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Time series of brain activity recorded from different anatomical regions and in different behavioural states and pathologies can be summarised by the power spectrum. Recently, attention has shifted to characterising the properties of changing temporal dynamics in rhythmic neural activity. Here, we present evidence from electrocorticography recordings made from the motor cortex to show that, dependent on the specific motor context, the statistics of temporal transients in beta frequency (14-30 Hz) rhythms (i.e., bursts) can significantly add to the description of states such rest, movement preparation, movement execution, and movement imagery. We show that the statistics of burst duration and amplitude can significantly improve the classification of motor states and that burst features reflect nonlinearities not detectable in the power spectrum, with states increasing in order of nonlinearity from movement execution to movement preparation to rest. Further, we provide mechanistic explanations for these features by fitting models of the motor cortical microcircuit to the empirical data and investigate how dynamical instabilities interact with noise to generate burst dynamics. Finally, we examine how beta bursting in motor cortex may influence the integration of exogenous inputs to the cortex and suggest that properties of spontaneous activity cannot be reliably used to infer the response of the cortex to external inputs. These findings have significance for the classification of motor states, for instance in novel brain-computer interfaces. Critically, we increase the understanding of how transient brain rhythms may contribute to cortical processing, which in turn, may inform novel approaches for its modulation with brain stimulation.

BackgroundIn Essential tremor (ET), involuntary shaking of the upper limbs during isometric muscle contraction closely reflects the patterns of neural activity measured in the thalamus - a key element of the tremorgenic circuit. Phase-specific deep brain stimulation (DBS) builds upon this observation while using accelerometery of the trembling limb to trigger repetitive electrical perturbations to the thalamus and surrounding areas at a specific time within the tremor cycle. This closed-loop strategy has been shown to induce clinically significant postural tremor relief while delivering less than half the energy of conventional DBS.ObjectiveThe main aim of the study was to evaluate treatment efficacy across different contexts and movement states.MethodsWe used accelerometery and a digitizing tablet to record the peripheral tremor dynamics of 4 DBS implanted ET patients while alternating stimulation strategies (no stimulation, continuous open-loop and phase-specific) and movement states (intermittent posture holding and spiral drawing).ResultsIn addition to observing a suppressive effect of phase-specific DBS on both postural and kinetic tremor, our results reinforce the key role of phase-specificity to achieve tremor control in postural motor states and highlight the difficulty of quantifying phase-dependent effects during continuous movement. Moreover, this study supports the hypothesis that ET patients with more stable tremor characteristics benefit the most from phase-specific DBS.ConclusionsBy creating a better understanding of the dynamic relationship between central and peripheral tremor activity, this study provides important insights for the development of effective patient and context-specific therapeutic approaches for ET.

SUMMARYSelective attention implements preferential routing of attended stimuli, likely through increasing the influence of the respective synaptic inputs on higher-area neurons. As the inputs of competing stimuli converge onto postsynaptic neurons, presynaptic circuits might offer the best target for attentional top-down influences. If those influences enabled presynaptic circuits to selectively entrain postsynaptic neurons, this might lead to selective routing. Indeed, when two visual stimuli induce two gamma rhythms in V1, only the gamma induced by the attended stimulus entrains gamma in V4. Here, we modeled this selective entrainment with a Dynamic Causal Model for Cross-Spectral Densities and found that it can be explained by attentional modulation of intrinsic V1 connections. Specifically, local inhibition was decreased in the granular input layer and increased in the supragranular output layer of the V1 circuit that processed the attended stimulus. Thus, presynaptic attentional influences and ensuing entrainment were sufficient to mediate selective routing.

Significance Prevalence and temporal dynamics of transient oscillations in the beta frequency band (15 to 35 Hz), referred to as β bursts, are correlated with motor performance. Disturbance of these activities is a candidate mechanism for motor impairment in Parkinson’s disease (PD), where the excessively long bursts correlate with symptom severity and are reduced by pharmacological and surgical treatments. Here we describe the changes in action potential firing that take place across multiple nodes of the cortical and basal ganglia circuit as these transient oscillations evolve. These analyses provide fresh insights into the network dynamics of β bursts that can guide novel strategies to interfere with their generation and maintenance in PD., Beta frequency oscillations (15 to 35 Hz) in cortical and basal ganglia circuits become abnormally synchronized in Parkinson’s disease (PD). How excessive beta oscillations emerge in these circuits is unclear. We addressed this issue by defining the firing properties of basal ganglia neurons around the emergence of cortical beta bursts (β bursts), transient (50 to 350 ms) increases in the beta amplitude of cortical signals. In PD patients, the phase locking of background spiking activity in the subthalamic nucleus (STN) to frontal electroencephalograms preceded the onset and followed the temporal profile of cortical β bursts, with conditions of synchronization consistent within and across bursts. Neuronal ensemble recordings in multiple basal ganglia structures of parkinsonian rats revealed that these dynamics were recapitulated in STN, but also in external globus pallidus and striatum. The onset of consistent phase-locking conditions was preceded by abrupt phase slips between cortical and basal ganglia ensemble signals. Single-unit recordings demonstrated that ensemble-level properties of synchronization were not underlain by changes in firing rate but, rather, by the timing of action potentials in relation to cortical oscillation phase. Notably, the preferred angle of phase-locked action potential firing in each basal ganglia structure was shifted during burst initiation, then maintained stable phase relations during the burst. Subthalamic, pallidal, and striatal neurons engaged and disengaged with cortical β bursts to different extents and timings. The temporal evolution of cortical and basal ganglia synchronization is cell type-selective, which could be key for the generation/ maintenance of excessive beta oscillations in parkinsonism.

Essential tremor is a common neurological disorder, characterised by involuntary shaking of a limb. Patients are usually treated using medications which have limited effects on tremor and may cause side-effects. Surgical therapies are effective in reducing essential tremor, however, the invasive nature of these therapies together with the high cost, greatly limit the number of patients benefiting from them. Non-invasive therapies have gained increasing traction to meet this clinical need. Here, we test a non-invasive and closed-loop electrical stimulation paradigm which tracks peripheral tremor and targets thalamic afferents to modulate the central oscillators underlying tremor. To this end, 9 patients had electrical stimulation delivered to the median nerve locked to different phases of tremor. Peripheral stimulation induced a subtle but significant modulation in five out of nine patients—this modulation consisted mainly of amplification rather than suppression of tremor amplitude. Modulatory effects of stimulation were more pronounced when patient’s tremor was spontaneously weaker at stimulation onset, when significant modulation became more frequent amongst subjects. This data suggests that for selected individuals, a more sophisticated control policy entailing an online estimate of both tremor phase and amplitude, should be considered in further explorations of the treatment potential of tremor phase-locked peripheral stimulation.

Patients with advanced Parkinson's can be treated by deep brain stimulation (DBS) of the subthalamic nucleus (STN). This affords a unique opportunity to record from this nucleus and stimulate it in a controlled manner. Previous work has shown that activity in the STN is modulated in a rhythmic pattern when Parkinson's patients perform stepping movements, raising the question whether the STN is involved in the dynamic control of stepping., Patients with advanced Parkinson's can be treated by deep brain stimulation (DBS) of the subthalamic nucleus (STN). This affords a unique opportunity to record from this nucleus and stimulate it in a controlled manner. Previous work has shown that activity in the STN is modulated in a rhythmic pattern when Parkinson's patients perform stepping movements, raising the question whether the STN is involved in the dynamic control of stepping. To answer this question, we tested whether an alternating stimulation pattern resembling the stepping-related modulation of activity in the STN could entrain patients' stepping movements as evidence of the STN's involvement in stepping control. Group analyses of 10 Parkinson's patients (one female) showed that alternating stimulation significantly entrained stepping rhythms. We found a remarkably consistent alignment between the stepping and stimulation cycle when the stimulation speed was close to the stepping speed in the five patients that demonstrated significant individual entrainment to the stimulation cycle. Our study suggests that the STN is causally involved in dynamic control of step timing and motivates further exploration of this biomimetic stimulation pattern as a potential basis for the development of DBS strategies to ameliorate gait impairments. SIGNIFICANCE STATEMENT We tested whether the subthalamic nucleus (STN) in humans is causally involved in controlling stepping movements. To this end, we studied patients with Parkinson's disease who have undergone therapeutic deep brain stimulation (DBS), as in these individuals we can stimulate the STNs in a controlled manner. We developed an alternating pattern of stimulation that mimics the pattern of activity modulation recorded in this nucleus during stepping. The alternating DBS (altDBS) could entrain patients' stepping rhythm, suggesting a causal role of the STN in dynamic gait control. This type of stimulation may potentially form the basis for improved DBS strategies for gait.

This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain – using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison – asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software., Highlights • A method is introduced for Bayesian fusion of M/EEG and BOLD fMRI data using dynamic causal modelling. • The key novel contribution is the introduction of neural drive functions, which link models of the two modalities. • Bayesian model comparison is used to select plausible hypotheses about the function of neurovascular coupling.

Essential tremor manifests predominantly as a tremor of the upper limbs. One therapy option is high-frequency deep brain stimulation, which continuously delivers electrical stimulation to the ventral intermediate nucleus of the thalamus at about 130 Hz. Constant stimulation can lead to side effects, it is therefore desirable to find ways to stimulate less while maintaining clinical efficacy. One strategy, phase-locked deep brain stimulation, consists of stimulating according to the phase of the tremor. To advance methods to optimise deep brain stimulation while providing insights into tremor circuits, we ask the question: can the effects of phase-locked stimulation be accounted for by a canonical Wilson–Cowan model? We first analyse patient data, and identify in half of the datasets significant dependence of the effects of stimulation on the phase at which stimulation is provided. The full nonlinear Wilson–Cowan model is fitted to datasets identified as statistically significant, and we show that in each case the model can fit to the dynamics of patient tremor as well as to the phase response curve. The vast majority of top fits are stable foci. The model provides satisfactory prediction of how patient tremor will react to phase-locked stimulation by predicting patient amplitude response curves although they were not explicitly fitted. We also approximate response curves of the significant datasets by providing analytical results for the linearisation of a stable focus model, a simplification of the Wilson–Cowan model in the stable focus regime. We report that the nonlinear Wilson–Cowan model is able to describe response to stimulation more precisely than the linearisation.

Currently employed strategies for therapeutic brain stimulation take a static approach to determining stimulation parameters. However, it is well understood that brain states fluctuate over time, depending for instance upon differing behavioural or disease states. Here, we characterize the impact of changes in connectivity upon the emergence of rhythmic neural activity in the circuits formed by the cortex, basal-ganglia, and thalamus. Importantly, we show how the efficacy of interaction with these rhythms via phase-specific stimulation is highly dependent upon the current network state. We take a computational approach to do this, modelling the population activity of the cortico-basal ganglia-thalamic circuit and fitting model parameters to match the spectral features of empirical data obtained from a 6-OHDA lesioned rat model of Parkinson’s disease. Using this fitted model, we then dissect the role of the circuit’s multiple loops in the maintenance of subcortical beta rhythms and their synchronization. We show that a competition of cortical and striato-pallidal inputs to the subthalamic nucleus, a main input hub of the basal-ganglia, determines the frequency, amplitude, and timing of beta band (14-30 Hz) activity. In addition, we demonstrate how the efficacy of cortical inputs in modulating ongoing subthalamic beta activity is dependent upon their relative phase alignment- with their precise effects in turn determined by the connectivity state of the network. These results inform our understanding of: (a) how alterations in circuit connectivity can lead to the emergence of pathologically amplified rhythms; (b) how precisely timed phasic stimulation can be leveraged to modulate aberrant brain activity; and (c) how effective stimulation parameters depend on the “connectivity state” of the circuit; highlighting the importance of incorporating an estimation of brain state in the determination of optimum stimulation parameters.

We present a technical development in the dynamic causal modelling of electrophysiological responses that combines qualitatively different neural mass models within a single network. This affords the option to couple various cortical and subcortical nodes that differ in their form and dynamics. Moreover, it enables users to implement new neural mass models in a straightforward and standardized way. This generic framework hence supports flexibility and facilitates the exploration of increasingly plausible models. We illustrate this by coupling a basal ganglia-thalamus model to a (previously validated) cortical model developed specifically for motor cortex. The ensuing DCM is used to infer pathways that contribute to the suppression of beta oscillations induced by dopaminergic medication in patients with Parkinson's disease. Experimental recordings were obtained from deep brain stimulation electrodes (implanted in the subthalamic nucleus) and simultaneous magnetoencephalography. In line with previous studies, our results indicate a reduction of synaptic efficacy within the circuit between the subthalamic nucleus and external pallidum, as well as reduced efficacy in connections of the hyperdirect and indirect pathway leading to this circuit. This work forms the foundation for a range of modelling studies of the synaptic mechanisms (and pathophysiology) underlying event-related potentials and cross-spectral densities.

Brain networks and the neural dynamics that unfold upon them are of great interest across the many scales of systems neuroscience. The tools of inverse modelling provide a way of both constraining and selecting models of large scale brain networks from empirical data. Such models have the potential to yield broad theoretical insights in the understanding of the physiological processes behind the integration and segregation of activity in the brain. In order to make inverse modelling computationally tractable, simplifying model assumptions have often been adopted that appeal to steady-state approximations to neural dynamics and thus prevent the investigation of stochastic or intermittent dynamics such as gamma or beta burst activity. In this work we describe a framework that uses the Approximate Bayesian Computation (ABC) algorithm for the inversion of neural models that can flexibly represent any statistical feature of empirically recorded data and eschew the need to assume a locally linearized system. Further, we demonstrate how Bayesian model comparison can be applied to fitted models to enable the selection of competing hypotheses regarding the causes of neural data. This work establishes a validation of the procedures by testing for both the face validity (i.e. the ability to identify the original model that has generated the observed data) and predictive validity (i.e. the consistency of the parameter estimation across multiple realizations of the same data). From the validation and example applications presented here we conclude that the proposed framework provides a novel opportunity to researchers aiming to explain how complex brain dynamics emerge from neural circuits.

Deep brain stimulation (DBS) is an effective treatment for common movement disorders and has been used to modulate neural activity through delivery of electrical stimulation to key brain structures. The long-term efficacy of stimulation in treating disorders, such as Parkinson’s disease and essential tremor, has encouraged its application to a wide range of neurological and psychiatric conditions. Nevertheless, adoption of DBS remains limited, even in Parkinson’s disease. Recent failed clinical trials of DBS in major depression, and modest treatment outcomes in dementia and epilepsy, are spurring further development. These improvements focus on interaction with disease circuits through complementary, spatially and temporally specific approaches. Spatial specificity is promoted by the use of segmented electrodes and field steering, and temporal specificity involves the delivery of patterned stimulation, mostly controlled through disease-related feedback. Underpinning these developments are new insights into brain structure–function relationships and aberrant circuit dynamics, including new methods with which to assess and refine the clinical effects of stimulation.

Essential tremor manifests predominantly as a tremor of the upper limbs. One therapy option is high-frequency deep brain stimulation, which continuously delivers electrical stimulation to the ventral intermediate nucleus of the thalamus at about 130 Hz. Investigators have been looking at stimulating less, chiefly to reduce side effects. One strategy, phase-locked deep brain stimulation, consists of stimulating according to the phase of the tremor, once per period. In this study, we aim to reproduce the phase dependent effects of stimulation seen in patient data with a biologically inspired Wilson-Cowan model. To this end, we first analyse patient data, and conclude that half of the datasets have response curves that are better described by sinusoidal curves than by straight lines, while an effect of phase cannot be consistently identified in the remaining half. Using the Hilbert phase we derive analytical expressions for phase and amplitude responses to phase-dependent stimulation and study their relationship in the linearisation of a stable focus model, a simplification of the Wilson-Cowan model in the stable focus regime. Analytical results provide a good approximation for response curves observed in patients with consistent significance. Additionally, we fitted the full non-linear Wilson-Cowan model to these patients, and we show that the model can fit in each case to the dynamics of patient tremor as well as the phase response curve, and the best fits are found to be stable foci for each patients (tied best fit in one instance). The model provides satisfactory prediction of how patient tremor will react to phase-locked stimulation by predicting patient amplitude response curves although they were not explicitly fitted. This can be partially explained by the relationship between the response curves in the model being compatible with what is found in the data. We also note that the non-linear Wilson-Cowan model is able to describe response to stimulation more precisely than the linearisation.

Synchronized oscillations within and between brain areas facilitate normal processing, but are often amplified in disease. A prominent example is the abnormally sustained beta-frequency (∼20 Hz) oscillations recorded from the cortex and subthalamic nucleus of Parkinson's disease patients. Computational modeling suggests that the amplitude of such oscillations could be modulated by applying stimulation at a specific phase. Such a strategy would allow selective targeting of the oscillation, with relatively little effect on other activity parameters. Here, activity was recorded from 10 awake, parkinsonian patients (6 male, 4 female human subjects) undergoing functional neurosurgery. We demonstrate that stimulation arriving on a particular patient-specific phase of the beta oscillation over consecutive cycles could suppress the amplitude of this pathophysiological activity by up to 40%, while amplification effects were relatively weak. Suppressive effects were accompanied by a reduction in the rhythmic output of subthalamic nucleus (STN) neurons and synchronization with the mesial cortex. While stimulation could alter the spiking pattern of STN neurons, there was no net effect on firing rate, suggesting that reduced beta synchrony was a result of alterations to the relative timing of spiking activity, rather than an overall change in excitability. Together, these results identify a novel intrinsic property of cortico-basal ganglia synchrony that suggests the phase of ongoing neural oscillations could be a viable and effective control signal for the treatment of Parkinson's disease. This work has potential implications for other brain diseases with exaggerated neuronal synchronization and for probing the function of rhythmic activity in the healthy brain.SIGNIFICANCE STATEMENTIn Parkinson's disease (PD), movement impairment is correlated with exaggerated beta frequency oscillations in the cerebral cortex and subthalamic nucleus (STN). Using a novel method of stimulation in PD patients undergoing neurosurgery, we demonstrate that STN beta oscillations can be suppressed when consecutive electrical pulses arrive at a specific phase of the oscillation. This effect is likely because of interrupting the timing of neuronal activity rather than excitability, as stimulation altered the firing pattern of STN spiking without changing overall rate. These findings show the potential of oscillation phase as an input for “closed-loop” stimulation, which could provide a valuable neuromodulation strategy for the treatment of brain disorders and for elucidating the role of neuronal oscillations in the healthy brain.

Prevalence and temporal dynamics of transient oscillations in the beta frequency band (15-35 Hz), referred to as beta bursts, are correlated with motor performance and tactile perception. Disturbance of these activities is a candidate mechanism for motor impairment in Parkinson’s disease (PD), where the excessively long bursts correlate with symptom severity and are reduced by pharmacological and surgical treatments. To date, characterization of beta bursts in PD has been limited to the local field potentials in the subthalamic nucleus (STN) and cortical EEG. Here, we describe the changes that take place in spiking activity across the cortico-basal ganglia circuit, providing a unique insight into the network dynamics of these transient oscillations. Firstly, we demonstrate that rhythmic subthalamic spiking activity emerges at a fixed phase relationship with respect to cortical beta bursts in PD patients. Using multichannel recordings of ensembles of neurons in the 6-OHDA rat model of PD, we then dissect the beta burst dynamics across the sensorimotor cortex and several basal ganglia structures: striatum (Str), globus pallidus externus (GPe) and STN. Each subcortical structure exhibits enhanced rhythmic activity in the beta band locked to the onset of cortical beta bursts and longer cortical bursts lead to stronger subcortical rhythmicity. Crucially, enhanced subcortical rhythmic activity emerges at a fixed phase relationship with respect to the motor cortex, comparable to the relationship observed in PD patients. Striatal beta bursts terminate prior to the recruitment of those in the STN and GPe, suggesting that while they could play an important role in establishing synchrony in the beta band, they do not extensively contribute to its maintenance in other basal ganglia structures. Critically, changes in cortico-subcortical phase coupling precede the onset of a cortical beta burst, supporting the hypothesis that phase alignment across the cortico-basal ganglia network could recruit these structures into synchronous network oscillations. We provide a powerful approach that not only examines pathophysiology of PD across the motor circuit, but also offer insights that could aid in the design of novel neuromodulation strategies to manipulate the state of the motor system before pathological activities emerge.

Parkinson's disease (PD) is a neurodegenerative condition in which aberrant oscillatory synchronization of neuronal activity at beta frequencies (15–35 Hz) across the cortico-basal ganglia-thalamocortical circuit is associated with debilitating motor symptoms, such as bradykinesia and rigidity. Mounting evidence suggests that the magnitude of beta synchrony in the parkinsonian state fluctuates over time, but the mechanisms by which thalamocortical circuitry regulates the dynamic properties of cortical beta in PD are poorly understood. Using the recently developed generic Dynamic Causal Modelling (DCM) framework, we recursively optimized a set of plausible models of the thalamocortical circuit (n = 144) to infer the neural mechanisms that best explain the transitions between low and high beta power states observed in recordings of field potentials made in the motor cortex of anesthetized Parkinsonian rats. Bayesian model comparison suggests that upregulation of cortical rhythmic activity in the beta-frequency band results from changes in the coupling strength both between and within the thalamus and motor cortex. Specifically, our model indicates that high levels of cortical beta synchrony are mainly achieved by a delayed (extrinsic) input from thalamic relay cells to deep pyramidal cells and a fast (intrinsic) input from middle pyramidal cells to superficial pyramidal cells. From a clinical perspective, our study provides insights into potential therapeutic strategies that could be utilized to modulate the network mechanisms responsible for the enhancement of cortical beta in PD. Specifically, we speculate that cortical stimulation aimed to reduce the enhanced excitatory inputs to either the superficial or deep pyramidal cells could be a potential non-invasive therapeutic strategy for PD.

Purpose of Review: This review aims to survey recent trends in electrical forms of neuromodulation, with a specific application to Parkinson’s disease (PD). Emerging trends are identified, highlighting synergies in state-of-the-art neuromodulation strategies, with directions for future improvements in stimulation efficacy suggested. Recent Findings: Deep brain stimulation remains the most common and effective form of electrical stimulation for the treatment of PD. Evidence suggests that transcranial direct current stimulation (tDCS) most likely impacts the motor symptoms of the disease, with the most prominent results relating to rehabilitation. However, utility is limited due to its weak effects and high variability, with medication state a key confound for efficacy level. Recent innovations in transcranial alternating current stimulation (tACS) offer new areas for investigation. Summary: Our understanding of the mechanistic foundations of electrical current stimulation is advancing and as it does so, trends emerge which steer future clinical trials towards greater efficacy.

Synchronized oscillations within and between brain areas facilitate normal processing, but are often amplified in disease. A prominent example is the abnormally sustained beta-frequency (~20Hz) oscillations recorded from the cortex and subthalamic nucleus of Parkinson’s Disease patients. Computational modelling suggests that the amplitude of such oscillations could be modulated by applying stimulation at a specific phase. Such a strategy would allow selective targeting of the oscillation, with relatively little effect on other activity parameters. Here we demonstrate in awake, parkinsonian patients undergoing functional neurosurgery, that electrical stimulation arriving on consecutive cycles of a specific phase of the subthalamic oscillation can suppress its amplitude and coupling to cortex. Stimulus-evoked changes in spiking did not have a consistent time course, suggesting that the oscillation was modulated independently of net output. Phase-dependent stimulation could thus be a valuable strategy for treating brain diseases and probing the function of oscillations in the healthy brain.

Phase alignment between oscillatory circuits is thought to optimize information flow, but excessive synchrony within the motor circuit may impair network function. Cagnan et al. characterize the processes that underscore excessive synchronization and its termination, as well as their modulation by levodopa, before suggesting interventions that might prevent pathological circuit interactions., Optimal phase alignment between oscillatory neural circuits is hypothesized to optimize information flow and enhance system performance. This theory is known as communication-through-coherence. The basal ganglia motor circuit exhibits exaggerated oscillatory and coherent activity patterns in Parkinson's disease. Such activity patterns are linked to compromised motor system performance as evinced by bradykinesia, rigidity and tremor, suggesting that network function might actually deteriorate once a certain level of net synchrony is exceeded in the motor circuit. Here, we characterize the processes underscoring excessive synchronization and its termination. To this end, we analysed local field potential recordings from the subthalamic nucleus and globus pallidus of five patients with Parkinson's disease (four male and one female, aged 37–64 years). We observed that certain phase alignments between subthalamic nucleus and globus pallidus amplified local neural synchrony in the beta frequency band while others either suppressed it or did not induce any significant change with respect to surrogates. The increase in local beta synchrony directly correlated with how long the two nuclei locked to beta-amplifying phase alignments. Crucially, administration of the dopamine prodrug, levodopa, reduced the frequency and duration of periods during which subthalamic and pallidal populations were phase-locked to beta-amplifying alignments. Conversely ON dopamine, the total duration over which subthalamic and pallidal populations were aligned to phases that left beta-amplitude unchanged with respect to surrogates increased. Thus dopaminergic input shifted circuit dynamics from persistent periods of locking to amplifying phase alignments, associated with compromised motoric function, to more dynamic phase alignment and improved motoric function. This effect of dopamine on local circuit resonance suggests means by which novel electrical interventions might prevent resonance-related pathological circuit interactions.

Deep brain stimulation (DBS) has markedly changed how we treat movement disorders including Parkinson's disease (PD), dystonia, and essential tremor (ET). However, despite its demonstrable clinical benefit, DBS is often limited by side effects and partial efficacy. These limitations may be due in part to the fact that DBS interferes with both pathological and physiological neural activities. DBS could, therefore, be potentially improved were it applied selectively and only at times of enhanced pathological activity. This form of stimulation is known as closed-loop or adaptive DBS (aDBS). An aDBS approach has been shown to be superior to conventional DBS in PD in primates using cortical neuronal spike triggering and in humans employing local field potential biomarkers. Likewise, aDBS studies for essential and Parkinsonian tremor are advancing and show great promise, using both peripheral or central sensing and stimulation. aDBS has not yet been trialed in dystonia and yet exciting and promising biomarkers suggest it could be beneficial here too. In this chapter, we will review the existing literature on aDBS in movement disorders and explore potential biomarkers and stimulation algorithms for applying aDBS in PD, ET, and dystonia.

Deep brain stimulation (DBS) has markedly changed how we treat movement disorders including Parkinson's disease (PD), dystonia, and essential tremor (ET). However, despite its demonstrable clinical benefit, DBS is often limited by side effects and partial efficacy. These limitations may be due in part to the fact that DBS interferes with both pathological and physiological neural activities. DBS could, therefore, be potentially improved were it applied selectively and only at times of enhanced pathological activity. This form of stimulation is known as closed-loop or adaptive DBS (aDBS). An aDBS approach has been shown to be superior to conventional DBS in PD in primates using cortical neuronal spike triggering and in humans employing local field potential biomarkers. Likewise, aDBS studies for essential and Parkinsonian tremor are advancing and show great promise, using both peripheral or central sensing and stimulation. aDBS has not yet been trialed in dystonia and yet exciting and promising biomarkers suggest it could be beneficial here too. In this chapter, we will review the existing literature on aDBS in movement disorders and explore potential biomarkers and stimulation algorithms for applying aDBS in PD, ET, and dystonia.

Continuous high‐frequency DBS is an established treatment for essential tremor and Parkinson's disease. Current developments focus on trying to widen the therapeutic window of DBS. Adaptive DBS (aDBS), where stimulation is dynamically controlled by feedback from biomarkers of pathological brain circuit activity, is one such development. Relevant biomarkers may be central, such as local field potential activity, or peripheral, such as inertial tremor data. Moreover, stimulation may be directed by the amplitude or the phase (timing) of the biomarker signal. In this review, we evaluate existing aDBS studies as proof‐of‐principle, discuss their limitations, most of which stem from their acute nature, and propose what is needed to take aDBS into a chronic setting. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society

Parkinson's disease (PD) and essential tremor (ET) are the two most common movement disorders. Both have been associated with similar patterns of network activation leading to the suggestion that they may result from similar network dysfunction, specifically involving the cerebellum. Here, we demonstrate that parkinsonian tremors and ETs result from distinct patterns of interactions between neural oscillators. These patterns are reflected in the tremors' derived frequency tolerance, a novel measure readily attainable from bedside accelerometry. Frequency tolerance characterizes the temporal evolution of tremor by quantifying the range of frequencies over which the tremor may be considered stable. We found that patients with PD (N= 24) and ET (N= 21) were separable based on their frequency tolerance, with PD associated with a broad range of stable frequencies whereas ET displayed characteristics consistent with a more finely tuned oscillatory drive. Furthermore, tremor was selectively entrained by transcranial alternating current stimulation applied over cerebellum. Narrow frequency tolerances predicted stronger entrainment of tremor by stimulation, providing good evidence that the cerebellum plays an important role in pacing those tremors. The different patterns of frequency tolerance could be captured with a simple model based on a broadly coupled set of neural oscillators for PD, but a more finely tuned set of oscillators in ET. Together, these results reveal a potential organizational principle of the human motor system, whose disruption in PD and ET dictates how patients respond to empirical, and potentially therapeutic, interventions that interact with their underlying pathophysiology.

SEE MOLL AND ENGEL DOI101093/AWW308 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Brain regions dynamically engage and disengage with one another to execute everyday actions from movement to decision making. Pathologies such as Parkinson's disease and tremor emerge when brain regions controlling movement cannot readily decouple, compromising motor function. Here, we propose a novel stimulation strategy that selectively regulates neural synchrony through phase-specific stimulation. We demonstrate for the first time the therapeutic potential of such a stimulation strategy for the treatment of patients with pathological tremor. Symptom suppression is achieved by delivering stimulation to the ventrolateral thalamus, timed according to the patient's tremor rhythm. Sustained locking of deep brain stimulation to a particular phase of tremor afforded clinically significant tremor relief (up to 87% tremor suppression) in selected patients with essential tremor despite delivering less than half the energy of conventional high frequency stimulation. Phase-specific stimulation efficacy depended on the resonant characteristics of the underlying tremor network. Selective regulation of neural synchrony through phase-locked stimulation has the potential to both increase the efficiency of therapy and to minimize stimulation-induced side effects.