Abstract
Pers Pective published: 09 April 2018 doi: 10.3389/fneur.2018.00212 technological Approaches for Neurorehabilitation: From robotic Devices to Brain stimulation and Beyond 1 1 2 3 Marianna Semprini , Matteo Laffranchi , Vittorio Sanguineti , Laura Avanzino , 4,5 1† 1 † Roberto De Icco , Lorenzo De Michieli and Michela Chiappalone * 1 2 Rehab Technologies, Istituto Italiano di Tecnologia, Genova, Italy, Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy, Section of Human Physiology, Department of Experimental Medicine (DIMES), University of Genova, Genova, Italy, Department of Neurology and Neurorehabilitation, Istituto Neurologico Nazionale C. Mondino, Pavia, Italy, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy Neurological diseases causing motor/cognitive impairments are among the most com- mon causes of adult-onset disability. More than one billion of people are affected world- Edited by: wide, and this number is expected to increase in upcoming years, because of the rapidly Fabio Blandini, aging population. The frequent lack of complete recovery makes it desirable to develop Fondazione Istituto Neurologico Nazionale Casimiro Mondino novel neurorehabilitative treatments, suited to the patients, and better targeting the spe- (IRCCS), Italy cific disability. To date, rehabilitation therapy can be aided by the technological support Reviewed by: of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this Stefano Tamburin, University of Verona, Italy perspective, we will review the above methods by referring to the most recent advances Alessio Baricich, in each field. Then, we propose and discuss current and future approaches based on Azienda Ospedaliero Universitaria the combination of the above. As pointed out in the recent literature, by combining tra- Maggiore della Carita, Italy ditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or *Correspondence: Michela Chiappalone novel robotic and wearable assistive devices, several studies have proven it is possible [email protected] to sensibly improve the amount of recovery with respect to traditional treatments. We Senior equal author contribution. will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy Specialty section: This article was submitted to based on patient and clinician needs and preferences. Headache Medicine and Facial Pain, Keywords: brain–computer interface, motor impairment, neurologic disorder, neuromodulation, personalization a section of the journal Frontiers in Neurology Received: 22 December 2017 Accepted: 16 March 2018 iNtr ODUcti ON Published: 09 April 2018 According to the World Health Organization (WHO), neurological disorders and injuries account Citation: for the 6.3% of the global burden of disease (GBD) (1, 2). With more than 6% of DALY (disability- Semprini M, Laffranchi M, Sanguineti V, Avanzino L, De Icco R, adjusted life years) in the world, neurological disorders represent one of the most widespread clinical De Michieli L and Chiappalone M condition. Among neurological disorders, more than half of the burden in DALYs is constituted (2018) Technological Approaches by cerebral-vascular disease (55%), such as stroke. Stroke, together with spinal cord injury (SCI), for Neurorehabilitation: From accounts for 52% of the adult-onset disability and, over a billion people (i.e., about a 15% of the Robotic Devices to Brain population worldwide) suffer from some form of disability ( 3). es Th e numbers are likely to increase Stimulation and Beyond. in the coming years due to the aging of the population (4), since disorders ae ff cting people aged Front. Neurol. 9:212. doi: 10.3389/fneur.2018.00212 60 years and older contribute to 23% of the total GBD (5). Frontiers in Neurology | www.frontiersin.org 1 April 2018 | Volume 9 | Article 212 Semprini et al. Technological Approaches for Neurorehabilitation Standard physical rehabilitation favors the functional recovery are also available for postural rehabilitation. For instance, Hunova aer s ft troke, as compared to no treatment (6). However, the func- (Movendo Technology, Italy, launched in 2017) is equipped with tional recovery is not always satisfactory as only 20% of patients a seat and a platform that induce multidirectional movements to fully resume their social life and job activities (7). Hence, the need improve postural stability (Figure 1A1, right). of more effective and patient-tailored rehabilitative approaches to Typical lower limb exoskeletons range from large systems, maximize the functional outcome of neurological injuries as well equipped with treadmill and weight support, and intended for as patients’ quality of life (8). Modern technological methodologies hospital use, like the Lokomat (Hocoma, Switzerland) and the represent one of the most recent advances in neurorehabilitation, LOPES system (26), to more lightweight devices intended for and an increasing body of evidence supports their role in the overground walking, like Ekso (Ekso Bionics, USA), Indego recovery from brain and/or medullary insults. This manuscript (Parker Hannafin, USA), Rewalk (Rewalk Robotics, USA), and provides a perspective on how technologies and methodo- the most recent one, Twin (IIT-INAIL, Italy). Notably, Twin has logies could be combined in order to maximize the outcome of been developed according to long interactions with focus groups neurorehabilitation. of disabled patients (Figure 1A2). A few exoskeletons for the upper limb have also been developed. They also range from lab systems—e.g., the KINARM Exoskeleton (BKIN, Canada) or the cUrreNt sYsteMs AND tHerAPeUtic Armeo Spring and Power (Hocoma, Switzerland)—to wearable, APPrOAcHes FOr modular devices (27–29). NeUrOreHABiLit AtiON One common feature of rehabilitation robots, is that they are equipped with movement and/or force sensors, so that they e g Th reat progress made in interdisciplinary fields, such as neu- integrate functionalities both for the assessment [i.e., quantify ral engineering (9, 10), has allowed to investigate many neural users’ movements and exchanged forces (30)] and the treatment mechanisms, by detecting and processing the neural signals at (i.e., administer highly reproducible, repetitive exercise protocols, high spatio-temporal resolution, and by interfacing the nervous and interaction modalities). system with external devices, thus restoring neurological func- In spite of the increasing volume of published studies, the tions lost due to disease/injury. The progress continues in parallel number of high-quality clinical trials on robot-assisted therapy to technological advancements. e Th last two decades there has is still relatively low. A large multi-center RCT comparing robot seen a large proliferation of technological approaches for human therapy, intensive physical therapy, and usual care (31) confirmed rehabilitation, such as robots, wearable systems, brain stimula- that robots are indeed effective, but found no significant advantage tion, and virtual environments. In the next sections, we will focus over conventional physical therapy. A systematic comparison of on: robotic therapy, non-invasive brain stimulation (NIBS), and different approaches (32) suggested that robot therapy is among the neural interfaces. most effective techniques for the rehabilitation of both upper and lower limbs. Moreover, recent studies concluded that robot-assisted robotic Devices gait training in combination with physiotherapy is more likely to Robots for neurorehabilitation are designed to support the achieve independent walking than gait training alone (33, 34). administration of physical exercises to the upper or lower extremi- A major limitation of endpoint robotic approach is that the ties, with the purpose of promoting neuro-motor recovery. This improvement is limited to the body regions involved in train- technology has a relatively long history, dating back to the early ing. In a clinical setting, robotic rehabilitation may be cost and 1990s (11). Robot devices for rehabilitation differ widely in terms time-consuming, and for this reason, it is difficult to imagine of mechanical design, number of degrees of freedom, and control the combination of different endpoint robotic devices in the architectures. As regards the mechanical design, robots may have patient who have an impairment that ae ff cts multiple body areas, either a single point of interaction (i.e., end effector) with the i.e., post-stroke hemiplegia. Moreover, early robots for neuroeha- user body (endpoint robots or manipulanda) or multiple points bilitation were specifically aimed at substituting labor-intensive of interaction (exoskeletons and wearable robots) (12). physical rehabilitation with minimal human intervention, pro- Endpoint robots for the upper extremity, include Inmotion2 ducing an automatic and repetitive treatment. This initial trend, (IMT, USA) (13), KINARM End-Point (BKIN, Canada), and however, minimizes the importance of both therapist knowledge Braccio di Ferro (14) (Figure 1A1, left). Only some of these and patient–physician relationship. However, the ability to devices have been tested in randomized clinical trials (15), precisely quantify sensorimotor performance during exercise in confirming an improvement of upper limb motor function aer ft terms of movement kinematics and exchanged forces is leading stroke (16). However, convincing evidence in favor of significant to a new revolution in rehabilitation, toward evidence-based and changes in activities of daily living (ADL) indicators is lacking knowledge-driven approaches. Modern rehabilitation devices (17), possibly because performance in ADL is highly ae ff cted automatically adapt task difficulty and assistance modalities to by hand functionality. A good example of lower limb endpoint individual performance (35). In the future, they may incorporate robot is represented by gait trainer GT1 (Reha-Stim, Germany). models of the recovery process (36) to predict the rehabilitation Its efficacy was tested by Picelli et al. (18), who demonstrated outcome (37) that will be fitted on patient’s features. an improvement in multiple clinical measures in subjects with Another stimulating challenge is the development of light- Parkinson’s disease following robotic-assisted rehabilitation when weight robots suitable for the use outside of the hospitals, in compared to physical rehabilitation alone (18). Endpoint robots domestic or community environments and in conjunction with Frontiers in Neurology | www.frontiersin.org 2 April 2018 | Volume 9 | Article 212 Semprini et al. Technological Approaches for Neurorehabilitation FigUre 1 | Neurorehabilitation therapies. (A1) Endpoint robots: on the left the “Braccio di Ferro” manipulandum, on the right the postural robot Hunova. Braccio di ferro (14) is a planar manipulandum with 2-DOF, developed at the University of Genoa (Italy). It is equipped with direct-drive brushless motors and is specially designed to minimize endpoint inertia. It uses the H3DAPI programming environment, which allows to share exercise protocol with other devices. Written informed consent was obtained from the subject depicted in the panel. Movendo Technology’s Hunova is a robotic device that permits full-body rehabilitation. It has two 2-DOF actuated and sensorized platforms located under the seat and on the floor level that allow it to rehabilitate several body districts, including lower limb (thanks to the floor-level platform), the core, and the back, using the platform located underneath the seat. Different patient categories (orthopedic, neurological, and geriatric) can be treated, and interact with the machine through a GUI based on serious games. (A2) Wearable device: the recent exoskeleton Twin. Twin is a fully modular device developed at IIT and co-funded by INAIL (the Italian National Institute for Insurance against Accidents at Work). The device can be easily assembled/ disassembled by the patient/therapist. It provides total assistance to patients in the 5–95th percentile range with a weight up to 110 kg. Its modularity is implemented by eight quick release connectors, each located at both mechanical ends of each motor, that allow mechanical and electrical connection with the rest of the structure. It can implement three different walking patterns that can be fully customized according to the patient’s needs via a GUI on mobile device, thus enabling personalization of the therapy. Steps can be triggered via an IMU-based machine state controller. (B1) Repetitive transcranial magnetic stimulation (rTMS) representation. rTMS refers to the application of magnetic pulses in a repetitive mode. Conventional rTMS applied at low frequency (0.2–1 Hz) results in plastic inhibition of cortical excitability, whereas when it is applied at high frequency (≥5Hz), it leads to excitation (19). rTMS can also be applied in a “patterned mode.” Theta burst stimulation involves applying bursts of high frequency magnetic stimulation (three pulses at 50 Hz) repeated at intervals of 200 ms (20). Intermittent TBS increases cortical excitability for a period of 20–30 min, whereas continuous TBS leads to a suppression of cortical activity for approximately the same amount of time (20). (B2) Transcranial current stimulation (tCS) representation. tCS uses ultra-low intensity current, to manipulate the membrane potential of neurons and modulate spontaneous firing rates, but is insufficient on its own to discharge resting neurons or axons (21). tCS is an umbrella term for a number of brain modulating paradigms, such as transcranial direct current stimulation (22), transcranial alternating current stimulation (23), and transcranial random noise stimulation (24). (c) A typical BCI system. Five stages are represented: brain-signal acquisition, preprocessing, feature extraction/selection, classification, and application interface. In the first stage, brain-signal acquisition, suitable signals are acquired using an appropriate modality. Since the acquired signals are normally weak and contain noise (physiological and instrumental) and artifacts, preprocessing is needed, which is the second stage. In the third stage, some useful data or so-called “features” are extracted. These features, in the fourth stage, are classified using a suitable classifier. Finally, in the fifth stage, the classified signals are transmitted to a computer or other external devices for generating the desired control commands to the devices. In neurofeedback applications, the application interface is a real-time display of brain activity, which enables self-regulation of brain functions (25). ADL, e.g., over ground walking in unstructured environments. can be used to influence cortical excitability, neuroplasticity, and This implies a modular structure, which facilitates donning and behavior (39, 40). Repetitive transcranial magnetic stimulation transportability. (rTMS, Figure 1B1) and transcranial current stimulation (tCS, Figure 1B2) are the most common and widely used techniques Non-invasive Brain stimulation (39). Because of its relative ease of use, portability and decreased Non-invasive brain stimulation techniques are a promising safety risk compared to rTMS, tCS is emerging as an effective adjuvant strategy for enhancing post-injury recovery. In recent and versatile clinical tool to prime the brain activity prior to years, more than 1,400 studies were performed in humans, or during neurorehabilitation. Starting from the hypothesis with at least one-fifth of these focusing on stroke rehabilitation. on training-induced plasticity, NIBS could be applied to foster NIBS techniques involve modulation of the central nervous plasticity induction, also in the spinal cord as shown in animals system by electrically activating neurons in the brain (38) and (41, 42) and in humans (43). Frontiers in Neurology | www.frontiersin.org 3 April 2018 | Volume 9 | Article 212 Semprini et al. Technological Approaches for Neurorehabilitation Related to rehabilitation, one of the major challenges is to speller device (68) or to control the movement of an end effector, design interventions that are efficient, promote motor learning, either virtual (69) or real (70). From a clinical point of view, consolidate skills, and augment retention. For example, NIBS the BCI approach proves to be beneficial in potentiating the approach to stroke rehabilitation has focused on excitation of the impaired motor function, as demonstrated for stroke (71, 72). unae ff cted hemisphere, of the ae ff cted hemisphere, or inhibition of MEG BCI training allowed patients with chronic stroke to unae ff cted hemisphere, also combining neuromodulation of both voluntary modulate the μ-rhythm amplitude over the affected hemispheres (44). To date, a number of sham-controlled studies hemisphere with the possibility to voluntary control grasping based on NIBS have been performed, but the evidence remains using a robotic hand orthosis (73). More recently, a BCI- inconsistent. A Cochrane review failed to support the efficacy orthosis training was tested as add-on to physical therapy in a of rTMS for stroke rehabilitation (45), although other studies sham-controlled study (74); after an EEG BCI training protocol (46, 47) concluded that low frequency rTMS was effective in the strength in hand muscles significantly improved when com- improving ADL and aphasia. A recent review (48) concluded that pared to sham group. Noteworthy, the results of the above cited rTMS may produce both short- and long-term improvement on studies were achieved in patients in a chronic stage, for whom motor recovery in stroke patients, in particular when neuromodu- very limited possibilities are available if treated with standard lation is initiated early aer s ft troke, and with better results in case rehabilitative care. The motor improvement is a consequence of sub-cortical lesions with respect to cortical ones. As regards tCS, of the cortical changes occurring during the interaction with it appeared to be useful for motor recovery in a sub population of the controlled object, as demonstrated both with invasive and patients with chronic stroke and low functional impairment (49) non-invasive studies (75, 76). This promising evidence made and very well tolerated (50), but a Cochrane review (51) failed to BCIs appealing for different types of neurorehabilitation support its effectiveness. A possible explanation for these incon- practices, not only in presence of motor disability, but also for sistent conclusions is the lack of a correct patient stratification, and the recovery of impaired cognitive functions (76–80). In this thus a tailored stimulation protocol (51, 52). framework, a particular form of BCI is that of neurofeedback, Some ethical and technical considerations deserve discus- in which neural data are visually displayed to the user (81). This sion. First, use of NIBS, calls for greater caution on pediatric technique has proven to be mainly effective in the treatment population, given the higher stakes and uncertain future effects of attention deficits/hyperactivity, but also for other cognitive for brains still undergoing rapid and formative development dysfunctions (82, 83) and in stroke (84, 85). (53). Second, a careful evaluation of the use of NIBS must also Over the past decade also invasive brain-machine interfaces be warranted in adults, regarding informed consent and patient and neural prostheses in general have been the subject of exten- selection (54). Any direct interference with neural activity, even sive research with promising findings for the treatment of neuro- beneficial, might be described more accurately as “minimally related impairments (86). The development of these devices will invasive” (55). Moreover, when considering that most of the hopefully have a profound social impact on the quality of life, studies mainly focused on the short-term, short-lasting effects of although translation to clinical application is far to be imple- NIBS, it is important to evaluate the long-term effects of modulat- mented due to the technological barriers (e.g., wired systems or ing cortical electric fields in patients with cortical impairment. limited bandwidth for wireless systems) and to the limits imposed Careful monitoring is particularly important when consider- by the invasiveness of the procedure (e.g., tissue reaction to the ing that, despite researchers’ discussion of and explicit warnings brain implant) (87). against unsupervised use of NIBS (56), brain stimulation products Neural prosthesis can be combined with functional electrical are already commercially available and without proper guidance stimulation (88, 89). In this scenario, the use of a controlled end or information. Thus NIBS could be conducted carelessly with effector is substituted by direct stimulation of the involved mus- unknown and potentially harmful effects. cles, therefore, natural movements are recreated by bridging two areas disconnected because of the impairment/disease (88, 89). Neural interfaces A system was recently developed allowing a quadriplegic patient In recent years, it is possible to include also neural interfaces chronically implanted with microwire arrays to move the arm among the strategies for neurorehabilitation and indeed the use by means of muscle stimulation triggered by the recorded and of these systems in clinical applications is increasing (57, 58). decoded brain signals (90). A neural interface is essentially a system mediating the com- Examples of latest-generation neural prostheses involve direct munication between the brain and an external device (59, 60). stimulation of central or peripheral neural tissue. Recent animal Several modalities have been used for brain signal acquisi- studies demonstrated locomotion restoration aer SCI b ft y spa- tion (61), which include electroencephalography (EEG) (62), tiotemporal modulation of the spinal cord (91) and restoration magnetoencephalography (MEG) (63), functional magnetic of motor function aer s ft troke by activity-dependent stimula- resonance imaging (64), and functional near-infrared spec- tion of the motor cortex (92). Whereas, recent human studies troscopy (65). Among neural interfaces, the so-called “BCIs” demonstrated the restoration of hand reaching and grasping by (Figure 1C) were essentially conceived as communication tools non-invasive neuromuscular stimulation of hand muscles (93) for paralyzed or locked-in patients (62) and were mainly based and prosthetic hand control by invasive stimulation of peripheral on the use of the processed EEG signal. Typical BCI techniques nerve (94). In addition, faster and more effective closed loop include the use of evoked potentials (such as P300) (66) or stimulation protocols are being investigated also in in vitro motor imagery (67), and enable the user to communicate with a preparations (95). Frontiers in Neurology | www.frontiersin.org 4 April 2018 | Volume 9 | Article 212 Semprini et al. Technological Approaches for Neurorehabilitation potentially useful synergy in the rehabilitative field is represented cHALLeNges AND OPeN issUes by the association of biofeedback (104, 105) with robotic reha- We have so far presented the main methodologies for neuroreha- bilitation (106, 107). bilitation and, for each field, the most innovative trends currently Besides the technological and scientific improvement of under investigation. However, novel rehabilitation approaches are neurorehabilitative treatment, a very important trend followed by characterized by a synergistic tactic, in which these techniques are current research is that of a personalized treatment. This is not just used in combination and also mixed with kinematic information intended to focus on a particular disease and address the symptoms (from the robot) and patients’ biosignals, such as EEG or EMG shared by different populations of patients, but is truly envisioned (electromyography) (Figure 2A). as a personalized method for a single individual. The idea of a An example of such a multimodal approach was shown to the patient-tailored approach is not new: standardized algorithms general public during the world cup in 2014, when the kick o ff have been proposed for stroke, based on the clinical history of the was given by a paraplegic man using a lower limbs exoskeleton patient, time elapsed aer in ft sult, topography of the lesion, type, controlled by brain activity. Two years later, it was demonstrated and severity of functional impairment (108). In this view, it will be that the combined use of gait rehabilitation with a BMI was able desirable to identify solid biomarkers not only in the acute settings, to induce partial neurological recovery in paraplegic patients but also in the middle and chronic stages of neurological diseases. (96). This represents a valid proof-of-concept for the combina- This modern approach, recently named as “Rehabilomics,” will tion of robotic devices driven by neural activity. Moreover, the be useful not only for outcome prediction, but also to foresee the number of clinical-oriented versions of this approach is increas- best personalized rehabilitative treatment. Well known biomarkers ing: exoskeletons powered by BCI have been used during post in stroke are represented by measures of function and structure stroke rehabilitation (97, 98). Similar results were obtained with through neuroimaging aer s ft troke (109) and by biochemical a BCI system for locomotion rehabilitation, based on the use of dosages (for example, uric acid, Cu/Zn superoxide dismutase, and an avatar in a virtual reality environment (99). urinary 8-OHdG) (110, 111). The technological improvement will Experiences where assisted locomotion has been used in help to identify novel biomarkers in neurorehabilitation. For exam- conjunction with neuromodulation are already present in the ple, a non-linear, composite, model made of robotic measurement literature. Spinal tDCS was applied in patients with SCI under- in the upper limb was able to predict motor recovery at 90 days from going assisted locomotion using driven gait orthosis (Lokomat, stroke (112). “Technological” measures seem to be complementary Hocoma AG, Volketswil, Switzerland) (100). Results showed that rather than substitutive to standard biomarkers (113). anodal spinal tDCS and assisted locomotion increased spinal Overall, there is a great need for the development and testing reflexes amplitude, suggesting functional effects when the spinal of novel innovative interventional strategies individually tailored cord is detached from the rest of the central nervous system. These to patients’ prerequisites. The neurorehabilitation scientific com- findings open an important avenue of research designed to rescue munity is finally showing an effort in this direction, by taking into residual spinal functions by spinal tDCS in SCI patients (100). account patients’ specific requests (Figure 2B). For example, dur - Although the combined effect of neuromodulation with robotic ing the sixth International Brain–Computer Interface Meeting in therapy still needs to be clinically validated (101), it clearly shows 2016 (114), a virtual forum of BCI users was presented, allowing the combined direction of neuromodulation-based rehabilita- patients to remotely participate at the conference sessions and tion. Explorative directions in neuromodulation also include a also to send video-message with their views and requirements in combination with traditional BMI approaches (102) as well as order to help the scientists shaping the future research directions. investigation of non-classical stimulation sites (103). Another This is exactly the approach that should be taken when designing FigUre 2 | Innovative patient-tailored approach. (A) Example of multimodal rehabilitative approach. Subject is using an exoskeleton while receiving brain stimulation. Both exoskeleton motors and stimulation parameters are updated based on subject’s biofeedback signals (electroencephalography and/or EMG) and on subject performance (ROBOT) while, at the same time brain stimulation non-invasive brain stimulation and exoskeleton assistance (ROBOT) influence the biosignals. (B) Motto of the disabled population. The motto means that any choice (in any field) regarding them must be taken with their direct participation. Rehabilitation research must follow the same policy. Frontiers in Neurology | www.frontiersin.org 5 April 2018 | Volume 9 | Article 212 Semprini et al. Technological Approaches for Neurorehabilitation a therapy or experimental protocol targeting a specific set of AUtHOr cONtriBUtiONs population. The patients’ motto “Nothing about us, without us” MS, LDM, and MC conceived the manuscript. MC and LDM clearly indicates that patients must be involved in experimental coordinated the activities. MS and MC prepared the figures. MS, studies, since the very beginning. MC, RDI, and LA revised the manuscript. All the authors wrote and approved the final version of the manuscript. cONcLUsiON In the coming years, science and medicine have to create a inte- AcKNOWLeDgMeNts grated dialog with patients, since they will be the first end-users of any technological development. To date, important advances e o Th riginal graphics used in the figures of the manuscript were developed by Silvia Chiappalone. e Th authors wish to tank have been made in robotic-based therapy, NIBS and neural interfaces, as integrations and/or alternatives to standard therapy. Dr. Giovanni Milandri for carefully proofreading the manuscript. However, in order to be really effective, neurorehabilitation research must be primarily person-centered (i.e., “personalized”). FUNDiNg Personalization calls for flexible solutions, such as combining the This work was supported by the Istituto Nazionale per main technologies, in order to adapt to the different patient’s features and preferences. And this is exactly the direction which l’Assicurazione contro gli Infortuni sul Lavoro (INAIL) under grant agreement POR1. should be undertaken in neurorehabilitation. technology-and-health-care/Preprint/Preprint (Accessed: December 22, reFereNces 2017). 1. WHO. 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Uric acid and Cu/Zn superoxide dismutase: potential strategies and e h Th andling Editor declared a shared affiliation, though no other collaboration, biomarkers in functional recovery of post-acute ischemic stroke patients with one of the authors RI. aer in ft tensive neurorehabilitation. Curr Neurovasc Res (2015) 12(2):120–7. doi:10.2174/1567202612666150311104900 Copyright © 2018 Semprini, Laffranchi, Sanguineti, Avanzino, De Icco, De Michieli 112. Krebs HI, Krams M, Agrafiotis DK, DiBernardo A, Chavez JC, Littman GS, and Chiappalone. This is an open-access article distributed under the terms of the et al. Robotic measurement of arm movements aer s ft troke establishes Creative Commons Attribution License (CC BY). The use, distribution or reproduction biomarkers of motor recovery. Stroke (2014) 45(1):200–4. doi:10.1161/ in other forums is permitted, provided the original author(s) and the copyright owner STROKEAHA.113.002296 are credited and that the original publication in this journal is cited, in accordance 113. Mostafavi SM, Mousavi P, Dukelow SP, Scott SH. Robot-based assessment with accepted academic practice. No use, distribution or reproduction is permitted of motor and proprioceptive function identifies biomarkers for prediction which does not comply with these terms. Frontiers in Neurology | www.frontiersin.org 9 April 2018 | Volume 9 | Article 212
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