Australas Phys Eng Sci Med (2014) 37:619–634 DOI 10.1007/s13246-014-0297-2

REVIEW

Closed loop deep brain stimulation: an evolving technology Md Kamal Hosain • Abbas Kouzani Susannah Tye

•

Received: 19 February 2014 / Accepted: 25 August 2014 / Published online: 7 September 2014 Ó Australasian College of Physical Scientists and Engineers in Medicine 2014

Abstract Deep brain stimulation is an effective and safe medical treatment for a variety of neurological and psychiatric disorders including Parkinson’s disease, essential tremor, dystonia, and treatment resistant obsessive compulsive disorder. A closed loop deep brain stimulation (CLDBS) system automatically adjusts stimulation parameters by the brain response in real time. The CLDBS continues to evolve due to the advancement in the brain stimulation technologies. This paper provides a study on the existing systems developed for CLDBS. It highlights the issues associated with CLDBS systems including feedback signal recording and processing, stimulation parameters setting, control algorithm, wireless telemetry, size, and power consumption. The benefits and limitations of the existing CLDBS systems are also presented. Whilst robust clinical proof of the benefits of the technology remains to be achieved, it has the potential to offer several advantages over open loop DBS. The CLDBS can improve efficiency and efficacy of therapy, eliminate lengthy start-up period for programming and adjustment, provide a personalized treatment, and make parameters setting automatic and adaptive. Keywords Deep brain stimulation
Closed loop DBS
Open loop DBS
Biomarker
Automatic stimulation

M. K. Hosain (&)
A. Kouzani School of Engineering, Deakin University, Geelong, VIC 3216, Australia e-mail: [email protected] S. Tye Department of Psychiatry & Psychology, Mayo Clinic Depression Center, Rochester, MN 55905, USA

Introduction Deep brain stimulation (DBS) is a medical treatment for a range of neurological and psychiatric disorders. DBS is a non-destructive surgical procedure which is carried out in two stages. First, the stimulation electrodes and the associated connecting leads are implanted in a target location, with the position of electrodes in the brain is determined by the patient’s response to stimulation. Next, a pulse generator (IPG) is implanted below the clavicle or chest. The IPG delivers electrical pulses to the electrodes via the connecting leads. An external programmer adjusts the stimulation parameters of the IPG wirelessly according to the patient’s needs [1]. Parkinson disease (PD) and other movement disorders such as essential tremor and dystonia are now being treated with DBS. Over the past decade, more than 30,000 PD patients worldwide have benefited from DBS, as well as many others with essential tremor and dystonia [2]. It is estimated that patients worldwide are receiving DBS with the rate of approximately 8,000 –10,000 new patients per year [3]. The field is now expanding to treat other nervous system related conditions even those for which there is no current medical treatment other than behavioural or physical rehabilitation. Research is underway to determine the clinical efficacy of DBS for treatment of new diseases including depression, obsessive compulsive disorder, Tourette’s syndrome, epilepsy, chronic pain, treatment refractory obesity [4], cluster headache, Alzheimer’s disease, addiction, and minimally conscious state disorder [2, 5–7]. By considering the relationship of a DBS process with the patient, it can be classified as an open loop or a closed loop DBS. A closed loop DBS (CLDBS) system is one in which an input parameters are set by the actual brain/body response in real time whereas the open loop DBS (OLDBS)

123

620

system does not use a feedback of the actual state of the brain/body in order to adjust the parameters of the DBS system [8]. Currently, in standard DBS systems, the stimulation parameters setting is done either by neurologist or trained physician according to gaits. The stimulation parameter adjustment by the clinician is not in real-time and automated, meaning that such systems are not closed loop systems [9]. Standard OLDBS systems suffer from some limitations including: having no effect on some symptoms, deteriorating certain conditions, causing some side effects, becoming less effective with time, causing speech impairment, increasing the cost of therapy, applying the same stimulation signal regardless of the state of patient, and so on [10]. The invariant stimulation pattern in OLDBS leads to habituation and tolerance resulting in the loss of efficacy. All these limitations are related to the open loop nature of stimulation. The CLDBS can alleviate these limitations and improve the outcome of DBS. A CLDBS system comprises stimulating electrodes, a physiological bio-marker recorder, a controller, and a control algorithm. The physiological bio-marker recorder provides feedback signals to the controller that in turn modifies stimulation parameters based on the feedback signal adaptively [11]. Internal brain biomarkers as well as external behaviour change can be used as a feedback signal in CLDBS. The internal brain biomarkers can include local field potential, spike, and electrocorticogram. electroencephalogram, magnetoencephalography, near infrared spectroscopy, and functional magnetic resonance imaging etc. These biomarkers are recordable with electrodes and other imaging systems [12]. Moreover the external bio-marker includes physical behaviour change of patients which can be recorded with wearable sensors (e.g. accelerometers) and video based monitoring systems. The recorded signals can also be used as feedback signals in a CLDBS system to adjust stimulation parameters from outside the body [13, 14]. An important area for exploration in designing a CLDBS system is the adaptive optimization of the stimulation parameters. The stimulation parameters are electrode configurations, combinations of negative and positive contacts, frequency, pulse width, and pulse amplitude. A real-time adaptation of the stimulation parameters is often necessary because the disease condition of a brain disorder patient changes with time [10]. The adaptive optimization of the stimulation parameters can minimize side effects, maximize therapeutic efficacy, and prolong battery life [15]. A closed loop global optimization algorithm, e.g. genetic algorithm, may identify the best DBS waveforms for more effective brain stimulation [16]. Other considerations for further exploration of CLDBS are stimulus artefact free feedback signal recording and processing, automatic feedback control algorithm, wireless telemetry, small size, and low power consumption.

123

Australas Phys Eng Sci Med (2014) 37:619–634

CLDBS will offers a number of features for making it superior to OLDBS [17–22]. The features of CLDBS are as follows: (i) CLDBS has automatic and constant adaptation to changing aspects of the disease in patients over time. If the rating scales of a disease changes, the controller adapts to them, and then updates the stimulation signal. However, for OLDBS, the stimulation parameters remain unchanged between manual adjustments by clinicians; (ii) CLDBS is an independent approach. It does not require any periodic medical assistance from a trained clinician to control the stimulation parameters for the dynamic disease; (iii) CLDBS does not rely on the statistics of the stimulation pattern that endorses the behavioural improvements, but rather the stimulation pattern reflects ongoing activity; (iv) CLDBS reduces the risk of applying inappropriate stimulation parameters consequently reducing the risk of tissue damage. There is less chance of setting parameters wrongly since it is internal biomarkers dependent. In contrast, OLDBS can be programmed for excessive signal amplitude and frequency if the operator is not trained properly, or has not studied the statistics of the stimulation. Recently, researchers have conducted numerous experiments for evaluating the performance of CLDBS systems on patients and laboratory animal models. These experimental results showed the superiority of CLDBS over OLDBS. For example, Little et al. [23] reported that CLDBS improves motor score for PD over standard OLDBS. Rosin et al. [19] and Lee et al. [24] also proved closed loop functionality of DBS on animal model of PD. Moreover, CLDBS was demonstrated to be a feasible and safe method for epilepsy treatment on human patients [25, 26]. In another study, Good et al. [27] presented that the efficacy of the close loop seizure control method is better than the open loop scheme in epilepsy rat model. In addition, the function of CLDBS system on human with essential tremor was verified by Graupe et al. [28]. These articles are the proof of concept for the implementation of CLDBS in treatment of various movement disorders, psychiatric diseases, and other pharmaco-resistant brain pathologies. Thus, these published works promote the need for CLDBS, an automatic and dynamic system that continually adjusts the stimulus. This article reviews recent technological advancements relating to CLDBS, and issues and benefits of CLDBS. Moreover, the technological development required to implement it for controlled and automated operation are identified. The limitations to implement a CLDBS system are also acknowledged.

CLDBS system Currently, most of the DBS systems have no sensing and recording capability, thus stimulation parameters setting

Australas Phys Eng Sci Med (2014) 37:619–634

Controllable Electrode Array

Sense Biomarker Using Electrodes

Bio Marker Analyser

No

Is the Sensed Biomarker Different from Normal Condition Yes Modify the Stimulation Parameters

Fig. 1 Flow diagram of a closed loop DBS [29]

are done either by a neurologist or an expert clinician according. The neurologist works as a feedback controller. The stimulation parameter adjustment by the clinician is not in real-time and automated, meaning that the systems are not closed loop type. To make DBS systems more effective, independent, and adaptive, the implementation of CLDBS has been considered. A CLBDS system needs to have some essential functionalities including a neural signal recording, signal processing to extract neurophysiological features, adjustment of stimulation parameters, controlling the entire system [12]. Figure 1 represents a flow chart for a CLDBS system [29]. This system will comprise electrode array for not only stimulation but also recording biomarkers. An analyser will process the recorded brain signal. A controller will perform an optimization algorithm to select best possible signal adaptively based on the analysed brain bio-marker. If the difference between the recorded bio-marker and the bio-marker in normal brain condition is observed then stimulation parameter will be adjusted. On the other hand, if the recorded bio-marker becomes same as that in normal brain condition, then no stimulation will be provided. However, some significant difficulties including biomarker detection from brain, real time operation of feedback loop, reliable functionality, stable operation, controlled switch off for safety might arise to implement CLDBS. These difficulties have been discussed in section III [19]. Figure 2 represents block diagram of a CLDBS system. It includes an implant and an external system. In the

621

external system, a transceiver transmits modulated RF signals to the implant as a command and power signal, and receives feedback signals wirelessly from the implant. A computer based analyser analyse the feedback signal and generate necessary command signal for the implant. The implant includes an antenna, a transceiver, a power scavenger, a demodulator, a controller, a signal generator, electrode array, and signal processor. The transceiver receives modulated RF signals containing control command and energy. The received RF signal is converted to regulated DC power in the power scavenging circuit. The power scavenging circuit comprises a high frequency rectifier and a voltage stabilizer. The scavenged energy is used to power the implant components. The received RF signal is demodulated and sent to the on-board controller. The controller regulates the signal generator to produce biphasic pulses with the desired frequency, amplitude, and pulse width. The pulse signal is delivered to the brain through the electrode array. On the other hand, the sensing electrode senses the biomarker from the target brain region and sends it back to the transceiver after processing and amplifying it with an amplifier and a filter. Since the sensed signal may have small magnitude, a low noise amplifier should be used. The feedback signals are modulated with high frequency carrier and then transmitted back to the external antenna. Recently some reports have been published on CLDBS system. Among some of these reported articles, Santaniello et al. [18] modelled a CLDBS system that automatically adjusts the stimulation amplitude based on electrical signals recorded from the stimulation electrodes. They used the local field potentials (LFPs) generated by the neural population of a patient with essential tremor as a feedback signal. This system regulates the spectrum of the LFPs based on the feedback variable and thereby normalizing the aberrant pattern of neuronal activity present in tremor. Although this is a simulation based study, it suggests the feasibility of a closed loop control of DBS parameters to increase the performance of brain stimulation system. In another study Lee et al. [30] developed a 64-channel programmable closed loop deep brain stimulator with 8-channel neural amplifier and a logarithmic analogue to digital converter for use in research and treatment of PD. The system sensed and filtered neural activity, and generated programmable stimulation currents. The device interfaced directly with the recording and stimulation electrodes in which the recording electrodes were implanted in the motor cortex whereas the stimulating electrodes were implanted in the STN. A feedback of recorded neural activity secures better control and optimization of stimulation parameters. The prototype system incorporated more functionality yet consumes less power and area. This system consisted of eight front-end low noise neural

123

622

Australas Phys Eng Sci Med (2014) 37:619–634

Fig. 2 Block diagram of CLDBS

Power Scavenging

RF power and command transmission Computerized Analyser

RF Transceiver

Feedback signal reception

RF Transceiver

RF Demodulator

Digital Controller IC

Signal Generator

Amplifier and signal processor Implantable device

amplifiers. Instead of an analogue filter, an on-chip digital filter was used to separates the low frequency neural field potential signal from the neural spike energy. The digital filter separates neural voltage spikes (400 Hz–5 kHz, with amplitude levels up to ±500 lV) and low frequency field potential (10–50 Hz, with amplitude levels up to ±10 mV). The 64-channels stimulator generated independent, biphasic, stimulation current signals. The stimulation parameters such as pulse amplitude, duration, and repetition rate were programmed with an on-chip controller. Avestruz et al. [31] designed a spectral processing chip for extracting biomarkers to use them drive therapy devices such as a deep brain neurostimulator. To exhibit the chip functionality, they included a prototype of a closed loop neurostimulator implementing adaptive titration of therapy based on measured field potential activity. This prototype system initiated stimulation after the detection of a burst of field potential energy in the b-band (15–40 Hz), which was indicative of several pathological events. The microprocessor sampled the LFP signals at 200 Hz for the time domain signal, and 5 Hz for the band power signal after amplification, and band power extraction was performed by the neural processing chip. A control algorithm normalized the mean energy in the last 2 s to the median energy over the last 30 min. If this normalized ratio exceeded a predefined threshold for a fixed time span, a detection flag was passed to the stimulation controller over the bus, and stimulation at 140 Hz was initiated on the stimulation electrodes. The sensing chip also interfaced to an off-chip memory to streamline loop recording of the chronic biometric data. The system emphasised on efficiently extracting neuronal biomarkers contained in field potentials using analogue preprocessing prior to data conversion and digital processing. The architecture had broad tuning capability to a range of biomarkers and provides robustness with modest tradeoffs in noise and area. The system also had the ability to run flexible control algorithms for adaptive therapy. In another approach, a brain-machine interface (BMI) prototype which represents a practical milestone towards a closed loop neurostimulation system was developed by Stanslaski et al. [9]. Although this implantable bi-

123

Array electrode

Feedback signal

directional BMI prototype is not exactly a CLDBS system, it is designed for chronic studies exploring closed loop and diagnostic opportunities for neuroprosthetics with sensing, algorithm, wireless telemetry, and stimulation therapy capabilities. It practically measured and classified disease relevant brain states from derived biomarkers using a custom chip and machine learning techniques. It also linked sensing and stimulation through a control algorithm as a bi-directional BMI. A sensing interface IC (BASIC) based system extracted band power at key physiological frequencies from LFPs. LFP was used as biomarker because it generally represent the ensemble activity of an in vivo neural population around the electrode and are more chronically robust. A spectrogram of LFP data was used classify the patient’s state in real time with high sensitivity and specificity applying support vector classification. These states were then fed to the therapy prototype controller to discover optimizing stimulator settings using algorithms or provide clinician feedback based on quantitative diagnostics. Moreover, wireless telemetry was used in this system for system configuration, algorithm programming, and data uplink. The system proved a promising ability to sense meaningful signals in the presence of therapeutic stimulation thereby demonstrated as a key to providing closed loop control.

Issues in CLDBS system There are some issues including biomarker recording, developing a feedback control algorithm, size reduction, wireless telemetry, battery-less operation, and optimum parameters setting to implement a CLDBS system, which are discussed in the following subsections. Feedback signal recording and processing One of the issues of designing a CLDBS system is the feedback signal recording and processing in real-time. The feedback signal could be either internal bio-marker or external bio-marker. The internal bio-markers are recorded

Australas Phys Eng Sci Med (2014) 37:619–634

from the brain itself whereas the external bio-markers are detailed from behavioural change in human body. The internal biomarker recoding can be invasive and noninvasive. Different internal and external biomarkers recoded during DBS are shown in Table 1. These biomarkers can be used as feedback signal for closed loop action of a DBS device. The internal brain biomarkers include local field potential, spike, electrocorticogram, electroencephalogram, magnetoencephalography, near infrared spectroscopy, and functional magnetic resonance imaging. While the external bio-markers may include, tremor, bradykinesia, and rigidity of hand, arm or body. PD and other movement disorders are dynamic diseases with time varying symptoms, as well as a range of different symptoms. The biomarker as a feedback signal for disease symptoms should actually be recorded in a chronic clinically relevant situation with high signal to noise ratio, stability over years, casually linked to a symptom, not disrupted by outside influences like movement, talking, thinking, and so on. Moreover, it needs to be proven whether brain signals are causally important before stimulation. Currently, large volume of research is being conducted to identify and record stable and relevant biomarkers [32–34]. Among many of these reported articles, Santaniello et al. [18] showed that the patterns, magnitude and the rate of neuronal rhythms for the internal biomarkers vary depending on the status of the patient. For example, the spectral content of local field potential in a Parkinson patent is not same in tremor conditions and tremor free conditions. In another study, Hirata et al. [12]. reported that the internal biomarkers are recordable with electrodes and other imaging systems. The sensing of signals in the presence of therapeutic stimulation with microelectrode array were implemented and reported. Moreover, McCreery et al. [35] designed and tested an electrode array including 16 activated iridium microelectrodes for stimulating subthalamic nucleus or the globus pallidus of animal, and recording the extracellular action potential. This microelectrode array can be used therapeutically even for a single neuron. Mohseni et al. [36] also presented an active implantable system for biopotential recording using an integrated telemetry circuit. It recorded spontaneous neural activity from the auditory cortex of an awake marmoset monkey. Two types of micromachined neural recording microelectrodes were used including a polyimide sieve electrode of 30 mm in length, three electroplated gold recording sites, and a silicon electrode of 5 mm length, and 16 iridium recording sites. Among numbers of technical difficulties in biomarker recording, the stimulus artefact-free recording is most important. The stimulus artefact is effect generated on the recorded signal from stimulation signal. The artefact is created due the simultaneous recording of biomarker and stimulation of same brain area. Numerous reports have been

623

published to supress artefact during bio-marker recording. Rossi et al. reported [37] a novel amplification system to suppress the artefact for LEP recording and to increase signal to noise (SNR) of the recorded signal. Since the recorded biomarker usually has very small SAR, it should be amplified with high performance amplifier to improve it. Kent and Grill [38] designed a novel instrument to record evoked potentials for closed loop control of DBS. This novel instrumentation system suppressed the stimulus artefact and amplified the small magnitude, short latency evoked compound action potentials response during DBS with clinically relevant parameters. This high fidelity recordings system provides insight into the type and spatial extent of neural elements activated during DBS, and could serve as a promising feedback control signals for CLDBS. In another approach, Yoon-Kyu et al. [39] developed a wireless prototype of the cortical neural recording microsystem for brain implantable neuro engineering applications. Moreover, a prototype integrated circuit for wireless neural recording from a 100-channel microelectrode array was presented by Harrison et al. [40] and another 128-channel neural recording device was described by Moo Sung et al. [41]. In recent time, some neurochemicals including neurotransmitters and neuromodulators which involve in the physiological effects of DBS are being considered as internal bio-markers. Since some diseases like essential tremor have neuromodulatory effects on neurotransmitter systems, the concentration of these neurotransmitters indicates the status of the patient. A wireless instantaneous neurotransmitter concentration sensing system as reported by Kimble et al. [42], can measure the concentration of various extracellular neurotransmitters such as dopamine, glutamate, adenosine and serotonin. Generally, two electroanalytical methods including fast scan cyclic voltammetry and fixed potential amperometry can be employed to measure neurotransmitter concentrations at carbon fibre microelectrode sensors. The electrochemical monitoring of real time, spatially resolved neurotransmitter of dopamine and adenosine using fast scan cyclic voltammetry system at carbon fibre microelectrode was reported in Ref. [43]. Although, the fast scan cyclic voltammetry parameters are not suitable for serotonin measurement, the incorporation of N-shaped waveform applied at a high scan rate allowed the serotonin measurement capability [44]. In another study, Agnesi et al. [45] developed neurotransmitter concentration system based on fixed potential amperometry to measure extracellular concentrations of dopamine, glutamate and adenosine. Although the accessibility to deep regions of the brain through the DBS implanted electrodes can give the opportunity to monitor the local activity of nuclei, there exist some limitations including stimulus artefacts. Therefore, some researches have been conducted to record internal biomarker from outside human body. This approach may be

123

123 A time-series electrical signal recorded from individual neurons in the brain Recording of the electrical activity of the brain made using electrodes placed on the surface of the exposed cerebral cortex.

Distributed signals representing global brain activity

A measure of the tiny magnetic components of the electromagnetic signals provoked by neural activity A measure of blood-oxygenation signals A neuroimaging process that measures brain activity through detecting related changes in blood flow

Spike

Electrocorticogram (ECoG)

Electroencephalogram (EEG)

Magnetoencephalography(MEG)

Near infrared spectroscopy

Functional magnetic resonance imaging (fMRI)

Motor symptoms of PD An electrical recording of muscle activity

Tremor, bradykinesia, rigidity of hand, arm or body

Electromyography (EMG)

External biomarkers

Electric potential in the extracellular space around neurons

Description

Local field potential (LFP)

Internal biomarkers

Biomarkers

Table 1 DBS-related biomarkers and a comparison of their advantages

Essential tremor patient

Patients with PD

Patients with PD

Patient with PD and with essential tremor

Phantom limb

A rodent model of PD

Eight patients with intractable epilepsy

Essential tremor or PD tremor

PD in rat study

Diseases

It allows predicting onset of tremor to expedite initiation of a next stimulation packet before tremor reappears. The analysis of this signal is fast and can be performed on-line in real-time. It is noninvasive and easy to extract technique

It produces reliable results

During deep brain stimulation, fMRI is a method with substantial potential for exposing the functional connectivity of the stimulated nuclei

It presents various patterns of cerebral blood oxygenation alterations in the frontal lobe during cognitive tasks

It is a non-invasive and without risk to patients technique. It can deliver novel spatiotemporal information of the underlying whole-brain activity

It measures whole-brain electrophysiological activity i.e. global field potentials using far-field sensors situated on the scalp

The seizures can be detected by filtering the ictal component from the raw ECoG. It is a real-time computation of median power of the filtered foreground. The recorded signal is stable over time

The spike timing of a neuron can be controlled by relying on a phase model

It is robust to high-frequency stimulation artifacts

Advantages

[28]

[80]

[79]

[78]

[58]

[77]

[25]

[10]

[51]

Reference

624 Australas Phys Eng Sci Med (2014) 37:619–634

Australas Phys Eng Sci Med (2014) 37:619–634

feasible because some diseases like PD show movement disorder such as rigidity, bradykinesia (slowness), tremor and gait disorder. These disorders which are measurable with sensors situated outside the body. A prototype of such a wearable(e.g. external) device for recording of extremely low voltage signals like LFPs was proposed by Amaru et al. [46]. This device is wearable, adaptable, and modular. It can be adapted to other biomedical signals like electroencephalographic or electrocardiographic ones. The data from the device would be stored locally on a flash drive or sent through a wireless connection to a remote server where they could be analysed subsequently, and adjust the stimulation parameters accordingly. On the other hand, a wearable sensor e.g. accelerometer can measure disease symptoms. Patel et al. [14] reported that wearable sensors have the ability to closely monitor changes in the severity of symptoms for the patient with brain disorder, and that could be used to quantify the effect of changing the DBS parameters [14]. Niazmand et al. [47] developed a wireless wearable sensor system which is integrated into a smart glove for the evaluation of the severity of the motor dysfunction in PD. The glove contains two touch sensors, two 3D-accelerometers, and a force sensor to assess the cardinal motor symptoms of PD (tremor, bradykinesia, and rigidity of hand and arm) [47]. Another wearable sensor that could be utilized to help clinicians achieve optimal settings of the stimulator was proposed by Patel et al. [48]. Wearable sensors are also being developed to assess neurological tremor instantaneously which is a nonlinear and non-stationary for movement disorder [49]. Thus, the data acquired by the sensors can accurately estimate the clinical scores associated with the motor activities, and subsequently used as feedback signals in CLDBS. Feedback control The closed loop system like CLDBS is characterized by a transfer function which determines the control criteria. The transfer function is a mathematical expression formulating the net effects of a feedback signal on the input signal. Generally, negative feedback is used to make a system selfregulating, ensure stability, and reduce the consequence of fluctuations. In a negative feedback system, an error signal is determined and then correction is applied to the input signal to force error to zero [50]. Figure 3 illustrates a dynamical systems view of a controller and plant model for the CLDBS. The inputs u(t) to the neural circuit comprise sensory inputs, pharmaceuticals, and electrical stimulation. The function f (u,t) consists not only the dynamics of the neural circuit but also the effects of inputs such as electrical stimulation on the neural state. The function g(x, u, t) encapsulates the measurement of biomarkers y(t) as well as

625

Fig. 3 Structure of a closed loop dynamic system. u(t) is a vector of the electrical stimulation. x(t) is the states of the neural circuit. y(t) is the biomarkers. The function f maps the inputs to the neural states, and the function g maps the neural states to biomarkers. The controller k senses and processes biomarkers and controls u(t) based on y(t) [31]

the effects of input to the measurement. The controller can be nonlinear, time varying, and adaptive [31]. The controller estimates the neural states x from the biomarkers y regardless of the corruptions contained in g. These corruptions comprise interference from electrical stimulation, artefacts from other parts of the nervous system, and physical motion. In the quantity of LFPs, g might contain the aggregation and low pass filtering of signals from individual neuron firings. The design requirements for the control algorithm are that it must be safe and reliable. The term safe refers to the condition where the processing chip does not corrupt the existing therapeutic stimulation, is single-fault tolerant to electrosurgery events and electrostatic discharge, and sustains DC electrode leakage. The term reliable refers to that condition where the circuit performance is set by well controlled parameters to guarantee consistent performance of the system, and the circuit is immune from common electromagnetic interference sources [31]. Stability of feedback control system is also an important concern which must be consider avoiding oscillating characteristic of the system. Moreover, a flexible and programmable CLDBS architecture together with a tuneable neuromodulation controller is necessary because of changing understanding of neural circuit, evolving new relevant biomarker. However, developing a CLDBS controller which is capable of adapting to the patient’s condition in real-time is highly challenging task. Recently, some interesting reports have been published on this type of controller and controlling mechanism. Rhew et al. [51] reported a CLDBS system which can adjust the stimulation current in real time according to the energy of feedback LFP signals using a PIcontroller. The feedback signal was processed digitally before being supplied to the controller. In another study Santaniello et al. [18] reported an adaptive minimum variance controller to develop a CLDBS system based on LFP power spectrum. The controller worked by a recursively

123

626

Australas Phys Eng Sci Med (2014) 37:619–634

Pulse width

Amplitude

Period = 1/ frequency (rate) Fig. 4 Parameters of the stimulation signal.

identified autoregressive model of the correlation between stimulation input signal and LFP output. This simulation based controller performed well in the theta (2–7 Hz), alpha (7–13 Hz), and beta (13–35 Hz) frequency ranges. Moreover, a nonlinear delayed feedback and desynchronizing effect of the nonlinear delayed feedback based method was presented in Ref. [52]. This feedback based stimulation technique is reliable, regardless of the stimulated network. Santos et al. [20] also used a delayed feedback control algorithm in a CLDBS system. In another report, Lee [53] described a dedicated microcontroller which could be programmed to adjust the stimulation parameters. The controller generated effective biphasic signal with desired parameters of pulse amplitude, duration and repetition rate. In addition, a model based rational feedback controller for PD was reported in Ref. [54]. Stimulation parameters setting The clinical success of DBS for treating different diseases depends on the quality of postoperative neurologic management such as optimal adaptation of the stimulation parameters and medication after implantation of the DBS system. The adaptation of stimulation parameter means the real-time change of parameter values for better patient outcome. Important stimulation parameters include electrode configurations, combination of negative and positive contacts, frequency, pulse width, and current/voltage amplitude. In CLDBS these stimulation parameters will be selected based on the feedback signal with advanced programmed controller. Effective DBS programming is steered by biological and electrical principles. Selecting the best parameters out of wide range of values which give patient the maximum benefit is called programming. The success of programming depends on several factors such as minimizing side effects, maximizing therapeutic efficacy, prolonging battery life, and using time proficiently [15]. Most commonly programmable electrical parameters including amplitude, frequency and pulse width are shown

123

Fig. 5 The effects of voltage and pulse duration on neural elements [58]

in Fig. 4. If the patients do not get the maximum benefit after optimizing these three parameters, then other parameters for instance combinations of negative and positive contacts should be changed. However, optimum programming is patient dependent. Therefore, most common stimulation parameters should be considered as starting points for programming, and then it should be optimize according to flowcharts developed by Montgomery [15] based on patient’s outcomes. The frequency of stimulating pulse trains typically changes from 30 to 185 Hz. Often high frequency stimulation (130–185 Hz) is the effective setting for most of the patient but some patients with dystonia get good result at the lower frequency of 50 Hz. Usually, the pulse width changes from 60 to 450 microseconds. Therapeutic amplitude of the stimulated signal normally varies from 1 to 3.5 V though available constant voltage stimulators allow voltage variation between 0 and 10.5 V with 0.05–0.1 V increment [55–57]. In another approach Kringelbach et al. [58] showed that the effects of DBS on neural elements rely on the relationship between the voltage and pulse duration as illustrated in Fig. 5. The DBS electrode with four leads (grey bars) was used to apply voltage into target area. The DBS with low voltage activated only some of the target neural elements (dark blue), while most target (light blue) and all non-target neural elements (light red) were not stimulated. Rising the voltage (middle) stimulated both target and nontarget elements (dark blue and dark red). On the other hand, increasing the pulse duration (right) activated only the target element [58]. However, the selection of optimum DBS parameters including frequency, amplitude, and duration is a major challenge because of the complex relationship between the

Australas Phys Eng Sci Med (2014) 37:619–634

DBS parameters and performance. A closed loop global optimization algorithm may identify the best DBS waveforms. Normally, model free optimization can be achieved by using adaptive optimization methods [59]. As the dependence of therapeutic effects on the DBS parameters can be rugged, search algorithms which do not depend on local estimates of slope are required and there may be many different waveforms that are comparably effective. The genetic algorithm is a global method which can simultaneously identify such multiple effective waveforms, opening the possibility of a therapeutic schedule which cycles through several of these. If the optimal DBS settings are identified, then either local or global control methods can be employed to continually adjust DBS settings to gradual physiological changes during chronic therapy [59]. Wireless telemetry Wireless telemetry is required to control power and data into a CLDBS system. Since CLDBS device dynamically generates stimulating signal and supplies it to the brain, it requires pausing or shouting down in worst case of patient, in malfunction of the device. Wireless telemetry can do it easily. Thus, wireless controlling of CLDBS is badly needed for emergency and safely. Recently, numerous articles on wireless telemetry for neural stimulation and recording system have been published. Among many of these reported works Arfin et al. [60] developed a wireless neural stimulation system. The system consisted of an external transmitter which is controllable through a computer interface, and a miniature, implantable wireless receiver-stimulator as well as four addressable bipolar electrodes. The transmitter transmited commands to the implantable system for delivering biphasic current pulses to electrodes at 32 selectable current levels (10 lA–1 mA). However, the system is battery dependent and an open loop system [60]. Another wireless implantable microelectronic device for transmitting cortical signals transcutaneously to interface a cortical microelectrode array to an external computer for neural control applications was developed by Song et al. [61]. The scalable 16-channel implantable active microsystem employed integrated ultra-low power amplification with analogue multiplexing, an analogue to digital converter, a low power digital controller chip, and infrared telemetry. The neural recording signal in a nonhuman primate brain was converted to a digital stream of infrared light pulses for transmission through the skin to a neural signal processor outside body, while clock/command signals were wirelessly acquired and electrical powered was generated by radio frequency (RF) inductive coupling. Although this implant system received power inductively, but it had the limitations of infrared technology. Furthermore a fully implantable wireless system for human brain machine

627

Fig. 6 Inductive coupled power transfer system [63]

Fig. 7 Far field powering system

interface was proposed by Hirata et al. [12]. It recorded electrocorticogram using brain surface microelectrodes for controling a machine from direct brain signals. In another study Harrison et al. [40] proposed a complete neural recording system which received power and commands wirelessly over a 2.64 MHz inductive link and transmited neural data back using a fully integrated 433 MHz FSK transmitter. An ultra-wideband wireless telemetry was also designed by Moo Sung et al. [41] to wirelessly transfer raw data from 128 recording channels. Power source Another concern on implementing a CLBDS device is the source of power. The implantable devices can be divided into two categories: passive and active. The active device is powered by battery whereas the passive device serves its function without battery. The passive implant is operated by external energy supply or generated energy from surrounding tissues and environment. Most of the existing implantable DBS devices are battery-operated. Human implantable devices demand a passive approach because an active approach has limitations including short lifetime of battery, surgery for battery replacement, battery malfunction, larger battery size, and environmental concern. In passive implantable device two types of power harvesting techniques such as inductive coupling and electromagnetic coupling can be used [62]. Inductive coupling works when the external energy transmitter is in very a short distance from the implant. Figure 6 illustrates the constitutive element for inductive coupled powering of an implant devices [63]. On the other hand, electromagnetic (far-field) coupling uses electromagnetic waves from the antenna in the far field region to power the passive implant. The far-field

123

628

electromagnetic energy radiated from an external transmitter is received by the implanted receiving antenna. The received RF wave is then converted to the required DC power for entire implant using necessary circuitry [64]. Radiom et al. [64] proposed a far field RF powering system for radio frequency identification and implantable devices with a monolithically integrated on-chip antenna. Figure 7 shows the structure of the far field powering system [65]. Size and power consumption The size and power consumption of a CLDBS device are more issues in implementing it. Since the CLDBS will be implanted in human head, it should be as small as possible due to the space volume limitation. Moreover, the device should consume less power because less power with chronic implant reduces the risk of tissue damage. Currently, the brain machine interface systems, and implantable systems are reducing in size and operating at low power with discovery of single chip telemetric method. For example, a compact CLDBS system of size of 2.67 mm2 implemented in 0.18 lm CMOS technology was reported by Lee et al. [30]. This device consumes only 271 lW from a 1.8 V supply. Avestruz et al. [31] also built a neural spectral processing IC (NPIC) based CLDBS system to extract the relevant biomarker signal with a total current draw for both analogue sensing and algorithmic processing on the order of 10 lW/channel, making it practical to implement within a chronic implantable DBS system. The NPIC is fabricated in a 0.8 lm CMOS process with a total die area of 6.7 mm 9 6.700 mm. Moreover, a cortical recording implant was fabricated on a 2.2 mm 9 2.2 mm chip in 1.5 lm standard n-well CMOS process by Yoon-Kyu et al. [39]. Ghovanloo and Najafi, [66] reported another wireless microstimulating chip for neural prosthesis applications with dimension of 4.6 mm 9 4.6 mm fabricated in 1.5 lm CMOS technology. Currently research is being carried out to develop a power efficient communication algorithm in order to both preserve the battery power in case of battery operated implants and reduce the health risks associated with energy transmission. To reduce the power requirement of a system, a joint source-channel coding/decoding, low density generator matrix code, Markov chain source correlation model, turbo iterative receiver algorithm can be used [67]. For example, Avestruz et al. [31] reported a NPIC that amplifies and processes neuronal activity for exploring adaptive neuromodulation concepts. This NPIC used chopper stabilization and heterodyne conversion to achieve power efficient extraction of neuronal biomarker. The power consumption of 6 mW was obtained in a neural recording chip designed by Moo Sung et al. [41] with spike feature extraction. The power consumption of this chip was reduced by employing a sequential turn-on architecture that selectively powers off idle analogue circuit blocks.

123

Australas Phys Eng Sci Med (2014) 37:619–634

Benefits and limitations of CLDBS Whilst the chronic clinical trials of CLDBS are not still validated, CLDBS is reported to offer certain benefits over OLDBS. In OLDBS, the stimulation parameters setting is accomplished after electrode implantation by an experienced neurologist and physician [68]. To get satisfactory results, programming may take about six months and the parameters are adjusted every 3–12 months during ambulatory visits [69]. DBS settings remain unchanged between two subsequent visits, regardless of fluctuation in patients’ experiences. Accordingly, OLDBS is not well suited for managing the dynamic nature of the disease. In contrary, CLDBS automatically adjusts stimulation setting based on the patients’ state. Therefore, it eliminates lengthy start-up period for programming and adjustment which are beneficial for some patients, particularly those who live far away from DBS clinics [17]. In OLDBS, patients experience adverse effects when the stimulator generates linearly identical signals and the disease has temporarily fallen. However, CLDBS prohibits adverse reaction of the patient with prompt change of stimulation parameters [21]. In OLDBS, the stimulation pattern and intensity are set manually through programmer while CLDBS adjusts the parameters automatically. Another advantage of CLDBS over OLDBS is the adaptation to the dynamics of the disease over time [20]. CLDBS provides a personalized approach and a variable charge transfer according to the patients’ specific need. Moreover, a CLDBS device may be included in a body area network with telemetric capability to further improve the patient’s quality of life [70]. Although CLDBS has become an attractive area of research for brain stimulation researchers, it has some limitations including the reliability of signal detection criteria, robustness of the signal for particular brain diseases, computational aspects of the stimulation parameters, and real time acquisition of feedback signal [11]. The extra circuitry that is needed for closed loop operation increases the system complexity and fabrication cost, but these are less significant considering the benefits offered by CLDBS. More importantly, a key issue that limits wide scale implementation of CLDBS is the absence of proven clinical utility at this stage. Other drawbacks include the lack of direct evidence documenting a relationship between disease disturbances and abnormal biomarker, and the absence of proven correlation between stimulus and response [17]. In addition, the biomarker detection is not easy in all patients. To get consistent feedback signals multiple biomarkers need to be combined together [32]. A study was carried out by Adhikari et al. [71] to explore the relationship between the thalamic stimulation and the cortical response using mean field models. The relationship is considered as the first step towards developing a closed

Australas Phys Eng Sci Med (2014) 37:619–634

loop feedback control protocol for curing epileptic activity. Stanslaski et al. [72] developed and validated the correlation of seizure activity and stimulation parameters using an embedded algorithm for a fully implantable, chronic closed loop neuromodulation system. They have minimized the time delays between closed loop actuation and response to stimulation using concurrent sensing and stimulation. This chronic CLDBS system having concurrent sensing and stimulation capabilities was tested in a large animal model of epilepsy. The experimental result shows that the stimulation generally suppresses seizures. The CLDBS also addresses the risk of stimulation through monitoring inducted seizures [72]. However, before applying CLDBS in patients, the variables which reflect the patients’ clinical state must be extracted from biomarkers, and feedback algorithms need to be developed. CLDBS needs to be tested thoroughly on animal models and then through clinical trials to verify the advantages it offers over OLDBS.

CLDBS is better than standard OLDBS Although OLDBS is an established brain stimulation strategy for treatment of some neurological and neuropsychiatric disorders, CLDBS offers the potential to further improve the applicability of DBS therapy. Rosin et al. [19] supports the idea that CLDBS is more efficient than OLDBS for delivering optimized stimulation to PD patients. CLDBS works by modulating pathological oscillatory activity rather than the discharge rate of the basal ganglia cortical networks. Rosin et al. clinically proved that cortico-pallidal CLDBS has a considerably greater impact on akinesia and on cortical and pallidal discharge patterns than OLDBS [19]. Priori et al. [17] reported that the automatic adjustment of stimulation parameters in CLDBS improves device performance, and enhances battery life if the device. Current evidence supports that CLDBS may help decrease the risk of non-motor cognitive and behavioural complications. A closed loop neuromodulation minimizes both clinical and patient burden [72]. An implantable closed loop responsive neurostimulation system was developed by Neuropace. The device comprises of a programmer, and quadripolar subdural strip or depth leads is under clinical trial phase for refractory focal epilepsy patient. The programmer triggers electrical stimulation using patient’s electrocortigrams. The operator can manipulate detection as well as stimulation parameters. The preliminary results suggest that it is safe and relatively simple surgical procedure whilst the efficacy of the technique needs to be determined through clinical study [73]. A computational model of the Parkinsonian basal ganglia was conducted by Feng et al. [59] to identify DBS waveforms

629

for CLDBS using a global optimization algorithm. This CLDBS algorithm outperforms its OLDBS counterparts. Lee et al. [30] proposed a low power, stand alone, neurostimulation device for closed loop stimulation through sensing both high frequency pulsatile neuronal spike trains and low frequency continuous LFP neural signals. The device still needs comprehensive experimental trails for performance validation.

Discussions DBS stimulates particular brain regions for specific diseases, thus it has less effect on non-target brain cells [2], resulting in an effective therapy for definite diseases. The success of DBS for treating different diseases considerably depends on the setting of the optimal stimulation parameters and medication after implantation of the DBS device. Possible adjustable stimulation variables are electrode configurations, combinations of negative and positive contacts, frequency, pulse width, and current/voltage amplitude [56]. OLDBS is an established treatment method for a range of neurological and psychiatric disorders and further progress in DBS depends on the implementation of CLDBS [17]. CLDBS can improve efficiency and efficacy, and make DBS parameters setting independent of physicians [19, 53]. CLDBS also makes DBS parameter setting adaptive [20]. Moreover, CLDBS personalizes DBS therapy by using device data to spontaneously adjust therapy to suit the needs of individual patients [70]. Whilst CLDBS alleviates clinical burden of DBS programming, and may improve patient outcomes, it faces some challenges. One of the challenges is biomarker detection with high fidelity during DBS, because stimulus makes artefact on recording. This artefact can be suppressed with recording instrument as reported by Kent and Grill [38]. Other techniques to reduce the stimulus artefact have relied on signal processing methods, such as filtering or template subtraction, performed after amplification [37]. Recent research shows the capability to make high fidelity recordings of biomarker. To get consistent feedback signals more than one biomarker can be combined together [32]. Another issue is to establish a correlation between the biomarker and the stimulation parameters. Research is being conducting to formulate this relationship. For instance Adhikari et al. [71] have explored the relationship between the thalamic stimulation and the cortical response using mean field models. The real time acquisition of feedback signal is also of concern. To get real time feedback signals, time delays between the closed loop actuation and response to the stimulation has to be minimized. The concurrent sensing and stimulation may provide a solution to this issue [72]. Another challenge is the algorithm design which determines control criteria. An optimization algorithm may be

123

630

Australas Phys Eng Sci Med (2014) 37:619–634

Table 2 A summary of the performances of recent CLDBS systems Diseases

No. of patients involved in test

Performance

Ref

PD

8 patients

Motor scores improved by 66 % (unblinded) and 50 % (blinded) during CLDBS, which were 29 and 27 % better than conventional OLDBS, respectively. It required 56 % less in stimulation time compared to OLDBS. This reduction in stimulation time has impacts on both side effects and the lifetime of implanted battery systems

[23]

PD

1 primate model

The cortico-pallidal closed-loop stimulation has a considerably more effect on akinesia and on cortical and pallidal discharge patterns than standard OLDBS. Thus, CLDBS may able to manage advanced PD more effectively and provide insights into PD pathophysiology

[19]

PD

1 rat model

The closed loop system adapted the stimulation current in real-time based on the LFP energy. Neural recordings in an animal and stimulation accordingly showed the feasibility of closed loop operation in the in vivo setting

[24]

Epilepsy

8 patients

Mean change in seizure rate in the one group of four patient was -55.5 % while in other group was -40.8 %. Mean effect on epileptogenic tissue was zero/negligible. Automated seizure detection and contingent delivery of stimulating signal with realtime feedback system were not only feasible, but also safe and efficacious

[25]

Epilepsy

4 patients

Electrographic seizures of epilepsy was suppressed during responsive neurostimulation of 68 h. This neurostimulation had no major side effect. The baseline EEG was improved for one patient among four

[26]

Epilepsy

6 rat models

[27]

Essential tremor

1 patient

The seizure frequency and duration for CLDBS system was reduced in 5 of the 6 rats. The efficacy of the close loop seizure control method was improved significantly over periodic control open loop scheme in the same animals A tremor-free interval averaging over 20 s existed followed by the stimulation packets of 20–35 s. The tremors disappeared within 0.5 s on average after stimulation was restarted. The detected signal can predict onset of tremor. Thus next stimulation packet can be activated based on predicted signal before tremor reappears

Physiology measures of hippocampal dynamics

1 ovine model

The moderate levels of stimulation suppressed hippocampal beta activity and beta band power was reduced from baseline. However, high levels of stimulation generated seizure-like after-discharge activity and beta band power was increased, resulting in occasional after-discharge. These results suggested that closed loop operation based on neural feedback may be an important consideration for optimal stimulation amplitude

effective for identifying novel DBS waveforms that diminish rhythmic symptoms of psychiatric disorders. A genetic algorithm is well suited to CLDBS applications because of the model free structure, global search capacity, and favourable scaling with the number of optimization parameters [59]. Since the changes of any kind of symptoms usually are not expected to be abrupt in nature, the parameters adjustment operation may likely be achieved by linear feedback algorithms hardwired into the stimulator [59]. CLDBS can be considered as a safe and convenient approach, however, its efficacy needs to be determined by proper clinical studies [73]. Currently, the main issue that is limiting the wide scale use of CLDBS is the absence of robust clinical demonstration of the approach. On the other hand, although CLDBS increases the system complexity and the development cost, it is beneficial for the patients living far away from DBS clinics because no ambulatory visits will be required. Moreover, OLDBS is unable to eradicate the adverse event or fluctuation the patient experiences whereas

123

[28]

[81]

CLDBS is apatite in nature which is essential to manage the dynamic nature of diseases [17, 69]. Some experimental and simulation based studies have been already reported in the literature confirming the better performance of CLDBS over OLDBS. For instance, Rosin et al. [19] noted that the performance of CLDBS is better than that of the OLDBS. They conducted CLDBS on primate model of PD. They reported that CLDBS of the globus pallidum (GPi) according to ongoing activity in primary motor cortex (M1) is more effective in reducing motor symptoms than standard high frequency GPi DBS paradigm. CLDBS also significantly reduced oscillatory activity in primary motor cortex and pallidum than OLDBS. The success of CLDBS is indeed owing to its adaptive nature. Although the study by Rosin and his team was conducted on primate model, but Solages et al. [74] suggested that a delay will best fit this CLDBS for primates in human patients. In another study, Frankemolle et al. [75] showed that the efficacy of DBS improves by frequent parameter adjustments through closed loop operation. This

Australas Phys Eng Sci Med (2014) 37:619–634

hypothesis is further supported by Priori et al. [17], Stanslaski et al. [72], and Fountas et al. [73]. According to Bronte-Stewart ‘‘what we have right now for DBS works, but it’s very much the first generation’’. Researchers are working on recording-capable DBS implants which are the stepping stone towards closed loop neurostimulators [76]. A CLDBS device might help with epilepsy treatment because it is a comparatively simple disorder, normally consisting of discrete episodes of abnormal brain activity. For instance, Osorio et al. [25] and Kossoff et al. [26] studied automatic neurostimulator (e.g. closed loop DBS) for seizure abatement in humans with epilepsy. US Food and Drug Administration (FDA) has approved the first closed-loop, implantable neurostimulator made by NeuroPace for intractable epilepsy in 2013. By contrast, the CLDBS technology is not immediately applicable to other conditions including PD because they involve a mishmash of symptoms that rise, fall and morph over time. Therefore, researchers are still working for the relevant neural signatures in these conditions, and producing the computational tools necessary to keep up with changing symptoms [76]. Among some of the most recent published works, Little et al. [23] demonstrated a CLDBS system for PD. In this system, the patients’ DBS implants was plugged into an external machine, which triggered stimulation in the subthalamic nucleus when certain abnormal brain rhythms were identified. This CLDBS improved the symptoms by almost 30 % compared with standard OLDBS treatments. Although it is far short of being introduced into patients, but this demonstration shows that the closed-loop concept could work for PD. Table 2 summaries the performances of recent CLDBS systems which are experimentally validated on human patients or animal models. These studies could be a proof of concept for the implementation of CLDBS in the treatment of PD and other brain disorders. Moreover, the outcomes from few implemented CLDBS systems are encouraging. However, further comprehensive studies should be carried out to maintain safety and obtain maximum efficacy of closed loop parameters in laboratory animal models and human patients [19]. There is not much doubt that CLDBS will be proved helpful and beneficial to any patients having movement disorders, psychiatric diseases, and other pharmaco-resistant brain pathologies. In addition, we are hopeful that CLDBS will enable us to learn how DBS works [10]. It is therefore our optimism that we will see new era of DBS e.g. closed loop DBS paradigms for different pathological brain diseases in near future. However, CLDBS poses several questions for further research [20]: Which are the optimal parameters for CLDBS? What type of triggering signals can be used for long term stability? What kind of structures can be used as reference or targets?

631

Conclusion This paper reported that CLDBS based on internal or external biomarkers is feasible and can be useful for patients with neurological and psychiatric disorders. Whilst most of the published articles focused on Parkinson’s disease, some recent investigations reported on other neurological and psychiatric disorders. To implement a CLDBS system, the main technological challenges are biomarkers recording, correlation between biomarker and patient symptoms, and a control algorithm for stimulation signal setting. Recent scientific research suggested that biomarkers as a feedback signal are detectable, and there exist a strong correlation between internal biomarkers and the patient’s clinical condition. Some modern feedback control algorithms enabled implementation of CLDBS systems that are able to adjust stimulation parameters dynamically, and automatically according to the patients’ needs. The paper also stated that CLDBS can improve efficiency and efficacy of therapy, minimise the side effects, and improve the quality of life for certain diseases that require extensive care and precision. However, CLDBS systems require comprehensive experimental validation to prove their effectiveness because there are no adequate clinical trials on this technology. In summary, the key objectives of this paper have been fulfilled by highlighting the components and operation of CLDBS systems, identifying their benefits and limitations, and comparing their performance against OLDBS systems. The readers will get all the relevant and recent information about CLDBS systems in this paper.

Conflict of interest

None.

References 1. Flora ED, Perera CL, Cameron AL, Maddern GJ (2010) Deep brain stimulation for essential tremor: a systematic review. Mov Disord 25:1550–1559 2. Liker MA, Won DS, Rao VY, Hua SE (2008) Deep brain stimulation: an evolving technology. Proc IEEE 96:1129–1141 3. Hamani C, Nobrega JN, Lozano AM (2010) Deep brain stimulation in clinical practice and in animal models. Clin Pharmacol Ther 88:559–562 4. Covalin A, Feshali A, Judy J (2005) Deep brain stimulation for obesity control: analyzing stimulation parameters to modulate energy expenditure. In: Conference proceedings. 2nd international IEEE EMBS conference on neural engineering, 2005, pp 482–485 5. Schiff ND, Giacino J, Kalmar K, Victor J, Baker K, Gerber M, Fritz B, Eisenberg B, O’Connor J, Kobylarz E (2007) Behavioural improvements with thalamic stimulation after severe traumatic brain injury. Nature 448:600–603 6. Sillay KA, Sani S, Starr PA (2010) Deep brain stimulation for medically intractable cluster headache. Neurobiol Dis 38:361–368

123

632 7. Lu L, Wang X, Kosten TR (2009) Stereotactic neurosurgical treatment of drug addiction. Am J Drug Alcohol Abuse 35:391–393 8. Castrioto A, Moro E (2013) New targets for deep brain stimulation treatment of Parkinson’s disease. Expert Rev Neurother 13:1319–1328 9. Stanslaski S, Cong P, Carlson D, Santa W, Jensen R, Molnar G, Marks WJ, Shafquat A, Denison T (2009) An implantable bidirectional brain-machine interface system for chronic neuroprosthesis research. In: Annual international conference of the IEEE engineering in medicine biology society, 2009, pp 5494–5497 10. Carron R, Chaillet A, Filipchuk A, Pasillas-Le´pine W, Hammond C (2013) Closing the loop of deep brain stimulation. Front Syst Neurosci 7: 112 11. Leondopulos SS, Micheli-Tzanakou E. (2010) Advances toward closed-loop deep brain stimulation. In: Computational neuroscience, Springer, New York, pp 227–253 12. Hirata M, Matsushita K, Suzuki T, Yoshida T, Sato F, Morris S, Yanagisawa T, Goto T, Kawato M, Yoshimine T (2011) A fullyimplantable wireless system for human brain-machine interfaces using brain surface electrodes: W-HERBS. IEICE Trans Commun E94b:2448–2453 13. Pham J, Cabrera SM, Sanchis-Segura C, Wood MA (2009) Automated scoring of fear-related behavior using EthoVision software. J Neurosci Methods 178:323–326 14. Patel S, Hester T, Hughes R, Huggins N, Standaert D, Flaherty A, Bonato P (2006) Using wearable sensors to enhance DBS parameter adjustment for Parkinson’s disease patients through measures of motor response. In: 3rd IEEE/EMBS international summer school on medical devices biosensors, 2006, pp 141–144 15. Montgomery EB (2010) Deep brain stimulation programming: principles and practice. Oxford University Press, Oxford 16. Feng X-J, Shea-Brown E, Greenwald B, Kosut R, Rabitz H (2007) Optimal deep brain stimulation of the subthalamic nucleus—a computational study. J Comput Neurosci 23:265–282 17. Priori A, Foffani G, Rossi L, Marceglia S (2012) Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations. Exp Neurol 245:77–86 18. Santaniello S, Fiengo G, Glielmo L, Grill WM (2011) Closedloop control of deep brain stimulation: a simulation study. IEEE Trans Neural Syst Rehabil Eng 19:15–24 19. Rosin B, Slovik M, Mitelman R, Rivlin-Etzion M, Haber SN, Israel Z, Vaadia E, Bergman H (2011) Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron 72:370–384 20. Santos FJ, Costa RM, Tecuapetla F (2011) Stimulation on demand: closing the loop on deep brain stimulation. Neuron 72:197–198 21. Bazavan R, Strungaru R (2010) Option to provide the necessary feedback for closed-loop neurostimulation. In: 10th international conference development application systems, Suceava 22. Ba˘za˘van R (2005) The brain closed-loop stimulation method with the biopotential mediation of the subthalamic nucleus for the parkinson’s disease therapy. U.P.B. Sci. Bull., Series C, 75: 157–168 23. Little S, Pogosyan A, Neal S, Zavala B, Zrinzo L, Hariz M, Foltynie T, Limousin P, Ashkan K, FitzGerald J (2013) Adaptive deep brain stimulation in advanced Parkinson disease. Ann Neurol 74:449–457 24. Lee J, Rhew H-G, Kipke DR, Flynn MP (2010) A 64 channel programmable closed-loop neurostimulator with 8 channel neural amplifier and logarithmic ADC. Solid-State Circuits IEEE J 45:1935–1945 25. Osorio I, Frei MG, Sunderam S, Giftakis J, Bhavaraju NC, Schaffner SF, Wilkinson SB (2005) Automated seizure abatement in humans using electrical stimulation. Ann Neurol 57:258–268

123

Australas Phys Eng Sci Med (2014) 37:619–634 26. Kossoff EH, Ritzl EK, Politsky JM, Murro AM, Smith JR, Duckrow RB, Spencer DD, Bergey GK (2004) Effect of an external responsive neurostimulator on seizures and electrographic discharges during subdural electrode monitoring. Epilepsia 45:1560–1567 27. Good LB, Sabesan S, Marsh ST, Tsakalis K, Treiman D, Iasemidis L (2009) Control of synchronization of brain dynamics leads to control of epileptic seizures in rodents. Int J Neural Syst 19:173–196 28. Graupe D, Basu I, Tuninetti D, Vannemreddy P, Slavin KV (2010) Adaptively controlling deep brain stimulation in essential tremor patient via surface electromyography. Neurol Res 32:899–904 29. Makous J, Schleicher B, Carbunaru R, Jaax K, DiGiore A (2010) Methods and apparatus for using sensors with a deep brain stimulation system. Google Patents, 2010 30. Lee J, Rhew H-G, Kipke DR, Flynn MP (2010) A 64 channel programmable closed-loop neurostimulator with 8 channel neural amplifier and logarithmic ADC. IEEE J Solid-State Circuits 45:1935–1945 31. Avestruz AT, Santa W, Carlson D, Jensen R, Stanslaski S, Helfenstine A, Denison T (2008) A 5 uW/channel spectral analysis IC for chronic bidirectional brain-machine interfaces. IEEE J Solid-State Circuits 43:3006–3024 32. Giannicola G, Rosa M, Servello D, Menghetti C, Carrabba G, Pacchetti C, Zangaglia R, Cogiamanian F, Scelzo E, Marceglia S (2012) Subthalamic local field potentials after seven-year deep brain stimulation in Parkinson’s disease. Exp Neurol 237:312–317 33. Kent AR, Grill WM (2011) Instrumentation to record evoked potentials for closed-loop control of deep brain stimulation. In: Annual international conference of the IEEE engineering in medicine biology society, EMBC, 2011, pp 6777–6780 34. Little S, Brown P (2012) What brain signals are suitable for feedback control of deep brain stimulation in Parkinson’s disease? Ann N Y Acad Sci 1265:9–24 35. McCreery D, Lossinsky A, Pikov V, Xindong L (2006) Microelectrode array for chronic deep-brain microstimulation and recording. IEEE Trans Bio-med Eng 53:726–737 36. Mohseni P, Najafi K, Eliades SJ, Xiaoqin W (2014) Wireless multichannel biopotential recording using an integrated FM telemetry circuit. IEEE Trans Neural Syst Rehabil Eng 8:1–11 37. Rossi L, Foffani G, Marceglia S, Bracchi F, Barbieri S, Priori A (2007) An electronic device for artefact suppression in human local field potential recordings during deep brain stimulation. J Neural Eng 4:96–106 38. Kent A, Grill W (2011) Instrumentation to record evoked potentials for closed-loop control of deep brain stimulation. In: 33rd annual international conference on IEEE EMBS, Boston, pp 6777–6780 39. Yoon-Kyu S, Patterson WR, Bull CW, Borton DA, Yanqiu L, Nurmikko AV, Simeral JD (2007) A brain implantable microsystem with Hybrid RF/IR telemetry for advanced neuroengineering applications. In: Engineering in medicine and biology Society, 2007. EMBS 2007. 29th annual international conference of the IEEE, pp 445–448 40. Harrison RR, Watkins PT, Kier RJ, Lovejoy RO, Black DJ, Greger B, Solzbacher F (2007) A low-power integrated circuit for a wireless 100-electrode neural recording system. IEEE J solidstate Circuits 42:123–133 41. Moo Sung C, Zhi Y, Yuce MR, Linh H, Liu W (2009) A 128-channel 6 mW wireless neural recording IC with spike feature extraction and UWB transmitter. IEEE Trans Neural Syst Rehabil Eng 17:312–321 42. Kimble CJ, Johnson DM, Winter BA, Whitlock SV, Kressin KR, Horne AE, Robinson JC, Bledsoe JM, Tye SJ, Chang S-Y (2009) Wireless instantaneous neurotransmitter concentration sensing

Australas Phys Eng Sci Med (2014) 37:619–634

43.

44.

45.

46.

47.

48.

49. 50. 51.

52.

53.

54.

55. 56.

57.

58.

59.

60.

system (WINCS) for intraoperative neurochemical monitoring. In: Engineering in medicine and biology society, 2009. EMBC 2009. Annual international conference of the IEEE, pp 4856–4859 Shon Y-M, Chang S-Y, Tye SJ, Kimble CJ, Bennet KE, Blaha CD, Lee KH (2010) Comonitoring of adenosine and dopamine using the wireless instantaneous neurotransmitter concentration system: proof of principle. J Neurosurg 112:539 Griessenauer CJ, Chang S-Y, Tye SJ, Kimble CJ, Bennet KE, Garris PA, Lee KH (2010) wireless instantaneous neurotransmitter concentration system: electrochemical monitoring of serotonin using fast-scan cyclic voltammetry-a proof-of-principle study: laboratory investigation. J Neurosurg 113:656–665 Agnesi F, Tye SJ, Bledsoe JM, Griessenauer CJ, Kimble CJ, Sieck GC, Bennet KE, Garris PA, Blaha CD, Lee KH (2009) Wireless instantaneous neurotransmitter concentration system– based amperometric detection of dopamine, adenosine, and glutamate for intraoperative neurochemical monitoring. J Neurosurg 111:701 Amaru F, Arena P, Latteri A, Lombardo D, Mazzone P, Vagliasindi G (2009)Towards a wearable device for deep brain signals monitoring. In: Human system interactions, 2009. HSI ‘09. 2nd conference on, Catania, pp 128–131 Niazmand K, Tonn K, Kalaras A, Fietzek UM, Mehrkens JH, Lueth TC (2011) Quantitative evaluation of Parkinson’s disease using sensor based smart glove. In: 24th international symposium on computer-based medical systems (CBMS), Bristol Patel S, Mancinelli C, Hughes R, Dalton A, Shih L, Bonato P (2009) Optimizing deep brain stimulation settings using wearable sensing technology. In: 4th international IEEE/EMBS conference on neural engineering, 2009. NER ‘09, pp 6–9 Grimaldi G, Manto M (2010) Neurological tremor: sensors, signal processing and emerging applications. Sensors 10:1399–1422 Warwick K (1996) Introduction to control systems, vol 8. World Scientific, Singapore Rhew H-G, Jeong J, Fredenburg JA, Dodani S, Patil P, Flynn MP (2012) A wirelessly powered log-based closed-loop deep brain stimulation SoC with two-way wireless telemetry for treatment of neurological disorders. In: Symposium on VLSI circuits (VLSIC), Honolulu, HI Popovych OV, Hauptmann C, Tass PA (2005) Effective desynchronization by nonlinear delayed feedback. Phys Rev Lett 94:164102 Lee J (2008) A Closed-loop deep brain stimulation device with a logarithmic pipeline ADC. Doctor of Philosophy, Electrical Engineering, University of Michigan Gorzelic P, Schiff S, Sinha A (2013) Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson’s disease. J Neural Eng 10:026016 Tarsy D (2008) Deep brain stimulation in neurological and psychiatric disorders. Humana Press, Totowa O’Suilleabhain PE, Frawley W, Giller C, Dewey RB (2003) Tremor response to polarity, voltage, pulsewidth and frequency of thalamic stimulation. Neurology 60:786–790 Moro E, Esselink R, Xie J, Hommel M, Benabid A, Pollak P (2002) The impact on Parkinson’s disease of electrical parameter settings in STN stimulation. Neurology 59:706–713 Kringelbach ML, Jenkinson N, Owen SL, Aziz TZ (2007) Translational principles of deep brain stimulation. Nat Rev Neurosci 8:623–635 Feng XJ, Greenwald B, Rabitz H, Shea-Brown E, Kosut R (2007) Toward closed-loop optimization of deep brain stimulation for Parkinson’s disease: concepts and lessons from a computational model. J Neural Eng 4:L14–L21 Arfin SK, Long MA, Fee MS, Sarpeshkar R (2009) Wireless neural stimulation in freely behaving small animals. J Neurophysiol 102:598–605

633 61. Song YK, Borton DA, Park S, Patterson WR, Bull CW, Laiwalla F, Mislow J, Simeral JD, Donoghue JP, Nurmikko AV (2009) Active microelectronic neurosensor arrays for implantable brain communication interfaces. IEEE Trans Neural Syst Rehabil Eng 17:339–345 62. Aubert H (2011) RFID technology for human implant devices. CR Phys 12:675–683 63. Finkenzeller K (2003) RFID handbook : fundamentals and applications in contactless smart cards and identification, 2nd edn. Wiley, Chichester 64. Radiom S, Baghaei-Nejad M, Vandenbosch G, Li-Rong Z, Gielen G (2010) Far-field RF powering system for RFID and implantable devices with monolithically integrated on-chip antenna. In: 2010 IEEE radio frequency integrated circuits symposium (RFIC), Anaheim, CA, 2010, pp 113–116 65. Lehpamer H (2008) RFID design principles. Artech House, London 66. Ghovanloo M, Najafi K (2004) A Modular 32-site wireless neural stimulation microsystem. IEEE J Solid-State Circuits 39:2457–2466 67. Yao N, Lee HN, Chang CC, Sclabassi RJ, Sun M (2007) A power-efficient communication system between brain-implantable devices and external computers. In: 29th annual international conference of the IEEE EMBS. France, Lyon, pp 6588–6591 68. Bronstein JM, Tagliati M, Alterman RL, Lozano AM, Volkmann J, Stefani A, Horak FB, Okun MS, Foote KD, Krack P (2011) Deep brain stimulation for Parkinson disease: an expert consensus and review of key issues. Arch Neurol 68:165 69. Deuschl G, Schade-Brittinger C, Krack P, Volkmann J, Scha¨fer H, Bo¨tzel K, Daniels C, Deutschla¨nder A, Dillmann U, Eisner W (2006) A randomized trial of deep-brain stimulation for Parkinson’s disease. N Engl J Med 355:896–908 70. Eberle W, Penders J, Yazicioglu RF (2011) Closing the loop for deep brain stimulation implants enables personalized healthcare for Parkinson’s disease patients. In: Engineering in medicine and biology society, EMBC. Annual international conference of the IEEE 2011:1556–1558 71. Adhikari MH, Heeroma JH, di Bernardo M, Krauskopf B, Richardson MP, Walker MC, Terry JR (2009) Characterisation of cortical activity in response to deep brain stimulation of ventral– lateral nucleus: modelling and experiment. J Neurosci Methods 183:77–85 72. Stanslaski S, Afshar P, Cong P, Giftakis J, Stypulkowski P, Carlson D, Linde D, Ullestad D, Avestruz A, Denison T (2012) Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation. IEEE Trans Neural Syst Rehabil Eng 20:410–421 73. Fountas KN, Smith JR, Murro AM, Politsky J, Park YD, Jenkins PD (2005) Implantation of a closed-loop stimulation in the management of medically refractory focal epilepsy. Stereotact Funct Neurosurg 83:153–158 74. de Solages C, Hill BC, Koop MM, Henderson JM, BronteStewart H (2010) Bilateral symmetry and coherence of subthalamic nuclei beta band activity in Parkinson’s disease. Exp Neurol 221:260–266 75. Frankemolle AM, Wu J, Noecker AM, Voelcker-Rehage C, Ho JC, Vitek JL, McIntyre CC, Alberts JL (2010) Reversing cognitive–motor impairments in Parkinson’s disease patients using a computational modelling approach to deep brain stimulation programming. Brain 133:746–761 76. Shen H (2014) Neuroscience: tuning the brain. Nature 507:290–292 77. Grahn PJ, Khurram O, Berry BM, Hachmann JT, Mallory GW, Bieber AJ, Bennet KE, Lee KH, Lujan JL (2014) A neurochemical closed-loop controller for deep brain stimulation: toward individualized smart neuromodulation therapies. Front Neurosci, p 0

123

634 78. Sakatani K, Katayama Y, Yamamoto T, Suzuki S (1999) Changes in cerebral blood oxygenation of the frontal lobe induced by direct electrical stimulation of thalamus and globus pallidus: a near infrared spectroscopy study. J Neurol Neurosurg Psychiatry 67:769–773 79. Jech R, Urgosˇ´ık D, Tinteˇra J, Nebuzˇelsky´ A, Kra´sensky´ J, Lisˇcˇa´k R, Roth J, Ru˚zˇicˇka E (2001) Functional magnetic resonance imaging during deep brain stimulation: a pilot study in four patients with Parkinson’s disease. Mov Disord 16:1126–1132

123

Australas Phys Eng Sci Med (2014) 37:619–634 80. Niazmand K, Tonn K, Kalaras A, Fietzek UM, Mehrkens JH, Lueth TC (2011) Quantitative evaluation of Parkinson’s disease using sensor based smart glove. In: 24th international symposium on computer-based medical systems (CBMS), Bristol 81. Afshar P, Khambhati A, Stanslaski S, Carlson D, Jensen R, Linde D, Dani S, Lazarewicz M, Cong P, Giftakis J (2012) A translational platform for prototyping closed-loop neuromodulation systems. Front Neural Circuits 6:117