Subthalamic Nucleus Local Field Potentials Research Articles

Intraoperative localization of spatially and spectrally distinct resting-state networks in Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for symptomatic Parkinson's disease (PD); the clinical benefit may not only mirror modulation of local STN activity but also reflect consecutive network effects on cortical oscillatory activity. Moreover, STN-DBS selectively suppresses spatially and spectrally distinct patterns of synchronous oscillatory activity within cortical-subcortical loops. These STN-cortical circuits have been described in PD patients using magnetoencephalography after surgery. This network information, however, is currently not available during surgery to inform the implantation strategy.The authors recorded spontaneous brain activity in 3 awake patients with PD (mean age 67 ± 14 years; mean disease duration 13 ± 7 years) during implantation of DBS electrodes into the STN after overnight withdrawal of dopaminergic medication. Intraoperative propofol was discontinued at least 30 minutes prior to the electrophysiological recordings. The authors used a novel approach for performing simultaneous recordings of STN local field potentials (LFPs) and multichannel electroencephalography (EEG) at rest. Coherent oscillations between LFP and EEG sensors were computed, and subsequent dynamic imaging of coherent sources was performed.The authors identified coherent activity in the upper beta range (21-35 Hz) between the STN and the ipsilateral mesial (pre)motor area. Coherence in the theta range (4-6 Hz) was detected in the ipsilateral prefrontal area.These findings demonstrate the feasibility of detecting frequency-specific and spatially distinct synchronization between the STN and cortex during DBS surgery. Mapping the STN with this technique may disentangle different functional loops relevant for refined targeting during DBS implantation.

Inferring sleep stage from local field potentials recorded in the subthalamic nucleus of Parkinson's patients.

Parkinson's disease (PD) is highly comorbid with sleep dysfunction. In contrast to motor symptoms, few therapeutic interventions exist to address sleep symptoms in PD. Subthalamic nucleus (STN) deep brain stimulation (DBS) treats advanced PD motor symptoms and may improve sleep architecture. As a proof of concept toward demonstrating that STN-DBS could be used to identify sleep stages commensurate with clinician-scored polysomnography (PSG), we developed a novel artificial neural network (ANN) that could trigger targeted stimulation in response to inferred sleep state from STN local field potentials (LFPs) recorded from implanted DBS electrodes. STN LFP recordings were collected from nine PD patients via a percutaneous cable attached to the DBS lead, during a full night's sleep (6-8hr) with concurrent polysomnography (PSG). We trained a feedforward neural network to prospectively identify sleep stage with PSG-level accuracy from 30-s epochs of LFP recordings. Our model's sleep-stage predictions match clinician-identified sleep stage with a mean accuracy of 91% on held-out epochs. Furthermore, leave-one-group-out analysis also demonstrates 91% mean classification accuracy for novel subjects. These results, which classify sleep stage across a typical heterogenous sample of PD patients, may indicate spectral biomarkers for automatically scoring sleep stage in PD patients with implanted DBS devices. Further development of this model may also focus on adapting stimulation during specific sleep stages to treat targeted sleep deficits.

Parkinsonian Tremor Detection from Subthalamic Nucleus Local Field Potentials for Closed-Loop Deep Brain Stimulation.

Deep Brain Stimulation (DBS) is a widely used therapy to ameliorate symptoms experienced by patients with Parkinson's Disease (PD). Conventional DBS is continuously ON even though PD symptoms fluctuate over time leading to undesirable side-effects and high energy requirements. This study investigates the use of a Iogistic regression-based classifier to identify periods when PD patients have rest tremor exploiting Local Field Potentials (LFPs) recorded with DBS electrodes implanted in the Subthalamic Nucleus in 7 PD patients (8 hemispheres). Analyzing 36.1 minutes of data with a 512 milliseconds non-overlapping window, the classification accuracy was well above chance-level for all patients, with Area Under the Curve (AUC) ranging from 0.67 to 0.93. The features with the most discriminative ability were, in descending order, power in the 31-45 Hz, 5-7 Hz, 21-30 Hz, 46-55 Hz, and 56-95 Hz frequency bands. These results suggest that using a machine learning-based classifier, such as the one proposed in this study, can form the basis for on-demand DBS therapy for PD tremor, with the potential to reduce side-effects and lower battery consumption.

Decoding Movement States in Stepping Cycles Based on Subthalamic LFPs in Parkinsonian Patients.

Gait disturbances are a prominent feature of Parkinson's disease (PD), often refractory to medication or continuous deep brain stimulation (DBS) on basal ganglia targets such as the subthalamic nucleus (STN). Here we sought to identify movement states during stepping cycles, such as left leg stance and right leg stance. To this end we analyzed local field potential (LFP) activity in STN using a combination of the multivariate autoregressive (MAR) model and the Hidden Markov model (HMM). Our results confirm that information is present in the STN related to movement states in stepping cycles, and that it is feasible to decode movement states based on STN LFPs recorded from DBS electrodes. This information can be used to implement temporally flexible stimulation strategies in order to facilitate patterns of neural modulation associated with better gait performance.

F183. Oscillatory [formula omitted] activities in basal ganglia are associated with gait speed in PD

Gait disturbance in PD is complex and its pathogenesis remains unclear and the effect of medical and surgical is limited. Subthalamic nucleus (STN) DBS ameliorates main motor symptoms parkinsonism but the effect for gait disturbance is variable. Local field potential (LFP) recorded from such patients demonstrated synchronized activity at β frequency band (13–35 Hz). In addition, recent study suggested that LFP recorded from PPN at α range (4–10 Hz) correlated with gait speed in PD. Whether oscillations in STN is associated with gait disturbance in PD remains unknown. Here we investigate the relationship between STN LFP and gait performance of PD patients who underwent STN DBS surgery. Correlations were evaluated between STN LFP at different frequency range and gait speed in PD. 15 PD patients underwent bilateral STN DBS surgery for the treatments of advanced PD were recruited. Experiments took place 3–5 days after electrodes implantation. Patients were asked to rest comfortably with eye open (REST), then walk at their preferred speed (GAIT). LFPs were recorded from STN bilaterally, a triaxial accelerometry (ACC) was fixed over the spinous lumbar to record trunk acceleration. Relative gait speed (rGS) is estimated from ACC. 1. Significant POSITIVE correlation was seen between peak frequency of LFP at REST and GAIT at β band but not α frequency band. 2. Significant POSITIVE correlation was seen between power of LFP at high β band (25–35 Hz) and gait speed. 3. Negative correlation was seen between power of LFP at low β band (15–25 Hz) and gait speed but didn’t reach significance. 1. The gait speed of patients with PD is associated with STN LFP synchronized at high β band (25–35 Hz). 2. This study provided insight to how the basal ganglia modulate the gait performance in PD and shed light to the development of future adaptive DBS therapy for PD.

Subthalamic Neural Activity Patterns Anticipate Economic Risk Decisions in Gambling.

Economic decision-making is disrupted in individuals with gambling disorder, an addictive behavior observed in Parkinson’s disease (PD) patients receiving dopaminergic therapy. The subthalamic nucleus (STN) is involved in the inhibition of impulsive behaviors; however, its role in impulse control disorders and addiction is still unclear. Here, we recorded STN local field potentials (LFPs) in PD patients with and without gambling disorder during an economic decision-making task. Reaction times analysis showed that for all patients, the decision whether to risk preceded task onset. We compared then for both groups the STN LFP preceding high- and low-risk economic decisions. We found that risk avoidance in gamblers correlated with larger STN LFP low-frequency (<12-Hz) fluctuations preceding task onset. In particular, the amplitude of low-frequency LFP fluctuations carried significant information about future decisions. Decisions of patients not affected by gambling disorder were instead not correlated with pretask STN LFP. Our results suggest that STN activity preceding task onset affects risk decisions by preemptively inhibiting attraction to high but unlikely rewards in favor of a long-term payoff.

Stimulation of Cortico-Subthalamic Projections Amplifies Resting Motor Circuit Activity and Leads to Increased Locomotion in Dopamine-Depleted Mice.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves motor function in patients with Parkinson’s disease (PD). STN-DBS enables similar improved motor function, including increased movement speed (reduced bradykinesia), in the 6-OHDA dopamine-depletion mouse model of PD. Previous analyses of electrophysiological recordings from STN and motor cortex (M1) have explored signaling changes that correspond to PD and amelioration of PD symptoms. The most common results show an increase in beta frequency power during ‘off’ states and a reduction in beta during ‘on’ states. Surprisingly, however, few studies have analyzed whole signal measures of amplitude and coherence during stimulation in freely moving subjects. In previous work by the author, specific transfection of layer five motor cortex projections to the STN revealed an axonal network with collaterals reaching to multiple non-dopaminergic subcortical areas of the brain. The large excitatory shift that stimulation of this axonal network could potentially induce inspired the current study’s hypothesis that amplification of excitatory signaling occurs during stimulation of cortico-subthalamic projections. The results show that, in awake mice, (1) the root-mean-square amplitudes of STN and M1 local field potentials (LFPs) are significantly decreased ipsilateral to chronic unilateral 6-OHDA lesions, (2) stimulation of cortico-subthalamic projections increases the amplitude of M1- and STN-LFPs, and 3) M1-LFP amplitude correlates strongly with locomotion speed in lesioned mice. Together, these findings demonstrate that bradykinesia-reducing stimulation of cortico-subthalamic projections amplifies both cortical and subcortical motor circuit activity in unilaterally dopamine-depleted mice. Most PD treatments are focused on increasing dopamine in the dorsal striatum. However, in this study, stimulation of layer five cortico-subthalamic glutamatergic axons that do not directly project to dopaminergic neurons increased movement and amplified cortico-subthalamic excitatory signaling in dopamine-depleted mice. The correlation between M1-LFP amplitude and locomotion speed observed in these mice points to a role for upregulated hyperdirect pathway excitatory signaling in bradykinesia amelioration. In addition to providing insight into the elusive mechanisms of DBS, these motor circuit amplification relationships suggest that specific manipulation of NMDA, AMPA, and/or metabotropic glutamate receptors in the hyperdirect pathway may be beneficial for upregulating signaling and movement in PD.

Motor Task Detection From Human STN Using Interhemispheric Connectivity.

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders, such as Parkinson's disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient's behavior. Subthalamic nucleus (STN) local field potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. In this paper, we introduce a behavior detection method capable of asynchronously detecting the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from the STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity for detecting finger movement. Our experimental results, using the recordings from 11 patients with PD, demonstrate that this approach is applicable for behavior detection in the majority of subjects (average area under curve of 70±12%).

149 Human Subthalamic Nucleus Neurons Exhibit Increased Theta-band Phase-locking During High-conflict Decision Making

Abstract INTRODUCTION The subthalamic nucleus (STN) is thought to be preferentially engaged during high-conflict decision making in humans. The population neuronal spike rate in the STN has been reported to increase during decision conflict. Conflict and feedback-related activity is also reflected in theta-band (4-8 Hz) oscillations in the STN. It remains unknown how single-neuron activity and theta-band local field potentials (LFP) oscillations interact to support decision making. METHODS We simultaneously recorded single-neuron spike activity and LFP from the STN of eight Parkinson's disease (PD) patients while they performed a novel Aversion-Reward conflict (ARC) task. Subjects decide whether to accept an offer of a monetary reward paired with a variable risk of an aversive air puff to the eye. By varying the reward and risk, we are able to study approach-avoidance decision making across a range of conflict. Using this task, we examined the mechanism of how theta-frequency oscillation and entrained single neurons involve humans' integration of cost and benefit and decision at various conflict statuses. RESULTS &gt;The ARC task reveals diverse risk-reward tradeoff strategies of patients. Consistent across patients, there is a positive correlation between the degree of decision conflict and reaction time (e.g., higher conflict offers require longer for subjects to decide). During high-conflict decisions, LFP in STN had increased activity of sub-theta oscillation, while increased activity of theta was found during low-conflict decisions. Single-trial STN theta-band power was correlated with degree of decision conflict. Interestingly, the decision to take or forgo the reward is predicted by theta-frequency phase-locked of STN neurons. CONCLUSION Our findings support the hypothesis that theta-band oscillations in single-neurons reflect the engagement of STN during conflict decision making. Furthermore, STN neurons with theta-band entrainment correlate with willingness to approach risk to pursue reward.

Human subthalamic nucleus – Automatic auditory change detection as a basis for action selection

The subthalamic nucleus (STN) shapes motor behavior and is important for the initiation and termination of movements. Here we ask whether the STN takes aggregated sensory information into account, in order to exert this function. To this end, local field potentials (LFP) were recorded in eight patients suffering from Parkinson’s disease and receiving deep-brain stimulation of the STN bilaterally. Bipolar recordings were obtained postoperatively from the externalized electrode leads. Patients were passively exposed to trains of auditory stimuli containing global deviants, local deviants or combined global/local deviants. The surface event-related potentials of the Parkinson’s patients as well as those of 19 age-matched healthy controls were characterized by a mismatch negativity (MMN) that was most pronounced for the global/local double deviants and less prominent for the other deviant conditions. The left and right STN LFPs similarly were modulated by stimulus deviance starting at about 100ms post-stimulus onset. The MMN has been viewed as an index of an automatic auditory change detection system, more recently phrased in terms of predictive coding theory, which prepares the organism for attention shifts and for action. The LFP-data from the STN clearly demonstrate that the STN receives information on stimulus deviance, possibly as a means to bias the system to interrupt ongoing and to allow alternative actions.

Long-Term Task- and Dopamine-Dependent Dynamics of Subthalamic Local Field Potentials in Parkinson's Disease.

Subthalamic nucleus (STN) local field potentials (LFP) are neural signals that have been shown to reveal motor and language behavior, as well as pathological parkinsonian states. We use a research-grade implantable neurostimulator (INS) with data collection capabilities to record STN-LFP outside the operating room to determine the reliability of the signals over time and assess their dynamics with respect to behavior and dopaminergic medication. Seven subjects were implanted with the recording augmented deep brain stimulation (DBS) system, and bilateral STN-LFP recordings were collected in the clinic over twelve months. Subjects were cued to perform voluntary motor and language behaviors in on and off medication states. The STN-LFP recorded with the INS demonstrated behavior-modulated desynchronization of beta frequency (13–30 Hz) and synchronization of low gamma frequency (35–70 Hz) oscillations. Dopaminergic medication did not diminish the relative beta frequency oscillatory desynchronization with movement. However, movement-related gamma frequency oscillatory synchronization was only observed in the medication on state. We observed significant inter-subject variability, but observed consistent STN-LFP activity across recording systems and over a one-year period for each subject. These findings demonstrate that an INS system can provide robust STN-LFP recordings in ambulatory patients, allowing for these signals to be recorded in settings that better represent natural environments in which patients are in a variety of medication states.

Decoding gripping force based on local field potentials recorded from subthalamic nucleus in humans

The basal ganglia are known to be involved in the planning, execution and control of gripping force and movement vigour. Here we aim to define the nature of the basal ganglia control signal for force and to decode gripping force based on local field potential (LFP) activities recorded from the subthalamic nucleus (STN) in patients with deep brain stimulation (DBS) electrodes. We found that STN LFP activities in the gamma (55-90 Hz) and beta (13-30m Hz) bands were most informative about gripping force, and that a first order dynamic linear model with these STN LFP features as inputs can be used to decode the temporal profile of gripping force. Our results enhance the understanding of how the basal ganglia control gripping force, and also suggest that deep brain LFPs could potentially be used to decode movement parameters related to force and movement vigour for the development of advanced human-machine interfaces.

Electrophysiological Evidence for Alternative Motor Networks in REM Sleep Behavior Disorder.

This study provides evidence that nocturnal movements during REM sleep in Parkinson's disease (PD) patients are not processed by the same corticobasal ganglia network as movements in the waking state. This implicates the existence of an alternative motor network that does not depend directly on the availability of l-Dopa in the basal ganglia. These findings further indicate that some PD patients are able to perform movements in the dopamine depleted state, possibly by bypassing the pathological basal ganglia network. The existence and direct activation of such alternative motor networks might finally have potential therapeutic effects for PD patients.

Sixty-hertz stimulation improves bradykinesia and amplifies subthalamic low-frequency oscillations.

The objective of this study was to investigate the hypothesis that attenuation of subthalamic nucleus (STN) alpha-/beta-band oscillations is causal to improvement in bradykinesia. STN local field potentials from a sensing neurostimulator (Activa® PC+S; Medtronic, Inc.) and kinematics from wearable sensors were recorded simultaneously during 60- and 140-Hz deep brain stimulation (DBS) in 9 freely moving PD subjects (15 STNs) performing repetitive wrist flexion-extension. Kinematics were recorded during 20-Hz DBS in a subgroup. Both 60- and 140-Hz DBS improved the angular velocity and frequency of movement (P=0.002 and P=0.029, respectively, for 60 Hz; P<0.001 and P<0.001, respectively, for 140 Hz), but 60-Hz DBS did not attenuate beta-band power (13-30 Hz). In fact, 60-Hz DBS amplified alpha/low-beta (11-15 Hz, P=0.007) and attenuated high-beta power (19-27 Hz, P<0.001), whereas 140-Hz DBS broadly attenuated beta power (15-30 Hz, P<0.001). Only 60-Hz DBS improved the regularity of angular range (P=0.046) and 20-Hz DBS did not worsen bradykinesia. There was no correlation between beta-power modulation and bradykinesia. These novel results obtained from freely moving PD subjects demonstrated that both 140- and 60-Hz DBS improved bradykinesia and attenuated high beta oscillations; however, 60-Hz DBS amplified a subband of alpha/low-beta oscillations, and DBS at a beta-band frequency did not worsen bradykinesia. Based on recent literature, we suggest that both 140- and 60-Hz DBS decouple the cortico-STN hyperdirect pathway, whereas 60-Hz DBS increases coupling within striato-STN circuitry. These results inform future algorithms for closed-loop DBS in PD. © 2016 International Parkinson and Movement Disorder Society.

Subthalamic beta oscillations are attenuated after withdrawal of chronic high frequency neurostimulation in Parkinson's disease

Subthalamic nucleus (STN) local field potential (LFP) recordings demonstrate beta (13–30Hz) band oscillations in Parkinson's disease (PD) defined as elevations of spectral power. The amount of attenuation of beta band power on therapeutic levels of high frequency (HF) deep brain stimulation (DBS) and/or dopaminergic medication has been correlated with the degree of improvement in bradykinesia and rigidity from the therapy, which has led to the suggestion that elevated beta band power is a marker of PD motor disability. A fundamental question has not been answered: whether there is a prolonged attenuation of beta band power after withdrawal of chronic HF DBS and whether this is related to a lack of progression or even improvement in the underlying motor disability.Until now, in human PD subjects, STN LFP recordings were only attainable in the peri-operative period and after short periods of stimulation. For the first time, using an investigational, implanted sensing neurostimulator (Activa® PC+S, Medtronic, Inc.), STN LFPs and motor disability were recorded/assessed after withdrawal of chronic (6 and 12month) HF DBS in freely moving PD subjects. Beta band power was similar within 14s and 60min after stimulation was withdrawn, suggesting that “off therapy” experiments can be conducted almost immediately after stimulation is turned off. After withdrawal of 6 and 12months of STN DBS, beta band power was significantly lower (P<0.05 at 6 and 12months) and off therapy UPDRS scores were better (P<0.05 at 12months) compared to before DBS was started. The attenuation in beta band power was correlated with improvement in motor disability scores (P<0.05). These findings were supported by evidence of a gradual increase in beta band power in two unstimulated STNs after 24months and could not be explained by changes in lead impedance. This suggests that chronic HF DBS exerts long-term plasticity in the sensorimotor network, which may contribute to a lack of progression in underlying motor disability in PD.

A Multiple Kernel Learning approach for human behavioral task classification using STN-LFP signal.

Deep Brain Stimulation (DBS) has gained increasing attention as an effective method to mitigate Parkinson's disease (PD) disorders. Existing DBS systems are open-loop such that the system parameters are not adjusted automatically based on patient's behavior. Classification of human behavior is an important step in the design of the next generation of DBS systems that are closed-loop. This paper presents a classification approach to recognize such behavioral tasks using the subthalamic nucleus (STN) Local Field Potential (LFP) signals. In our approach, we use the time-frequency representation (spectrogram) of the raw LFP signals recorded from left and right STNs as the feature vectors. Then these features are combined together via Support Vector Machines (SVM) with Multiple Kernel Learning (MKL) formulation. The MKL-based classification method is utilized to classify different tasks: button press, mouth movement, speech, and arm movement. Our experiments show that the lp-norm MKL significantly outperforms single kernel SVM-based classifiers in classifying behavioral tasks of five subjects even using signals acquired with a low sampling rate of 10 Hz. This leads to a lower computational cost.

Closed-Loop Deep Brain Stimulation Effects on Parkinsonian Motor Symptoms in a Non-Human Primate - Is Beta Enough?

Incorporating feedback controls based on real-time measures of pathological brain activity may improve deep brain stimulation (DBS) approaches for the treatment of Parkinson's disease (PD). Excessive beta oscillations in subthalamic nucleus (STN) local field potentials (LFP) have been proposed as a potential biomarker for closed-loop DBS (CL-DBS). In a non-human primate PD model we compared CL-DBS, which delivered stimulation only when STN LFP beta activity was elevated, to traditional continuous DBS (tDBS). Therapeutic effects of CL-DBS and tDBS relative to the Off-DBS condition were evaluated via a clinical rating scale and objective measures of movement speed during a cued reaching task. CL-DBS was comparable to tDBS at reducing rigidity, while reducing the amount of time DBS was on by ≈50%; however, only tDBS improved bradykinesia during the reaching behavior. This was likely due to reach-related reductions in beta amplitude that influence the timing and duration of stimulation in the CL-DBS condition. These results illustrate the potential utility of closed-loop DBS devices for PD based on STN beta LFP levels. They also point to possible consequences in behavioral tasks when restricting real-time sensing to a single LFP frequency that itself is modulated during performance of such tasks. The present study provides data that suggest alternate algorithms or more than one physiological biomarker may be required to optimize the performance of behavioral tasks and demonstrates the value of using multiple objective measures when evaluating the efficacy of closed-loop DBS systems.

Surprise disrupts cognition via a fronto-basal ganglia suppressive mechanism.

Surprising events markedly affect behaviour and cognition, yet the underlying mechanism is unclear. Surprise recruits a brain mechanism that globally suppresses motor activity, ostensibly via the subthalamic nucleus (STN) of the basal ganglia. Here, we tested whether this suppressive mechanism extends beyond skeletomotor suppression and also affects cognition (here, verbal working memory, WM). We recorded scalp-EEG (electrophysiology) in healthy participants and STN local field potentials in Parkinson's patients during a task in which surprise disrupted WM. For scalp-EEG, surprising events engage the same independent neural signal component that indexes action stopping in a stop-signal task. Importantly, the degree of this recruitment mediates surprise-related WM decrements. Intracranially, STN activity is also increased post surprise, especially when WM is interrupted. These results suggest that surprise interrupts cognition via the same fronto-basal ganglia mechanism that interrupts action. This motivates a new neural theory of how cognition is interrupted, and how distraction arises after surprising events.

Motor task event detection using Subthalamic Nucleus Local Field Potentials.

Deep Brain Stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson's disease. Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and DBS side effects. In such systems, DBS parameters are adjusted based on patient's behavior, which means that behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local Field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. A practical behavior detection method should be able to detect behaviors asynchronously meaning that it should not use any prior knowledge of behavior onsets. In this paper, we introduce a behavior detection method that is able to asynchronously detect the finger movements of Parkinson patients. As a result of this study, we learned that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We used non-linear regression method to measure this connectivity and use it to detect the finger movements. Performance of this method is evaluated using Receiver Operating Characteristic (ROC).

Long-term detection of Parkinsonian tremor activity from subthalamic nucleus local field potentials.

Current deep brain stimulation paradigms deliver continuous stimulation to deep brain structures to ameliorate the symptoms of Parkinson's disease. This continuous stimulation has undesirable side effects and decreases the lifespan of the unit's battery, necessitating earlier replacement. A closed-loop deep brain stimulator that uses brain signals to determine when to deliver stimulation based on the occurrence of symptoms could potentially address these drawbacks of current technology. Attempts to detect Parkinsonian tremor using brain signals recorded during the implantation procedure have been successful. However, the ability of these methods to accurately detect tremor over extended periods of time is unknown. Here we use local field potentials recorded during a deep brain stimulation clinical follow-up visit 1 month after initial programming to build a tremor detection algorithm and use this algorithm to detect tremor in subsequent visits up to 8 months later. Using this method, we detected the occurrence of tremor with accuracies between 68-93%. These results demonstrate the potential of tremor detection methods for efficacious closed-loop deep brain stimulation over extended periods of time.