Functional MRI using robotic MRI compatible devices for monitoring rehabilitation from chronic stroke in the molecular medicine era (Review)

The number of individuals suffering from stroke is increasing daily, and its consequences are a major contributor to invalidity in today’s society. Stroke rehabilitation is relatively new, having been hampered from the longstanding view that lost functions were not recoverable. Nowadays, robotic devices, which aid by stimulating brain plasticity, can assist in restoring movement compromised by stroke-induced pathological changes in the brain which can be monitored by MRI. Multiparametric magnetic resonance imaging (MRI) of stroke patients participating in a training program with a novel Magnetic Resonance Compatible Hand-Induced Robotic Device (MR_CHIROD) could yield a promising biomarker that, ultimately, will enhance our ability to advance hand motor recovery following chronic stroke. Using state-of-the art MRI in conjunction with MR_CHIROD-assisted therapy can provide novel biomarkers for stroke patient rehabilitation extracted by a meta-analysis of data. Successful completion of such studies may provide a ground breaking method for the future evaluation of stroke rehabilitation therapies. Their results will attest to the effectiveness of using MR-compatible hand devices with MRI to provide accurate monitoring during rehabilitative therapy. Furthermore, such results may identify biomarkers of brain plasticity that can be monitored during stroke patient rehabilitation. The potential benefit for chronic stroke patients is that rehabilitation may become possible for a longer period of time after stroke than previously thought, unveiling motor skill improvements possible even after six months due to retained brain plasticity

Design and Validation of an MR Conditional Upper Extremity Evaluation System to Study Brain Activation Patterns after Stroke

Stroke is the third leading cause of death and second most frequent cause of disability in the United States. Stroke rehabilitation methods have been developed to induce the cortical reorganization and motor-relearning that leads to stroke recovery. In this thesis, we designed and developed an MR conditional upper extremity reach and grasp movement evaluation system for the stroke survivors to study their kinematic performances in reach and grasp movement and the relationship between kinematic metrics and the recovery level measured by clinical assessment methods. We also applied the system into the functional MRI experiments to identify the ability to study motor performance with the system inside the scanner and the reach, grasp and reach-to-grasp movements related brain activation patterns. Our experiments demonstrates that ours system is an MR conditional system in the 3.0 Tesla magnetic field. It is able to measure the stroke survivors\u27 reach and grasp movement in terms of grasp aperture and elbow joint angles. We used the Mann Whitney U test to examine the significant metrics in each tasks and principle component analysis to decide the major metrics that are associated with the outcome. Then we discovered better recovery scores are associated with these major kinematic metrics such as larger maximal velocity, larger mean velocity, larger maximal movement angle, and longer time to peak velocity. Additional to these metrics, time to maximal angle, time to target and time to peak velocity could also be used as additional metrics to help predict the recovery and assess robot-assisted therapy and optimize task-oriented rehabilitation strategy. We also identified the movement related brain activations in the motor and sensory areas as well as cerebellum in both normal and stroke survivors

Sensorimotor experience in virtual environments

The goal of rehabilitation is to reduce impairment and provide functional improvements resulting in quality participation in activities of life, Plasticity and motor learning principles provide inspiration for therapeutic interventions including movement repetition in a virtual reality environment, The objective of this research work was to investigate functional specific measurements (kinematic, behavioral) and neural correlates of motor experience of hand gesture activities in virtual environments stimulating sensory experience (VE) using a hand agent model. The fMRI compatible Virtual Environment Sign Language Instruction (VESLI) System was designed and developed to provide a number of rehabilitation and measurement features, to identify optimal learning conditions for individuals and to track changes in performance over time. Therapies and measurements incorporated into VESLI target and track specific impairments underlying dysfunction. The goal of improved measurement is to develop targeted interventions embedded in higher level tasks and to accurately track specific gains to understand the responses to treatment, and the impact the response may have upon higher level function such as participation in life. To further clarify the biological model of motor experiences and to understand the added value and role of virtual sensory stimulation and feedback which includes seeing one\u27s own hand movement, functional brain mapping was conducted with simultaneous kinematic analysis in healthy controls and in stroke subjects. It is believed that through the understanding of these neural activations, rehabilitation strategies advantaging the principles of plasticity and motor learning will become possible. The present research assessed successful practice conditions promoting gesture learning behavior in the individual. For the first time, functional imaging experiments mapped neural correlates of human interactions with complex virtual reality hands avatars moving synchronously with the subject\u27s own hands, Findings indicate that healthy control subjects learned intransitive gestures in virtual environments using the first and third person avatars, picture and text definitions, and while viewing visual feedback of their own hands, virtual hands avatars, and in the control condition, hidden hands. Moreover, exercise in a virtual environment with a first person avatar of hands recruited insular cortex activation over time, which might indicate that this activation has been associated with a sense of agency. Sensory augmentation in virtual environments modulated activations of important brain regions associated with action observation and action execution. Quality of the visual feedback was modulated and brain areas were identified where the amount of brain activation was positively or negatively correlated with the visual feedback, When subjects moved the right hand and saw unexpected response, the left virtual avatar hand moved, neural activation increased in the motor cortex ipsilateral to the moving hand This visual modulation might provide a helpful rehabilitation therapy for people with paralysis of the limb through visual augmentation of skills. A model was developed to study the effects of sensorimotor experience in virtual environments, and findings of the effect of sensorimotor experience in virtual environments upon brain activity and related behavioral measures. The research model represents a significant contribution to neuroscience research, and translational engineering practice, A model of neural activations correlated with kinematics and behavior can profoundly influence the delivery of rehabilitative services in the coming years by giving clinicians a framework for engaging patients in a sensorimotor environment that can optimally facilitate neural reorganization

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Simultaneous measurements of kinematics and fMRI: compatibility assessment and case report on recovery evaluation of one stroke patient

<p>Abstract</p> <p>Background</p> <p>Correlating the features of the actual executed movement with the associated cortical activations can enhance the reliability of the functional Magnetic Resonance Imaging (fMRI) data interpretation. This is crucial for longitudinal evaluation of motor recovery in neurological patients and for investigating detailed mutual interactions between activation maps and movement parameters.</p> <p>Therefore, we have explored a new set-up combining fMRI with an optoelectronic motion capture system, which provides a multi-parameter quantification of the performed motor task.</p> <p>Methods</p> <p>The cameras of the motion system were mounted inside the MR room and passive markers were placed on the subject skin, without any risk or encumbrance. The versatile set-up allows 3-dimensional multi-segment acquisitions including recording of possible mirror movements, and it guarantees a high inter-sessions repeatability.</p> <p>We demonstrated the integrated set-up reliability through compatibility tests. Then, an fMRI block-design protocol combined with kinematic recordings was tested on a healthy volunteer performing finger tapping and ankle dorsal- plantar-flexion. A preliminary assessment of clinical applicability and perspectives was carried out by pre- and post rehabilitation acquisitions on a hemiparetic patient performing ankle dorsal- plantar-flexion. For all sessions, the proposed method integrating kinematic data into the model design was compared with the standard analysis.</p> <p>Results</p> <p>Phantom acquisitions demonstrated the not-compromised image quality. Healthy subject sessions showed the protocols feasibility and the model reliability with the kinematic regressor. The patient results showed that brain activation maps were more consistent when the images analysis included in the regression model, besides the stimuli, the kinematic regressor quantifying the actual executed movement (movement timing and amplitude), proving a significant model improvement. Moreover, concerning motor recovery evaluation, after one rehabilitation month, a greater cortical area was activated during exercise, in contrast to the usual focalization associated with functional recovery. Indeed, the availability of kinematics data allows to correlate this wider area with a higher frequency and a larger amplitude of movement.</p> <p>Conclusions</p> <p>The kinematic acquisitions resulted to be reliable and versatile to enrich the fMRI images information and therefore the evaluation of motor recovery in neurological patients where large differences between required and performed motion can be expected.</p

Robot-based hand motor therapy after stroke.

Robots can improve motor status after stroke with certain advantages, but there has been less emphasis to date on robotic developments for the hand. The goal of this study was to determine whether a hand-wrist robot would improve motor function, and to evaluate the specificity of therapy effects on brain reorganization. Subjects with chronic stroke producing moderate right arm/hand weakness received 3 weeks therapy that emphasized intense active movement repetition as well as attention, speed, force, precision and timing, and included virtual reality games. Subjects initiated hand movements. If necessary, the robot completed movements, a feature available at all visits for seven of the subjects and at the latter half of visits for six of the subjects. Significant behavioural gains were found at end of treatment, for example, in Action Research Arm Test (34 +/- 20 to 38 +/- 19, P&lt; 0.0005) and arm motor Fugl-Meyer score (45 +/- 10 to 52 +/- 10, P &lt; 0.0001). Results suggest greater gains for subjects receiving robotic assistance in all sessions as compared to those receiving robotic assistance in half of sessions. The grasp task practiced during robotic therapy, when performed during functional MRI, showed increased sensorimotor cortex activation across the period of therapy, while a non-practiced task, supination/pronation, did not. A robot-based therapy showed improvements in hand motor function after chronic stroke. Reorganization of motor maps during the current therapy was task-specific, a finding useful when considering generalization of rehabilitation therapy

A magnetic compatible supernumerary robotic finger for functional magnetic resonance imaging (fMRI) acquisitions: Device description and preliminary results

The Supernumerary robotic limbs are a recently introduced class of wearable robots that, differently from traditional prostheses and exoskeletons, aim at adding extra effectors (i.e., arms, legs, or fingers) to the human user, rather than substituting or enhancing the natural ones. However, it is still undefined whether the use of supernumerary robotic limbs could specifically lead to neural modifications in brain dynamics. The illusion of owning the part of body has been already proven in many experimental observations, such as those relying on multisensory integration (e.g., rubber hand illusion), prosthesis and even on virtual reality. In this paper we present a description of a novel magnetic compatible supernumerary robotic finger together with preliminary observations from two functional magnetic resonance imaging (fMRI) experiments, in which brain activity was measured before and after a period of training with the robotic device, and during the use of the novel MRI-compatible version of the supernumerary robotic finger. Results showed that the usage of the MR-compatible robotic finger is safe and does not produce artifacts on MRI images. Moreover, the training with the supernumerary robotic finger recruits a network of motor-related cortical regions (i.e. primary and supplementary motor areas), hence the same motor network of a fully physiological voluntary motor gestures

An fMRI Study on Supra-Spinal Contributions to Upper and Lower Limb Motor Control

The differences in the neural mechanisms contributing to upper and lower extremity movement have not been fully elucidated, and this might be a factor that leads to the ineffectiveness of rehabilitation techniques for most stroke survivors. It is unclear whether therapies designed for upper extremities should also be used for the lower extremities, and vice versa. In this study, fMRI was used to examine the supraspinal control of UE and LE movement in both neurologically intact individuals and people with post-stroke hemiparesis. We compared the location, volume, and intensity of brain activity associated with upper and lower extremity pedaling and unilateral flexion/extension of the hand and ankle. We hypothesized that if the supraspinal control strategies were the same for upper and lower extremities, then the pattern of brain activity would be the same across upper and lower limb movement. Alternatively, if the strategies were not the same, then brain activation would differ for each task. We found movement related brain activity in three cortical regions (S1, M1, and Brodmann Area 6) among healthy subjects. The location of activity complied with the somatotopic order in the sensorimotor cortex, but upper extremity produced greater activities during both pedaling and flexion/extension movement compared to the lower extremities. These observations suggested that the general brain activation strategies were similar between upper and lower extremities, while the involvement of cortical structures was more substantial for upper than lower limb movements. The four stroke subjects showed activity in the same regions as compared to the healthy group, yet the volume, intensity and symmetry of activation varied across the subjects and motor tasks. These observations suggested that there were multiple strategies for cortical reorganization after stroke and the controlling strategies for the effectors differed

Focal Spot, Fall/Winter 2002/2003

https://digitalcommons.wustl.edu/focal_spot_archives/1092/thumbnail.jp