha oscillations in visual perception and exemplify how real-time BCI systems can be used as a test bench for brain-behavioral theories.

Measuring the nonselective effects of motor inhibition using isometric force recordings

Inhibition is a key cognitive control mechanism humans use to enable goal-directed behavior. When rapidly exerted, inhibitory control has broad, nonselective motor effects, typically demonstrated using corticospinal excitability measurements (CSE) elicited by transcranial magnetic stimulation (TMS). For example, during rapid action-stopping, CSE is suppressed at both stopped and task-unrelated muscles. While such TMS-based CSE measurements have provided crucial insights into the fronto-basal ganglia circuitry underlying inhibitory control, they have several downsides. TMS is contraindicated in many populations (e.g., epilepsy or deep-brain stimulation patients), has limited temporal resolution, produces distracting auditory and haptic stimulation, is difficult to combine with other imaging methods, and necessitates expensive, immobile equipment. Here, we attempted to measure the nonselective motor effects of inhibitory control using a method unaffected by these shortcomings. Thirty male and female human participants exerted isometric force on a high-precision handheld force transducer while performing a foot-response stop-signal task. Indeed, when foot movements were successfully stopped, force output at the task-irrelevant hand was suppressed as well. Moreover, this nonselective reduction of isometric force was highly correlated with stop-signal performance and showed frequency dynamics similar to established inhibitory signatures typically found in neural and muscle recordings. Together, these findings demonstrate that isometric force recordings can reliably capture the nonselective effects of motor inhibition, opening the door to many applications that are hard or impossible to realize with TMS.

A guide towards optimal detection of transient oscillatory bursts with unknown parameters

Objectives. Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a neural signature that bridges dynamic neural states to cognition and behaviors. Following this insight, our study aimed to (1) compare the efficacy of common burst detection algorithms under varying signal-to-noise ratios and event durations using synthetic signals and (2) establish a strategic guideline for selecting the optimal algorithm for real datasets with undefined properties.Approach.We tested the robustness of burst detection algorithms using a simulation dataset comprising bursts of multiple frequencies. To systematically assess their performance, we used a metric called 'detection confidence', quantifying classification accuracy and temporal precision in a balanced manner. Given that burst properties in empirical data are often unknown in advance, we then proposed a selection rule to identify an optimal algorithm for a given dataset and validated its application on local field potentials of basolateral amygdala recorded from male mice (n=8) exposed to a natural threat.Main Results.Our simulation-based evaluation demonstrated that burst detection is contingent upon event duration, whereas accurately pinpointing burst onsets is more susceptible to noise level. For real data, the algorithm chosen based on the selection rule exhibited superior detection and temporal accuracy, although its statistical significance differed across frequency bands. Notably, the algorithm chosen by human visual screening differed from the one recommended by the rule, implying a potential misalignment between human priors and mathematical assumptions of the algorithms.Significance.Therefore, our findings underscore that the precise detection of transient bursts is fundamentally influenced by the chosen algorithm. The proposed algorithm-selection rule suggests a potentially viable solution, while also emphasizing the inherent limitations originating from algorithmic design and volatile performances across datasets. Consequently, this study cautions against relying solely on heuristic-based approaches, advocating for a careful algorithm selection in burst detection studies.

Average power and burst analysis revealed complementary information on drug-related changes of motor performance in Parkinson's disease

In patients with Parkinson's disease (PD), suppression of beta and increase in gamma oscillations in the subthalamic nucleus (STN) have been associated with both levodopa treatment and motor functions. Recent results suggest that modulation of the temporal dynamics of theses oscillations (bursting activity) might contain more information about pathological states and behaviour than their average power. Here we directly compared the information provided by power and burst analyses about the drug-related changes in STN activities and their impact on motor performance within PD patients. STN local field potential (LFP) signals were recorded from externalized patients performing self-paced movements ON and OFF levodopa. When normalised across medication states, both power and burst analyses showed an increase in low-beta oscillations in the dopamine-depleted state during rest. When normalised within-medication state, both analyses revealed that levodopa increased movement-related modulation in the alpha and low-gamma bands, with higher gamma activity around movement predicting faster reaches. Finally, burst analyses helped to reveal opposite drug-related changes in low- and high-beta frequency bands, and identified additional within-patient relationships between high-beta bursting and movement performance. Our findings suggest that although power and burst analyses share a lot in common they also provide complementary information on how STN-LFP activity is associated with motor performance, and how levodopa treatment may modify these relationships in a way that helps explain drug-related changes in motor performance. Different ways of normalisation in the power analysis can reveal different information. Similarly, the burst analysis is sensitive to how the threshold is defined - either for separate medication conditions separately, or across pooled conditions. In addition, the burst interpretation has far-reaching implications about the nature of neural oscillations - whether the oscillations happen as isolated burst-events or are they sustained phenomena with dynamic amplitude variations? This can be different for different frequency bands, and different for different medication states even for the same frequency band.

Attenuated post-movement beta rebound reflects psychomotor alterations in major depressive disorder during a simple visuomotor task: a MEG study

BACKGROUND

Psychomotor alterations are a common symptom in patients with major depressive disorder (MDD). The primary motor cortex (M1) plays a vital role in the mechanism of psychomotor alterations. Post-movement beta rebound (PMBR) in the sensorimotor cortex is abnormal in patients with motor abnormalities. However, the changes in M1 beta rebound in patients with MDD remain unclear. This study aimed to primarily explore the relationship between psychomotor alterations and PMBR in MDD.

METHODS

One hundred thirty-two subjects were enrolled in the study, comprising 65 healthy controls (HCs) and 67 MDD patients. All participants performed a simple right-hand visuomotor task during MEG scanning. PMBR was measured in the left M1 at the source reconstruction level with the time-frequency analysis method. Retardation factor scores and neurocognitive test performance, including the Digit Symbol Substitution Test (DSST), the Making Test Part A (TMT-A), and the Verbal Fluency Test (VFT), were used to measure psychomotor functions. Pearson correlation analyses were used to assess relationships between PMBR and psychomotor alterations in MDD.

RESULTS

The MDD group showed worse neurocognitive performance than the HC group in all three neurocognitive tests. The PMBR was diminished in patients with MDD compared to HCs. In a group of MDD patients, the reduced PMBR was negatively correlated with retardation factor scores. Further, there was a positive correlation between the PMBR and DSST scores. PMBR is negatively associated with the TMT-A scores.

CONCLUSION

Our findings suggested that the attenuated PMBR in M1 could illustrate the psychomotor disturbance in MDD, possibly contributing to clinical psychomotor symptoms and deficits of cognitive functions.

First-in-human prediction of chronic pain state using intracranial neural biomarkers

Chronic pain syndromes are often refractory to treatment and cause substantial suffering and disability. Pain severity is often measured through subjective report, while objective biomarkers that may guide diagnosis and treatment are lacking. Also, which brain activity underlies chronic pain on clinically relevant timescales, or how this relates to acute pain, remains unclear. Here four individuals with refractory neuropathic pain were implanted with chronic intracranial electrodes in the anterior cingulate cortex and orbitofrontal cortex (OFC). Participants reported pain metrics coincident with ambulatory, direct neural recordings obtained multiple times daily over months. We successfully predicted intraindividual chronic pain severity scores from neural activity with high sensitivity using machine learning methods. Chronic pain decoding relied on sustained power changes from the OFC, which tended to differ from transient patterns of activity associated with acute, evoked pain states during a task. Thus, intracranial OFC signals can be used to predict spontaneous, chronic pain state in patients.

Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.

Beta Oscillations in Monkey Striatum Encode Reward Prediction Error Signals

Reward prediction error (RPE) signals are crucial for reinforcement learning and decision-making as they quantify the mismatch between predicted and obtained rewards. RPE signals are encoded in the neural activity of multiple brain areas, such as midbrain dopaminergic neurons, prefrontal cortex, and striatum. However, it remains unclear how these signals are expressed through anatomically and functionally distinct subregions of the striatum. In the current study, we examined to which extent RPE signals are represented across different striatal regions. To do so, we recorded local field potentials (LFPs) in sensorimotor, associative, and limbic striatal territories of two male rhesus monkeys performing a free-choice probabilistic learning task. The trial-by-trial evolution of RPE during task performance was estimated using a reinforcement learning model fitted on monkeys' choice behavior. Overall, we found that changes in beta band oscillations (15-35 Hz), after the outcome of the animal's choice, are consistent with RPE encoding. Moreover, we provide evidence that the signals related to RPE are more strongly represented in the ventral (limbic) than dorsal (sensorimotor and associative) part of the striatum. To conclude, our results suggest a relationship between striatal beta oscillations and the evaluation of outcomes based on RPE signals and highlight a major contribution of the ventral striatum to the updating of learning processes.SIGNIFICANCE STATEMENT Reward prediction error (RPE) signals are crucial for reinforcement learning and decision-making as they quantify the mismatch between predicted and obtained rewards. Current models suggest that RPE signals are encoded in the neural activity of multiple brain areas, including the midbrain dopaminergic neurons, prefrontal cortex and striatum. However, it remains elusive whether RPEs recruit anatomically and functionally distinct subregions of the striatum. Our study provides evidence that RPE-related modulations in local field potential (LFP) power are dominant in the striatum. In particular, they are stronger in the rostro-ventral rather than the caudo-dorsal striatum. Our findings contribute to a better understanding of the role of striatal territories in reward-based learning and may be relevant for neuropsychiatric and neurologic diseases that affect striatal circuits.

Two modes of midfrontal theta suggest a role in conflict and error processing

Midfrontal theta increases during scenarios when conflicts are successfully resolved. Often considered a generic signal of cognitive control, its temporal nature has hardly been investigated. Using advanced spatiotemporal techniques, we uncover that midfrontal theta occurs as a transient oscillation or "event" at single trials with their timing reflecting computationally distinct modes. Single-trial analyses of electrophysiological data from participants performing the Flanker (N = 24) and Simon task (N = 15) were used to probe the relationship between theta and metrics of stimulus-response conflict. We specifically investigated "partial errors", in which a small burst of muscle activity in the incorrect response effector occurred, quickly followed by a correction. We found that transient theta events in single trials could be categorized into two distinct theta modes based on their relative timing to different task events. Theta events from the first mode occurred briefly after the task stimulus and might reflect conflict-related processing of the stimulus. In contrast, theta events from the second mode were more likely to occur around the time partial errors were committed, suggesting they were elicited by a potential upcoming error. Importantly, in trials in which a full error was committed, this "error-related theta" occurred too late with respect to the onset of the erroneous muscle response, supporting the role of theta also in error correction. We conclude that different modes of transient midfrontal theta can be adopted in single trials not only to process stimulus-response conflict, but also to correct erroneous responses.

Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts

Well-known haemodynamic resting-state networks are better mirrored in power correlation networks than phase coupling networks in electrophysiological data. However, what do these power correlation networks reflect? We address this long-outstanding question in neuroscience using rigorous mathematical analysis, biophysical simulations with ground truth and application of these mathematical concepts to empirical magnetoencephalography (MEG) data. Our mathematical derivations show that for two non-Gaussian electrophysiological signals, their power correlation depends on their coherence, cokurtosis and conjugate-coherence. Only coherence and cokurtosis contribute to power correlation networks in MEG data, but cokurtosis is less affected by artefactual signal leakage and better mirrors haemodynamic resting-state networks. Simulations and MEG data show that cokurtosis may reflect co-occurrent bursting events. Our findings shed light on the origin of the complementary nature of power correlation networks to phase coupling networks and suggests that the origin of resting-state networks is partly reflected in co-occurent bursts in neuronal activity.

β-Bursts over Frontal Cortex Track the Surprise of Unexpected Events in Auditory, Visual, and Tactile Modalities

One of the fundamental ways in which the brain regulates and monitors behavior is by making predictions about the sensory environment and adjusting behavior when those expectations are violated. As such, surprise is one of the fundamental computations performed by the human brain. In recent years, it has been well established that one key aspect by which behavior is adjusted during surprise is inhibitory control of the motor system. Moreover, because surprise automatically triggers inhibitory control without much proactive influence, it can provide unique insights into largely reactive control processes. Recent years have seen tremendous interest in burst-like β frequency events in the human (and nonhuman) local field potential-especially over (p)FC-as a potential signature of inhibitory control. To date, β-bursts have only been studied in paradigms involving a substantial amount of proactive control (such as the stop-signal task). Here, we used two cross-modal oddball tasks to investigate whether surprise processing is accompanied by increases in scalp-recorded β-bursts. Indeed, we found that unexpected events in all tested sensory domains (haptic, auditory, visual) were followed by low-latency increases in β-bursting over frontal cortex. Across experiments, β-burst rates were positively correlated with estimates of surprise derived from Shannon's information theory, a type of surprise that represents the degree to which a given stimulus violates prior expectations. As such, the current work clearly implicates frontal β-bursts as a signature of surprise processing. We discuss these findings in the context of common frameworks of inhibitory and cognitive control after unexpected events.

Using convolutional dictionary learning to detect task-related neuromagnetic transients and ageing trends in a large open-access dataset

Human neuromagnetic activity is characterised by a complex combination of transient bursts with varying spatial and temporal characteristics. The characteristics of these transient bursts change during task performance and normal ageing in ways that can inform about underlying cortical sources. Many methods have been proposed to detect transient bursts, with the most successful ones being those that employ multi-channel, data-driven approaches to minimize bias in the detection procedure. There has been little research, however, into the application of these data-driven methods to large datasets for group-level analyses. In the current work, we apply a data-driven convolutional dictionary learning (CDL) approach to detect neuromagnetic transient bursts in a large group of healthy participants from the Cam-CAN dataset. CDL was used to extract repeating spatiotemporal motifs in 538 participants between the ages of 18-88 during a sensorimotor task. Motifs were then clustered across participants based on similarity, and relevant task-related clusters were analysed for age-related trends in their spatiotemporal characteristics. Seven task-related motifs resembling known transient burst types were identified through this analysis, including beta, mu, and alpha type bursts. All burst types showed positive trends in their activation levels with age that could be explained by increasing burst rate with age. This work validated the data-driven CDL approach for transient burst detection on a large dataset and identified robust information about the complex characteristics of human brain signals and how they change with age.

When do bursts matter in the primary motor cortex? Investigating changes in the intermittencies of beta rhythms associated with movement states

Brain activity exhibits significant temporal structure that is not well captured in the power spectrum. Recently, attention has shifted to characterising the properties of intermittencies in rhythmic neural activity (i.e. bursts), yet the mechanisms that regulate them are unknown. Here, we present evidence from electrocorticography recordings made over the motor cortex to show that the statistics of bursts, such as duration or amplitude, in the beta frequency (14-30 Hz) band, significantly aid the classification of motor states such as rest, movement preparation, execution, and imagery. These features reflect nonlinearities not detectable in the power spectrum, with states increasing in nonlinearity from movement execution to preparation to rest. Further, we show using a computational model of the cortical microcircuit, constrained to account for burst features, that modulations of laminar specific inhibitory interneurons are responsible for the temporal organisation of activity. Finally, we show that the temporal characteristics of spontaneous activity can be used to infer the balance of cortical integration between incoming sensory information and endogenous activity. Critically, we contribute to the understanding of how transient brain rhythms may underwrite cortical processing, which in turn, could inform novel approaches for brain state classification, and modulation with novel brain-computer interfaces.

Subthalamic beta bursts correlate with dopamine-dependent motor symptoms in 106 Parkinson's patients

Pathologically increased beta power has been described as a biomarker for Parkinson's disease (PD) and related to prolonged bursts of subthalamic beta synchronization. Here, we investigate the association between subthalamic beta dynamics and motor impairment in a cohort of 106 Parkinson's patients in the ON- and OFF-medication state, using two different methods of beta burst determination. We report a frequency-specific correlation of low beta power and burst duration with motor impairment OFF dopaminergic medication. Furthermore, reduction of power and burst duration correlated significantly with symptom alleviation through dopaminergic medication. Importantly, qualitatively similar results were yielded with two different methods of beta burst definition. Our findings validate the robustness of previous results on pathological changes in subcortical oscillations both in the frequency- as well as in the time-domain in the largest cohort of PD patients to date with important implications for next-generation adaptive deep brain stimulation control algorithms.

Phase-adaptive brain stimulation of striatal D1 medium spiny neurons in dopamine-depleted mice

Brain rhythms are strongly linked with behavior, and abnormal rhythms can signify pathophysiology. For instance, the basal ganglia exhibit a wide range of low-frequency oscillations during movement, but pathological "beta" rhythms at ~ 20 Hz have been observed in Parkinson's disease (PD) and in PD animal models. All brain rhythms have a frequency, which describes how often they oscillate, and a phase, which describes the precise time that peaks and troughs of brain rhythms occur. Although frequency has been extensively studied, the relevance of phase is unknown, in part because it is difficult to causally manipulate the instantaneous phase of ongoing brain rhythms. Here, we developed a phase-adaptive, real-time, closed-loop algorithm to deliver optogenetic stimulation at a specific phase with millisecond latency. We combined this Phase-Adaptive Brain STimulation (PABST) approach with cell-type-specific optogenetic methods to stimulate basal ganglia networks in dopamine-depleted mice that model motor aspects of human PD. We focused on striatal medium spiny neurons expressing D1-type dopamine receptors because these neurons can facilitate movement. We report three main results. First, we found that our approach delivered PABST within system latencies of 13 ms. Second, we report that closed-loop stimulation powerfully influenced the spike-field coherence of local brain rhythms within the dorsal striatum. Finally, we found that both 4 Hz PABST and 20 Hz PABST improved movement speed, but we found differences between phase only with 4 Hz PABST. These data provide causal evidence that phase is relevant for brain stimulation, which will allow for more precise, targeted, and individualized brain stimulation. Our findings are applicable to a broad range of preclinical brain stimulation approaches and could also inform circuit-specific neuromodulation treatments for human brain disease.

Modulation of cortical beta oscillations influences motor vigor: A rhythmic TMS-EEG study

Previous electro- or magnetoencephalography (Electro/Magneto EncephaloGraphic; E/MEG) studies using a correlative approach have shown that β (13-30 Hz) oscillations emerging in the primary motor cortex (M1) are implicated in regulating motor response vigor and associated with an anti-kinetic role, that is, slowness of movement. However, the functional role of M1 β oscillations in regulation of motor responses remains unclear. To address this gap, we combined EEG with rhythmic TMS (rhTMS) delivered to M1 at the β (20 Hz) frequency shortly before subjects performed an isometric ramp-and-hold finger force production task at three force levels. rhTMS is a novel approach that can modulate rhythmic patterns of neural activity. β-rhTMS over M1 induced a modulation of neural oscillations to β frequency in the sensorimotor area and reduced peak force rate during the ramp-up period relative to sham and catch trials. Interestingly, this rhTMS effect occurred only in the large force production condition. To distinguish whether the effects of rhTMS on EEG and behavior stemmed from phase-resetting by each magnetic pulse or neural entrainment by the periodicity of rhTMS, we performed a control experiment using arrhythmic TMS (arTMS). arTMS did not induce changes in EEG oscillations nor peak force rate during the rump-up period. Our results provide novel evidence that β neural oscillations emerging the sensorimotor area influence the regulation of motor response vigor. Furthermore, our findings further demonstrate that rhTMS is a promising tool for tuning neural oscillations to the target frequency.

Stable, interactive modulation of neuronal oscillations produced through brain-machine equilibrium

Closed-loop interaction has the potential to regulate ongoing brain activity by continuously binding an external stimulation to specific dynamics of a neural circuit. Achieving interactive modulation requires a stable brain-machine feedback loop. Here, we demonstrate that it is possible to maintain oscillatory brain activity in a desired state by delivering stimulation accurately aligned with the timing of each cycle. We develop a fast algorithm that responds on a cycle-by-cycle basis to stimulate basal ganglia nuclei at predetermined phases of successive cortical beta cycles in parkinsonian rats. Using this approach, an equilibrium emerges between the modified brain signal and feedback-dependent stimulation pattern, leading to sustained amplification or suppression of the oscillation depending on the phase targeted. Beta amplification slows movement speed by biasing the animal's mode of locomotion. Together, these findings show that highly responsive, phase-dependent stimulation can achieve a stable brain-machine interaction that leads to robust modulation of ongoing behavior.

Developments in the mechanistic understanding and clinical application of deep brain stimulation for Parkinson's disease

INTRODUCTION. Deep brain stimulation (DBS) is a life-changing treatment for patients with Parkinson's disease (PD) and gives the unique opportunity to directly explore how basal ganglia work. Despite the rapid technological innovation of the last years, the untapped potential of DBS is still high.

AREAS COVERED. This review summarizes the developments in the mechanistic understanding of DBS and the potential clinical applications of cutting-edge technological advances. Rather than a univocal local mechanism, DBS exerts its therapeutic effects through several multimodal mechanisms and involving both local and network-wide structures, although crucial questions remain unexplained. Nonetheless, new insights in mechanistic understanding of DBS in PD have provided solid bases for advances in preoperative selection phase, prediction of motor and non-motor outcomes, leads placement and postoperative stimulation programming.

EXPERT OPINION. DBS has not only strong evidence of clinical effectiveness in PD treatment, but technological advancements are revamping its role of neuromodulation of brain circuits and key to better understanding PD pathophysiology. In the next few years, the worldwide use of new technologies in clinical practice will provide large data to elucidate their role and to expand their applications for PD patients, providing useful insights to personalize DBS treatment and follow-up.

Cortical network formation based on subthalamic beta bursts in Parkinson's disease

Recent evidence suggests that beta bursts in subthalamic nucleus (STN) play an important role in Parkinsonian pathophysiology. We studied the spatio-temporal relationship between STN beta bursts and cortical activity in 26 Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery. Postoperatively, we simultaneously recorded STN local field potentials (LFP) from externalized DBS leads and cortical activity using whole-brain magnetoencephalography. Event-related magnetic fields (ERF) were averaged time-locked to STN beta bursts and subjected to source localization. Our results demonstrate that ERF exhibiting activity significantly different from baseline activity were localized within areas functionally related to associative, limbic, and motor systems as well as regions pertinent for visual and language processing. Our data suggest that STN beta bursts are involved in network formation between STN and cortex. This interaction is in line with the idea of parallel processing within the basal ganglia-cortex loop, specifically within the functional subsystems of the STN (i.e., associative, limbic, motor, and the related cortical areas). ERFs within visual and language-related cortical areas indicate involvement of beta bursts in STN-cortex networks beyond the associative, limbic, and motor loops. In sum, our results highlight the involvement of STN beta bursts in the formation of multiple STN - cortex loops in patients with PD.