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

Chronic Pallidal Local Field Potentials Are Associated With Dystonic Symptoms in Children

BACKGROUND

Novel deep brain stimulation devices can record local field potentials (LFPs), which represent the synchronous synaptic activity of neuronal populations. The clinical relevance of LFPs in patients with dystonia remains unclear.

OBJECTIVES

We sought to determine whether chronic LFPs recorded from the globus pallidus internus (GPi) were associated with symptoms of dystonia in children.

MATERIALS AND METHODS

Ten patients with heterogeneous forms of dystonia (genetic and acquired) were implanted with neurostimulators that recorded LFP spectral snapshots. Spectra were compared across parent-reported asymptomatic and symptomatic periods, with daily narrowband data superimposed in 24 one-hour bins.

RESULTS

Spectral power increased during periods of registered dystonic symptoms: mean increase = 102%, CI: (76.7, 132). Circadian rhythms within the LFP narrowband time series correlated with dystonic symptoms: for delta/theta-waves, correlation = 0.33, CI: (0.18, 0.47) and for alpha waves, correlation = 0.27, CI: (0.14, 0.40).

CONCLUSIONS

LFP spectra recorded in the GPi indicate a circadian pattern and are associated with the manifestation of dystonic symptoms.

Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system

Introduction

Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices.

Methods

In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms.

Results

Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage.

Discussion

This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.

Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives

Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.

Long Term Performance of a Bi-Directional Neural Interface for Deep Brain Stimulation and Recording

Background: In prior reports, we described the design and initial performance of a fully implantable, bi-directional neural interface system for use in deep brain and other neurostimulation applications. Here we provide an update on the chronic, long-term neural sensing performance of the system using traditional 4-contact leads and extend those results to include directional 8-contact leads.

Methods: Seven ovine subjects were implanted with deep brain stimulation (DBS) leads at different nodes within the Circuit of Papez: four with unilateral leads in the anterior nucleus of the thalamus and hippocampus; two with bilateral fornix leads, and one with bilateral hippocampal leads. The leads were connected to either an Activa PC+S^® (Medtronic) or Percept PC^°ledR (Medtronic) deep brain stimulation and recording device. Spontaneous local field potentials (LFPs), evoked potentials (EPs), LFP response to stimulation, and electrode impedances were monitored chronically for periods of up to five years in these subjects.

Results: The morphology, amplitude, and latencies of chronic hippocampal EPs evoked by thalamic stimulation remained stable over the duration of the study. Similarly, LFPs showed consistent spectral peaks with expected variation in absolute magnitude dependent upon behavioral state and other factors, but no systematic degradation of signal quality over time. Electrode impedances remained within expected ranges with little variation following an initial stabilization period. Coupled neural activity between the two nodes within the Papez circuit could be observed in synchronized recordings up to 5 years post-implant. The magnitude of passive LFP power recorded from directional electrode segments was indicative of the contacts that produced the greatest stimulation-induced changes in LFP power within the Papez network.

Conclusion: The implanted device performed as designed, providing the ability to chronically stimulate and record neural activity within this network for up to 5 years of follow-up.

Decreased Phase-Amplitude Coupling Between the mPFC and BLA During Exploratory Behaviour in Chronic Unpredictable Mild Stress-Induced Depression Model of Rats

Depression is a common neuropsychiatric illness observed worldwide, and reduced interest in exploration is one of its symptoms. The control of dysregulated medial prefrontal cortex (mPFC) over the basolateral amygdala (BLA) is related to depression. However, the oscillation interaction in the mPFC-BLA circuit has remained elusive. Therefore, this study used phase-amplitude coupling (PAC), which provides complicated forms of information transmission by the phase of low-frequency rhythm, modulating the amplitude of high-frequency rhythm, and has a potential application for the treatment of neurological disease. The chronic unpredictable mild stress (CUMS) was used to prepare the rat models of depression. Moreover, multichannel in vivo recording was applied to obtain the local field potentials (LFPs) of the mPFC, the BLA in rats in control, and CUMS groups, while they explored the open field. The results showed prominent coupling between the phase of theta oscillation (4-12 Hz) in the mPFC and the amplitude of high-gamma oscillation (70-120 Hz) in the BLA. Compared to the control group, this theta-gamma PAC was significantly decreased in the CUMS group, which was accompanied by the diminished exploratory behaviour. The results indicate that the coupling between the phase of theta in the mPFC and the amplitude of gamma in the BLA is involved in exploratory behaviour, and this decreased coupling may inhibit exploratory behaviour of rats exposed to CUMS.

Double-blind cross-over pilot trial protocol to evaluate the safety and preliminary efficacy of long-term adaptive deep brain stimulation in patients with Parkinson's disease

INTRODUCTION

After several years of brain-sensing technology development and proof-of-concept studies, adaptive deep brain stimulation (aDBS) is ready to better treat Parkinson's disease (PD) using aDBS-capable implantable pulse generators (IPGs). New aDBS devices are capable of continuous sensing of neuronal activity from the subthalamic nucleus (STN) and contemporaneous stimulation automatically adapted to match the patient's clinical state estimated from the analysis of STN activity using proprietary algorithms. Specific studies are necessary to assess superiority of aDBS vs conventional DBS (cDBS) therapy. This protocol describes an original innovative multicentre international study aimed to assess safety and efficacy of aDBS vs cDBS using a new generation of DBS IPG in PD (AlphaDBS system by Newronika SpA, Milan, Italy).

METHODS

The study involves six investigational sites (in Italy, Poland and The Netherlands). The primary objective will be to evaluate the safety and tolerability of the AlphaDBS System, when used in cDBS and aDBS mode. Secondary objective will be to evaluate the potential efficacy of aDBS. After eligibility screening, 15 patients with PD already implanted with DBS systems and in need of battery replacement will be randomised to enter a two-phase protocol, including a 'short-term follow-up' (2 days experimental sessions during hospitalisation, 1 day per each mode) and a 'long-term follow-up' (1 month at home, 15 days per each mode).

ETHICS AND DISSEMINATION

The trial was approved as premarket study by the Italian, Polish, and Dutch Competent Authorities: Bioethics Committee at National Oncology Institute of Maria Skłodowska-Curie-National Research Institute in Warsaw; Comitato Etico Milano Area 2; Comitato Etico IRCCS Istituto Neurologico C. Besta; Comitato Etico interaziendale AOUC Città della Salute e della Scienza-AO Ordine Mauriziano di Torino-ASL Città di Torino; De Medisch Ethisch Toetsingscommissie van Maastricht UMC. The study started enrolling patients in January 2021.

TRIAL REGISTRATION NUMBER

NCT04681534.

Deep brain stimulation: is it time to change gears by closing the loop?

Objective.Adaptive deep brain stimulation (aDBS) is a form of invasive stimulation that was conceived to overcome the technical limitations of traditional DBS, which delivers continuous stimulation of the target structure without considering patients' symptoms or status in real-time. Instead, aDBS delivers on-demand, contingency-based stimulation. So far, aDBS has been tested in several neurological conditions, and will be soon extensively studied to translate it into clinical practice. However, an exhaustive description of technical aspects is still missing.Approach.in this topical review, we summarize the knowledge about the current (and future) aDBS approach and control algorithms to deliver the stimulation, as reference for a deeper undestending of aDBS model.Main results.We discuss the conceptual and functional model of aDBS, which is based on the sensing module (that assesses the feedback variable), the control module (which interpretes the variable and elaborates the new stimulation parameters), and the stimulation module (that controls the delivery of stimulation), considering both the historical perspective and the state-of-the-art of available biomarkers.Significance.aDBS modulates neuronal circuits based on clinically relevant biofeedback signals in real-time. First developed in the mid-2000s, many groups have worked on improving closed-loop DBS technology. The field is now at a point in conducting large-scale randomized clinical trials to translate aDBS into clinical practice. As we move towards implanting brain-computer interfaces in patients, it will be important to understand the technical aspects of aDBS.

Information transmission in mPFC-BLA network during exploratory behavior in the open field

Exploratory behavior plays a fundamental role in motivation, learning, and well-being of organisms. The open field test (OFT) is a classic method to investigate the exploratory behavior in rodents, also a widely adopted and pharmacologically validated procedure for evaluating anxiety and depression. Several lines of evidence have shown that medial prefrontal cortex (mPFC) and basolateral amygdala (BLA) play crucial roles in anxiety-like or depression-like exploratory behavior. However, the dynamic characterization of the mPFC-BLA network in exploratory behavior is less well understood. Therefore, this study aimed to investigate the information transmission mechanism in the mPFC-BLA network during exploratory behavior. Local field potentials (LFPs) from mPFC and BLA were simultaneously recorded while the rats performed the OFT. Directed transfer function (DTF), which was derived from Granger causal connectivity analysis, was applied to measure the functional connectivity among LFPs. Information flow (IF) was calculated to explore the dynamics of information transmission in the mPFC-BLA network. Our results revealed that, for both mPFC and BLA, the theta-band functional connectivity in periphery was significantly higher than that in center of the open field. The IF from BLA to mPFC in the open field task was significantly higher than that from mPFC to BLA. These results suggest that the functional connectivity and IF in the mPFC-BLA network are related to the exploratory behavior, and information transmission from BLA to mPFC could be predominant for exploratory behavior.

Reduced Information Transmission of Medial Prefrontal Cortex to Basolateral Amygdala Inhibits Exploratory Behavior in Depressed Rats

Depression is a mental and neurological disease that reduces the desire for exploration. Dysregulation of the information transmission between medial prefrontal cortex (mPFC) and basolateral amygdala (BLA) is associated with depression. However, which direction of information transmission (mPFC-BLA or BLA-mPFC) related to the decline of exploratory interests in depression is unclear. Therefore, it is important to determine what specific changes occur in mPFC and BLA information transmission in depressed rats during exploratory behavior. In the present study, local field potentials (LFPs) were recorded via multi-electrodes implanted in the mPFC and BLA for the control and depression groups of rats when they were exploring in an open field. The theta band was determined to be the characteristic band of exploratory behavior. The direct transfer function (DTF) was used to calculate the mPFC and BLA bidirectional information flow (IF) to measure information transmission. Compared with the control group, the theta IF of mPFC-BLA in the depression group was significantly reduced, and there was no significant difference in theta IF of BLA-mPFC between the two groups. Our results indicated that the reduction of mPFC-BLA information transmission can inhibit the exploratory behavior of depressed rats.

Toward a closed-loop deep brain stimulation in Parkinson's disease using local field potential in parkinsonian rat model

Deep brain stimulation (DBS) is an invasive method used for treating Parkinson's disease in its advanced stages. Nowadays, the initial adjustment of DBS parameters and their automatic matching proportion to the progression of the disease is viewed as one of the research areas discussed by the researchers, which is called closed-loop DBS. Various studies were conducted regarding finding the signal(s) which reflects different symptoms of the disease. Local Field Potential (LFP) is one of the signals that is suitable for using as feedback, because it can be recorded by the same implemented electrodes for stimulation. The present study aimed to identify the distinguishing features of patients from healthy individuals using LFP signals.

METHODS

In the present study, LFP was recorded from the rats in sham and parkinsonian model groups. After evaluating the signals in the frequency domain, sixty-six features were extracted from power spectral density of LFPs. The features were classified by Support Vector Machine (SVM) to determine the ability of features for separating parkinsonian rats from healthy ones. Finally, the most effective features were selected for distinguishing between the sham and parkinsonian model groups using a genetic algorithm.

RESULTS

The results indicated that the frequency domain features of LFP signals from rats have capacity of using them as a feedback for closed-loop DBS. The accuracy of the Support Vector Machine classification using all 66 features was 80.42% which increased to 84.41% using 38 features selected by genetic algorithm. The proposed method not only increase the accuracy, but it also reduce computation by decreasing the number of the effective features. The results indicate the significant capacity of the proposed method for identifying the effective high-frequency features to control the closed-loop DBS.

CONCLUSIONS

The ability of using LFP signals as feedback in closed-loop DBS was shown by extracting useful information in frequency bands below and above 100 Hz regarding LFP signals of parkinsonian rats and sham ones. Based on the results, features at frequencies above 100 Hz were more powerful and robust than below 100 Hz. The genetic algorithm was used for optimizing the classification problem.

Neurobiological Mechanisms of Metacognitive Therapy - An Experimental Paradigm

INTRODUCTION

The neurobiological mechanisms underlying the clinical effects of psychotherapy are scarcely understood. In particular, the modifying effects of psychotherapy on neuronal activity are largely unknown. We here present data from an innovative experimental paradigm using the example of a patient with treatment resistant obsessive-compulsive disorder (trOCD) who underwent implantation of bilateral electrodes for deep brain stimulation (DBS). The aim of the paradigm was to examine the short term effect of metacognitive therapy (MCT) on neuronal local field potentials (LFP) before and after 5 MCT sessions.

METHODS

DBS electrodes were implanted bilaterally with stereotactic guidance in the bed nucleus of the stria terminalis/ internal capsule (BNST/IC). The period between implantation of the electrodes and the pacemaker was used for the experimental paradigm. DBS electrodes were externalized via extension cables, yielding the opportunity to record LFP directly from the BNST/IC. The experimental paradigm was designed as follows: (a) baseline recording of LFP from the BNST/IC, (b) application of 5 MCT sessions over 3 days, (c) post-MCT recording from the BNST/IC. The Obsessive-Compulsive Disorder- scale (OCD-S) was used to evaluate OCD symptoms.

RESULTS

OCD symptoms decreased after MCT. These reductions were accompanied by a decrease of the relative power of theta band activity, while alpha, beta, and gamma band activity was significantly increased after MCT. Further, analysis of BNST/IC LFP and frontal cortex EEG coherence showed that MCT decreased theta frequency band synchronization.

DISCUSSION

Implantation of DBS electrodes for treating psychiatric disorders offers the opportunity to gather data from neuronal circuits, and to compare effects of therapeutic interventions. Here, we demonstrate direct effects of MCT on neuronal oscillatory behavior, which may give possible cues for the neurobiological changes associated with psychotherapy.

Fornical Closed-Loop Stimulation for Alzheimer's Disease

Pharmacological neuromodulation strategies have shown limited efficacy in treating memory deficits related to Alzheimer's disease (AD). Despite encouraging results from a few preclinical studies, clinical trials investigating open-loop deep brain stimulation (DBS) for AD have not been successful. Recent refinements in understanding the various phases of memory processes, animal studies investigating phase-specific modulation of hippocampal activity during memorization, and clinical studies using closed-loop DBS strategies to treat patients with movement disorders, all point to the need to investigate closed-loop fornical DBS strategies to better understand memory dynamics and potentially treat memory deficits in AD preclinical models.

Optimizing the learning rate for adaptive estimation of neural encoding models

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.

Strengthened functional connectivity among LFPs in rat medial prefrontal cortex during anxiety

Theta oscillations in medial prefrontal cortex (mPFC) have been consistently implicated in the regulation of anxiety-related behaviors. However, the theta-band functional connectivity in mPFC is less well characterized. Therefore, we simultaneously recorded local filed potentials (LFPs) from mPFC in freely behaving rats in the elevated plus maze (EPM). Functional connectivity among LFPs was measured by directed transfer function (DTF) via spectral Granger causal connectivity analysis. Causal network was then identified based on DTF. Global efficiency (E_glob) was selected to quantitatively describe the characteristic of the network. Our results showed that a significant difference in theta-band functional connectivity between safe and aversive location in the maze anxiety test. Strikingly, DTF and E_glob were higher specifically in the closed arms and decreased sharply prior to entrying into the open arms. These results indicate strengthened theta-band functional connectivity may be related to anxiety.

A new bio-inspired stimulator to suppress hyper-synchronized neural firing in a cortical network

Hyper-synchronous neural oscillations are the character of several neurological diseases such as epilepsy. On the other hand, glial cells and particularly astrocytes can influence neural synchronization. Therefore, based on the recent researches, a new bio-inspired stimulator is proposed which basically is a dynamical model of the astrocyte biophysical model. The performance of the new stimulator is investigated on a large-scale, cortical network. Both excitatory and inhibitory synapses are also considered in the simulated spiking neural network. The simulation results show that the new stimulator has a good performance and is able to reduce recurrent abnormal excitability which in turn avoids the hyper-synchronous neural firing in the spiking neural network. In this way, the proposed stimulator has a demand controlled characteristic and is a good candidate for deep brain stimulation (DBS) technique to successfully suppress the neural hyper-synchronization.

An external portable device for adaptive deep brain stimulation (aDBS) clinical research in advanced Parkinson's Disease

Compared to conventional deep brain stimulation (DBS) for patients with Parkinson's Disease (PD), the newer approach of adaptive DBS (aDBS), regulating stimulation on the basis of the patient's clinical state, promises to achieve better clinical outcomes, avoid adverse-effects and save time for tuning parameters. A remaining challenge before aDBS comes into practical use is to prove its feasibility and its effectiveness in larger groups of patients and in more ecological conditions. We developed an external portable aDBS system prototype designed for clinical testing in freely-moving PD patients with externalized DBS electrodes. From a single-channel bipolar artifact-free recording, it analyses local field potentials (LFPs), during ongoing DBS for tuning stimulation parameters, independent from the specific feedback algorithm implemented. We validated the aDBS system in vitro, by testing both its sensing and closed-loop stimulation capabilities, and then tested it in vivo, focusing on the sensing capabilities. By applying the aDBS system prototype in a patient with PD, we provided evidence that it can track levodopa and DBS-induced LFP spectral power changes among different patient's clinical states. Our system, intended for testing LFP-based feedback strategies for aDBS, should help understanding how and whether aDBS therapy works in PD and indicating future technical and clinical advances.

Application of higher-order spectral analysis to local field potentials recorded in patients treated with deep brain stimulation

Local field potentials (LFPs) recorded from implanted deep brain electrodes demonstrated the oscillatory nature of human basal ganglia. LFP rhythms were mainly characterized by means od power spectral analysis, thus loosing information related to rhythm phase synchronization and to event related phase modulations. Because the application of higher-order spectral analysis methodology can overcome such limitation, here we review the present applications of bispectral and cross-bispectral analysis to LFP recordings. The results obtained up to now showed that higher-order spectral analysis was able to clarify detect different rhythm synchronizations and interactions characterizing different pathologies and patient's states.

Closed-loop stimulation of a delayed neural fields model of parkinsonian STN-GPe network: a theoretical and computational study

Several disorders are related to pathological brain oscillations. In the case of Parkinson's disease, sustained low-frequency oscillations (especially in the β-band, 13-30 Hz) correlate with motor symptoms. It is still under debate whether these oscillations are the cause of parkinsonian motor symptoms. The development of techniques enabling selective disruption of these β-oscillations could contribute to the understanding of the underlying mechanisms, and could be exploited for treatments. A particularly appealing technique is Deep Brain Stimulation (DBS). With clinical electrical DBS, electrical currents are delivered at high frequency to a region made of potentially heterogeneous neurons (the subthalamic nucleus (STN) in the case of Parkinson's disease). Even more appealing is DBS with optogenetics, which is until now a preclinical method using both gene transfer and deep brain light delivery and enabling neuromodulation at the scale of one given neural network. In this work, we rely on delayed neural fields models of STN and the external Globus Pallidus (GPe) to develop, theoretically validate and test in silico a closed-loop stimulation strategy to disrupt these sustained oscillations with optogenetics. First, we rely on tools from control theory to provide theoretical conditions under which sustained oscillations can be attenuated by a closed-loop stimulation proportional to the measured activity of STN. Second, based on this theoretical framework, we show numerically that the proposed closed-loop stimulation efficiently attenuates sustained oscillations, even in the case when the photosensitization effectively affects only 50% of STN neurons. We also show through simulations that oscillations disruption can be achieved when the same light source is used for the whole STN population. We finally test the robustness of the proposed strategy to possible acquisition and processing delays, as well as parameters uncertainty.

Update on deep brain stimulation in Parkinson's disease

Deep brain stimulation (DBS) is considered a safe and well tolerated surgical procedure to alleviate Parkinson’s disease (PD) and other movement disorders symptoms along with some psychiatric conditions. Over the last few decades DBS has been shown to provide remarkable therapeutic effect on carefully selected patients. Although its precise mechanism of action is still unknown, DBS improves motor functions and therefore quality of life. To date, two main targets have emerged in PD patients: the globus pallidus pars interna and the subthalamic nucleus. Two other targets, the ventralis intermedius and zona incerta have also been selectively used, especially in tremor-dominant PD patients. The main indications for PD DBS have traditionally been motor fluctuations, debilitating medication induced dyskinesias, unpredictable “off time” state, and medication refractory tremor. Medication refractory tremor and intolerable dyskinesia are potential palliative indications. Besides aforementioned targets, the brainstem pedunculopontine nucleus (PPN) is under investigation for the treatment of ON-state freezing of gait and postural instability. In this article, we will review the most recent literature on DBS therapy for PD, including cutting-edge advances and data supporting the role of DBS in advanced neural-network modulation.

Chasing tics in the human brain: development of open, scheduled and closed loop responsive approaches to deep brain stimulation for tourette syndrome

Tourette syndrome is a childhood-onset disorder characterized by a combination of motor and vocal tics, often associated with psychiatric comorbidities including attention deficit and hyperactivity disorder and obsessive-compulsive disorder. Despite an onset early in life, half of patients may present symptoms in adulthood, with variable degrees of severity. In select cases, the syndrome may lead to significant physical and social impairment, and a worrisome risk for self injury. Evolving research has provided evidence supporting the idea that the pathophysiology of Tourette syndrome is directly related to a disrupted circuit involving the cortex and subcortical structures, including the basal ganglia, nucleus accumbens, and the amygdala. There has also been a notion that a dysfunctional group of neurons in the putamen contributes to an abnormal facilitation of competing motor responses in basal ganglia structures ultimately underpinning the generation of tics. Surgical therapies for Tourette syndrome have been reserved for a small group of patients not responding to behavioral and pharmacological therapies, and these therapies have been directed at modulating the underlying pathophysiology. Lesion therapy as well as deep brain stimulation has been observed to suppress tics in at least some of these cases. In this article, we will review the clinical aspects of Tourette syndrome, as well as the evolution of surgical approaches and we will discuss the evidence and clinical responses to deep brain stimulation in various brain targets. We will also discuss ongoing research and future directions as well as approaches for open, scheduled and closed loop feedback-driven electrical stimulation for the treatment of Tourette syndrome.

Sixty hertz neurostimulation amplifies subthalamic neural synchrony in Parkinson's disease

High frequency subthalamic nucleus (STN) deep brain stimulation (DBS) improves the cardinal motor signs of Parkinson's disease (PD) and attenuates STN alpha/beta band neural synchrony in a voltage-dependent manner. While there is a growing interest in the behavioral effects of lower frequency (60 Hz) DBS, little is known about its effect on STN neural synchrony. Here we demonstrate for the first time that during intra-operative 60 Hz STN DBS, one or more bands of resting state neural synchrony were amplified in the STN in PD. We recorded intra-operative STN resting state local field potentials (LFPs) from twenty-eight STNs in seventeen PD subjects after placement of the DBS lead (model 3389, Medtronic, Inc.) before and during three randomized neurostimulation sets (130 Hz/1.35V, 130 Hz/2V, 60 Hz/2V). During 130 Hz/2V DBS, baseline (no DBS) STN alpha (8-12 Hz) and beta (13-35 Hz) band power decreased (N=14, P < 0.001 for both), whereas during 60 Hz/2V DBS, alpha band and peak frequency power increased (P = 0.012, P = 0.007, respectively). The effect of 60 Hz/2V DBS opposed that of power-equivalent (130 Hz/1.35V) DBS (alpha: P < 0.001, beta: P = 0.006). These results show that intra-operative 60 Hz STN DBS amplified whereas 130 Hz STN DBS attenuated resting state neural synchrony in PD; the effects were frequency-specific. We demonstrate that neurostimulation may be useful as a tool to selectively modulate resting state resonant bands of neural synchrony and to investigate its influence on motor and non-motor behaviors in PD and other neuropsychiatric diseases.

Web-based telemonitoring and delivery of caregiver support for patients with Parkinson disease after deep brain stimulation: protocol

Background

The increasing number of patients, the high costs of management, and the chronic progress of the disease that prevents patients from performing even simple daily activities make Parkinson disease (PD) a complex pathology with a high impact on society. In particular, patients implanted with deep brain stimulation (DBS) electrodes face a highly fragile stabilization period, requiring specific support at home. However, DBS patients are followed usually by untrained personnel (caregivers or family), without specific care pathways and supporting systems.

Objective

This projects aims to (1) create a reference consensus guideline and a shared requirements set for the homecare and monitoring of DBS patients, (2) define a set of biomarkers that provides alarms to caregivers for continuous home monitoring, and (3) implement an information system architecture allowing communication between health care professionals and caregivers and improving the quality of care for DBS patients.

Methods

The definitions of the consensus care pathway and of caregiver needs will be obtained by analyzing the current practices for patient follow-up through focus groups and structured interviews involving health care professionals, patients, and caregivers. The results of this analysis will be represented in a formal graphical model of the process of DBS patient care at home. To define the neurophysiological biomarkers to be used to raise alarms during the monitoring process, neurosignals will be acquired from DBS electrodes through a new experimental system that records while DBS is turned ON and transmits signals by radiofrequency. Motor, cognitive, and behavioral protocols will be used to study possible feedback/alarms to be provided by the system. Finally, a set of mobile apps to support the caregiver at home in managing and monitoring the patient will be developed and tested in the community of caregivers that participated in the focus groups. The set of developed apps will be connected to the already existing WebBioBank Web-based platform allowing health care professionals to manage patient electronic health records and neurophysiological signals. New modules in the WebBioBank platform will be implemented to allow integration and data exchange with mobile health apps.

Results

The results of this project will provide a novel approach to long-term evaluation of patients with chronic, severe conditions in the homecare environment, based on caregiver empowerment and tailored applications developed according to consensus care pathways established by clinicians.

Conclusions

The creation of a direct communication channel between health care professionals and caregivers can benefit large communities of patients and would represent a scalable experience in integrating data and information coming from a clinical setting to those in home monitoring.

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

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

Inhibition of propofol anesthesia on functional connectivity between LFPs in PFC during rat working memory task

Working memory (WM) refers to the temporary storage and manipulation of information necessary for performance of complex cognitive tasks. There is a growing interest in whether and how propofol anesthesia inhibits WM function. The aim of this study is to investigate the possible inhibition mechanism of propofol anesthesia based on the functional connections of multi-local field potentials (LFPs) and behavior during WM tasks. Adult SD rats were randomly divided into 3 groups: pro group (0.5 mg·kg⁻¹·min⁻¹,2 h), PRO group (0.9 mg·kg⁻¹·min⁻¹, 2 h) and control group. The experimental data were 16-channel LFPs obtained at prefrontal cortex with implanted microelectrode array in SD rats during WM tasks in Y-maze at 24, 48, 72, 96, 120 hours (day 1-day 5) after propofol anesthesia, and the behavior results of WM were recoded at the same time. Directed transfer function (DTF) method was applied to analyze the connections among LFPs directly. Furthermore, the causal networks were identified by DTF. The clustering coefficient (C), network density (D) and global efficiency (E_global) were selected to describe the functional connectivity quantitatively. The results show that: comparing with the control group, the LFPs functional connectivity in pro group were no significantly difference (p>0.05); the connectivity in PRO group were significantly decreased (p<0.05 at 24 hours, p<0.05 at 48 hours), while no significant difference at 72, 96 and 120 hours for rats (p>0.05), which were consistent with the behavior results. These findings could lead to improved understanding the mechanism of inhibition of anesthesia on WM functions from the view of connections among LFPs.

Closing the loop of deep brain stimulation

High-frequency deep brain stimulation is used to treat a wide range of brain disorders, like Parkinson's disease. The stimulated networks usually share common electrophysiological signatures, including hyperactivity and/or dysrhythmia. From a clinical perspective, HFS is expected to alleviate clinical signs without generating adverse effects. Here, we consider whether the classical open-loop HFS fulfills these criteria and outline current experimental or theoretical research on the different types of closed-loop DBS that could provide better clinical outcomes. In the first part of the review, the two routes followed by HFS-evoked axonal spikes are explored. In one direction, orthodromic spikes functionally de-afferent the stimulated nucleus from its downstream target networks. In the opposite direction, antidromic spikes prevent this nucleus from being influenced by its afferent networks. As a result, the pathological synchronized activity no longer propagates from the cortical networks to the stimulated nucleus. The overall result can be described as a reversible functional de-afferentation of the stimulated nucleus from its upstream and downstream nuclei. In the second part of the review, the latest advances in closed-loop DBS are considered. Some of the proposed approaches are based on mathematical models, which emphasize different aspects of the parkinsonian basal ganglia: excessive synchronization, abnormal firing-rate rhythms, and a deficient thalamo-cortical relay. The stimulation strategies are classified depending on the control-theory techniques on which they are based: adaptive and on-demand stimulation schemes, delayed and multi-site approaches, stimulations based on proportional and/or derivative control actions, optimal control strategies. Some of these strategies have been validated experimentally, but there is still a large reservoir of theoretical work that may point to ways of improving practical treatment.

Abnormal patterns of theta frequency oscillations during the temporal evolution of freezing of gait in Parkinson's disease

OBJECTIVE

We sought to characterize the electrophysiological signature of Freezing of gait in Parkinson's disease.

METHODS

We examined 24 patients with idiopathic Parkinson's disease and significant freezing of gait as they performed a series of timed up-and-go tasks in their 'off' state while electroencephalographic data was collected from four scalp leads. Fast Fourier Transformation was utilized to explore the power spectral density between periods of normal walking and periods of freezing, as well as during the transition between the two states. In addition, Cross Spectrum and Cross Frequency analyses were used to explore the role of impaired temporal and spatial connectivity.

RESULTS

When compared to walking, episodes of freezing were associated with a significant increase in theta band power within the central and frontal leads. The transition from normal walking to freezing of gait was also associated with increased theta frequency coupling between the central and frontal leads, along with an increase in cross-frequency coupling in the central lead.

CONCLUSIONS

Episodes of freezing of gait in Parkinson's disease are associated with abnormal oscillatory activity in the brain.

SIGNIFICANCE

These results provide novel insights into the pattern of spatiotemporal dynamics underlying freezing of gait and may provide a potential means for therapeutic prediction and alleviation of freezing episodes in susceptible patients.

A low-power configurable neural recording system for epileptic seizure detection

This paper describes a low-power configurable neural recording system capable of capturing and digitizing both neural action-potential (AP) and fast-ripple (FR) signals. It demonstrates the functionality of epileptic seizure detection through FR recording. This system features a fixed-gain, variable-bandwidth (BW) front-end circuit and a sigma-delta ADC with scalable bandwidth and power consumption. The ADC employs a 2nd-order single-bit sigma-delta modulator (SDM) followed by a low-power decimation filter. Direct impulse-response implementation of a sinc(3) filter and 8-cycle data pipelining in an IIR filter are proposed for the decimation filter design to improve the power and area efficiency. In measurements, the front end exhibits 39.6-dB DC gain, 0.8 Hz to 5.2 kHz of BW, 5.86- μVrms input-referred noise, and 2.4- μW power consumption in AP mode, while showing 38.5-dB DC gain, 250 to 486 Hz of BW, 2.48- μVrms noise, and 4.5- μW power consumption in FR mode. The noise efficiency factor (NEF) is 2.93 and 7.6 for the AP and FR modes, respectively. At 77-dB dynamic range (DR), the ADC has a peak SNR and SNDR of 75.9 dB and 67 dB, respectively, while consuming 2.75-mW power in AP mode. It achieves 78-dB DR, 76.2-dB peak SNR, 73.2-dB peak SNDR, and 588- μW power consumption in FR mode. Both analog and digital power supply voltages are 2.8 V. The chip is fabricated in a standard 0.6- μm CMOS process. The die size is 11.25 mm(2).

Neural origin of evoked potentials during thalamic deep brain stimulation

Closed-loop deep brain stimulation (DBS) systems could provide automatic adjustment of stimulation parameters and improve outcomes in the treatment of Parkinson's disease and essential tremor. The evoked compound action potential (ECAP), generated by activated neurons near the DBS electrode, may provide a suitable feedback control signal for closed-loop DBS. The objectives of this work were to characterize the ECAP across stimulation parameters and determine the neural elements contributing to the signal. We recorded ECAPs during thalamic DBS in anesthetized cats and conducted computer simulations to calculate the ECAP of a population of thalamic neurons. The experimental and computational ECAPs were similar in shape and had characteristics that were correlated across stimulation parameters (R(2) = 0.80-0.95, P < 0.002). The ECAP signal energy increased with larger DBS amplitudes (P < 0.0001) and pulse widths (P < 0.002), and the signal energy of secondary ECAP phases was larger at 10-Hz than at 100-Hz DBS (P < 0.002). The computational model indicated that these changes resulted from a greater extent of neural activation and an increased synchronization of postsynaptic thalamocortical activity, respectively. Administration of tetrodotoxin, lidocaine, or isoflurane abolished or reduced the magnitude of the experimental and computational ECAPs, glutamate receptor antagonists 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) and D(-)-2-amino-5-phosphonopentanoic acid (APV) reduced secondary ECAP phases by decreasing postsynaptic excitation, and the GABAA receptor agonist muscimol increased the latency of the secondary phases by augmenting postsynaptic hyperpolarization. This study demonstrates that the ECAP provides information about the type and extent of neural activation generated during DBS, and the ECAP may serve as a feedback control signal for closed-loop DBS.

Surgical treatment of Parkinson disease: past, present, and future

Advances in functional neurosurgery have expanded the treatment of Parkinson disease (PD) to targeted electrical stimulation of specific nodes in the basal ganglia circuitry. Deep brain stimulation (DBS), applied to selected patients and difficult-to-manage motor fluctuations, yields substantial reductions in off time and dyskinesia. Emerging concepts in DBS include examination of new targets, such as the potential efficacy of pedunculopontine nucleus stimulation for treatment of freezing and falls, the use of pathologic oscillations in the beta band to construct an adaptive "closed-loop" DBS, and new technologies, including segmented electrodes to steer current toward specific neural populations.

Pathological gambling in Parkinson's disease: subthalamic oscillations during economics decisions

Pathological gambling develops in up to 8% of patients with Parkinson's disease. Although the pathophysiology of gambling remains unclear, several findings argue for a dysfunction in the basal ganglia circuits. To clarify the role of the subthalamic nucleus in pathological gambling, we studied its activity during economics decisions. We analyzed local field potentials recorded from deep brain stimulation electrodes in the subthalamic nucleus while parkinsonian patients with (n = 8) and without (n = 9) pathological gambling engaged in an economics decision-making task comprising conflictual trials (involving possible risk-taking) and non conflictual trials. In all parkinsonian patients, subthalamic low frequencies (2-12 Hz) increased during economics decisions. Whereas, in patients without gambling, low-frequency oscillations exhibited a similar pattern during conflictual and non conflictual stimuli, in those with gambling, low-frequency activity increased significantly more during conflictual than during non conflictual stimuli. The specific low-frequency oscillatory pattern recorded in patients with Parkinson's disease who gamble could reflect a subthalamic dysfunction that makes their decisional threshold highly sensitive to risky options. When parkinsonian patients process stimuli related to an economics task, low-frequency subthalamic activity increases. This task-related change suggests that the cognitive-affective system that drives economics decisional processes includes the subthalamic nucleus. The specific subthalamic neuronal activity during conflictual decisions in patients with pathological gambling supports the idea that the subthalamic nucleus is involved in behavioral strategies and in the pathophysiology of gambling.

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

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

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

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

A chronic generalized bi-directional brain-machine interface

A bi-directional neural interface (NI) system was designed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an existing neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain-machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson's disease). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.

Thalamic single-unit and local field potential activity in Tourette syndrome

Deep brain stimulation (DBS) of the ventralis oralis (VO) complex of the thalamus improves tics in patients with Tourette syndrome (TS). To neurophysiologically describe the VO complex we recorded, in seven patients with TS undergoing DBS electrode implantation, single-unit activity during surgery and local field potentials (LFPs) a few days after surgery. Single unit recordings showed that the VO complex is characterized by a localized pattern of bursting neuronal activity. LFP spectra demonstrated that VO of TS patients has a prominent oscillatory activity at low frequencies (2-7 Hz) and in the alpha-band (8-13 Hz), and a virtually absent beta activity. In each patient, the main LFP frequency significantly correlated with single-unit interburst frequency. In conclusion, we observed an oscillatory bursting activity in the VO as target region in patients with severe TS undergoing DBS surgery.

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

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