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The adaptive deep brain stimulation challenge
Accepted Manuscript The Adaptive Deep Brain Stimulation Challenge Mattia Arlotti, Manuela Rosa, Sara Marceglia, Sergio Barbieri, Alberto Priori PII:
S1353-8020(16)30074-8
DOI:
10.1016/j.parkreldis.2016.03.020
Reference:
PRD 2970
To appear in:
Parkinsonism and Related Disorders
Received Date: 26 October 2015 Revised Date:
25 March 2016
Accepted Date: 28 March 2016
Please cite this article as: Arlotti M, Rosa M, Marceglia S, Barbieri S, Priori A, The Adaptive Deep Brain Stimulation Challenge, Parkinsonism and Related Disorders (2016), doi: 10.1016/ j.parkreldis.2016.03.020. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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The Adaptive Deep Brain Stimulation Challenge
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Mattia Arlotti12, Manuela Rosa1, Sara Marceglia1,3, Sergio Barbieri1,4, Alberto Priori5
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Centro clinico per la Neurostimolazione, le Neurotecnologie ed i Disordini del Movimento,
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Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milan, Italy 2
Department of Electronics, Computer Science and Systems, University of Bologna, Cesena,
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Dipartimento di Ingegneria e Architettura, Università degli Studi di Trieste, Trieste, Italy
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Unità di Neurofisiopatologia, Fondazione IRCCS Ca' Granda Ospedale Maggiore
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Policlinico di Milano, Milan, Italy 5
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Università degli Studi di Milano,Polo Ospedaliero San Paolo, Milan, Italy
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Corresponding author
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Prof. Alberto Priori MD PhD
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Direttore III Clinica Neurologica
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Università degli Studi di Milano
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Polo Ospedaliero San Paolo
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Milano, Italy
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e-mail: [email protected]
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Abstract
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Sub-optimal clinical outcomes of conventional deep brain stimulation (cDBS) in treating Parkinson’s Disease (PD) have boosted the development of new solutions to improve DBS therapy.
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Adaptive DBS (aDBS), consisting of closed-loop, real-time changing of stimulation parameters
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according to the patient’s clinical state, promises to achieve this goal and is attracting increasing
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interest in overcoming all of the challenges posed by its development and adoption. In the design,
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implementation, and application of aDBS, the choice of the control variable and of the control
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algorithm represents the core challenge. The proposed approaches, in fact, differ in the choice of the
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control variable and control policy, in the system design and its technological limits, in the patient’s
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target symptom, and in the surgical procedure needed. Here, we review the current proposals for
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aDBS systems, focusing on the choice of the control variable and its advantages and drawbacks,
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thus providing a general overview of the possible pathways for the clinical translation of aDBS with
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its benefits, limitations and unsolved issues.
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Keywords:
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Adaptive deep brain stimulation, Parkinson’s Disease, Control variable, Basal ganglia local
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field potentials
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1. Introduction
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Deep brain stimulation (DBS) is an established treatment for Parkinson’s Disease (PD) based on chronic high-frequency (100-180 Hz) stimulation of the basal ganglia nuclei [1]. It has
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remained almost unchanged since its first application. Recently, however, advances in
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neurophysiology, neuroimaging and neural engineering are opening up new possibilities to improve
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DBS clinical outcomes by tailoring the therapy to individual patients’ needs. To this end,
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technological innovations have been developed to increase DBS space resolution, i.e., current
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steering [2, 3] and dual stimulation [4], and DBS time resolution, i.e., adaptive deep brain
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stimulation (aDBS) [5] and coordinated reset [6]. However, because the technical possibilities are
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evolving faster than basic research, whether these developments represent true innovations in
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treatment still needs to be demonstrated [7].
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Among these innovations, aDBS promises to improve DBS outcomes by automatically adapting stimulation parameters moment-by-moment to the patient’s clinical state, thus overcoming
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the suboptimal symptomatic control and adverse effects of DBS [8]. Although this idea is widely
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accepted by the clinical and scientific community, concrete evidence of its effectiveness and
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feasibility remains lacking. Additionally, different approaches are under development to translate
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aDBS research into clinical practice. In this review, we will discuss the basic principles of the aDBS
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strategy and the challenges faced by the different technological strategies currently available to
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concretize the aDBS philosophy.
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2. Closed-loop model
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The aDBS strategy is based on a classical closed-loop model: the aDBS device should
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measure and analyze a specific variable/set of variables that reflects ongoing changes in the
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patient's clinical state. Based on the analysis of the control variable, the aDBS system calculates a
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new set of stimulation parameters that are better suited to control the patient’s symptoms and sends
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them to the implanted stimulator that delivers DBS therapy.
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The choice of the control variable with the related technological constraints, the surgical procedures needed for additional devices, and the feedback algorithm providing the individual
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patient’s symptom management are the most critical issues to be solved in developing aDBS.
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In principle, the ideal aDBS system should alleviate PD symptoms and side effects better than traditional DBS without introducing changes in the surgical procedures or in the design of the
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device unless they are profitable for the patient. Additionally, because the PD phenotype can vary
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across subjects, the ideal aDBS system could use a combination of control variables and feedback
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algorithms that represent and interpret the personal characteristics of the individual patient. Finally,
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because the feedback algorithm should run on the implanted aDBS device, the processing and
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computational costs should be kept low to ensure low power consumption. In the next sections, we will present the different approaches, beginning with the choice of
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the control variable, and discuss their implications in terms of technological design, surgical
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procedure, and value as biomarkers of the specific symptoms, according to the above-mentioned
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basic requirements.
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3. Control variables
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3.1 Surface EMG and accelerometers.
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Surface EMG (sEMG) and accelerometers have proven useful in detecting and predicting
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tremor onset in PD [9, 10] and in essential tremor (ET) patients [11-13]. Basu and colleagues
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proposed an algorithm based on a set of spectral and nonlinear features extracted by sEMG and
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accelerometer signals recorded at the symptomatic limb that could achieve an overall accuracy of
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80% and a sensitivity of 100% in tremor prediction[9]. This result suggests that sEMG and
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accelerometers are suitable biomarkers for tremor and thus can be used in aDBS strategies for
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patients showing this symptom.
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Because sEMG and accelerometer recording is not invasive, no changes are required to the current DBS implantable pulse generators (IPG), thus leaving the surgical procedures unchanged.
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The feedback algorithm can run externally, and the new stimulation parameters can be transmitted
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to the IPG by the existing telemetry link. However, the continuous wireless communication may
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shorten the battery life, and the additional external devices that the patients need to wear may be
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uncomfortable and potentially unmanageable. In fact, recording sEMG requires specific skills to
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control the various sources of noise [14], and the consistency of the recordings over time is affected
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by electrode positioning, motion, and contact impedances. Finally, whereas tremor onset can be
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predicted with high accuracy and sensitivity, sEMG and accelerometers leave rigidity and
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bradykinesia undescribed and uncontrolled. 3.2 Cortical neurosignals
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Cortical signals such as electrocorticography (ECoG), single- and multi-unit activity are
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widely studied for neuroprotheses [15] and epilepsy seizure detection [16]. Neuroprotheses are
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mainly based on micro-electrode array (MEA) recordings that ensure high temporal and spatial
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resolution [17], although signal stability decreases over time [18]. However, closed-loop systems
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for seizure detection based on ECoG signals analysis are already commercially available for chronic
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implants [19].
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Considering closed-loop DBS for PD, ECoG [20] and multiunit activity [21] proved to be
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helpful in unveiling mechanisms in the cortico-basal ganglia loop in PD patients and monkey PD
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models, respectively. Rosin and colleagues showed that in the 1-methil-4-phenyl-1,2,3,6-
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tetrahyhydropyridine (MPTP) primate model of PD, short stimulation trains (7 pulses at 130 Hz) in
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the Gpi at a pre-determined and fixed latency of 80 ms after the occurrence of an action potential 5
ACCEPTED MANUSCRIPT recorded from the M1 motor cortex area reduced akinesia, and suppressed the pallidal discharge
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rate and oscillations more than standard DBS. ECoG data recorded in a PD patient in the M1 motor
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cortex revealed that the phase amplitude coupling (PAC) between beta and gamma oscillations
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decreased with motor tasks and the reduction of rigidity.
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For use as control variables, while cortical signals should not be sensitive to stimulation artifacts (the recording area is far enough from the stimulation site), the sensors should
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hypothetically cover a wide cortical area to map cortical function connections. Additionally, the
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implantation of these additional electrodes would change the surgical procedure.
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3.3 Subcortical neurosignals: Basal ganglia local field potentials
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DBS electrodes have been used since the introduction of the technique to record the
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neuronal activity of the target area and study the neurophysiology of the basal ganglia [22]. These
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signals, known as Local Field Potentials (LFPs), represent the oscillatory activity of a neuronal
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population around the recording electrode and carry information about the state of synchrony of a
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neural ensemble.
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More than ten years of research have shown that LFP oscillations, as well as EEG
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oscillations, are clustered into frequency bands ranging from very low frequencies (2–8 Hz) to beta
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frequencies (8–20 Hz, alpha/low beta and 20–35 Hz, high beta), gamma frequencies (60–80 Hz),
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and very high frequencies (250–350 Hz). Band-specific changes are detectable in response to the
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patient's clinical state, movement execution, and also cognitive and behavioral stimuli [23-25].
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More specifically, beta band LFP oscillations recorded from the subthalamic nucleus (STN)
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are the most studied and debated because they have been found to be modulated by dopaminergic
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medication [26] and electrical stimulation [27, 28] and to correlate with movement preparation and
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execution [29], akinesia [24], and the freezing of gait [30]. However, other bands have been shown 6
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to correlate with PD motor and non-motor symptoms: the low frequencies (LF) are representative of
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dyskinesias [31, 32], the alpha frequencies correlate with the gait speed [33], and the gamma
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frequencies have a prokinetic role [34].
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This correlation between symptoms and LFPs provides a basis for the hypothesis that these
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signals can be used as control variables for aDBS.
From a technological viewpoint, LFPs can be recorded from the contacts of the implanted
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DBS electrode. Conventional models of DBS macroelectrode (e.g., Model 3389, Medtronic,
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Minneapolis, USA) have platinum-iridium (Pt-I) cylindrical contacts with large surface contacts,
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low impedances and stable recording proprieties, implying that the DBS electrode is suitable for
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long-term LFP recordings. As a consequence, LFP-based aDBS systems do not require any
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additional electrodes or external implants or, in turn, any specific changes in neurosurgical
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procedures. Conversely, they require changing the electronic board of the IPGs by adding analogue
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circuits for neural sensing, which has been demonstrated to be technically feasible [35].
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How DBS acts on the neurophysiological mechanisms of PD remains under investigation.
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One study showed that both DBS and levodopa can disrupt beta oscillations, but only in patients
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showing a clear beta oscillation before DBS and levodopa administration [36]. Another study using
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a similar custom-made technology showed that DBS suppressed global beta oscillations in all STN
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nuclei selected [27]. Although the exact mechanisms are still under debate, recent evidence shows
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that beta oscillations in freely moving patients with PD are similar in rest conditions (lying, sitting
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and standing) and are modulated by DBS in an amplitude-dependent manner [37].
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The main limitation of all of these encouraging results is that the LFP recordings took place
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during perioperative experimental sessions, only a few days after brain surgery, and could be
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affected by lesion effects and local brain edema. Therefore, before concluding that LFP oscillations
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are suitable as a control variable for aDBS strategies, their consistency over time, in chronic 7
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sessions and one month after electrode implantation [38]. They showed a comparable amplitude of
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beta activity two days after surgery and thirty days after surgery, thus suggesting that the beta
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activity results during perioperative LFP recording sessions remain valid in the long term [39]. As
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additional proof, it has been shown that LFP beta oscillations are still modulated by DBS 7 years
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after electrode implantation [40].
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Because the beta band is not easily detectable in all patients and alone does not capture all of
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the aspects of PD, other LFP oscillations such as low frequencies or gamma frequencies that
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correlate with other PD symptoms can be considered to complement the missing information.
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Complex LFP processing algorithms may be needed to extract markers other than spectral power,
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which may increase the power consumption necessary for on-line detection and analysis.
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Despite these limitations, LFP-based aDBS was tested in 8 patients with advanced PD [41].
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The LFPs were band-pass filtered to remove oscillations other than the beta frequencies and then
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rectified to obtain a measure of the power time course. When the beta power decreased below an
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arbitrary threshold, DBS was switched off. Motor outcomes were 30% better with aDBS than with
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cDBS, and aDBS was also more effective than no stimulation and than random intermittent
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stimulation.
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conventional continuous DBS for PD with reduced power consumption. The same algorithm was
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tested bilaterally in 4 patients for approximately two hours, and the results showed that PD patients
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can be effectively alleviated by aDBS,despite no comparisons with cDBS outcomes have been
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provided [45].
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This study demonstrated that aDBS can be more efficient and efficacious than
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In a single-case study, our group tested whether a portable DBS device we developed was
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suitable to compare the clinical benefits induced by aDBS and by cDBS in a freely moving PD
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patient [43]. We treated a blinded patient with cDBS and aDBS in two separate experimental
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sessions, each lasting 120 minutes, five and six days after DBS electrode implant, respectively. The 8
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patient experienced a more stable condition during aDBS than during cDBS, with better control of
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symptoms over time [43].
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3.4 Neurochemical signals:
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In addition to electrophysiological signals, stimulation-evoked dopamine responses have
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been proposed as a control variable for closed-loop systems [44]. The relationship between the
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stimulation parameters and dopamine release was characterized by in vivo fast scan
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cyclovoltammetry, using a carbon fiber microelectrode (CFM) implanted in the striatum of four
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anaesthetized rats [45]. The real-time measurement of neurotransmitter release opened up
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opportunities to investigate DBS optimization strategies: the time duration of the tremor-free period
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was comparable to the duration of the increased levels of stimulation-induced dopamine release
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after DBS pulse trains [46].
Despite important implications found in basic research on animal models, the experimental
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and clinical applications of real-time neurotransmitter release measurement in humans still require
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clarification of the relationship between neurotransmitter levels and symptoms of disease and the
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overcoming of several technical issues (for example, the method requires at least 4 adjunctive
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electrodes, and CFM has a lifetime of only a few months) [44].
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4. Discussion
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Because the main challenge of aDBS consists in finding the best control variable and the
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optimal closed-loop strategy (Fig. 1), we reviewed the implications of each candidate signal,
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starting from the choice of the control variable, in terms of physiological meaning, technical
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constraints and surgical procedures (Tab.1). 9
ACCEPTED MANUSCRIPT The present bulk of evidence suggests LFPs as the primary choice because they match most
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of the requirements for a control variable. In particular, in addition to technological aspects, they
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carry information about all of the main PD symptoms, and the rich oscillatory pattern may account
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for inter-subject variability, thus allowing sufficient personalization. However, whether the
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statistical correlation between LFP and clinical symptoms is sufficient for controlling stimulation
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remains unclear. Different oscillatory patterns may have a causal role in symptom manifestation or
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may be merely an epiphenomenon. If LFP oscillations have a causal role in compromising the
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normal basal ganglia activity, it will certainly be possible to study the ideal algorithm to rebalance
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the affected network. Alternatively, if they are only a parallel manifestation of more complex
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mechanisms, their value as biomarkers must be demonstrated though empirical approaches and
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neurophysiological studies. The three previous proofs of concept of LFP-based aDBS suggest the
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feasibility of the approach [41-43] but suffer from two main limitations: aDBS was tested only for
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short periods and in acute settings. Increasing the testing time and performing chronic clinical
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studies are, therefore, necessary to confirm and expand these preliminary results.
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Both algorithms [41-43] were based on amplitude changes (on-off and proportionally to beta
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activity) with the frequency and the pulse width kept constant. However, there is emerging evidence
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that network oscillations and symptoms may be controlled through frequency modulation
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mechanisms [47]. Because of the cronaxie properties of the target neural structures, the pulse width
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must be as short as possible [48]. As a consequence, changes in the pulse width will play only a
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marginal role in the next adaptive strategies, which will focus instead on amplitude and frequency
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changes.
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While empirical approaches represent the main way to prove aDBS feasibility and
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effectiveness, they require a parallel effort in basic research to clarify and obtain further information
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about DBS anatomical and physiological mechanisms in PD and other movement disorders.
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information technology platforms) will prove useful for quantifying symptoms and clinical
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outcomes and for data and patient clustering. In general, the progressive use of sensors and methods
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for collecting data will play a fundamental role in the process of therapy personalization for the
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patient’s specific needs and, ultimately, in the definition of adaptive paradigms.
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In this review, we decided not to include theoretical algorithms, focusing only on more
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practical aDBS aspects that may impact the patient’s quality of life. In fact, computational modeling
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tries to develop in-vitro control policies [49] to suggest clinical applications based on a-priori
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hypotheses regarding DBS mechanisms of action and on the variables representing such
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mechanisms. Therefore, as the mechanisms of action of DBS are still under debate, and as the
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control variable affects the control policy, we chose a more practical approach and focused on the
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experimental choice of the control variable.
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Because DBS is clinically indicated for dystonia and ET, the adaptive philosophy may also
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be successful in these classes of patients. In PD and ET, the effect of DBS on main symptoms is
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detectable early after it is switched on [50,13]. In dystonia, the benefits of DBS may appear after
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days, weeks or even months [51]. This aspect makes the study of adaptive systems for PD and
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tremors easier than for dystonias. In fact, on-demand DBS based on accelerometer signals and
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EMGs has already been applied in essential tremor patients [13] (see Table.1), and
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neurophysiological signs of tremor in DBS LFP recording have been deeply studied [52-54]. A
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recent interesting finding in phasic dystonia patients is that, similarly to beta oscillations in PD,
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low-frequency oscillations are suppressed by high-frequency stimulation [55], and thus could serve
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as a potential biomarker.
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Keeping in mind the implications of each choice and considering the heterogeneity of inter-
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subject phenotypical manifestation, it is possible that a combination of signals/biomarkers may act
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better than a single one. Although LFP-based aDBS seems to be the most promising approach, 11
ACCEPTED MANUSCRIPT proceeding on different pathways is necessary to exploit the maximal benefits achievable and to
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weight them based on their unavoidable drawbacks. In conclusion, the ideal aDBS system should be
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designed to achieve the optimal trade-off between effectiveness and technological issues, thus
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ameliorating the patient’s quality of life.
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Conflict of interest
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Sara Marceglia, Sergio Barbieri and Alberto Priori are shareholders of Newronika S.r.l. (Milan,
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Italy), a spin-off company of the Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico
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and Università degli Studi di Milano.
Author contributions
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All authors have approved the final article.
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Acknowledgements
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This study was supported by ERANET-Neuron Grant “PhysiolDBS” (Neuron-49-013), by
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Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Mangiagalli, Regina Elena (Milan,
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Italy), and by Università degli Studi di Milano (Italy).
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ACCEPTED MANUSCRIPT References
2
[1] Benabid AL, Chabardes S, Mitrofanis J, Pollak P. Deep brain stimulation of the subthalamic
3
nucleus for the treatment of Parkinson's disease. Lancet Neurol. 2009;8:67-81.
4
[2] Barbe MT, Dembek TA, Becker J, Raethjen J, Hartinger M, Meister IG, et al. Individualized
5
current-shaping reduces DBS-induced dysarthria in patients with essential tremor. Neurology.
6
2014;82:614-9.
7
[3] Pollo C, Kaelin-Lang A, Oertel MF, Stieglitz L, Taub E, Fuhr P, et al. Directional deep brain
8
stimulation: an intraoperative double-blind pilot study. Brain. 2014;137:2015-26.
9
[4] Sims-Williams HP, Selbi WR, Javed S, Pickering AE, Patel NK. Two birds, one stone: single
M AN U
SC
RI PT
1
electrode dual target stimulation for the treatment of phantom limb pain. Stereotact Funct
11
Neurosurg. 2013;91:264.
12
[5] Priori A, Foffani G, Rossi L, Marceglia S. Adaptive deep brain stimulation (aDBS) controlled
13
by local field potential oscillations. Exp Neurol. 2013;245:77-86.
14
[6] Adamchic I, Hauptmann C, Barnikol UB, Pawelczyk N, Popovych O, Barnikol TT, et al.
15
Coordinated reset neuromodulation for Parkinson's disease: proof-of-concept study. Mov Disord.
16
2014;29:1679-84.
17
[7] Priori A. Technology for deep brain stimulation at a gallop. Mov Disord. 2015;30:1206-12.
18
[8] Carron R, Chaillet A, Filipchuk A, Pasillas-Lepine W, Hammond C. Closing the loop of deep
19
brain stimulation. Front Syst Neurosci. 2013;7:112.
20
[9] Basu I, Graupe D, Tuninetti D, Shukla P, Slavin KV, Metman LV, et al. Pathological tremor
21
prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand
22
driven deep brain stimulator design. J Neural Eng. 2013;10:036019.
23
[10] Shukla P, Basu I, Graupe D, Tuninetti D, Slavin KV. A neural network-based design of an on-
24
off adaptive control for Deep Brain Stimulation in movement disorders. Conf Proc IEEE Eng Med
25
Biol Soc. 2012;2012:4140-3.
AC C
EP
TE D
10
13
ACCEPTED MANUSCRIPT [11] Basu I, Tuninetti D, Graupe D, Slavin KV. Adaptive control of deep brain stimulator for
2
essential tremor: entropy-based tremor prediction using surface-EMG. Conf Proc IEEE Eng Med
3
Biol Soc. 2011;2011:7711-4.
4
[12] Shukla P, Basu I, Tuninetti D. Towards closed-loop deep brain stimulation: decision tree-based
5
essential tremor patient's state classifier and tremor reappearance predictor. Conf Proc IEEE Eng
6
Med Biol Soc. 2013;2014:2605-8.
7
[13] Yamamoto T, Katayama Y, Ushiba J, Yoshino H, Obuchi T, Kobayashi K, et al. On-demand
8
control system for deep brain stimulation for treatment of intention tremor. Neuromodulation.
9
2012;16:230-5; discussion 5.
SC
RI PT
1
[14] Clancy EA, Morin EL, Merletti R. Sampling, noise-reduction and amplitude estimation issues
11
in surface electromyography. J Electromyogr Kinesiol. 2002;12:1-16.
12
[15] Tsu AP, Burish MJ, GodLove J, Ganguly K. Cortical neuroprosthetics from a clinical
13
perspective. Neurobiol Dis. 2015.
14
[16] Vonck K, Boon P. Epilepsy: closing the loop for patients with epilepsy. Nat Rev Neurol.
15
2015;11:252-4.
16
[17] Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV. A high performing brain-machine
17
interface driven by low-frequency local field potentials alone and together with spikes. J Neural
18
Eng. 2015;12:036009.
19
[18] Shenoy KV, Carmena JM. Combining decoder design and neural adaptation in brain-machine
20
interfaces. Neuron. 2014;84:665-80.
21
[19] Sun FT, Morrell MJ. The RNS System: responsive cortical stimulation for the treatment of
22
refractory partial epilepsy. Expert Rev Med Devices. 2014;11:563-72.
23
[20] de Hemptinne C, Swann NC, Ostrem JL, Ryapolova-Webb ES, San Luciano M, Galifianakis
24
NB, et al. Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in
25
Parkinson's disease. Nat Neurosci. 2015;18:779-86.
AC C
EP
TE D
M AN U
10
14
ACCEPTED MANUSCRIPT [21] Rosin B, Slovik M, Mitelman R, Rivlin-Etzion M, Haber SN, Israel Z, et al. Closed-loop deep
2
brain stimulation is superior in ameliorating parkinsonism. Neuron. 2011;72:370-84.
3
[22] Rosa M, Giannicola G, Marceglia S, Fumagalli M, Barbieri S, Priori A. Neurophysiology of
4
deep brain stimulation. Int Rev Neurobiol. 2012;107:23-55.
5
[23] Foffani G, Bianchi AM, Baselli G, Priori A. Movement-related frequency modulation of beta
6
oscillatory activity in the human subthalamic nucleus. J Physiol. 2005;568:699-711.
7
[24] Kuhn AA, Tsui A, Aziz T, Ray N, Brucke C, Kupsch A, et al. Pathological synchronisation in
8
the subthalamic nucleus of patients with Parkinson's disease relates to both bradykinesia and
9
rigidity. Exp Neurol. 2009;215:380-7.
SC
RI PT
1
[25] Marceglia S, Fumagalli M, Priori A. What neurophysiological recordings tell us about
11
cognitive and behavioral functions of the human subthalamic nucleus. Expert Rev Neurother.
12
2011;11:139-49.
13
[26] Priori A, Foffani G, Pesenti A, Tamma F, Bianchi AM, Pellegrini M, et al. Rhythm-specific
14
pharmacological modulation of subthalamic activity in Parkinson's disease. Exp Neurol.
15
2004;189:369-79.
16
[27] Eusebio A, Thevathasan W, Doyle Gaynor L, Pogosyan A, Bye E, Foltynie T, et al. Deep brain
17
stimulation can suppress pathological synchronisation in parkinsonian patients. J Neurol Neurosurg
18
Psychiatry. 2011;82:569-73.
19
[28] Giannicola G, Marceglia S, Rossi L, Mrakic-Sposta S, Rampini P, Tamma F, et al. The effects
20
of levodopa and ongoing deep brain stimulation on subthalamic beta oscillations in Parkinson's
21
disease. Exp Neurol. 2010;226:120-7.
22
[29] Foffani G, Ardolino G, Meda B, Egidi M, Rampini P, Caputo E, et al. Altered subthalamo-
23
pallidal synchronisation in parkinsonian dyskinesias. J Neurol Neurosurg Psychiatry. 2005;76:426-
24
8.
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10
15
ACCEPTED MANUSCRIPT [30] Toledo JB, Lopez-Azcarate J, Garcia-Garcia D, Guridi J, Valencia M, Artieda J, et al. High
2
beta activity in the subthalamic nucleus and freezing of gait in Parkinson's disease. Neurobiol Dis.
3
2014;64:60-5.
4
[31] Alonso-Frech F, Zamarbide I, Alegre M, Rodriguez-Oroz MC, Guridi J, Manrique M, et al.
5
Slow oscillatory activity and levodopa-induced dyskinesias in Parkinson's disease. Brain.
6
2006;129:1748-57.
7
[32] Rodriguez-Oroz MC, Lopez-Azcarate J, Garcia-Garcia D, Alegre M, Toledo J, Valencia M, et
8
al. Involvement of the subthalamic nucleus in impulse control disorders associated with Parkinson's
9
disease. Brain. 2010;134:36-49.
SC
RI PT
1
[33] Thevathasan W, Pogosyan A, Hyam JA, Jenkinson N, Foltynie T, Limousin P, et al. Alpha
11
oscillations in the pedunculopontine nucleus correlate with gait performance in parkinsonism.
12
Brain. 2012;135:148-60.
13
[34] Florin E, Erasmi R, Reck C, Maarouf M, Schnitzler A, Fink GR, et al. Does increased gamma
14
activity in patients suffering from Parkinson's disease counteract the movement inhibiting beta
15
activity? Neuroscience. 2013;237:42-50.
16
[35] Rouse AG, Stanslaski SR, Cong P, Jensen RM, Afshar P, Ullestad D, et al. A chronic
17
generalized bi-directional brain-machine interface. J Neural Eng. 2011;8:036018.
18
[36] Rossi L, Marceglia S, Foffani G, Cogiamanian F, Tamma F, Rampini P, et al. Subthalamic
19
local field potential oscillations during ongoing deep brain stimulation in Parkinson's disease. Brain
20
Res Bull. 2008;76:512-21.
21
[37] Quinn EJ, Blumenfeld Z, Velisar A, Koop MM, Shreve LA, Trager MH, et al. Beta oscillations
22
in freely moving Parkinson's subjects are attenuated during deep brain stimulation. Mov Disord.
23
2015.
24
[38] Rosa M, Giannicola G, Servello D, Marceglia S, Pacchetti C, Porta M, et al. Subthalamic local
25
field beta oscillations during ongoing deep brain stimulation in Parkinson's disease in hyperacute
26
and chronic phases. Neurosignals. 2011;19:151-62.
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ACCEPTED MANUSCRIPT [39] Rosa M, Marceglia S, Servello D, Foffani G, Rossi L, Sassi M, et al. Time dependent
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subthalamic local field potential changes after DBS surgery in Parkinson's disease. Exp Neurol.
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2010;222:184-90.
4
[40] Giannicola G, Rosa M, Servello D, Menghetti C, Carrabba G, Pacchetti C, et al. Subthalamic
5
local field potentials after seven-year deep brain stimulation in Parkinson's disease. Exp Neurol.
6
2012;237:312-7.
7
[41] Little S, Pogosyan A, Neal S, Zavala B, Zrinzo L, Hariz M, et al. Adaptive deep brain
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stimulation in advanced Parkinson disease. Ann Neurol. 2013;74:449-57.
9
[42] Little S, Beudel M, Zrinzo L, Foltynie T, Limousin P, Hariz M, et al. Bilateral adaptive deep
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brain stimulation is effective in Parkinson's disease. J Neurol Neurosurg Psychiatry. 2015.
11
[43] Rosa M, Arlotti M, Ardolino G, Cogiamanian F, Marceglia S, DiFonzo A, et al. Adaptive Deep
12
Brain Stimulation in a freely-moving parkinsonian patient. Mov Disorder. 2015;In press.
13
[44] Chang SY, Kimble CJ, Kim I, Paek SB, Kressin KR, Boesche JB, et al. Development of the
14
Mayo Investigational Neuromodulation Control System: toward a closed-loop electrochemical
15
feedback system for deep brain stimulation. J Neurosurg. 2013;119:1556-65.
16
[45] Grahn PJ, Mallory GW, Khurram OU, Berry BM, Hachmann JT, Bieber AJ, et al. A
17
neurochemical closed-loop controller for deep brain stimulation: toward individualized smart
18
neuromodulation therapies. Front Neurosci. 2014;8:169.
19
[46] Graupe D, Tuninetti D, Slavin KV, Basu I. Closed-loop electrochemical feedback system for
20
DBS. J Neurosurg. 2014;121:762-3.
21
[47] Fasano A, Lozano AM. The FM/AM world is shaping the future of deep brain stimulation.
22
Mov Disord. 2014;29(2):161-3.
23
[48] Volkmann J, Herzog J, Kopper F, Deuschl G. Introduction to the programming of deep brain
24
stimulators. Mov Disord. 2002;17 Suppl 3:S181-7.
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ACCEPTED MANUSCRIPT [49] Santaniello S, Fiengo G, Glielmo L, Grill WM. Closed-loop control of deep brain stimulation:
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a simulation study. IEEE Trans Neural Syst Rehabil Eng. 2011;19:15-24.
3
[50] Lopiano L, Torre E, Benedetti F, Bergamasco B, Perozzo P, Pollo A, et al. Temporal changes
4
in movement time during the switch of the stimulators in Parkinson's disease patients treated by
5
subthalamic nucleus stimulation. Eur Neurol. 2003;50:94-99.
6
[51] Yianni J, Bain PG, Gregory RP, Nandi D, Joint C, Scott RB, et al. Post-operative progress of
7
dystonia patients following globus pallidus internus deep brain stimulation. Eur J
8
Neurol. 2003;10(3):239-47.
9
[52] Reck C, Himmel M, Florin E, Maarouf M, Sturm V, Wojtecki L, et al. Coherence analysis of
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local field potentials in the subthalamic nucleus: differences in parkinsonian rest and postural
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tremor. Eur J Neurosci. 2010;32(7):1202-14.
12
[53] Pedrosa DJ, Reck C, Florin E, Pauls KA, Maarouf M, Wojtecki L, et al. Essential tremor and
13
tremor in Parkinson's disease are associated with distinct 'tremor clusters' in the ventral
14
thalamus.Exp Neurol. 2012;237(2):435-43.
15
[54] Heida T, Wentink EC, Marani E. Power spectral density analysis of physiological, rest and
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action tremor in Parkinson’s disease patients treated with deep brain stimulation. J Neuroeng
17
Rehabil. 2013;10:70.
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[55] Barow E, Neumann WJ, Brucke C, Huebl J, Horn A, Brown P, Krauss JK, et al. Deep brain
19
stimulation suppresses pallidal low frequency activity in patients with phasic dystonic movements.
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Brain. 2014;137:3012-24.
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Figure and table legends
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Table 1. Rationale for the choice of the control variable.
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CFM = carbon fiber microelectrode; ET: Essential Tremor; LFP = local field potential; M1 = motor cortex area M1; IPG = implanted pulse generator; PD: Parkinson’s Disease.
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Figure 1. Control variables for adaptive deep brain stimulation systems.
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sEEG = surface electroencephalography; iEEG = intracranial electroencephalography; LFP
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= local field potentials; acc = accelerometers, sEMG = surface electromyography, ECoG =
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electrocorticography; IPG = implantable pulse generator; LFP = local field potential.
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ACCEPTED MANUSCRIPT Cortical neurosignals
Basal ganglia LFPs
Neurotransmitters
YES – external sensors required
YES – implanted cortical electrodes required
NO – LFPs are recorded from the implanted DBS electrode
YES – at least 4 additional CFM
NO – no additional implant during surgery
YES– the additional CFMs need to be implanted during surgery
YES – the patient perceives the same system as for traditional DBS
NO - CFMs have a time life of only a few months and have to be replaced
Additional implant/equipment
NO – it may be difficult to manage the recording sensors and the external equipment may be uncomfortable
YES/NO – optimal correlation with tremor, but no correlations with rigidity and bradikinesia
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NO – cannot be used if patients do not show tremor
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Low battery consumption
Proof of concept
YES – multiple YES – EcoG phase LFP oscillations YES - the time duration amplitude coupling are modulated by of tremor-free period is and M1 action levodopa comparable to the potentials correlate administration, duration of increased with main PD DBS, levels of stimulationsymptoms and can movements, and induced dopamine release be used to drive non-motor tasks after DBS pulse trains aDBS even years after electrode implant
YES/NO – it may encode patient specific information
YES – the presence of multiple rhythms correlating with different patient’s characteristics may account for inter-subject variability
NOT YET TESTED
YES/NO – the IPG needs to include the sensing circuit and the feedback algorithm
YES/NO – the IPG needs to include the sensing circuit and the feedback algorithm
NO – the IPG needs to include the sensing circuit and the feedback algorithm
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Correlation with the clinical state
Personalization and adaptability
YES – all the equipment is implanted
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Patient’s management/acceptability
NO – the additional YES – the surgery implant is external needs to include the and does not affect implant of cortical the surgical procedure electrodes
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Changes in the surgical procedures
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sEMG and accelerometers
YES/NO – the processing can be done externally, but triggers should be sent via telemetry links
YES in humans (ET) [Yamamoto et al., 2013]
YES in humans (PD) [Little et al., YES in animals 2013; Rosa et al., [Rosin et al., 2011] 2015; Little et al., 2015]
NO
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ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT Highlights: The clinical translation of adaptive DBS needs to overcome several challenges.
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Current proposals are classified on the base of the choice of the control variable.
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Basal-ganglia local field potentials are the best candidate.
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S1353-8020(16)30074-8
DOI:
10.1016/j.parkreldis.2016.03.020
Reference:
PRD 2970
To appear in:
Parkinsonism and Related Disorders
Received Date: 26 October 2015 Revised Date:
25 March 2016
Accepted Date: 28 March 2016
Please cite this article as: Arlotti M, Rosa M, Marceglia S, Barbieri S, Priori A, The Adaptive Deep Brain Stimulation Challenge, Parkinsonism and Related Disorders (2016), doi: 10.1016/ j.parkreldis.2016.03.020. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
The Adaptive Deep Brain Stimulation Challenge
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Mattia Arlotti12, Manuela Rosa1, Sara Marceglia1,3, Sergio Barbieri1,4, Alberto Priori5
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Centro clinico per la Neurostimolazione, le Neurotecnologie ed i Disordini del Movimento,
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Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milan, Italy 2
Department of Electronics, Computer Science and Systems, University of Bologna, Cesena,
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Dipartimento di Ingegneria e Architettura, Università degli Studi di Trieste, Trieste, Italy
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Unità di Neurofisiopatologia, Fondazione IRCCS Ca' Granda Ospedale Maggiore
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Italy
Policlinico di Milano, Milan, Italy 5
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Università degli Studi di Milano,Polo Ospedaliero San Paolo, Milan, Italy
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Corresponding author
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Prof. Alberto Priori MD PhD
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Direttore III Clinica Neurologica
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Università degli Studi di Milano
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Polo Ospedaliero San Paolo
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Milano, Italy
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e-mail: [email protected]
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Abstract
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Sub-optimal clinical outcomes of conventional deep brain stimulation (cDBS) in treating Parkinson’s Disease (PD) have boosted the development of new solutions to improve DBS therapy.
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Adaptive DBS (aDBS), consisting of closed-loop, real-time changing of stimulation parameters
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according to the patient’s clinical state, promises to achieve this goal and is attracting increasing
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interest in overcoming all of the challenges posed by its development and adoption. In the design,
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implementation, and application of aDBS, the choice of the control variable and of the control
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algorithm represents the core challenge. The proposed approaches, in fact, differ in the choice of the
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control variable and control policy, in the system design and its technological limits, in the patient’s
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target symptom, and in the surgical procedure needed. Here, we review the current proposals for
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aDBS systems, focusing on the choice of the control variable and its advantages and drawbacks,
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thus providing a general overview of the possible pathways for the clinical translation of aDBS with
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its benefits, limitations and unsolved issues.
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Keywords:
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Adaptive deep brain stimulation, Parkinson’s Disease, Control variable, Basal ganglia local
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field potentials
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1. Introduction
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Deep brain stimulation (DBS) is an established treatment for Parkinson’s Disease (PD) based on chronic high-frequency (100-180 Hz) stimulation of the basal ganglia nuclei [1]. It has
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remained almost unchanged since its first application. Recently, however, advances in
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neurophysiology, neuroimaging and neural engineering are opening up new possibilities to improve
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DBS clinical outcomes by tailoring the therapy to individual patients’ needs. To this end,
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technological innovations have been developed to increase DBS space resolution, i.e., current
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steering [2, 3] and dual stimulation [4], and DBS time resolution, i.e., adaptive deep brain
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stimulation (aDBS) [5] and coordinated reset [6]. However, because the technical possibilities are
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evolving faster than basic research, whether these developments represent true innovations in
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treatment still needs to be demonstrated [7].
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Among these innovations, aDBS promises to improve DBS outcomes by automatically adapting stimulation parameters moment-by-moment to the patient’s clinical state, thus overcoming
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the suboptimal symptomatic control and adverse effects of DBS [8]. Although this idea is widely
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accepted by the clinical and scientific community, concrete evidence of its effectiveness and
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feasibility remains lacking. Additionally, different approaches are under development to translate
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aDBS research into clinical practice. In this review, we will discuss the basic principles of the aDBS
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strategy and the challenges faced by the different technological strategies currently available to
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concretize the aDBS philosophy.
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2. Closed-loop model
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The aDBS strategy is based on a classical closed-loop model: the aDBS device should
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measure and analyze a specific variable/set of variables that reflects ongoing changes in the
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patient's clinical state. Based on the analysis of the control variable, the aDBS system calculates a
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new set of stimulation parameters that are better suited to control the patient’s symptoms and sends
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them to the implanted stimulator that delivers DBS therapy.
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The choice of the control variable with the related technological constraints, the surgical procedures needed for additional devices, and the feedback algorithm providing the individual
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patient’s symptom management are the most critical issues to be solved in developing aDBS.
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In principle, the ideal aDBS system should alleviate PD symptoms and side effects better than traditional DBS without introducing changes in the surgical procedures or in the design of the
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device unless they are profitable for the patient. Additionally, because the PD phenotype can vary
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across subjects, the ideal aDBS system could use a combination of control variables and feedback
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algorithms that represent and interpret the personal characteristics of the individual patient. Finally,
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because the feedback algorithm should run on the implanted aDBS device, the processing and
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computational costs should be kept low to ensure low power consumption. In the next sections, we will present the different approaches, beginning with the choice of
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the control variable, and discuss their implications in terms of technological design, surgical
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procedure, and value as biomarkers of the specific symptoms, according to the above-mentioned
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basic requirements.
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3. Control variables
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3.1 Surface EMG and accelerometers.
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Surface EMG (sEMG) and accelerometers have proven useful in detecting and predicting
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tremor onset in PD [9, 10] and in essential tremor (ET) patients [11-13]. Basu and colleagues
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proposed an algorithm based on a set of spectral and nonlinear features extracted by sEMG and
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accelerometer signals recorded at the symptomatic limb that could achieve an overall accuracy of
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80% and a sensitivity of 100% in tremor prediction[9]. This result suggests that sEMG and
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accelerometers are suitable biomarkers for tremor and thus can be used in aDBS strategies for
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patients showing this symptom.
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Because sEMG and accelerometer recording is not invasive, no changes are required to the current DBS implantable pulse generators (IPG), thus leaving the surgical procedures unchanged.
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The feedback algorithm can run externally, and the new stimulation parameters can be transmitted
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to the IPG by the existing telemetry link. However, the continuous wireless communication may
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shorten the battery life, and the additional external devices that the patients need to wear may be
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uncomfortable and potentially unmanageable. In fact, recording sEMG requires specific skills to
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control the various sources of noise [14], and the consistency of the recordings over time is affected
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by electrode positioning, motion, and contact impedances. Finally, whereas tremor onset can be
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predicted with high accuracy and sensitivity, sEMG and accelerometers leave rigidity and
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bradykinesia undescribed and uncontrolled. 3.2 Cortical neurosignals
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Cortical signals such as electrocorticography (ECoG), single- and multi-unit activity are
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widely studied for neuroprotheses [15] and epilepsy seizure detection [16]. Neuroprotheses are
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mainly based on micro-electrode array (MEA) recordings that ensure high temporal and spatial
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resolution [17], although signal stability decreases over time [18]. However, closed-loop systems
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for seizure detection based on ECoG signals analysis are already commercially available for chronic
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implants [19].
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Considering closed-loop DBS for PD, ECoG [20] and multiunit activity [21] proved to be
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helpful in unveiling mechanisms in the cortico-basal ganglia loop in PD patients and monkey PD
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models, respectively. Rosin and colleagues showed that in the 1-methil-4-phenyl-1,2,3,6-
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tetrahyhydropyridine (MPTP) primate model of PD, short stimulation trains (7 pulses at 130 Hz) in
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the Gpi at a pre-determined and fixed latency of 80 ms after the occurrence of an action potential 5
ACCEPTED MANUSCRIPT recorded from the M1 motor cortex area reduced akinesia, and suppressed the pallidal discharge
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rate and oscillations more than standard DBS. ECoG data recorded in a PD patient in the M1 motor
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cortex revealed that the phase amplitude coupling (PAC) between beta and gamma oscillations
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decreased with motor tasks and the reduction of rigidity.
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For use as control variables, while cortical signals should not be sensitive to stimulation artifacts (the recording area is far enough from the stimulation site), the sensors should
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hypothetically cover a wide cortical area to map cortical function connections. Additionally, the
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implantation of these additional electrodes would change the surgical procedure.
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3.3 Subcortical neurosignals: Basal ganglia local field potentials
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DBS electrodes have been used since the introduction of the technique to record the
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neuronal activity of the target area and study the neurophysiology of the basal ganglia [22]. These
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signals, known as Local Field Potentials (LFPs), represent the oscillatory activity of a neuronal
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population around the recording electrode and carry information about the state of synchrony of a
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neural ensemble.
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More than ten years of research have shown that LFP oscillations, as well as EEG
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oscillations, are clustered into frequency bands ranging from very low frequencies (2–8 Hz) to beta
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frequencies (8–20 Hz, alpha/low beta and 20–35 Hz, high beta), gamma frequencies (60–80 Hz),
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and very high frequencies (250–350 Hz). Band-specific changes are detectable in response to the
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patient's clinical state, movement execution, and also cognitive and behavioral stimuli [23-25].
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More specifically, beta band LFP oscillations recorded from the subthalamic nucleus (STN)
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are the most studied and debated because they have been found to be modulated by dopaminergic
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medication [26] and electrical stimulation [27, 28] and to correlate with movement preparation and
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execution [29], akinesia [24], and the freezing of gait [30]. However, other bands have been shown 6
ACCEPTED MANUSCRIPT 1
to correlate with PD motor and non-motor symptoms: the low frequencies (LF) are representative of
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dyskinesias [31, 32], the alpha frequencies correlate with the gait speed [33], and the gamma
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frequencies have a prokinetic role [34].
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This correlation between symptoms and LFPs provides a basis for the hypothesis that these
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signals can be used as control variables for aDBS.
From a technological viewpoint, LFPs can be recorded from the contacts of the implanted
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DBS electrode. Conventional models of DBS macroelectrode (e.g., Model 3389, Medtronic,
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Minneapolis, USA) have platinum-iridium (Pt-I) cylindrical contacts with large surface contacts,
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low impedances and stable recording proprieties, implying that the DBS electrode is suitable for
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long-term LFP recordings. As a consequence, LFP-based aDBS systems do not require any
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additional electrodes or external implants or, in turn, any specific changes in neurosurgical
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procedures. Conversely, they require changing the electronic board of the IPGs by adding analogue
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circuits for neural sensing, which has been demonstrated to be technically feasible [35].
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How DBS acts on the neurophysiological mechanisms of PD remains under investigation.
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One study showed that both DBS and levodopa can disrupt beta oscillations, but only in patients
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showing a clear beta oscillation before DBS and levodopa administration [36]. Another study using
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a similar custom-made technology showed that DBS suppressed global beta oscillations in all STN
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nuclei selected [27]. Although the exact mechanisms are still under debate, recent evidence shows
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that beta oscillations in freely moving patients with PD are similar in rest conditions (lying, sitting
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and standing) and are modulated by DBS in an amplitude-dependent manner [37].
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The main limitation of all of these encouraging results is that the LFP recordings took place
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during perioperative experimental sessions, only a few days after brain surgery, and could be
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affected by lesion effects and local brain edema. Therefore, before concluding that LFP oscillations
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are suitable as a control variable for aDBS strategies, their consistency over time, in chronic 7
ACCEPTED MANUSCRIPT conditions, needs to be proven. Rosa et al. recorded LFPs both in perioperative experimental
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sessions and one month after electrode implantation [38]. They showed a comparable amplitude of
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beta activity two days after surgery and thirty days after surgery, thus suggesting that the beta
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activity results during perioperative LFP recording sessions remain valid in the long term [39]. As
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additional proof, it has been shown that LFP beta oscillations are still modulated by DBS 7 years
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after electrode implantation [40].
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Because the beta band is not easily detectable in all patients and alone does not capture all of
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the aspects of PD, other LFP oscillations such as low frequencies or gamma frequencies that
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correlate with other PD symptoms can be considered to complement the missing information.
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Complex LFP processing algorithms may be needed to extract markers other than spectral power,
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which may increase the power consumption necessary for on-line detection and analysis.
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Despite these limitations, LFP-based aDBS was tested in 8 patients with advanced PD [41].
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The LFPs were band-pass filtered to remove oscillations other than the beta frequencies and then
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rectified to obtain a measure of the power time course. When the beta power decreased below an
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arbitrary threshold, DBS was switched off. Motor outcomes were 30% better with aDBS than with
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cDBS, and aDBS was also more effective than no stimulation and than random intermittent
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stimulation.
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conventional continuous DBS for PD with reduced power consumption. The same algorithm was
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tested bilaterally in 4 patients for approximately two hours, and the results showed that PD patients
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can be effectively alleviated by aDBS,despite no comparisons with cDBS outcomes have been
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provided [45].
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In a single-case study, our group tested whether a portable DBS device we developed was
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suitable to compare the clinical benefits induced by aDBS and by cDBS in a freely moving PD
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patient [43]. We treated a blinded patient with cDBS and aDBS in two separate experimental
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sessions, each lasting 120 minutes, five and six days after DBS electrode implant, respectively. The 8
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patient experienced a more stable condition during aDBS than during cDBS, with better control of
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symptoms over time [43].
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3.4 Neurochemical signals:
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In addition to electrophysiological signals, stimulation-evoked dopamine responses have
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been proposed as a control variable for closed-loop systems [44]. The relationship between the
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stimulation parameters and dopamine release was characterized by in vivo fast scan
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cyclovoltammetry, using a carbon fiber microelectrode (CFM) implanted in the striatum of four
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anaesthetized rats [45]. The real-time measurement of neurotransmitter release opened up
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opportunities to investigate DBS optimization strategies: the time duration of the tremor-free period
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was comparable to the duration of the increased levels of stimulation-induced dopamine release
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after DBS pulse trains [46].
Despite important implications found in basic research on animal models, the experimental
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and clinical applications of real-time neurotransmitter release measurement in humans still require
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clarification of the relationship between neurotransmitter levels and symptoms of disease and the
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overcoming of several technical issues (for example, the method requires at least 4 adjunctive
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electrodes, and CFM has a lifetime of only a few months) [44].
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4. Discussion
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Because the main challenge of aDBS consists in finding the best control variable and the
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optimal closed-loop strategy (Fig. 1), we reviewed the implications of each candidate signal,
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starting from the choice of the control variable, in terms of physiological meaning, technical
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constraints and surgical procedures (Tab.1). 9
ACCEPTED MANUSCRIPT The present bulk of evidence suggests LFPs as the primary choice because they match most
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of the requirements for a control variable. In particular, in addition to technological aspects, they
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carry information about all of the main PD symptoms, and the rich oscillatory pattern may account
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for inter-subject variability, thus allowing sufficient personalization. However, whether the
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statistical correlation between LFP and clinical symptoms is sufficient for controlling stimulation
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remains unclear. Different oscillatory patterns may have a causal role in symptom manifestation or
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may be merely an epiphenomenon. If LFP oscillations have a causal role in compromising the
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normal basal ganglia activity, it will certainly be possible to study the ideal algorithm to rebalance
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the affected network. Alternatively, if they are only a parallel manifestation of more complex
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mechanisms, their value as biomarkers must be demonstrated though empirical approaches and
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neurophysiological studies. The three previous proofs of concept of LFP-based aDBS suggest the
12
feasibility of the approach [41-43] but suffer from two main limitations: aDBS was tested only for
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short periods and in acute settings. Increasing the testing time and performing chronic clinical
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studies are, therefore, necessary to confirm and expand these preliminary results.
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Both algorithms [41-43] were based on amplitude changes (on-off and proportionally to beta
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activity) with the frequency and the pulse width kept constant. However, there is emerging evidence
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that network oscillations and symptoms may be controlled through frequency modulation
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mechanisms [47]. Because of the cronaxie properties of the target neural structures, the pulse width
19
must be as short as possible [48]. As a consequence, changes in the pulse width will play only a
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marginal role in the next adaptive strategies, which will focus instead on amplitude and frequency
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changes.
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While empirical approaches represent the main way to prove aDBS feasibility and
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effectiveness, they require a parallel effort in basic research to clarify and obtain further information
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about DBS anatomical and physiological mechanisms in PD and other movement disorders.
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ACCEPTED MANUSCRIPT Moreover, an increased adoption of “technology around the patient” (wearable sensors and
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information technology platforms) will prove useful for quantifying symptoms and clinical
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outcomes and for data and patient clustering. In general, the progressive use of sensors and methods
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for collecting data will play a fundamental role in the process of therapy personalization for the
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patient’s specific needs and, ultimately, in the definition of adaptive paradigms.
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In this review, we decided not to include theoretical algorithms, focusing only on more
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practical aDBS aspects that may impact the patient’s quality of life. In fact, computational modeling
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tries to develop in-vitro control policies [49] to suggest clinical applications based on a-priori
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hypotheses regarding DBS mechanisms of action and on the variables representing such
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mechanisms. Therefore, as the mechanisms of action of DBS are still under debate, and as the
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control variable affects the control policy, we chose a more practical approach and focused on the
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experimental choice of the control variable.
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Because DBS is clinically indicated for dystonia and ET, the adaptive philosophy may also
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be successful in these classes of patients. In PD and ET, the effect of DBS on main symptoms is
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detectable early after it is switched on [50,13]. In dystonia, the benefits of DBS may appear after
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days, weeks or even months [51]. This aspect makes the study of adaptive systems for PD and
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tremors easier than for dystonias. In fact, on-demand DBS based on accelerometer signals and
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EMGs has already been applied in essential tremor patients [13] (see Table.1), and
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neurophysiological signs of tremor in DBS LFP recording have been deeply studied [52-54]. A
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recent interesting finding in phasic dystonia patients is that, similarly to beta oscillations in PD,
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low-frequency oscillations are suppressed by high-frequency stimulation [55], and thus could serve
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as a potential biomarker.
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Keeping in mind the implications of each choice and considering the heterogeneity of inter-
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subject phenotypical manifestation, it is possible that a combination of signals/biomarkers may act
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better than a single one. Although LFP-based aDBS seems to be the most promising approach, 11
ACCEPTED MANUSCRIPT proceeding on different pathways is necessary to exploit the maximal benefits achievable and to
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weight them based on their unavoidable drawbacks. In conclusion, the ideal aDBS system should be
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designed to achieve the optimal trade-off between effectiveness and technological issues, thus
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ameliorating the patient’s quality of life.
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Conflict of interest
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Sara Marceglia, Sergio Barbieri and Alberto Priori are shareholders of Newronika S.r.l. (Milan,
8
Italy), a spin-off company of the Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico
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and Università degli Studi di Milano.
Author contributions
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All authors have approved the final article.
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Acknowledgements
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This study was supported by ERANET-Neuron Grant “PhysiolDBS” (Neuron-49-013), by
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Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Mangiagalli, Regina Elena (Milan,
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Italy), and by Università degli Studi di Milano (Italy).
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ACCEPTED MANUSCRIPT References
2
[1] Benabid AL, Chabardes S, Mitrofanis J, Pollak P. Deep brain stimulation of the subthalamic
3
nucleus for the treatment of Parkinson's disease. Lancet Neurol. 2009;8:67-81.
4
[2] Barbe MT, Dembek TA, Becker J, Raethjen J, Hartinger M, Meister IG, et al. Individualized
5
current-shaping reduces DBS-induced dysarthria in patients with essential tremor. Neurology.
6
2014;82:614-9.
7
[3] Pollo C, Kaelin-Lang A, Oertel MF, Stieglitz L, Taub E, Fuhr P, et al. Directional deep brain
8
stimulation: an intraoperative double-blind pilot study. Brain. 2014;137:2015-26.
9
[4] Sims-Williams HP, Selbi WR, Javed S, Pickering AE, Patel NK. Two birds, one stone: single
M AN U
SC
RI PT
1
electrode dual target stimulation for the treatment of phantom limb pain. Stereotact Funct
11
Neurosurg. 2013;91:264.
12
[5] Priori A, Foffani G, Rossi L, Marceglia S. Adaptive deep brain stimulation (aDBS) controlled
13
by local field potential oscillations. Exp Neurol. 2013;245:77-86.
14
[6] Adamchic I, Hauptmann C, Barnikol UB, Pawelczyk N, Popovych O, Barnikol TT, et al.
15
Coordinated reset neuromodulation for Parkinson's disease: proof-of-concept study. Mov Disord.
16
2014;29:1679-84.
17
[7] Priori A. Technology for deep brain stimulation at a gallop. Mov Disord. 2015;30:1206-12.
18
[8] Carron R, Chaillet A, Filipchuk A, Pasillas-Lepine W, Hammond C. Closing the loop of deep
19
brain stimulation. Front Syst Neurosci. 2013;7:112.
20
[9] Basu I, Graupe D, Tuninetti D, Shukla P, Slavin KV, Metman LV, et al. Pathological tremor
21
prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand
22
driven deep brain stimulator design. J Neural Eng. 2013;10:036019.
23
[10] Shukla P, Basu I, Graupe D, Tuninetti D, Slavin KV. A neural network-based design of an on-
24
off adaptive control for Deep Brain Stimulation in movement disorders. Conf Proc IEEE Eng Med
25
Biol Soc. 2012;2012:4140-3.
AC C
EP
TE D
10
13
ACCEPTED MANUSCRIPT [11] Basu I, Tuninetti D, Graupe D, Slavin KV. Adaptive control of deep brain stimulator for
2
essential tremor: entropy-based tremor prediction using surface-EMG. Conf Proc IEEE Eng Med
3
Biol Soc. 2011;2011:7711-4.
4
[12] Shukla P, Basu I, Tuninetti D. Towards closed-loop deep brain stimulation: decision tree-based
5
essential tremor patient's state classifier and tremor reappearance predictor. Conf Proc IEEE Eng
6
Med Biol Soc. 2013;2014:2605-8.
7
[13] Yamamoto T, Katayama Y, Ushiba J, Yoshino H, Obuchi T, Kobayashi K, et al. On-demand
8
control system for deep brain stimulation for treatment of intention tremor. Neuromodulation.
9
2012;16:230-5; discussion 5.
SC
RI PT
1
[14] Clancy EA, Morin EL, Merletti R. Sampling, noise-reduction and amplitude estimation issues
11
in surface electromyography. J Electromyogr Kinesiol. 2002;12:1-16.
12
[15] Tsu AP, Burish MJ, GodLove J, Ganguly K. Cortical neuroprosthetics from a clinical
13
perspective. Neurobiol Dis. 2015.
14
[16] Vonck K, Boon P. Epilepsy: closing the loop for patients with epilepsy. Nat Rev Neurol.
15
2015;11:252-4.
16
[17] Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV. A high performing brain-machine
17
interface driven by low-frequency local field potentials alone and together with spikes. J Neural
18
Eng. 2015;12:036009.
19
[18] Shenoy KV, Carmena JM. Combining decoder design and neural adaptation in brain-machine
20
interfaces. Neuron. 2014;84:665-80.
21
[19] Sun FT, Morrell MJ. The RNS System: responsive cortical stimulation for the treatment of
22
refractory partial epilepsy. Expert Rev Med Devices. 2014;11:563-72.
23
[20] de Hemptinne C, Swann NC, Ostrem JL, Ryapolova-Webb ES, San Luciano M, Galifianakis
24
NB, et al. Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in
25
Parkinson's disease. Nat Neurosci. 2015;18:779-86.
AC C
EP
TE D
M AN U
10
14
ACCEPTED MANUSCRIPT [21] Rosin B, Slovik M, Mitelman R, Rivlin-Etzion M, Haber SN, Israel Z, et al. Closed-loop deep
2
brain stimulation is superior in ameliorating parkinsonism. Neuron. 2011;72:370-84.
3
[22] Rosa M, Giannicola G, Marceglia S, Fumagalli M, Barbieri S, Priori A. Neurophysiology of
4
deep brain stimulation. Int Rev Neurobiol. 2012;107:23-55.
5
[23] Foffani G, Bianchi AM, Baselli G, Priori A. Movement-related frequency modulation of beta
6
oscillatory activity in the human subthalamic nucleus. J Physiol. 2005;568:699-711.
7
[24] Kuhn AA, Tsui A, Aziz T, Ray N, Brucke C, Kupsch A, et al. Pathological synchronisation in
8
the subthalamic nucleus of patients with Parkinson's disease relates to both bradykinesia and
9
rigidity. Exp Neurol. 2009;215:380-7.
SC
RI PT
1
[25] Marceglia S, Fumagalli M, Priori A. What neurophysiological recordings tell us about
11
cognitive and behavioral functions of the human subthalamic nucleus. Expert Rev Neurother.
12
2011;11:139-49.
13
[26] Priori A, Foffani G, Pesenti A, Tamma F, Bianchi AM, Pellegrini M, et al. Rhythm-specific
14
pharmacological modulation of subthalamic activity in Parkinson's disease. Exp Neurol.
15
2004;189:369-79.
16
[27] Eusebio A, Thevathasan W, Doyle Gaynor L, Pogosyan A, Bye E, Foltynie T, et al. Deep brain
17
stimulation can suppress pathological synchronisation in parkinsonian patients. J Neurol Neurosurg
18
Psychiatry. 2011;82:569-73.
19
[28] Giannicola G, Marceglia S, Rossi L, Mrakic-Sposta S, Rampini P, Tamma F, et al. The effects
20
of levodopa and ongoing deep brain stimulation on subthalamic beta oscillations in Parkinson's
21
disease. Exp Neurol. 2010;226:120-7.
22
[29] Foffani G, Ardolino G, Meda B, Egidi M, Rampini P, Caputo E, et al. Altered subthalamo-
23
pallidal synchronisation in parkinsonian dyskinesias. J Neurol Neurosurg Psychiatry. 2005;76:426-
24
8.
AC C
EP
TE D
M AN U
10
15
ACCEPTED MANUSCRIPT [30] Toledo JB, Lopez-Azcarate J, Garcia-Garcia D, Guridi J, Valencia M, Artieda J, et al. High
2
beta activity in the subthalamic nucleus and freezing of gait in Parkinson's disease. Neurobiol Dis.
3
2014;64:60-5.
4
[31] Alonso-Frech F, Zamarbide I, Alegre M, Rodriguez-Oroz MC, Guridi J, Manrique M, et al.
5
Slow oscillatory activity and levodopa-induced dyskinesias in Parkinson's disease. Brain.
6
2006;129:1748-57.
7
[32] Rodriguez-Oroz MC, Lopez-Azcarate J, Garcia-Garcia D, Alegre M, Toledo J, Valencia M, et
8
al. Involvement of the subthalamic nucleus in impulse control disorders associated with Parkinson's
9
disease. Brain. 2010;134:36-49.
SC
RI PT
1
[33] Thevathasan W, Pogosyan A, Hyam JA, Jenkinson N, Foltynie T, Limousin P, et al. Alpha
11
oscillations in the pedunculopontine nucleus correlate with gait performance in parkinsonism.
12
Brain. 2012;135:148-60.
13
[34] Florin E, Erasmi R, Reck C, Maarouf M, Schnitzler A, Fink GR, et al. Does increased gamma
14
activity in patients suffering from Parkinson's disease counteract the movement inhibiting beta
15
activity? Neuroscience. 2013;237:42-50.
16
[35] Rouse AG, Stanslaski SR, Cong P, Jensen RM, Afshar P, Ullestad D, et al. A chronic
17
generalized bi-directional brain-machine interface. J Neural Eng. 2011;8:036018.
18
[36] Rossi L, Marceglia S, Foffani G, Cogiamanian F, Tamma F, Rampini P, et al. Subthalamic
19
local field potential oscillations during ongoing deep brain stimulation in Parkinson's disease. Brain
20
Res Bull. 2008;76:512-21.
21
[37] Quinn EJ, Blumenfeld Z, Velisar A, Koop MM, Shreve LA, Trager MH, et al. Beta oscillations
22
in freely moving Parkinson's subjects are attenuated during deep brain stimulation. Mov Disord.
23
2015.
24
[38] Rosa M, Giannicola G, Servello D, Marceglia S, Pacchetti C, Porta M, et al. Subthalamic local
25
field beta oscillations during ongoing deep brain stimulation in Parkinson's disease in hyperacute
26
and chronic phases. Neurosignals. 2011;19:151-62.
AC C
EP
TE D
M AN U
10
16
ACCEPTED MANUSCRIPT [39] Rosa M, Marceglia S, Servello D, Foffani G, Rossi L, Sassi M, et al. Time dependent
2
subthalamic local field potential changes after DBS surgery in Parkinson's disease. Exp Neurol.
3
2010;222:184-90.
4
[40] Giannicola G, Rosa M, Servello D, Menghetti C, Carrabba G, Pacchetti C, et al. Subthalamic
5
local field potentials after seven-year deep brain stimulation in Parkinson's disease. Exp Neurol.
6
2012;237:312-7.
7
[41] Little S, Pogosyan A, Neal S, Zavala B, Zrinzo L, Hariz M, et al. Adaptive deep brain
8
stimulation in advanced Parkinson disease. Ann Neurol. 2013;74:449-57.
9
[42] Little S, Beudel M, Zrinzo L, Foltynie T, Limousin P, Hariz M, et al. Bilateral adaptive deep
SC
RI PT
1
brain stimulation is effective in Parkinson's disease. J Neurol Neurosurg Psychiatry. 2015.
11
[43] Rosa M, Arlotti M, Ardolino G, Cogiamanian F, Marceglia S, DiFonzo A, et al. Adaptive Deep
12
Brain Stimulation in a freely-moving parkinsonian patient. Mov Disorder. 2015;In press.
13
[44] Chang SY, Kimble CJ, Kim I, Paek SB, Kressin KR, Boesche JB, et al. Development of the
14
Mayo Investigational Neuromodulation Control System: toward a closed-loop electrochemical
15
feedback system for deep brain stimulation. J Neurosurg. 2013;119:1556-65.
16
[45] Grahn PJ, Mallory GW, Khurram OU, Berry BM, Hachmann JT, Bieber AJ, et al. A
17
neurochemical closed-loop controller for deep brain stimulation: toward individualized smart
18
neuromodulation therapies. Front Neurosci. 2014;8:169.
19
[46] Graupe D, Tuninetti D, Slavin KV, Basu I. Closed-loop electrochemical feedback system for
20
DBS. J Neurosurg. 2014;121:762-3.
21
[47] Fasano A, Lozano AM. The FM/AM world is shaping the future of deep brain stimulation.
22
Mov Disord. 2014;29(2):161-3.
23
[48] Volkmann J, Herzog J, Kopper F, Deuschl G. Introduction to the programming of deep brain
24
stimulators. Mov Disord. 2002;17 Suppl 3:S181-7.
AC C
EP
TE D
M AN U
10
17
ACCEPTED MANUSCRIPT [49] Santaniello S, Fiengo G, Glielmo L, Grill WM. Closed-loop control of deep brain stimulation:
2
a simulation study. IEEE Trans Neural Syst Rehabil Eng. 2011;19:15-24.
3
[50] Lopiano L, Torre E, Benedetti F, Bergamasco B, Perozzo P, Pollo A, et al. Temporal changes
4
in movement time during the switch of the stimulators in Parkinson's disease patients treated by
5
subthalamic nucleus stimulation. Eur Neurol. 2003;50:94-99.
6
[51] Yianni J, Bain PG, Gregory RP, Nandi D, Joint C, Scott RB, et al. Post-operative progress of
7
dystonia patients following globus pallidus internus deep brain stimulation. Eur J
8
Neurol. 2003;10(3):239-47.
9
[52] Reck C, Himmel M, Florin E, Maarouf M, Sturm V, Wojtecki L, et al. Coherence analysis of
SC
RI PT
1
local field potentials in the subthalamic nucleus: differences in parkinsonian rest and postural
11
tremor. Eur J Neurosci. 2010;32(7):1202-14.
12
[53] Pedrosa DJ, Reck C, Florin E, Pauls KA, Maarouf M, Wojtecki L, et al. Essential tremor and
13
tremor in Parkinson's disease are associated with distinct 'tremor clusters' in the ventral
14
thalamus.Exp Neurol. 2012;237(2):435-43.
15
[54] Heida T, Wentink EC, Marani E. Power spectral density analysis of physiological, rest and
16
action tremor in Parkinson’s disease patients treated with deep brain stimulation. J Neuroeng
17
Rehabil. 2013;10:70.
18
[55] Barow E, Neumann WJ, Brucke C, Huebl J, Horn A, Brown P, Krauss JK, et al. Deep brain
19
stimulation suppresses pallidal low frequency activity in patients with phasic dystonic movements.
20
Brain. 2014;137:3012-24.
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Figure and table legends
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Table 1. Rationale for the choice of the control variable.
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CFM = carbon fiber microelectrode; ET: Essential Tremor; LFP = local field potential; M1 = motor cortex area M1; IPG = implanted pulse generator; PD: Parkinson’s Disease.
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Figure 1. Control variables for adaptive deep brain stimulation systems.
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sEEG = surface electroencephalography; iEEG = intracranial electroencephalography; LFP
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= local field potentials; acc = accelerometers, sEMG = surface electromyography, ECoG =
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electrocorticography; IPG = implantable pulse generator; LFP = local field potential.
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ACCEPTED MANUSCRIPT Cortical neurosignals
Basal ganglia LFPs
Neurotransmitters
YES – external sensors required
YES – implanted cortical electrodes required
NO – LFPs are recorded from the implanted DBS electrode
YES – at least 4 additional CFM
NO – no additional implant during surgery
YES– the additional CFMs need to be implanted during surgery
YES – the patient perceives the same system as for traditional DBS
NO - CFMs have a time life of only a few months and have to be replaced
Additional implant/equipment
NO – it may be difficult to manage the recording sensors and the external equipment may be uncomfortable
YES/NO – optimal correlation with tremor, but no correlations with rigidity and bradikinesia
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NO – cannot be used if patients do not show tremor
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Low battery consumption
Proof of concept
YES – multiple YES – EcoG phase LFP oscillations YES - the time duration amplitude coupling are modulated by of tremor-free period is and M1 action levodopa comparable to the potentials correlate administration, duration of increased with main PD DBS, levels of stimulationsymptoms and can movements, and induced dopamine release be used to drive non-motor tasks after DBS pulse trains aDBS even years after electrode implant
YES/NO – it may encode patient specific information
YES – the presence of multiple rhythms correlating with different patient’s characteristics may account for inter-subject variability
NOT YET TESTED
YES/NO – the IPG needs to include the sensing circuit and the feedback algorithm
YES/NO – the IPG needs to include the sensing circuit and the feedback algorithm
NO – the IPG needs to include the sensing circuit and the feedback algorithm
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Correlation with the clinical state
Personalization and adaptability
YES – all the equipment is implanted
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Patient’s management/acceptability
NO – the additional YES – the surgery implant is external needs to include the and does not affect implant of cortical the surgical procedure electrodes
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sEMG and accelerometers
YES/NO – the processing can be done externally, but triggers should be sent via telemetry links
YES in humans (ET) [Yamamoto et al., 2013]
YES in humans (PD) [Little et al., YES in animals 2013; Rosa et al., [Rosin et al., 2011] 2015; Little et al., 2015]
NO
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ACCEPTED MANUSCRIPT Highlights: The clinical translation of adaptive DBS needs to overcome several challenges.
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Current proposals are classified on the base of the choice of the control variable.
•
Basal-ganglia local field potentials are the best candidate.
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