Effects of Current Robotic Stroke Training on Stroke Rehabilitation Proposal • Write your final proposal 6-7 PAGES. – What do you think is the problem wit

Effects of Current Robotic Stroke Training on Stroke Rehabilitation Proposal • Write your final proposal 6-7 PAGES.

– What do you think is the problem with the current training? • Is it the robot, the way the robot is used, or what?

– What can you do better?

• Then you will develop a hypothesis (or hypotheses)

– This will depend on your proposed idea
– Examples
“We hypothesize that the current robotic approaches lack …”
“It is hypothesized that … will significantly improve the efficacy of the …”
“We hypothesize that the used of … will significantly enhance the rehabilitative outcome of …”
– But you need to explain why you believe this would be the case

• This CANNOT be just your HUNCH!!

• Then you’ll propose a new study to test your hypothesis Restorative Neurology and Neuroscience 30 (2012) 497–510
DOI 10.3233/RNN-2012-120227
IOS Press
497
Inter-hemispheric coupling changes associate
with motor improvements after robotic stroke
rehabilitation
G. Pellegrinoa,1,∗ , L. Tomasevicb,1 , M. Tombinia , G. Assenzaa , M. Bravic , S. Sterzic , V. Giacobbed ,
L. Zollod , E. Guglielmellid , G. Cavallod , F. Vernieria and F. Tecchiob
a Department
of Neurology, Campus Bio-Medico University, Via A. del Portillo, Rome, Italy
– ISTC – CNR – Ospedale Fatebenefratelli – Via di Ponte Quattro Capi, Rome, Italy
c Medicina Fisica e Riabilitazione – Campus Bio-Medico University, Via A. del Portillo, Rome, Italy
d Laboratory of Biomedical Robotics and EMC – Campus Bio-Medico University, Via A. del Portillo, Rome, Italy
b LET’S
Abstract. Purpose: In the chronic phase of stroke brain plasticity plays a crucial role for further motor control improvements.
This study aims to assess the brain plastic reorganizations and their association with clinical progresses induced by a robot-aided
rehabilitation program in chronic stroke patients.
Methods: 7 stroke patients with an upper limb motor impairment in chronic phase underwent a multi-modal evaluation
before starting and at the end of a 12-week upper-limb neurorehabilitation program. Fugl-Meyer Assessment (FMA) Scale
scores and performance indices of hand movement performance (isometric pinch monitored through a visual feedback) were
collected. Cerebral reorganizations were characterized by 32-channel electroencephalography (EEG) focusing on ipsilesional
and contralesional resting state properties investigating both bipolar derivations overlying the middle cerebral artery territory
and the primary somatosensory sources (S1) obtained through the Functional Source Separation (FSS) method. Power Spectral
Density (PSD) and interhemispheric coherence (IHCoh) at rest were measured and correlated with clinical and hand control
robot-induced improvements.
Results: After the robotic rehabilitation we found an improvement of FMAS scores and hand motor control performance and
changes of brain connectivity in high frequency rhythms (24–90 Hz). In particular, the improvement of motor performance
correlated with the modulation of the interhemispheric S1 coherence in the high beta band (24–33 Hz).
Conclusions: Recently it has been shown that an upper limb robot-based rehabilitation improves motor performance in stroke
patients. We confirm this potential and demonstrate that a robot-aided rehabilitation program induces brain reorganizations.
Specifically, interhemispheric connectivity between primary somatosensory areas got closer to a ‘physiological level’ in parallel
with the acquisition of more accurate hand control.
Keywords: Chronic stroke, robotic rehabilitation, resting state EEG, primary somatosensory hand area (S1), interhemispheric
coherence, Functional Source Separation (FSS)
1 These
authors contributed equally to this work.
author: Giovanni Pellegrino, Department of
Neurology, Campus Bio-Medico University, Via A. del Portillo,
00128 Rome, Italy. Tel.: +39 06 225411286; Fax: +39 06 225411955;
E-mail: g.pellegrino@unicampus.it.
∗ Corresponding
0922-6028/12/$27.50 © 2012 – IOS Press and the authors. All rights reserved
498
G. Pellegrino et al. / Inter-hemispheric coupling changes associate
1. Introduction
Stroke is the third cause of mortality and the first
of disability. Annually, 15 million people worldwide
suffer from a stroke, and it has been evaluated that in
the next years the health burden due to this disease will
rapidly increase. Despite many different interventions
aimed to ameliorate patients’ clinical condition, 30%
or more among all stroke survivors are significantly
disabled due to a neurological impairment involving
force, motion, sensory perception and sensory-motor
integration (Roger et al., 2011).
Among mechanisms sustaining clinical recovery,
cerebral or synaptic plasticity, defined as the neural ability to change brain functional organization
over time producing different responses to the same
stimulus (Tecchio et al., 2007), seems to play a crucial role (Cramer, 2008; Cramer et al., 2011; Rossini
et al., 2003). The neuroplastic changes linked to a
stroke lesion in the middle cerebral artery (MCA) territory leading to a sensory/motor impairment of the
upper limb are able to modulate activity, position and
representation of hand sensorimotor areas bilaterally
(Rossini et al., 2003; Tecchio et al., 2007). Moreover,
such changes affect interhemispheric interactions as
shown by evaluating both cortical excitability through
transcranial magnetic stimulation (TMS) (Grefkes
et al., 2008; Shimizu et al., 2002; Takeuchi et al., 2010;
Traversa et al., 1998) and brain metabolism and activation through functional magnetic resonance imaging
(fMRI) (Grefkes et al., 2008).
A powerful method that gives us the opportunity to
investigate sensorimotor plasticity is the evaluation at
rest of brain connectivity between ipsilesional hemisphere (ILH) and contralesional hemisphere (CLH)
brain areas reactive to median nerve stimulation
(Wikstrom et al., 2000; Rossini et al., 2004). This technique is repeatable and not influenced by the patient’s
degree of motor impairment or attention (Allison et
al., 1991); it provides the same input amount to both
hemispheres, and is able to track sensorimotor regions,
thanks to an adhoc procedure named Functional Source
Separation (FSS) (Porcaro et al., 2008).
The FSS technique models the set of EEG signals as
a linear combination of several sources. FSS identifies
a single source at a time, building a contrast function for
that source that exploits some ‘fingerprint’ information
typical of the neuronal pool to be identified. FSS was
used to identify hemispheric cortical sources devoted
to hand representation within S1 (Barbati et al., 2006;
Porcaro et al., 2008; Porcaro et al., 2009), using as fingerprint information the earliest response to the median
nerve stimulation that is known to be generated within
S1 area 3b. Once the sources are identified, FSS allows
us to investigate them in other experimental conditions.
Here, S1 cortical patches in the two hemispheres were
tracked while the subject was at rest.
Furthermore, in patients with a chronic stroke lesion
in the MCA territory and sensorimotor hand impairment it has been demonstrated that primary sensory
cortical areas change their responsiveness to median
nerve stimulation and their topographical organization
involving regions usually not reached by a dense sensory input from the opposite hand (Forss et al., 1999;
Rossini et al., 1998; Rossini et al.; 2001). These plastic
mechanisms are linked to the hand functional recovery
(Altamura et al., 2007; Rossini et al., 1998; Rossini et
al., 2001; Tecchio et al., 2007). FSS allows to track
resting activity of such newly recruited areas devoted
to hand somatosensory perception.
Several studies clearly documented that the neuronal
reorganization occurring after an ischemic insult may
be positively influenced by motor practice, somatosensory input, pharmacological agents (Dimyan and
Cohen, 2011; Laufer and Elboim-Gabyzon, 2011;
Nudo et al., 1996) and neurorehabilitation techniques
including the most innovative ones (Cramer, 2008;
Dimyan and Cohen, 2011). In this sense, specific
robotic systems have been developed as a new and
promising tool to improve motor recovery following stroke (Brochard et al., 2010). Even if their
effectiveness compared to standard rehabilitation is
still under debate (Kwakkel et al., 2008), the use of
robotic devices shows several advantages: the ability to provide a repeatable, programmable, controlled
rehabilitation and the capability of assessing motor features in a quantitative manner (Volpe et al., 2009).
Recently, Lo and colleagues (Lo et al., 2010) conducted a multicentric, randomized, controlled trial
on stroke people with long-term disability, to test
the effectiveness of robot- based rehabilitation program: the benefit obtained was comparable to that
obtained when a therapist provided intensive therapy.
The study indirectly proved that robot-assisted rehabilitation procedures, even in a chronic phase of stroke,
are capable of inducing plasticity sustaining clinical
improvement.
The aim of the present study is to evaluate clinical
changes and brain plastic reorganizations induced by
a robot-aided rehabilitation program using InMotion2
G. Pellegrino et al. / Inter-hemispheric coupling changes associate
499
Fig. 1. Experimental setup. Left: Robot employed for the rehabilitation program. Centre: Experimental Flow- chart. Right: Hand motor control
and brain activity and connectivity evaluations applied pre- and post-robotic rehabilitation program.
and InMotion3 robots in a population of chronic stroke
patients.
2. Materials and methods
The study was approved by the local Ethical Committee and informed written consent from all subjects
was obtained.
2.1. Patients
We enrolled seven right-handed patients (age
60 ± 18y, 5 males), with an upper limb sensory/motor
impairment who had suffered a first ever ischemic
stroke in the MCA territory (5 right side, 2 left side).
Time since stroke ranged from 1 to 5 years. In order
to assess the stability of clinical conditions the Upper
Limb Fugl-Meyer Assessment Scale (FMA) (Sivan
et al., 2011) was administered once every six weeks
for three times before the start of the study. MRI
examination confirmed the diagnosis and provided
lesion site characteristics. Patients affected by peripheral neuropathy, dementia, severe aphasia or with an
impairment that could bias the correct execution of the
task were excluded.
At the beginning (Tpre ) and at the end (Tpost ) of the
12-week upper-limb robot-aided neurorehabilitation
program all patients underwent a multi-modal evaluation that included the assessment of clinical status,
quantitative motor performance and neurophysiological features (Fig. 1).
2.2. Clinical and hand motor control evaluation
The patients’ clinical state was evaluated through
the FMA before and after robotic therapy . Moreover,
in order to asses motor performance, we selected a
physiological and simple motor task: the isometric
opposition of the thumb to index and middle finger against resistance of a semicompliant specifically
designed device. The module used was the ALLADIN
Finger Device (AFD) which consists of a rigid
hand orthesis for isometric tasks, embedding three
force/torque sensors (JR3 model No. 50M31A-I25)
that are located on the outer side of the hand. During the
measurement the hand is positioned between the sensor for the thumb and the two sensors for the index and
middle finger, while the forearm is resting on the arm
support. The fingers are attached to the finger support
using Velcro straps (Figs. 2–3). Visual feedback was
provided by means of a purposely developed graphical
500
G. Pellegrino et al. / Inter-hemispheric coupling changes associate
Fig. 2. ILH and CLH sensorimotor areas variables. Centre: Spatial positioning according to the international 10–20 system of the electrodes
considered for the EEG bipolar derivations overlying the middle cerebral artery territory sensorimotor districts (MCA SM, red) and Primary
Somatosensory Sources (S1, blue) localization for an indicative patient. Left and Right: Power spectral densities (PSD) and Interhemispheric
Coherence (IHCoh) of the Ipsilateral Hemisphere (ILH) and of the Contralateral Hemisphere (CLH) obtained from both Primary Somatosensory
Sources (S1) and EEG bipolar derivations overlying the middle cerebral artery territory sensorimotor districts (MCA SM) at the beginning
(Tpre ) and at the end (Tpost ) of the rehabilitation program for an indicative patient. For MCA CM, PSDs were separately calculated in the
ipsilesional (ILH) and contralesional hemisphere (CLH) averaging the bipolar derivations of each hemisphere. The inter-hemispheric coherence
was calculated with a similar procedure. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/RNN-2012-120227)
interface to control that the applied pressure matched
the required level of force. Initially the subjects were
asked to perform the task at maximal voluntary contraction (MVC) force with each hand separately, to
calculate their residual ability. After a rest period of
at least 5 minutes, in order to avoid fatigue in the test
task, subjects alternated 20 seconds of isometric contraction to 20 seconds of rest for 15 times repeatedly
with each hand. The target force level was set to 20%
of MVC and for each hand about 300 s of contraction were recorded. Paretic and non-paretic hand motor
control abilities were estimated by: 1) MVC; 2) the
level of applied force along the whole task (average of
the applied force during all contraction periods, Contraction level); 3) an index incorporating the capability
to apply a strength within ±5% of the established level
of contraction and its duration (the product of time and
mean force level, Contraction quality).
2.3. Robotic rehabilitation
All patients underwent a 12-week robotic rehabilitation program with the shoulder- and -elbow and
wrist robotic machines, namely the InMotion2 (or
MIT-Manus) and the InMotion3 robots, respectively
(Zollo et al., 2011; Zollo et al., 2011). The MITManus machine is a planar, two degree-of-freedom
robot providing assistance to the patient upper extremity motion while executing a series of “video games”
that involve positioning the robot end effector (Krebs
et al., 2007). The wrist robot, instead, has three
active DOF: abduction–adduction; flexion–extension;
pronation–supination. The two side-mounted actuators are connected to a differential mechanism, which
enables flexion/extension, abduction/adduction movements and their combinations. Joint torque production
on the differential mechanism is a result of the
proper combination of motor torques. When the two
motors rotate in the same direction, motion is purely
abduction/adduction; when they rotate in the opposite direction, the resulting motion is flexion/extension.
The entire differential mechanism is mounted onto a
curved rack so that it can be actuated from beneath
the forearm, enabling pronation and supination movements (Krebs et al., 2007). Thus, a third motor is used
to actuate the pronation/supination degree of freedom.
Each subject has been trained 1 hour a day for three
days a week. All subjects underwent 6 weeks of training with the InMotion2 robot and 6 weeks with the
InMotion3 robot.
G. Pellegrino et al. / Inter-hemispheric coupling changes associate
MAXIMAL VOLUNTARY CONTRACTION (MVC)
*
60
**
*
15
*
**
**
PRE
CONTRACTION LEVEL
POST
Robotic rehabilitation
CONTRACTION QUAUTY
**
*
6
PRE
POST
Robotic rehabilitation
501
**
PRE
**
POST
Robotic rehabilitation
Moved hand
Paretic
Non Paretic
Fig. 3. Robotic rehabilitation effects on hand motor control. Top: Error bars indicate mean ±1 SE for the three indices considered for hand
motor control assessment: Maximal Voluntary Contraction (MVC), Contraction Level and Contraction Quality. X-axis PRE and POST Robotic
Rehabilitation. Y-axis: For Maximal Voluntary Contraction (MVC) and Contraction Level, strength applied expressed in N; for execution quality
strength within ±5% of the established level of contraction and its duration (N*sec). Blue Bars: Paretic Hand; Red Bars: Non Paretic Hand.
Significant differences: *corresponds to p < 0.05; **corresponds to p < 0.001. Bottom: Alladin device (finger module) employed for Hand Motor Control measures of the Paretic and Non Paretic Hand. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/RNN2012-120227) 2.4. Neurophysiological investigation 2.4.1. EEG recordings Thirty-two channel EEG (Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, P7, P8, T7, T8, FZ, CZ, PZ, FC1, FC2, CP1, CP2, FC5, FC6, FT9, FT10, FCZ, CP5, CP6, TP9, TP10) with binaural reference was recorded with scalp electrodes mounted on an elastic cap, according to the 10–20 international system. Vertical and horizontal electro-oculogram were recorded bipolarly in order to control for eye movement-related artifacts. Impedances of all electrodes were kept below 5 k. EEG data were sampled at 1024 Hz (pre-sampling analogical band-pass filter set at 0.48–256 Hz, BrainAmp System). EEG recording was performed at rest with eyes opened (5 minutes) and during median nerve stimulation. Stimulation consisted in short electric pulses (0.2 ms duration, intensity set at three times the subjective sensory threshold producing a painless clearly visible thumb opposition, and interstimulus interval of 631 ms) delivered unilaterally to the right and left median nerve at the wrist. Stimuli were delivered through a pair of nonmagnetic, 2.5-cm spaced, Ag–AgCl disc electrodes filled with conductive jelly. 2.4.2. Primary sensorimotor hand area identification Two different methods were applied to identify the sensorimotor area of the ILH and CLH: a) EEG bipolar derivations overlying the (MCA) territory sensorimotor districts (MCA SM) considering scalp EEG (for 502 G. Pellegrino et al. / Inter-hemispheric coupling changes associate the left hemisphere: F3-C3; C3-P3; FC1-CP1; FC5CP5 and the homologous sites for the right side). b) the somatosensory source extracted from the 32 EEG signals by applying the previously mentioned FSS procedure (Porcaro et al., 2009; Tecchio et al., 2007). FSS models a set of signals X a linear combination (through an unknown mixing matrix A) of sources S: X = AS (1) The single source FS is obtained optimizing the contrast function: F = J + λR (2) where J can be any function normally used for Independent Component Analysis (in our case kurtosis), while H accounts for the prior information we have on sources. Parameter λ is used to suitably weigh the two parts of the contrast function. To identify neural network devoted to somatosensory perception from the hand (territory supplied by the median nerve), the ‘reactivity’ to the stimuli was taken into account. It was defined as follows: the evoked activity (EA) was computed by averaging signal epochs centered on the stimulus of the median nerve. The reactivity coefficient (R) was then computed as: − 40 10 |EA(t)| dt R = |EA(t)| dt − 20 (3) −30 with t = 0 corresponding to the stimulus delivery to the median nerve at wrist. The time interval ranging from 20 to 40 ms includes the maximum activation (Tecchio et al., 1997) and the baseline (no response) was computed in the pre-stimulus time interval (−30 to −10 ms). The FSS algorithm was applied to EEG recordings during contralateral median nerve stimulation to identify time signal and field distribution of bilateral S1 neuronal pools. EEG channel weights, in turn, allowed position identification of the two sources by applying proper inverse problem algorithms. To investigate right and left S1 at rest, a semi-automatic artifact rejection procedure (Barbati et al., 2004) was applied to EEG data recorded in a resting state to minimize the contribution of non-cerebral sources (such as the heart, eyes, muscles) which can critically exceed the brain signal in absence of stimulus synchronized average noise-reduction. Thereafter, artifact-free rest EEG data were multiplied by the inverse of the demixing matrix of S1 (WS1 = 1/AS1) to obtain activity in the resting state. Once the source extraction was completed, the FSS method allowed to evaluate features such as activity, connectivity and position of the two primary cortical neuronal pools devoted to the contra-lateral hand (S1, ILH S1 for ipsilesional hemisphere and CLH S1 for contralesional hemisphere), also at rest. The S1 source localization was suitably obtained by applying the sLoreta algorithm (http://www.uzh.ch/ keyinst/NewLORETA/Methods/MethodsSloreta.htm) (Pascual-Marqui, 200... Purchase answer to see full attachment