An electronic device for artefact suppression in human local field potential recordings during deep brain stimulation

RECOVERING EEG SIGNALS: MUSCLE ARTIFACT SUPPRESSION USING WAVELET-ENHANCED, INDEPENDENT COMPONENT ANALYSIS INTEGRATED WITH ADAPTIVE FILTER

Independent component analysis (ICA) has been proven to be a powerful tool for removing artifacts from electroencephalogram (EEG) recordings in the form of blind source separation (BSS). Independent components (ICs) come from undesired sources that are mixed with the useful signal, and the assessment of such ICs allows them to be detected. But the unwanted ICs also can contain some useful information. To overcome this problem, wavelet-enhanced ICA (wICA) can be used, and this method applies a wavelet threshold for each wavelet coefficient to suppress abnormal deformation in each wavelet coefficient. Using the wICA algorithm to suppress artifacts provides an EEG signal with less distortion in the amplitude and in the phase of the cerebral part of the EEG, and the cerebral part of the EEG can be estimated and obtained very similar to control conditions. However, the EEG signals are affected by various artifact components, and those that have the greatest influence are electromyography (EMG) and electrooculography (EOG). These artifacts may appear simultaneously, randomly or interruptedly, so a fixed threshold level is not really appropriate. We proposed a system including wICA integrated with an adaptive filter model, and this combination system can provide the best prediction of the impacts of artifacts to set up a threshold value that is adaptive and suitable. Our experimental results showed that are approach provided better rejection of artifacts than the wICA system.

Clinical implications of local field potentials for understanding and treating movement disorders

BACKGROUND

Deep brain stimulation (DBS) for the treatment of movement disorders has provided researchers with an opportunity to record electrical oscillatory activity from electrodes implanted in deep brain structures. Extracellular activity recorded from a population of neurons, termed local field potentials (LFPs), has shed light on the pathophysiology of movement disorders and holds the potential to lead to refinement in existing treatments.

OBJECTIVE

This paper reviews the clinical significance of LFPs recorded from macroelectrodes implanted in basal ganglia and thalamic targets for the treatment of Parkinson's disease, essential tremor and dystonia.

METHODS

Neural population dynamics and subthreshold events, which are undetectable by single-unit recordings, can be examined with frequency band analysis of LFPs (frequency range: 1-250 Hz).

RESULTS

Of clinical relevance, reliable correlations between motor symptoms and components of the LFP power spectrum suggest that LFPs may serve as a biomarker for movement disorders. In particular, Parkinson's rigidity has been shown to correlate with the power of beta oscillations (13-30 Hz), and essential tremor coheres with oscillations of 8-27 Hz. Furthermore, evidence indicates that the optimal contacts for DBS programming can be predicted from the anatomic location of beta and gamma bands (48-200 Hz).

CONCLUSION

LFP analysis has implications for improved electrode targeting and the development of a real-time, individualized, 'closed-loop' stimulation system.

A neurochemical closed-loop controller for deep brain stimulation: toward individualized smart neuromodulation therapies

Current strategies for optimizing deep brain stimulation (DBS) therapy involve multiple postoperative visits. During each visit, stimulation parameters are adjusted until desired therapeutic effects are achieved and adverse effects are minimized. However, the efficacy of these therapeutic parameters may decline with time due at least in part to disease progression, interactions between the host environment and the electrode, and lead migration. As such, development of closed-loop control systems that can respond to changing neurochemical environments, tailoring DBS therapy to individual patients, is paramount for improving the therapeutic efficacy of DBS. Evidence obtained using electrophysiology and imaging techniques in both animals and humans suggests that DBS works by modulating neural network activity. Recently, animal studies have shown that stimulation-evoked changes in neurotransmitter release that mirror normal physiology are associated with the therapeutic benefits of DBS. Therefore, to fully understand the neurophysiology of DBS and optimize its efficacy, it may be necessary to look beyond conventional electrophysiological analyses and characterize the neurochemical effects of therapeutic and non-therapeutic stimulation. By combining electrochemical monitoring and mathematical modeling techniques, we can potentially replace the trial-and-error process used in clinical programming with deterministic approaches that help attain optimal and stable neurochemical profiles. In this manuscript, we summarize the current understanding of electrophysiological and electrochemical processing for control of neuromodulation therapies. Additionally, we describe a proof-of-principle closed-loop controller that characterizes DBS-evoked dopamine changes to adjust stimulation parameters in a rodent model of DBS. The work described herein represents the initial steps toward achieving a “smart” neuroprosthetic system for treatment of neurologic and psychiatric disorders.

Closed-loop cortical neuromodulation in Parkinson's disease: An alternative to deep brain stimulation?

Deep brain stimulation (DBS) is usually performed to treat advanced Parkinson's disease (PD) patients with electrodes permanently implanted in basal ganglia while the stimulator delivers electrical impulses continuously and independently of any feedback (open-loop stimulation). Conversely, in closed-loop stimulation, electrical stimulation is delivered as a function of neuronal activities recorded and analyzed online. There is an emerging development of closed-loop DBS in the treatment of PD and a growing discussion about proposing cortical stimulation rather than DBS for this purpose. Why does it make sense to "close the loop" to treat parkinsonian symptoms? Could closed-loop stimulation applied to the cortex become a valuable therapeutic strategy for PD? Can mathematical modeling contribute to the development of this technique? We review the various evidences in favor of the use of closed-loop cortical stimulation for the treatment of advanced PD, as an emerging technique which might offer substantial clinical benefits for PD patients.

Basal ganglia local field potentials: applications in the development of new deep brain stimulation devices for movement disorders

The analysis of neural rhythms measured in local field potentials (LFPs) through deep brain stimulation (DBS) electrodes have provided a new insight into brain mechanisms of information processing. The application of novel methodological approaches for LFP analysis is of key importance to uncover the complexity of such mechanisms, thereby clarifying the relationship between the LFP code and patient's clinical state. Thanks to a new device for recording artifact-free LFPs during high-frequency stimulation, DBS-induced neural rhythms modulations and their nonlinear features can be analyzed and used in the development of a new, adaptive DBS approach: the frequency, strength and site of DBS could be controlled, in a closed-loop system, through LFP-based variables obtained through the application of different methodological approaches.

Spatiotemporal visualization of deep brain stimulation-induced effects in the subthalamic nucleus

Deep brain stimulation (DBS) is a successful surgical therapy used to treat the disabling symptoms of movement disorders such as Parkinson's disease. It involves the chronic stimulation of disorder-specific nuclei. However, the mechanisms that lead to clinical improvements remain unclear. Consequently, this slows the optimization of present-day DBS therapy and hinders its future development and application. We used a computational model to calculate the distribution of electric potential induced by DBS and study the effect of stimulation on the spiking activity of a subthalamic nucleus (STN) projection neuron. We previously showed that such a model can reveal detailed spatial effects of stimulation in the vicinity of the electrode. However, this multi-compartmental STN neuron model can fire in either a burst or tonic mode and, in this study, we hypothesized that the firing mode of the cell will have a major impact on the DBS-induced effects. Our simulations showed that the bursting model exhibits behaviour observed in studies of high-frequency stimulation of STN neurons, such as the presence of a silent period at stimulation offset and frequency-dependent stimulation effects. We validated the model by simulating the clinical parameter settings used for a Parkinsonian patient and showed, in a patient-specific anatomical model, that the region of affected tissue is consistent with clinical observations of the optimal DBS site. Our results demonstrated a method of quantitatively assessing neuronal changes induced by DBS, to maximize therapeutic benefit and minimize unwanted side effects.

Resting beta hypersynchrony in secondary dystonia and its suppression during pallidal deep brain stimulation in DYT3+ Lubag dystonia

OBJECTIVES

1) To characterize patterns of globus pallidus interna neural synchrony in patients with secondary dystonia; 2) to determine whether neural hypersynchrony in the globus pallidus externa (GPe) and interna (GPi) is attenuated during high frequency deep brain stimulation (HF DBS) in a patient with DYT3+ dystonia and in a patient with secondary dystonia due to childhood encephalitis.

MATERIALS AND METHODS

We recorded local field potentials from the DBS lead in the GPi of four patients (seven hemispheres) with secondary dystonia and from one patient (two hemispheres) with primary DYT3+ dystonia. In two patients, we also recorded pallidal local field potentials during the administration of 10 sec epochs of HF DBS.

RESULTS

Power spectral densities during rest demonstrated visible peaks in the beta band in seven out of nine cases. In DYT3+ dystonia, power in the alpha and beta bands, but not theta band, was attenuated during HF DBS in the GPe and in GPi, and attenuation was most prominent in the high beta band. This patient demonstrated an early and maintained improvement in dystonia. There was no beta peak and the power spectrum was not attenuated during HF DBS in a patient with secondary dystonia due to childhood encephalitis.

CONCLUSIONS

These results suggest that beta hypersynchrony, demonstrated now in both primary and secondary dystonia, may play a pathophysiological role in pathological hyperkinesis. Further investigation is needed in a larger cohort of well-characterized primary and secondary dystonia patients.

On-demand control system for deep brain stimulation for treatment of intention tremor

OBJECTIVES

Intention tremor becomes evident only when patients intend to move their body and is characterized by dysmetria. We have developed an on-demand control system that triggers the switching on/off of deep brain stimulation (DBS) instantly for the control of intention tremor.

MATERIAL AND METHODS

We used surface electrodes for the recording of electromyographic (EMG) activity, and the power of EMG activity was analyzed instantly employing the fast Fourier transform. The on-demand control system switched on DBS when only the power of tremor frequency exceeded the on-trigger threshold, and the system switched off DBS when the total power of EMG activity decreased below the off-trigger threshold.

RESULTS

The on-demand control system triggered the switching on/off of DBS accurately, and controlled intention tremor completely. Our on-demand control system is small and portable, and suitable for clinical use.

CONCLUSIONS

The on-demand control system for DBS is useful for controlling intention tremor and may decrease the incidence of tolerance to DBS and may be a powerful tool for various applications of neuromodulation therapy.

Instrumentation to record evoked potentials for closed-loop control of deep brain stimulation

Closed-loop deep brain stimulation (DBS) systems offer promise in relieving the clinical burden of stimulus parameter selection and improving treatment outcomes. In such a system, a feedback signal is used to adjust automatically stimulation parameters and optimize the efficacy of stimulation. We explored the feasibility of recording electrically evoked compound action potentials (ECAPs) during DBS for use as a feedback control signal. A novel instrumentation system was developed to suppress the stimulus artifact and amplify the small magnitude, short latency ECAP response during DBS with clinically relevant parameters. In vitro testing demonstrated the capabilities to increase the gain by a factor of 1,000× over a conventional amplifier without saturation, reduce distortion of mock ECAP signals, and make high fidelity recordings of mock ECAPs at latencies of only 0.5 ms following DBS pulses of 50 to 100 μs duration. Subsequently, the instrumentation was used to make in vivo recordings of ECAPs during thalamic DBS in cats, without contamination by the stimulus artifact. The signal characteristics were similar across three experiments, suggesting common neural activation patterns. The ECAP recordings enabled with this novel instrumentation may provide insight into the type and spatial extent of neural elements activated during DBS, and could serve as feedback control signals for closed-loop systems.

What brain signals are suitable for feedback control of deep brain stimulation in Parkinson's disease?

Feedback control of deep brain stimulation (DBS) in Parkinson's disease has great potential to improve efficacy, reduce side effects, and decrease the cost of treatment. In this, the timing and intensity of stimulation are titrated according to biomarkers that capture current clinical state. Stimulation may be at standard high frequency or intelligently patterned to directly modify specific pathological rhythms. The search for and validation of appropriate feedback signals are therefore crucial. Signals recorded from the DBS electrode currently appear to be the most promising source of feedback. In particular, beta-frequency band oscillations in the local field potential recorded at the stimulation target may capture variation in bradykinesia and rigidity across patients, but this remains to be confirmed within patients. Biomarkers that reliably reflect other impairments, such as tremor, also need to be established. Finally, whether brain signals are causally important needs to be established before stimulation can be specifically patterned rather than delivered at empirically defined high frequency.

Does suppression of oscillatory synchronisation mediate some of the therapeutic effects of DBS in patients with Parkinson's disease?

There is growing evidence for exaggerated oscillatory neuronal synchronisation in patients with Parkinson's disease (PD). In particular, oscillations at around 20 Hz, in the so-called beta frequency band, relate to the cardinal symptoms of bradykinesia and rigidity. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) can significantly improve these motor impairments. Recent evidence has demonstrated reduction of beta oscillations concurrent with alleviation of PD motor symptoms, raising the possibility that suppression of aberrant activity may mediate the effects of DBS. Here we review the evidence supporting suppression of pathological oscillations during stimulation and discuss how this might underlie the efficacy of DBS. We also consider how beta activity may provide a feedback signal suitable for next generation closed-loop and intelligent stimulators.

Emerging technology for advancing the treatment of epilepsy using a dynamic control framework

We briefly describe a dynamic control system framework for neuromodulation for epilepsy, with an emphasis on its practical challenges and the preliminary validation of key prototype technologies in a chronic animal model. The current state of neuromodulation can be viewed as a classical dynamic control framework such that the nervous system is the classical "plant", the neural stimulator is the controller/actuator, clinical observation, patient diaries and/or measured bio-markers are the sensor, and clinical judgment applied to these sensor inputs forms the state estimator. Technology can potentially address two main factors contributing to the performance limitations of existing systems: "observability," the ability to observe the state of the system from output measurements, and "controllability," the ability to drive the system to a desired state. In addition to improving sensors and actuator performance, methods and tools to better understand disease state dynamics and state estimation are also critical for improving therapy outcomes. We describe our preliminary validation of key "observability" and "controllability" technology blocks using an implanted research tool in an epilepsy disease model. This model allows for testing the key emerging technologies in a representative neural network of therapeutic importance. In the future, we believe these technologies might enable both first principles understanding of neural network behavior for optimizing therapy design, and provide a practical pathway towards clinical translation.

Recording evoked potentials during deep brain stimulation: development and validation of instrumentation to suppress the stimulus artefact

The clinical efficacy of deep brain stimulation (DBS) for the treatment of movement disorders depends on the identification of appropriate stimulation parameters. Since the mechanisms of action of DBS remain unclear, programming sessions can be time consuming, costly and result in sub-optimal outcomes. Measurement of electrically evoked compound action potentials (ECAPs) during DBS, generated by activated neurons in the vicinity of the stimulating electrode, could offer insight into the type and spatial extent of neural element activation and provide a potential feedback signal for the rational selection of stimulation parameters and closed-loop DBS. However, recording ECAPs presents a significant technical challenge due to the large stimulus artefact, which can saturate recording amplifiers and distort short latency ECAP signals. We developed DBS-ECAP recording instrumentation combining commercial amplifiers and circuit elements in a serial configuration to reduce the stimulus artefact and enable high fidelity recording. We used an electrical circuit equivalent model of the instrumentation to understand better the sources of the stimulus artefact and the mechanisms of artefact reduction by the circuit elements. In vitro testing validated the capability of the instrumentation to suppress the stimulus artefact and increase gain by a factor of 1000 to 5000 compared to a conventional biopotential amplifier. The distortion of mock ECAP (mECAP) signals was measured across stimulation parameters, and the instrumentation enabled high fidelity recording of mECAPs with latencies of only 0.5 ms for DBS pulse widths of 50 to 100 µs/phase. Subsequently, the instrumentation was used to record in vivo ECAPs, without contamination by the stimulus artefact, during thalamic DBS in an anesthetized cat. The characteristics of the physiological ECAP were dependent on stimulation parameters. The novel instrumentation enables high fidelity ECAP recording and advances the potential use of the ECAP as a feedback signal for the tuning of DBS parameters.

Deep brain stimulation can suppress pathological synchronisation in parkinsonian patients

BACKGROUND

Although deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a highly effective therapeutic intervention in severe Parkinson's disease, its mechanism of action remains unclear. One possibility is that DBS suppresses local pathologically synchronised oscillatory activity.

METHODS

To explore this, the authors recorded from DBS electrodes implanted in the STN of 16 patients with Parkinson's disease during simultaneous stimulation (pulse width 60 μs; frequency 130 Hz) of the same target using a specially designed amplifier. The authors analysed data from 25 sides.

RESULTS

The authors found that DBS progressively suppressed peaks in local field potential activity at frequencies between 11 and 30 Hz as voltage was increased beyond a stimulation threshold of 1.5 V. Median peak power had fallen to 54% of baseline values by a stimulation intensity of 3.0 V.

CONCLUSION

The findings suggest that DBS can suppress pathological 11-30 Hz activity in the vicinity of stimulation in patients with Parkinson's disease. This suppression occurs at stimulation voltages that are clinically effective.

High frequency oscillations in the subthalamic nucleus: a neurophysiological marker of the motor state in Parkinson's disease

Increasing evidence suggests that abnormal oscillatory activity in basal ganglia and cortex plays a pivotal role in the pathophysiology of Parkinson's disease. Recordings of local field potentials from subthalamic nucleus of patients undergoing deep brain stimulation have focused on oscillations occurring at frequencies below 100 Hz in the alpha, beta and gamma range and suggested that, in particular, an increase of beta band oscillations underlies slowing of movement in Parkinson's disease. Recent findings showing that the amplitude of high frequency oscillations (>200 Hz) couples with the phase of beta activity have raised the important question about the role of subthalamic high frequency oscillations in Parkinson's disease. To investigate functional characteristics and clinical relevance of high frequency oscillations, we recorded local field potentials from 18 subthalamic nuclei of 9 akinetic-rigid Parkinsonian patients with implanted deep brain stimulation electrodes and still externalised leads before and after intake of levodopa. We identified two distinct bands of high frequency oscillations, one centred around 250 Hz and another one around 350 Hz that show characteristic levodopa dependent amplitude and coupling behaviours. Administration of levodopa changed the power ratio between the two high frequency bands towards the component centred around 350 Hz in all 18 nuclei under study (p<10(-4)). Moreover, this power ratio correlated significantly with the Unified Parkinson's Disease Rating Scale hemibody akinesia/rigidity subscore (r=0.3618, p=0.015), but interestingly not with beta peak power (p=0.1) suggesting that levodopa induced changes in high frequency and beta oscillations are at least potentially independent of each other. Accordingly, a combined parameter composed of power ratio of high frequency oscillations and beta peak power significantly increased the correlation with the motor state (r=0.45, p=0.004). These results indicate that a shift from slower to faster frequencies of the spectrum greater than 200 Hz represents a prokinetic neurophysiological marker underlying levodopa induced motor improvement in Parkinson's disease.

Deep brain stimulation: technology at the cutting edge

Deep brain stimulation (DBS) surgery has been performed in over 75,000 people worldwide, and has been shown to be an effective treatment for Parkinson's disease, tremor, dystonia, epilepsy, depression, Tourette's syndrome, and obsessive compulsive disorder. We review current and emerging evidence for the role of DBS in the management of a range of neurological and psychiatric conditions, and discuss the technical and practical aspects of performing DBS surgery. In the future, evolution of DBS technology may depend on several key areas, including better scientific understanding of its underlying mechanism of action, advances in high-spatial resolution imaging and development of novel electrophysiological and neurotransmitter microsensor systems. Such developments could form the basis of an intelligent closed-loop DBS system with feedback-guided neuromodulation to optimize both electrode placement and therapeutic efficacy.

Real-time decision fusion for multimodal neural prosthetic devices

BACKGROUND

The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device.

METHODOLOGY/PRINCIPAL FINDINGS

Here we propose a framework based on decision fusion, i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions.

CONCLUSIONS

Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future.

Time dependent subthalamic local field potential changes after DBS surgery in Parkinson's disease

Local field potentials (LFPs) recorded through electrodes implanted in patients with Parkinson's disease (PD) for deep brain stimulation (DBS) provided physiological information about the human basal ganglia. However, LFPs were always recorded 2-7 days after electrode implantation ("acute" condition). Because changes in the tissue surrounding the electrode occur after DBS surgery and could be relevant for LFPs, in this work we assessed whether impedance and LFP pattern are a function of the time interval between the electrode implant and the recordings. LFPs and impedances were recorded from 11 patients with PD immediately after (T-0h), 2 h after (T-2h), 2 days after (T-48h), and 1 month after (T-30d, "chronic" condition) surgery. Impedances at T-0h were significantly higher than at all the other time intervals (T-2h, p=0.0005; T-48h, p=0.0002; T-30d, p=0.003). Correlated with this change (p=0.005), the low-frequency band (2-7 Hz) decreased at all time intervals (p=0.0005). Conversely, the low- (8-20 Hz) and the high-beta (21-35 Hz) bands increased in time (low-beta, p=0.003; high beta, p=0.022), but did not change between T-48h and T-30d. Our results suggest that DBS electrode impedance and LFP pattern are a function of the time interval between electrode implant and LFP recordings. Impedance decrease could be related to changes in the electrode/tissue interface and in the low-frequency band. Conversely, beta band modulations could raise from the adaptation of the neural circuit. These findings confirm that results from LFP analysis in the acute condition can be extended to the chronic condition and that LFPs can be used in novel closed-loop DBS systems.

External trial deep brain stimulation device for the application of desynchronizing stimulation techniques

In the past decade deep brain stimulation (DBS)-the application of electrical stimulation to specific target structures via implanted depth electrodes-has become the standard treatment for medically refractory Parkinson's disease and essential tremor. These diseases are characterized by pathological synchronized neuronal activity in particular brain areas. We present an external trial DBS device capable of administering effectively desynchronizing stimulation techniques developed with methods from nonlinear dynamics and statistical physics according to a model-based approach. These techniques exploit either stochastic phase resetting principles or complex delayed-feedback mechanisms. We explain how these methods are implemented into a safe and user-friendly device.

Filtering Out Deep Brain Stimulation Artifacts Using a Nonlinear Oscillatory Model

This letter is devoted to the suppression of spurious signals (artifacts) in records of neural activity during deep brain stimulation. An approach based on nonlinear adaptive model with self-oscillations is proposed. We developed an algorithm of adaptive filtering based on this approach. The proposed algorithm was tested using recordings collected from patients during the stimulation. This was then compared to existing methods and showed the best performance.

Synchronisation in the beta frequency-band--the bad boy of parkinsonism or an innocent bystander?

Excessive synchronisation of basal ganglia neuronal activity in the beta frequency band has been implicated in Parkinson's disease. In a recent issue of Experimental Neurology, Bronte-Stewart, H., Barberini, C., Koop, M.M., Hill, B.C., Henderson, J.M., Wingeier, B., 2009. The STN beta-band profile in Parkinson's disease is stationary and shows prolonged attenuation after deep brain stimulation. Exp. Neurol. 215, 20–28. demonstrate that such activity is consistent over time and provide further evidence that deep brain stimulation is associated with its suppression. However, the extent to which beta synchrony has a mechanistic (rather than epiphenomenal) role in parkinsonism remains unclear, and the suppression of this activity by deep brain stimulation is contentious. This commentary discusses the evidence for and against a role for excessive beta synchrony in mediating the parkinsonian phenotype and in providing a possible mechanism to explain the therapeutic effects of deep brain stimulation in Parkinson's disease.

Interaction between rhythms in the human basal ganglia: application of bispectral analysis to local field potentials

The application of deep brain stimulation (DBS) for the treatment of Parkinson's disease offered a direct "insight" into the human electrical activity in subcortical structures. The analysis of the oscillatory activity [local field potentials (LFPs)] disclosed the importance of rhythms and of interactions between rhythms in the human basal ganglia information processing. The aim of this study was to investigate the existence of possible nonlinear interactions between LFP rhythms characterizing the output structure of the basal ganglia, the globus pallidus internus, by means of bispectral analysis. The results of this study disclosed that the rhythms expressed in the globus pallidus internus of the untreated parkinsonian patient are not independent and, in particular, the low-beta (13-20 Hz) band generates harmonics that are included in the high-beta (20-35 Hz) band. Conversely, in the dystonic globus pallidus, as well as in the parkinsonian globus pallidus after dopaminergic medication (i.e., in the more "normal" condition), the rhythms are substantially independent and characterized by a strong activity in the low-frequency band that generates a second harmonic (4-14 Hz), mostly included in the same band. The interactions between rhythms in the human globus pallidus are therefore different in different pathologies and in different patient's states. The interpretation of these interactions is likely critical for fully understanding the role of LFP rhythms in the pathophysiology of human basal ganglia.

Mechanisms of deep brain stimulation in movement disorders as revealed by changes in stimulus frequency

Deep brain stimulation (DBS) is an established treatment for symptoms in movement disorders and is under investigation for symptom management in persons with psychiatric disorders and epilepsy. Nevertheless, there remains disagreement regarding the physiological mechanisms responsible for the actions of DBS, and this lack of understanding impedes both the design of DBS systems for treating novel diseases and the effective tuning of current DBS systems. Currently available data indicate that effective DBS overrides pathological bursts, low frequency oscillations, synchronization, and disrupted firing patterns present in movement disorders, and replaces them with more regularized firing. Although it is likely that the specific mechanism(s) by which DBS exerts its effects varies between diseases and target nuclei, the overriding of pathological activity appears to be ubiquitous. This review provides an overview of changes in motor symptoms with changes in DBS frequency and highlights parallels between the changes in motor symptoms and the changes in cellular activity that appear to underlie the motor symptoms.