Chronic cortical and electromyographic recordings from a fully implantable device: preclinical experience in a nonhuman primate

Responsive Versus Continuous Deep Brain Stimulation for Speech in Essential Tremor: A Pilot Study

BACKGROUND

Responsive deep brain stimulation (rDBS) uses physiological signals to deliver stimulation when needed. rDBS is hypothesized to reduce stimulation-induced speech effects associated with continuous DBS (cDBS) in patients with essential tremor (ET).

OBJECTIVE

To determine if rDBS reduces cDBS speech-related side effects while maintaining tremor suppression.

METHODS

Eight ET participants with thalamic DBS underwent unilateral rDBS. Both speech evaluations and tremor severity were assessed across three conditions (DBS OFF, cDBS ON, and rDBS ON). Speech was analyzed using intelligibility ratings. Tremor severity was scored using the Fahn-Tolosa-Marin Tremor Rating Scale (TRS).

RESULTS

During unilateral cDBS, participants experienced reduced speech intelligibility (P = 0.025) compared to DBS OFF. rDBS was not associated with a deterioration of intelligibility. Both rDBS (P = 0.026) and cDBS (P = 0.038) improved the contralateral TRS score compared to DBS OFF.

CONCLUSIONS

rDBS maintained speech intelligibility without loss of tremor suppression. A larger prospective chronic study of rDBS in ET is justified. © 2024 International Parkinson and Movement Disorder Society.

Chronic subdural electrocorticography in nonhuman primates by an implantable wireless device for brain-machine interfaces

Background

Subdural electrocorticography (ECoG) signals have been proposed as a stable, good-quality source for brain-machine interfaces (BMIs), with a higher spatial and temporal resolution than electroencephalography (EEG). However, long-term implantation may lead to chronic inflammatory reactions and connective tissue encapsulation, resulting in a decline in signal recording quality. However, no study has reported the effects of the surrounding tissue on signal recording and device functionality thus far.

Methods

In this study, we implanted a wireless recording device with a customized 32-electrode-ECoG array subdurally in two nonhuman primates for 15 months. We evaluated the neural activities recorded from and wirelessly transmitted to the devices and the chronic tissue reactions around the electrodes. In addition, we measured the gain factor of the newly formed ventral fibrous tissue in vivo.

Results

Time-frequency analyses of the acute and chronic phases showed similar signal features. The average root mean square voltage and power spectral density showed relatively stable signal quality after chronic implantation. Histological examination revealed thickening of the reactive tissue around the electrode array; however, no evident inflammation in the cortex. From gain factor analysis, we found that tissue proliferation under electrodes reduced the amplitude power of signals.

Conclusion

This study suggests that subdural ECoG may provide chronic signal recordings for future clinical applications and neuroscience research. This study also highlights the need to reduce proliferation of reactive tissue ventral to the electrodes to enhance long-term stability.

Nine decades of electrocorticography: A comparison between epidural and subdural recordings

In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.

Algorithm and hardware considerations for real-time neural signal on-implant processing

Objective Various on-workstation neural-spike-based brain machine interface(BMI) systems have reached the point of in-human trials, but on-node and on-implant BMI systems are still under exploration. Such systems are constrained by the area and battery. Researchers should consider the algorithm complexity, available resources, power budgets, CMOS technologies, and the choice of platforms when designing BMI systems. However, the effect of these factors is currently still unclear. Approaches. Here we have proposed a novel real-time 128 channel spike detection algorithm and optimised it on Microcontroller(MCU) and Field Programmable Gate Array(FPGA) platforms towards consuming minimal power and memory/resources. It is presented as a use case to explore the different considerations in system design. Main results. The proposed spike detection algorithm achieved over 97% sensitivity and a smaller than 3% false detection rate. The MCU implementation occupies less than 3KB RAM and consumes 31.5μW/ch. The FPGA platform only occupies 299 logic cells and 3KB RAM for 128 channels and consumes 0.04μW/ch. Significance. On the spike detection algorithm front, we have eliminated the processing bottleneck by reducing the dynamic power consumption to lower than the hardware static power, without sacrificing detection performance. More importantly, we have explored the considerations in algorithm and hardware design with respect to scalability, portability, and costs. These findings can facilitate and guide the future development of real-time on-implant neural signal processing platforms.

Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients

Objective.The evaluation of the long-term stability of ElectroCorticoGram (ECoG) signals is an important scientific question as new implantable recording devices can be used for medical purposes such as Brain-Computer Interface (BCI) or brain monitoring.Approach.The long-term clinical validation of wireless implantable multi-channel acquisition system for generic interface with neurons (WIMAGINE), a wireless 64-channel epidural ECoG recorder was investigated. The WIMAGINE device was implanted in two quadriplegic patients within the context of a BCI protocol. This study focused on the ECoG signal stability in two patients bilaterally implanted in June 2017 (P1) and in November 2019 (P2).Methods. The ECoG signal was recorded at rest prior to each BCI session resulting in a 32 month and in a 14 month follow-up for P1 and P2 respectively. State-of-the-art signal evaluation metrics such as root mean square (RMS), the band power (BP), the signal to noise ratio (SNR), the effective bandwidth (EBW) and the spectral edge frequency (SEF) were used to evaluate stability of signal over the implantation time course. The time-frequency maps obtained from task-related motor activations were also studied to investigate the long-term selectivity of the electrodes.Mainresults.Based on temporal linear regressions, we report a limited decrease of the signal average level (RMS), spectral distribution (BP) and SNR, and a remarkable steadiness of the EBW and SEF. Time-frequency maps obtained during motor imagery, showed a high level of discrimination 1 month after surgery and also after 2 years.Conclusions.The WIMAGINE epidural device showed high stability over time. The signal evaluation metrics of two quadriplegic patients during 32 months and 14 months respectively provide strong evidence that this wireless implant is well-suited for long-term ECoG recording.Significance.These findings are relevant for the future of implantable BCIs, and could benefit other patients with spinal cord injury, amyotrophic lateral sclerosis, neuromuscular diseases or drug-resistant epilepsy.

A High Accuracy Electrographic Seizure Classifier Trained Using Semi-Supervised Labeling Applied to a Large Spectrogram Dataset

The objective of this study was to explore using ECoG spectrogram images for training reliable cross-patient electrographic seizure classifiers, and to characterize the classifiers’ test accuracy as a function of amount of training data. ECoG channels in ∼138,000 time-series ECoG records from 113 patients were converted to RGB spectrogram images. Using an unsupervised spectrogram image clustering technique, manual labeling of 138,000 ECoG records (each with up to 4 ECoG channels) was completed in 320 h, which is an estimated 5 times faster than manual labeling without ECoG clustering. For training supervised classifier models, five random folds of data were created; with each fold containing 72, 18, and 23 patients’ data for model training, validation and testing respectively. Five convolutional neural network (CNN) architectures, including two with residual connections, were trained. Cross-patient classification accuracies and F₁ scores improved with model complexity, with the shallowest 6-layer model (with ∼1.5 million trainable parameters) producing a class-balanced seizure/non-seizure classification accuracy of 87.9% on ECoG channels and the deepest ResNet50-based model (with ∼23.5 million trainable parameters) producing a classification accuracy of 95.7%. The trained ResNet50-based model additionally had 93.5% agreement in scores with an independent expert labeller. Visual inspection of gradient-based saliency maps confirmed that the models’ classifications were based on relevant portions of the spectrogram images. Further, by repeating training experiments with data from varying number of patients, it was found that ECoG spectrogram images from just 10 patients were sufficient to train ResNet50-based models with 88% cross-patient accuracy, while at least 30 patients’ data was required to produce cross-patient classification accuracies of >90%.

Device profile of the percept PC deep brain stimulation system for the treatment of Parkinson's disease and related disorders

INTRODUCTION

Several software and hardware advances in the field of deep brain stimulation (DBS) have been realized in recent years and devices from three manufacturers are available. The Percept™ PC platform (Medtronic, Inc.) enables brain sensing, the latest innovation. Clinicians should be familiar with the differences in devices, and with the latest technologies to deliver optimized patient care.Areas covered: In this device profile, the sensing capabilities of the Percept™ PC platform are described, and the system capabilities are differentiated from other available platforms. The development of the preceding Activa™ PC+S research platform, an investigational device to simultaneously sense brain signals and provide therapeutic stimulation, is provided to place Percept™ PC in the appropriate context.Expert opinion: Percept™ PC offers unique sensing and diary functions as a means to refine therapeutic stimulation, track symptoms and correlate them to neurophysiologic characteristics. Additional features enhance the patient experience with DBS, including 3 T magnetic resonance imaging compatibility, wireless telemetry, a smaller and thinner battery profile, and increased battery longevity. Future work will be needed to illustrate the clinical utility and added value of using sensing to optimize DBS therapy. Patients implanted with Percept™ PC will have ready access to future technology developments.

Differentiating tic electrophysiology from voluntary movement in the human thalamocortical circuit

OBJECTIVES

Tourette syndrome is a neurodevelopmental disorder commonly associated with involuntary movements, or tics. We currently lack an ideal animal model for Tourette syndrome. In humans, clinical manifestation of tics cannot be captured via functional imaging due to motion artefacts and limited temporal resolution, and electrophysiological studies have been limited to the intraoperative environment. The goal of this study was to identify electrophysiological signals in the centromedian (CM) thalamic nucleus and primary motor (M1) cortex that differentiate tics from voluntary movements.

METHODS

The data were collected as part of a larger National Institutes of Health-sponsored clinical trial. Four participants (two males, two females) underwent monthly clinical visits for collection of physiology for a total of 6 months. Participants were implanted with bilateral CM thalamic macroelectrodes and M1 subdural electrodes that were connected to two neurostimulators, both with sensing capabilities. MRI scans were performed preoperatively and CT scans postoperatively for localisation of electrodes. Electrophysiological recordings were collected at each visit from both the cortical and subcortical implants.

RESULTS

Recordings collected from the CM thalamic nucleus revealed a low-frequency power (3-10 Hz) increase that was time-locked to the onset of involuntary tics but was not present during voluntary movements. Cortical recordings revealed beta power decrease in M1 that was present during tics and voluntary movements.

CONCLUSION

We conclude that a human physiological signal was detected from the CM thalamus that differentiated tic from voluntary movement, and this physiological feature could potentially guide the development of neuromodulation therapies for Tourette syndrome that could use a closed-loop-based approach.

Basal ganglia oscillations as biomarkers for targeting circuit dysfunction in Parkinson's disease

Oscillations are a naturally occurring phenomenon in highly interconnected dynamical systems. However, it is thought that excessive synchronized oscillations in brain circuits can be detrimental for many brain functions by disrupting neuronal information processing. Because synchronized basal ganglia oscillations are a hallmark of Parkinson's disease (PD), it has been suggested that aberrant rhythmic activity associated with symptoms of the disease could be used as a physiological biomarker to guide pharmacological and electrical neuromodulatory interventions. We here briefly review the various manifestations of basal ganglia oscillations observed in human subjects and in animal models of PD. In this context, we also review the evidence supporting a pathophysiological role of different oscillations for the suppression of voluntary movements as well as for the induction of excessive motor activity. In light of these findings, it is discussed how oscillations could be used to guide a more precise targeting of dysfunctional circuits to obtain improved symptomatic treatment of PD.

Big data in status epilepticus

Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

Toward adaptive deep brain stimulation for dystonia

The presence of abnormal neural oscillations within the cortico-basal ganglia-thalamo-cortical (CBGTC) network has emerged as one of the current principal theories to explain the pathophysiology of movement disorders. In theory, these oscillations can be used as biomarkers and thereby serve as a feedback signal to control the delivery of deep brain stimulation (DBS). This new form of DBS, dependent on different characteristics of pathological oscillations, is called adaptive DBS (aDBS), and it has already been applied in patients with Parkinson's disease. In this review, the authors summarize the scientific research to date on pathological oscillations in dystonia and address potential biomarkers that might be used as a feedback signal for controlling aDBS in patients with dystonia.

Stability of a chronic implanted brain-computer interface in late-stage amyotrophic lateral sclerosis

OBJECTIVE

We investigated the long-term functional stability and home use of a fully implanted electrocorticography (ECoG)-based brain-computer interface (BCI) for communication by an individual with late-stage Amyotrophic Lateral Sclerosis (ALS).

METHODS

Data recorded from the cortical surface of the motor and prefrontal cortex with an implanted brain-computer interface device was evaluated for 36 months after implantation of the system in an individual with late-stage ALS. In addition, electrode impedance and BCI control accuracy were assessed. Key measures included frequency of use of the system for communication, user and system performance, and electrical signal characteristics.

RESULTS

User performance was high consistently over the three years. Power in the high frequency band, used for the control signal, declined slowly in the motor cortex, but control over the signal remained unaffected by time. Impedance increased until month 5, and then remained constant. Frequency of home use increased steadily, indicating adoption of the system by the user.

CONCLUSIONS

The implanted brain-computer interface proves to be robust in an individual with late-stage ALS, given stable performance and control signal for over 36 months.

SIGNIFICANCE

These findings are relevant for the future of implantable brain-computer interfaces along with other brain-sensing technologies, such as responsive neurostimulation.

Long-Term Sheep Implantation of WIMAGINE®, a Wireless 64-Channel Electrocorticogram Recorder

This article deals with the long-term preclinical validation of WIMAGINE^® (Wireless Implantable Multi-channel Acquisition system for Generic Interface with Neurons), a 64-channel wireless implantable recorder that measures the electrical activity at the cortical surface (electrocorticography, ECoG). The WIMAGINE^® implant was designed for chronic wireless neuronal signal acquisition, to be used e.g., as an intracranial Brain–Computer Interface (BCI) for severely motor-impaired patients. Due to the size and shape of WIMAGINE^®, sheep appeared to be the best animal model on which to carry out long-term in vivo validation. The devices were implanted in two sheep for a follow-up period of 10 months, including idle state cortical recordings and Somato-Sensory Evoked Potential (SSEP) sessions. ECoG and SSEP demonstrated relatively stable behavior during the 10-month observation period. Information recorded from the SensoriMotor Cortex (SMC) showed an SSEP phase reversal, indicating the cortical site of the sensorimotor activity was retained after 10 months of contact. Based on weekly recordings of raw ECoG signals, the effective bandwidth was in the range of 230 Hz for both animals and remarkably stable over time, meaning preservation of the high frequency bands valuable for decoding of the brain activity using BCIs. The power spectral density (in dB/Hz), on a log scale, was of the order of 2.2, –4.5 and –18 for the frequency bands (10–40), (40–100), and (100–200) Hz, respectively. The outcome of this preclinical work is the first long-term in vivo validation of the WIMAGINE^® implant, highlighting its ability to record the brain electrical activity through the dura mater and to send wireless digitized data to the external base station. Apart from local adhesion of the dura to the skull, the neurosurgeon did not face any difficulty in the implantation of the WIMAGINE^® device and post-mortem analysis of the brain revealed no side effect related to the implantation. We also report on the reliability of the system; including the implantable device, the antennas module and the external base station.

Direct Electrical Stimulation in Electrocorticographic Brain-Computer Interfaces: Enabling Technologies for Input to Cortex

Electrocorticographic brain computer interfaces (ECoG-BCIs) offer tremendous opportunities for restoring function in individuals suffering from neurological damage and for advancing basic neuroscience knowledge. ECoG electrodes are already commonly used clinically for monitoring epilepsy and have greater spatial specificity in recording neuronal activity than techniques such as electroencephalography (EEG). Much work to date in the field has focused on using ECoG signals recorded from cortex as control outputs for driving end effectors. An equally important but less explored application of an ECoG-BCI is directing input into cortex using ECoG electrodes for direct electrical stimulation (DES). Combining DES with ECoG recording enables a truly bidirectional BCI, where information is both read from and written to the brain. We discuss the advantages and opportunities, as well as the barriers and challenges presented by using DES in an ECoG-BCI. In this article, we review ECoG electrodes, the physics and physiology of DES, and the use of electrical stimulation of the brain for the clinical treatment of disorders such as epilepsy and Parkinson’s disease. We briefly discuss some of the translational, regulatory, financial, and ethical concerns regarding ECoG-BCIs. Next, we describe the use of ECoG-based DES for providing sensory feedback and for probing and modifying cortical connectivity. We explore future directions, which may draw on invasive animal studies with penetrating and surface electrodes as well as non-invasive stimulation methods such as transcranial magnetic stimulation (TMS). We conclude by describing enabling technologies, such as smaller ECoG electrodes for more precise targeting of cortical areas, signal processing strategies for simultaneous stimulation and recording, and computational modeling and algorithms for tailoring stimulation to each individual brain.

Longitudinal Recordings Reveal Transient Increase of Alpha/Low-Beta Power in the Subthalamic Nucleus Associated With the Onset of Parkinsonian Rest Tremor

Functional magnetic resonance imaging studies suggest that different subcortico-cortical circuits control different aspects of Parkinsonian rest tremor. The basal ganglia were proposed to drive tremor onset, and the cerebellum was suggested to be responsible for tremor maintenance (“dimmer-switch” hypothesis). Although several electrophysiological correlates of tremor have been described, it is currently unclear whether any of these is specific to tremor onset or maintenance. In this study, we present data from a single patient measured repeatedly within 2 years after implantation of a deep brain stimulation (DBS) system capable of recording brain activity from the target. Local field potentials (LFPs) from the subthalamic nucleus and the scalp electroencephalogram were recorded 1 week, 3 months, 6 months, 1 year, and 2 years after surgery. Importantly, the patient suffered from severe rest tremor of the lower limbs, which could be interrupted voluntarily by repositioning the feet. This provided the unique opportunity to record many tremor onsets in succession. We found that tremor onset and tremor maintenance were characterized by distinct modulations of subthalamic oscillations. Alpha/low-beta power increased transiently immediately after tremor onset. In contrast, beta power was continuously suppressed during tremor maintenance. Tremor maintenance was additionally associated with subthalamic and cortical power increases around individual tremor frequency. To our knowledge, this is the first evidence of distinct subthalamic LFP modulations in tremor onset and tremor maintenance. Our observations suggest the existence of an acceleration signal for Parkinsonian rest tremor in the basal ganglia, in line with the “dimmer-switch” hypothesis.

Toward Electrophysiology-Based Intelligent Adaptive Deep Brain Stimulation for Movement Disorders

Deep brain stimulation (DBS) represents one of the major clinical breakthroughs in the age of translational neuroscience. In 1987, Benabid and colleagues demonstrated that high-frequency stimulation can mimic the effects of ablative neurosurgery in Parkinson's disease (PD), while offering two key advantages to previous procedures: adjustability and reversibility. Deep brain stimulation is now an established therapeutic approach that robustly alleviates symptoms in patients with movement disorders, such as Parkinson's disease, essential tremor, and dystonia, who present with inadequate or adverse responses to medication. Currently, stimulation electrodes are implanted in specific target regions of the basal ganglia-thalamic circuit and stimulation pulses are delivered chronically. To achieve optimal therapeutic effect, stimulation frequency, amplitude, and pulse width must be adjusted on a patient-specific basis by a movement disorders specialist. The finding that pathological neural activity can be sampled directly from the target region using the DBS electrode has inspired a novel DBS paradigm: closed-loop adaptive DBS (aDBS). The goal of this strategy is to identify pathological and physiologically normal patterns of neuronal activity that can be used to adapt stimulation parameters to the concurrent therapeutic demand. This review will give detailed insight into potential biomarkers and discuss next-generation strategies, implementing advances in artificial intelligence, to further elevate the therapeutic potential of DBS by capitalizing on its modifiable nature. Development of intelligent aDBS, with an ability to deliver highly personalized treatment regimens and to create symptom-specific therapeutic strategies in real-time, could allow for significant further improvements in the quality of life for movement disorders patients with DBS that ultimately could outperform traditional drug treatment.

Cloud computing for seizure detection in implanted neural devices

OBJECTIVE

Closed-loop implantable neural stimulators are an exciting treatment option for patients with medically refractory epilepsy, with a number of new devices in or nearing clinical trials. These devices must accurately detect a variety of seizure types in order to reliably deliver therapeutic stimulation. While effective, broadly-applicable seizure detection algorithms have recently been published, these methods are too computationally intensive to be directly deployed in an implantable device. We demonstrate a strategy that couples devices to cloud computing resources in order to implement complex seizure detection methods on an implantable device platform.

APPROACH

We use a sensitive gating algorithm capable of running on-board a device to identify potential seizure epochs and transmit these epochs to a cloud-based analysis platform. A precise seizure detection algorithm is then applied to the candidate epochs, leveraging cloud computing resources for accurate seizure event detection. This seizure detection strategy was developed and tested on eleven human implanted device recordings generated using the NeuroVista Seizure Advisory System.

MAIN RESULTS

The gating algorithm achieved high-sensitivity detection using a small feature set as input to a linear classifier, compatible with the computational capability of next-generation implantable devices. The cloud-based precision algorithm successfully identified all seizures transmitted by the gating algorithm while significantly reducing the false positive rate. Across all subjects, this joint approach detected 99% of seizures with a false positive rate of 0.03 h^-1.

SIGNIFICANCE

We present a novel framework for implementing computationally intensive algorithms on human data recorded from an implanted device. By using telemetry to intelligently access cloud-based computational resources, the next generation of neuro-implantable devices will leverage sophisticated algorithms with potential to greatly improve device performance and patient outcomes.

Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson's disease

In this paper, we present a novel Bayesian adaptive dual controller (ADC) for autonomously programming deep brain stimulation devices. We evaluated the Bayesian ADC's performance in the context of reducing beta power in a computational model of Parkinson's disease, in which it was tasked with finding the set of stimulation parameters which optimally reduced beta power as fast as possible. Here, the Bayesian ADC has dual goals: (a) to minimize beta power by exploiting the best parameters found so far, and (b) to explore the space to find better parameters, thus allowing for better control in the future. The Bayesian ADC is composed of two parts: an inner parameterized feedback stimulator and an outer parameter adjustment loop. The inner loop operates on a short time scale, delivering stimulus based upon the phase and power of the beta oscillation. The outer loop operates on a long time scale, observing the effects of the stimulation parameters and using Bayesian optimization to intelligently select new parameters to minimize the beta power. We show that the Bayesian ADC can efficiently optimize stimulation parameters, and is superior to other optimization algorithms. The Bayesian ADC provides a robust and general framework for tuning stimulation parameters, can be adapted to use any feedback signal, and is applicable across diseases and stimulator designs.

A machine-learning approach to volitional control of a closed-loop deep brain stimulation system

OBJECTIVE

Deep brain stimulation (DBS) is a well-established treatment for essential tremor, but may not be an optimal therapy, as it is always on, regardless of symptoms. A closed-loop (CL) DBS, which uses a biosignal to determine when stimulation should be given, may be better. Cortical activity is a promising biosignal for use in a closed-loop system because it contains features that are correlated with pathological and normal movements. However, neural signals are different across individuals, making it difficult to create a 'one size fits all' closed-loop system.

APPROACH

We used machine learning to create a patient-specific, CL DBS system. In this system, binary classifiers are used to extract patient-specific features from cortical signals and determine when volitional, tremor-evoking movement is occurring to alter stimulation voltage in real time.

MAIN RESULTS

This system is able to deliver stimulation up to 87%-100% of the time that subjects are moving. Additionally, we show that the therapeutic effect of the system is at least as good as that of current, continuous-stimulation paradigms.

SIGNIFICANCE

These findings demonstrate the promise of CL DBS therapy and highlight the importance of using subject-specific models in these systems.

Totally Implantable Bidirectional Neural Prostheses: A Flexible Platform for Innovation in Neuromodulation

Implantable neural prostheses are in widespread use for treating a variety of brain disorders. Until recently, most implantable brain devices have been unidirectional, either delivering neurostimulation without brain sensing, or sensing brain activity to drive external effectors without a stimulation component. Further, many neural interfaces that incorporate a sensing function have relied on hardwired connections, such that subjects are tethered to external computers and cannot move freely. A new generation of neural prostheses has become available, that are both bidirectional (stimulate as well as record brain activity) and totally implantable (no externalized connections). These devices provide an opportunity for discovering the circuit basis for neuropsychiatric disorders, and to prototype personalized neuromodulation therapies that selectively interrupt neural activity underlying specific signs and symptoms.

Designing Patient-Specific Optimal Neurostimulation Patterns for Seizure Suppression

Neurostimulation is a promising therapy for abating epileptic seizures. However, it is extremely difficult to identify optimal stimulation patterns experimentally. In this study, human recordings are used to develop a functional 24 neuron network statistical model of hippocampal connectivity and dynamics. Spontaneous seizure-like activity is induced in silico in this reconstructed neuronal network. The network is then used as a testbed to design and validate a wide range of neurostimulation patterns. Commonly used periodic trains were not able to permanently abate seizures at any frequency. A simulated annealing global optimization algorithm was then used to identify an optimal stimulation pattern, which successfully abated 92% of seizures. Finally, in a fully responsive, or closed-loop, neurostimulation paradigm, the optimal stimulation successfully prevented the network from entering the seizure state. We propose that the framework presented here for algorithmically identifying patient-specific neurostimulation patterns can greatly increase the efficacy of neurostimulation devices for seizures.

A Materials Roadmap to Functional Neural Interface Design

Advancement in neurotechnologies for electrophysiology, neurochemical sensing, neuromodulation, and optogenetics are revolutionizing scientific understanding of the brain while enabling treatments, cures, and preventative measures for a variety of neurological disorders. The grand challenge in neural interface engineering is to seamlessly integrate the interface between neurobiology and engineered technology, to record from and modulate neurons over chronic timescales. However, the biological inflammatory response to implants, neural degeneration, and long-term material stability diminish the quality of interface overtime. Recent advances in functional materials have been aimed at engineering solutions for chronic neural interfaces. Yet, the development and deployment of neural interfaces designed from novel materials have introduced new challenges that have largely avoided being addressed. Many engineering efforts that solely focus on optimizing individual probe design parameters, such as softness or flexibility, downplay critical multi-dimensional interactions between different physical properties of the device that contribute to overall performance and biocompatibility. Moreover, the use of these new materials present substantial new difficulties that must be addressed before regulatory approval for use in human patients will be achievable. In this review, the interdependence of different electrode components are highlighted to demonstrate the current materials-based challenges facing the field of neural interface engineering.

Parkinsonian Rest Tremor Is Associated With Modulations of Subthalamic High-Frequency Oscillations

BACKGROUND

High frequency oscillations (>200 Hz) have been observed in the basal ganglia of PD patients and were shown to be modulated by the administration of levodopa and voluntary movement.

OBJECTIVE

The objective of this study was to test whether the power of high-frequency oscillations in the STN is associated with spontaneous manifestation of parkinsonian rest tremor.

METHODS

The electromyogram of both forearms and local field potentials from the STN were recorded in 11 PD patients (10 men, age 58 [9.4] years, disease duration 9.2 [6.3] years). Patients were recorded at rest and while performing repetitive hand movements before and after levodopa intake. High-frequency oscillation power was compared across epochs containing rest tremor, tremor-free rest, or voluntary movement and related to the tremor cycle.

RESULTS

We observed prominent slow (200-300 Hz) and fast (300-400 Hz) high-frequency oscillations. The ratio between slow and fast high-frequency oscillation power increased when tremor became manifest. This increase was consistent across nuclei (94%) and occurred in medication ON and OFF. The ratio outperformed other potential markers of tremor, such as power at individual tremor frequency, beta power, or low gamma power. For voluntary movement, we did not observe a significant difference when compared with rest or rest tremor. Finally, rhythmic modulations of high-frequency oscillation power occurred within the tremor cycle.

CONCLUSIONS

Subthalamic high-frequency oscillation power is closely linked to the occurrence of parkinsonian rest tremor. The balance between slow and fast high-frequency oscillation power combines information on motor and medication state. © 2016 International Parkinson and Movement Disorder Society.

Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease

OBJECTIVE Dysfunction of distributed neural networks underlies many brain disorders. The development of neuromodulation therapies depends on a better understanding of these networks. Invasive human brain recordings have a favorable temporal and spatial resolution for the analysis of network phenomena but have generally been limited to acute intraoperative recording or short-term recording through temporarily externalized leads. Here, the authors describe their initial experience with an investigational, first-generation, totally implantable, bidirectional neural interface that allows both continuous therapeutic stimulation and recording of field potentials at multiple sites in a neural network. METHODS Under a physician-sponsored US Food and Drug Administration investigational device exemption, 5 patients with Parkinson's disease were implanted with the Activa PC+S system (Medtronic Inc.). The device was attached to a quadripolar lead placed in the subdural space over motor cortex, for electrocorticography potential recordings, and to a quadripolar lead in the subthalamic nucleus (STN), for both therapeutic stimulation and recording of local field potentials. Recordings from the brain of each patient were performed at multiple time points over a 1-year period. RESULTS There were no serious surgical complications or interruptions in deep brain stimulation therapy. Signals in both the cortex and the STN were relatively stable over time, despite a gradual increase in electrode impedance. Canonical movement-related changes in specific frequency bands in the motor cortex were identified in most but not all recordings. CONCLUSIONS The acquisition of chronic multisite field potentials in humans is feasible. The device performance characteristics described here may inform the design of the next generation of totally implantable neural interfaces. This research tool provides a platform for translating discoveries in brain network dynamics to improved neurostimulation paradigms. Clinical trial registration no.: NCT01934296 (clinicaltrials.gov).

Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness

Despite deep brain stimulation's positive early results in psychiatric disorders, well-designed clinical trials have yielded inconsistent clinical outcomes. One path to more reliable benefit is closed-loop therapy: stimulation that is automatically adjusted by a device or algorithm in response to changes in the patient's electrical brain activity. These interventions may provide more precise and patient-specific treatments. This article first introduces the available closed-loop neuromodulation platforms, which have shown clinical efficacy in epilepsy and strong early results in movement disorders. It discusses the strengths and limitations of these devices in the context of psychiatric illness. It then describes emerging technologies to address these limitations, including pre-clinical developments such as wireless deep neurostimulation and genetically targeted neuromodulation. Finally, ongoing challenges and limitations for closed-loop psychiatric brain stimulation development, most notably the difficulty of identifying meaningful biomarkers for titration, are discussed. This is considered in the recently-released Research Domain Criteria (RDoC) framework, and how neuromodulation and RDoC are jointly very well suited to address the problem of treatment-resistant illness is described.

Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson’s disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation

After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery.

Neurofeedback Control in Parkinsonian Patients Using Electrocorticography Signals Accessed Wirelessly With a Chronic, Fully Implanted Device

Parkinson's disease (PD) is characterized by motor symptoms such as rigidity and bradykinesia that prevent normal movement. Beta band oscillations (13-30 Hz) in neural local field potentials (LFPs) have been associated with these motor symptoms. Here, three PD patients implanted with a therapeutic deep brain neural stimulator that can also record and wirelessly stream neural data played a neurofeedback game where they modulated their beta band power from sensorimotor cortical areas. Patients' beta band power was streamed in real-time to update the position of a cursor that they tried to drive into a cued target. After playing the game for 1-2 hours each, all three patients exhibited above chance-level performance regardless of subcortical stimulation levels. This study, for the first time, demonstrates using an invasive neural recording system for at-home neurofeedback training. Future work will investigate chronic neurofeedback training as a potentially therapeutic tool for patients with neurological disorders.

Phasic Burst Stimulation: A Closed-Loop Approach to Tuning Deep Brain Stimulation Parameters for Parkinson's Disease

We propose a novel, closed-loop approach to tuning deep brain stimulation (DBS) for Parkinson's disease (PD). The approach, termed Phasic Burst Stimulation (PhaBS), applies a burst of stimulus pulses over a range of phases predicted to disrupt pathological oscillations seen in PD. Stimulation parameters are optimized based on phase response curves (PRCs), which would be measured from each patient. This approach is tested in a computational model of PD with an emergent population oscillation. We show that the stimulus phase can be optimized using the PRC, and that PhaBS is more effective at suppressing the pathological oscillation than a single phasic stimulus pulse. PhaBS provides a closed-loop approach to DBS that can be optimized for each patient.

Histological evaluation of a chronically-implanted electrocorticographic electrode grid in a non-human primate

OBJECTIVE

Electrocorticography (ECoG), used as a neural recording modality for brain-machine interfaces (BMIs), potentially allows for field potentials to be recorded from the surface of the cerebral cortex for long durations without suffering the host-tissue reaction to the extent that it is common with intracortical microelectrodes. Though the stability of signals obtained from chronically implanted ECoG electrodes has begun receiving attention, to date little work has characterized the effects of long-term implantation of ECoG electrodes on underlying cortical tissue.

APPROACH

We implanted and recorded from a high-density ECoG electrode grid subdurally over cortical motor areas of a Rhesus macaque for 666 d.

MAIN RESULTS

Histological analysis revealed minimal damage to the cortex underneath the implant, though the grid itself was encapsulated in collagenous tissue. We observed macrophages and foreign body giant cells at the tissue-array interface, indicative of a stereotypical foreign body response. Despite this encapsulation, cortical modulation during reaching movements was observed more than 18 months post-implantation.

SIGNIFICANCE

These results suggest that ECoG may provide a means by which stable chronic cortical recordings can be obtained with comparatively little tissue damage, facilitating the development of clinically viable BMI systems.

Intraoperative electrocorticography for physiological research in movement disorders: principles and experience in 200 cases

OBJECTIVE Contemporary theories of the pathophysiology of movement disorders emphasize abnormal oscillatory activity in basal ganglia-thalamocortical loops, but these have been studied in humans mainly using depth recordings. Recording from the surface of the cortex using electrocorticography (ECoG) provides a much higher amplitude signal than depth recordings, is less susceptible to deep brain stimulation (DBS) artifacts, and yields a surrogate measure of population spiking via "broadband gamma" (50-200 Hz) activity. Therefore, a technical approach to movement disorders surgery was developed that employs intraoperative ECoG as a research tool. METHODS One hundred eighty-eight patients undergoing DBS for the treatment of movement disorders were studied under an institutional review board-approved protocol. Through the standard bur hole exposure that is clinically indicated for DBS lead insertion, a strip electrode (6 or 28 contacts) was inserted to cover the primary motor or prefrontal cortical areas. Localization was confirmed by the reversal of the somatosensory evoked potential and intraoperative CT or 2D fluoroscopy. The ECoG potentials were recorded at rest and during a variety of tasks and analyzed offline in the frequency domain, focusing on activity between 3 and 200 Hz. Strips were removed prior to closure. Postoperative MRI was inspected for edema, signal change, or hematoma that could be related to the placement of the ECoG strip. RESULTS One hundred ninety-eight (99%) strips were successfully placed. Two ECoG placements were aborted due to resistance during the attempted passage of the electrode. Perioperative surgical complications occurred in 8 patients, including 5 hardware infections, 1 delayed chronic subdural hematoma requiring evacuation, 1 intraparenchymal hematoma, and 1 venous infarction distant from the site of the recording. None of these appeared to be directly related to the use of ECoG. CONCLUSIONS Intraoperative ECoG has long been used in neurosurgery for functional mapping and localization of seizure foci. As applied during DBS surgery, it has become an important research tool for understanding the brain networks in movement disorders and the mechanisms of therapeutic stimulation. In experienced hands, the technique appears to add minimal risk to surgery.

Neurosurgery for schizophrenia: an update on pathophysiology and a novel therapeutic target

The main objectives of this review were to provide an update on the progress made in understanding specific circuit abnormalities leading to psychotic symptoms in schizophrenia and to propose rational targets for therapeutic deep brain stimulation (DBS). Refractory schizophrenia remains a major unsolved clinical problem, with 10%-30% of patients not responding to standard treatment options. Progress made over the last decade was analyzed through reviewing structural and functional neuroimaging studies in humans, along with studies of animal models of schizophrenia. The authors reviewed theories implicating dysfunction in dopaminergic and glutamatergic signaling in the pathophysiology of the disorder, paying particular attention to neurosurgically relevant nodes in the circuit. In this context, the authors focused on an important pathological circuit involving the associative striatum, anterior hippocampus, and ventral striatum, and discuss the possibility of targeting these nodes for therapeutic neuromodulation with DBS. Finally, the authors examined ethical considerations in the treatment of these vulnerable patients. The functional anatomy of neural circuits relevant to schizophrenia remains of great interest to neurosurgeons and psychiatrists and lends itself to the development of specific targets for neuromodulation. Ongoing progress in the understanding of these structures will be critical to the development of potential neurosurgical treatments of schizophrenia.

Implanted electrodes for multi-month EEG

An implanted electroencephalogram (EEG) recorder would help diagnose infrequent seizure-like events. A proof-of-concept study quantified the electrical characteristics of the electrodes planned for the proposed recorder. The electrodes were implanted in an ovine model for eight weeks. Electrode impedance was less than 800 Ohms throughout the study. A frequency-domain determination of sedation performed similarly for surface versus implanted electrodes throughout the study. The time-domain correlation between an implanted electrode and a surface electrode was almost as high as between two surface electrodes (0.86 versus 0.92). EEG-certified clinicians judged that the implanted electrode quality was adequate to excellent and that the implanted electrodes provided the same clinical information as surface electrodes except for a noticeable amplitude difference. No significant issues were found that would stop development of the EEG recorder.

Local field potential recordings in a non-human primate model of Parkinsons disease using the Activa PC + S neurostimulator

OBJECTIVE

Using the Medtronic Activa® PC + S system, this study investigated how passive joint manipulation, reaching behavior, and deep brain stimulation (DBS) modulate local field potential (LFP) activity in the subthalamic nucleus (STN) and globus pallidus (GP).

APPROACH

Five non-human primates were implanted unilaterally with one or more DBS leads. LFPs were collected in montage recordings during resting state conditions and during motor tasks that facilitate the expression of parkinsonian motor signs. These recordings were made in the naïve state in one subject, in the parkinsonian state in two subjects, and in both naïve and parkinsonian states in two subjects.

MAIN RESULTS

LFPs measured at rest were consistent over time for a given recording location and parkinsonian state in a given subject; however, LFPs were highly variable between subjects, between and within recording locations, and across parkinsonian states. LFPs in both naïve and parkinsonian states across all recorded nuclei contained a spectral peak in the beta band (10-30 Hz). Moreover, the spectral content of recorded LFPs was modulated by passive and active movement of the subjects' limbs. LFPs recorded during a cued-reaching task displayed task-related beta desynchronization in STN and GP. The bidirectional capabilities of the Activa® PC + S also allowed for recording LFPs while delivering DBS. The therapeutic effect of STN DBS on parkinsonian rigidity outlasted stimulation for 30-60 s, but there was no correlation with beta band power.

SIGNIFICANCE

This study emphasizes (1) the variability in spontaneous LFPs amongst subjects and (2) the value of using the Activa® PC + S system to record neural data in the context of behavioral tasks that allow one to evaluate a subject's symptomatology.

Deep Brain Recordings Using an Implanted Pulse Generator in Parkinson's Disease

OBJECTIVES

Recent studies suggest that oscillatory beta activity could be used as a state biomarker in patients with Parkinson's disease for subthalamic closed-loop stimulation with the intention of improving clinical benefit. Here we investigate the feasibility of subthalamic recordings via a novel chronically implanted pulse generator.

METHODS

Subthalamic local field potential recordings were obtained from eight patients before and during deep brain stimulation (DBS). All data were analyzed in the frequency domain using Fourier transform-based methods and compared between ON and OFF stimulation conditions.

RESULTS

Distinct peaks of oscillatory beta band activity were found in 12 of 15 electrodes. DBS induced a significant frequency specific suppression of oscillatory beta activity (p = 0.002).

CONCLUSION

The results of the study suggest that oscillatory beta band synchronization and its modulation by DBS is recordable with a system suitable for chronic implantation and may serve as a biomarker for subthalamic closed-loop stimulation in patients with Parkinson's disease.

Tremor Reduction by Deep Brain Stimulation Is Associated With Gamma Power Suppression in Parkinson's Disease

Objectives

Rest tremor is a cardinal symptom of Parkinson's disease (PD), and is readily suppressed by deep brain stimulation (DBS) of the subthalamic nucleus (STN). The therapeutic effect of the latter on bradykinesia and rigidity has been associated with the suppression of exaggerated beta (13–30 Hz) band synchronization in the vicinity of the stimulating electrode, but there is no correlation between beta suppression and tremor amplitude. In the present study, we investigate whether tremor suppression is related to suppression of activities at other frequencies.

Materials and Methods

We recorded hand tremor and contralateral local field potential (LFP) activity from DBS electrodes during stimulation of the STN in 15 hemispheres in 11 patients with PD. DBS was applied with increasing voltages starting at 0.5 V until tremor suppression was achieved or until 4.5 V was reached.

Results

Tremor was reduced to 48.9% ± 10.9% of that without DBS once stimulation reached 2.5–3 V (t14 = −4.667, p < 0.001). There was a parallel suppression of low gamma (31–45 Hz) power to 92.5% ± 3% (t14 = −2.348, p = 0.034). This was not seen over a band containing tremor frequencies and their harmonic (4–12 Hz), or over the beta band. Moreover, low gamma power correlated with tremor severity (mean r = 0.43 ± 0.14, p = 0.008) within subjects. This was not the case for LFP power in the other two bands.

Conclusions

Our findings support a relationship between low gamma oscillations and PD tremor, and reinforce the principle that the subthalamic LFP is a rich signal that may contain information about the severity of multiple different Parkinsonian features.

Sensing-enabled hippocampal deep brain stimulation in idiopathic nonhuman primate epilepsy

Epilepsy is a debilitating condition affecting 1% of the population worldwide. Medications fail to control seizures in at least 30% of patients, and deep brain stimulation (DBS) is a promising alternative treatment. A modified clinical DBS hardware platform was recently described (PC+S) allowing long-term recording of electrical brain activity such that effects of DBS on neural networks can be examined. This study reports the first use of this device to characterize idiopathic epilepsy and assess the effects of stimulation in a nonhuman primate (NHP). Clinical DBS electrodes were implanted in the hippocampus of an epileptic NHP bilaterally, and baseline local field potential (LFP) recordings were collected for seizure characterization with the PC+S. Real-time automatic detection of ictal events was demonstrated and validated by concurrent visual observation of seizure behavior. Seizures consisted of large-amplitude 8- to 25-Hz oscillations originating from the right hemisphere and quickly generalizing, with an average occurrence of 0.71 ± 0.15 seizures/day. Various stimulation parameters resulted in suppression of LFP activity or in seizure induction during stimulation under ketamine anesthesia. Chronic stimulation in the awake animal was studied to evaluate how seizure activity was affected by stimulation configurations that suppressed broadband LFPs in acute experiments. This is the first electrophysiological characterization of epilepsy using a next-generation clinical DBS system that offers the ability to record and analyze neural signals from a chronically implanted stimulating electrode. These results will direct further development of this technology and ultimately provide insight into therapeutic mechanisms of DBS for epilepsy.

Toward sophisticated basal ganglia neuromodulation: Review on basal ganglia deep brain stimulation

This review presents state-of-the-art knowledge about the roles of the basal ganglia (BG) in action-selection, cognition, and motivation, and how this knowledge has been used to improve deep brain stimulation (DBS) treatment of neurological and psychiatric disorders. Such pathological conditions include Parkinson's disease, Huntington's disease, Tourette syndrome, depression, and obsessive-compulsive disorder. The first section presents evidence supporting current hypotheses of how the cortico-BG circuitry works to select motor and emotional actions, and how defects in this circuitry can cause symptoms of the BG diseases. Emphasis is given to the role of striatal dopamine on motor performance, motivated behaviors and learning of procedural memories. Next, the use of cutting-edge electrochemical techniques in animal and human studies of BG functioning under normal and disease conditions is discussed. Finally, functional neuroimaging studies are reviewed; these works have shown the relationship between cortico-BG structures activated during DBS and improvement of disease symptoms.

Origins and suppression of oscillations in a computational model of Parkinson's disease

Efficacy of deep brain stimulation (DBS) for motor signs of Parkinson's disease (PD) depends in part on post-operative programming of stimulus parameters. There is a need for a systematic approach to tuning parameters based on patient physiology. We used a physiologically realistic computational model of the basal ganglia network to investigate the emergence of a 34 Hz oscillation in the PD state and its optimal suppression with DBS. Discrete time transfer functions were fit to post-stimulus time histograms (PSTHs) collected in open-loop, by simulating the pharmacological block of synaptic connections, to describe the behavior of the basal ganglia nuclei. These functions were then connected to create a mean-field model of the closed-loop system, which was analyzed to determine the origin of the emergent 34 Hz pathological oscillation. This analysis determined that the oscillation could emerge from the coupling between the globus pallidus external (GPe) and subthalamic nucleus (STN). When coupled, the two resonate with each other in the PD state but not in the healthy state. By characterizing how this oscillation is affected by subthreshold DBS pulses, we hypothesize that it is possible to predict stimulus frequencies capable of suppressing this oscillation. To characterize the response to the stimulus, we developed a new method for estimating phase response curves (PRCs) from population data. Using the population PRC we were able to predict frequencies that enhance and suppress the 34 Hz pathological oscillation. This provides a systematic approach to tuning DBS frequencies and could enable closed-loop tuning of stimulation parameters.

Deep brain stimulation for movement disorders

Deep brain stimulation (DBS) is an implanted electrical device that modulates specific targets in the brain resulting in symptomatic improvement in a particular neurologic disease, most commonly a movement disorder. It is preferred over previously used lesioning procedures due to its reversibility, adjustability, and ability to be used bilaterally with a good safety profile. Risks of DBS include intracranial bleeding, infection, malposition, and hardware issues, such migration, disconnection, or malfunction, but the risk of each of these complications is low--generally ≤ 5% at experienced, large-volume centers. It has been used widely in essential tremor, Parkinson's disease, and dystonia when medical treatment becomes ineffective, intolerable owing to side effects, or causes motor complications. Brain targets implanted include the thalamus (most commonly for essential tremor), subthalamic nucleus (most commonly for Parkinson's disease), and globus pallidus (Parkinson's disease and dystonia), although new targets are currently being explored. Future developments include brain electrodes that can steer current directionally and systems capable of "closed loop" stimulation, with systems that can record and interpret regional brain activity and modify stimulation parameters in a clinically meaningful way. New, image-guided implantation techniques may have advantages over traditional DBS surgery.