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, characterisation and validation of a haptic interface based on twisted string actuation.

This paper presents the design and experimental characterisation of a wrist haptic interface based on a twisted string actuator. The interface is designed for controlled actuation of wrist flexion/extension and is capable of rendering torque feedback through a rotary handle driven by the twisted string actuator and spring-loaded cable mechanisms. The interface was characterised to obtain its static and dynamic haptic feedback rendering capabilities. Compliance in the spring and actuation mechanism makes the interface suitable for smooth rendering of haptic feedback of large magnitudes due to the high motion transmission ratio of the twisted strings. Haptic virtual wall rendering capabilities are demonstrated

Establishing a Framework for the development of Multimodal Virtual Reality Interfaces with Applicability in Education and Clinical Practice

The development of Virtual Reality (VR) and Augmented Reality (AR) content with multiple sources of both input and output has led to countless contributions in a great many number of fields, among which medicine and education. Nevertheless, the actual process of integrating the existing VR/AR media and subsequently setting it to purpose is yet a highly scattered and esoteric undertaking. Moreover, seldom do the architectures that derive from such ventures comprise haptic feedback in their implementation, which in turn deprives users from relying on one of the paramount aspects of human interaction, their sense of touch. Determined to circumvent these issues, the present dissertation proposes a centralized albeit modularized framework that thus enables the conception of multimodal VR/AR applications in a novel and straightforward manner. In order to accomplish this, the aforesaid framework makes use of a stereoscopic VR Head Mounted Display (HMD) from Oculus Rift©, a hand tracking controller from Leap Motion©, a custom-made VR mount that allows for the assemblage of the two preceding peripherals and a wearable device of our own design. The latter is a glove that encompasses two core modules in its innings, one that is able to convey haptic feedback to its wearer and another that deals with the non-intrusive acquisition, processing and registering of his/her Electrocardiogram (ECG), Electromyogram (EMG) and Electrodermal Activity (EDA). The software elements of the aforementioned features were all interfaced through Unity3D©, a powerful game engine whose popularity in academic and scientific endeavors is evermore increasing. Upon completion of our system, it was time to substantiate our initial claim with thoroughly developed experiences that would attest to its worth. With this premise in mind, we devised a comprehensive repository of interfaces, amid which three merit special consideration: Brain Connectivity Leap (BCL), Ode to Passive Haptic Learning (PHL) and a Surgical Simulator

Assessing MR-compatibility of somatosensory stimulation devices: A systematic review on testing methodologies

Functional magnetic resonance imaging (fMRI) has been extensively used as a tool to map the brain processes related to somatosensory stimulation. This mapping includes the localization of task-related brain activation and the characterization of brain activity dynamics and neural circuitries related to the processing of somatosensory information. However, the magnetic resonance (MR) environment presents unique challenges regarding participant and equipment safety and compatibility. This study aims to systematically review and analyze the state-of-the-art methodologies to assess the safety and compatibility of somatosensory stimulation devices in the MR environment. A literature search, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines, was performed in PubMed, Scopus, and Web of Science to find original research on the development and testing of devices for somatosensory stimulation in the MR environment. Nineteen records that complied with the inclusion and eligibility criteria were considered. The findings are discussed in the context of the existing international standards available for the safety and compatibility assessment of devices intended to be used in the MR environment. In sum, the results provided evidence for a lack of uniformity in the applied testing methodologies, as well as an in-depth presentation of the testing methodologies and results. Lastly, we suggest an assessment methodology (safety, compatibility, performance, and user acceptability) that can be applied to devices intended to be used in the MR environment.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42021257838

Medical robots for MRI guided diagnosis and therapy

Magnetic Resonance Imaging (MRI) provides the capability of imaging tissue with fine resolution and superior soft tissue contrast, when compared with conventional ultrasound and CT imaging, which makes it an important tool for clinicians to perform more accurate diagnosis and image guided therapy. Medical robotic devices combining the high resolution anatomical images with real-time navigation, are ideal for precise and repeatable interventions. Despite these advantages, the MR environment imposes constraints on mechatronic devices operating within it. This thesis presents a study on the design and development of robotic systems for particular MR interventions, in which the issue of testing the MR compatibility of mechatronic components, actuation control, kinematics and workspace analysis, and mechanical and electrical design of the robot have been investigated. Two types of robotic systems have therefore been developed and evaluated along the above aspects. (i) A device for MR guided transrectal prostate biopsy: The system was designed from components which are proven to be MR compatible, actuated by pneumatic motors and ultrasonic motors, and tracked by optical position sensors and ducial markers. Clinical trials have been performed with the device on three patients, and the results reported have demonstrated its capability to perform needle positioning under MR guidance, with a procedure time of around 40mins and with no compromised image quality, which achieved our system speci cations. (ii) Limb positioning devices to facilitate the magic angle effect for diagnosis of tendinous injuries: Two systems were designed particularly for lower and upper limb positioning, which are actuated and tracked by the similar methods as the first device. A group of volunteers were recruited to conduct tests to verify the functionality of the systems. The results demonstrate the clear enhancement of the image quality with an increase in signal intensity up to 24 times in the tendon tissue caused by the magic angle effect, showing the feasibility of the proposed devices to be applied in clinical diagnosis

Effect of Error Augmentation on Brain Activation and Motor Learning of a Complex Locomotor Task

Up to date, the functional gains obtained after robot-aided gait rehabilitation training are limited. Error augmenting strategies have a great potential to enhance motor learning of simple motor tasks. However, little is known about the effect of these error modulating strategies on complex tasks, such as relearning to walk after a neurologic accident. Additionally, neuroimaging evaluation of brain regions involved in learning processes could provide valuable information on behavioral outcomes. We investigated the effect of robotic training strategies that augment errors—error amplification and random force disturbance—and training without perturbations on brain activation and motor learning of a complex locomotor task. Thirty-four healthy subjects performed the experiment with a robotic stepper (MARCOS) in a 1.5 T MR scanner. The task consisted in tracking a Lissajous figure presented on a display by coordinating the legs in a gait-like movement pattern. Behavioral results showed that training without perturbations enhanced motor learning in initially less skilled subjects, while error amplification benefited better-skilled subjects. Training with error amplification, however, hampered transfer of learning. Randomly disturbing forces induced learning and promoted transfer in all subjects, probably because the unexpected forces increased subjects' attention. Functional MRI revealed main effects of training strategy and skill level during training. A main effect of training strategy was seen in brain regions typically associated with motor control and learning, such as, the basal ganglia, cerebellum, intraparietal sulcus, and angular gyrus. Especially, random disturbance and no perturbation lead to stronger brain activation in similar brain regions than error amplification. Skill-level related effects were observed in the IPS, in parts of the superior parietal lobe (SPL), i.e., precuneus, and temporal cortex. These neuroimaging findings indicate that gait-like motor learning depends on interplay between subcortical, cerebellar, and fronto-parietal brain regions. An interesting observation was the low activation observed in the brain's reward system after training with error amplification compared to training without perturbations. Our results suggest that to enhance learning of a locomotor task, errors should be augmented based on subjects' skill level. The impacts of these strategies on motor learning, brain activation, and motivation in neurological patients need further investigation

A comprehensive review of haptic feedback in minimally invasive robotic liver surgery: Advancements and challenges

Background: Liver medical procedures are considered one of the most challenging because of the liver's complex geometry, heterogeneity, mechanical properties, and movement due to respiration. Haptic features integrated into needle insertion systems and other medical devices could support physicians but are uncommon. Additional training time and safety concerns make it difficult to implement in robot-assisted surgery. The main challenges of any haptic device in a teleoperated system are the stability and transparency levels required to develop a safe and efficient system that suits the physician's needs. Purpose: The objective of the review article is to investigate whether haptic-based teleoperation potentially improves the efficiency and safety of liver needle insertion procedures compared with insertion without haptic feedback. In addition, it looks into haptic technology that can be integrated into simulators to train novice physicians in liver procedures. Methods: This review presents the physician's needs during liver interventions and the consequent requirements of haptic features to help the physician. This paper provides an overview of the different aspects of a teleoperation system in various applications, especially in the medical field. It finally presents the state-of-the-art haptic technology in robot-assisted procedures for the liver. This includes 3D virtual models of the liver and force measurement techniques used in haptic rendering to estimate the real-time position of the surgical instrument relative to the liver. Results: Haptic feedback technology can be used to navigate the surgical tool through the desired trajectory to reach the target accurately and avoid critical regions. It also helps distinguish between various textures of liver tissue. Conclusion: Haptic feedback can complement the physician's experience to compensate for the lack of real-time imaging during Computed Tomography guided (CT-guided) liver procedures. Consequently, it helps the physician mitigate the destruction of healthy tissues and takes less time to reach the target.</p

Principles of sensorimotor control and learning in complex motor tasks

The brain coordinates a continuous coupling between perception and action in the presence of uncertainty and incomplete knowledge about the world. This mapping is enabled by control policies and motor learning can be perceived as the update of such policies on the basis of improving performance given some task objectives. Despite substantial progress in computational sensorimotor control and empirical approaches to motor adaptation, to date it remains unclear how the brain learns motor control policies while updating its internal model of the world. In light of this challenge, we propose here a computational framework, which employs error-based learning and exploits the brain’s inherent link between forward models and feedback control to compute dynamically updated policies. The framework merges optimal feedback control (OFC) policy learning with a steady system identification of task dynamics so as to explain behavior in complex object manipulation tasks. Its formalization encompasses our empirical findings that action is learned and generalised both with regard to a body-based and an object-based frame of reference. Importantly, our approach predicts successfully how the brain makes continuous decisions for the generation of complex trajectories in an experimental paradigm of unfamiliar task conditions. A complementary method proposes an expansion of the motor learning perspective at the level of policy optimisation to the level of policy exploration. It employs computational analysis to reverse engineer and subsequently assess the control process in a whole body manipulation paradigm. Another contribution of this thesis is to associate motor psychophysics and computational motor control to their underlying neural foundation; a link which calls for further advancement in motor neuroscience and can inform our theoretical insight to sensorimotor processes in a context of physiological constraints. To this end, we design, build and test an fMRI-compatible haptic object manipulation system to relate closed-loop motor control studies to neurophysiology. The system is clinically adjusted and employed to host a naturalistic object manipulation paradigm on healthy human subjects and Friedreich’s ataxia patients. We present methodology that elicits neuroimaging correlates of sensorimotor control and learning and extracts longitudinal neurobehavioral markers of disease progression (i.e. neurodegeneration). Our findings enhance the understanding of sensorimotor control and learning mechanisms that underlie complex motor tasks. They furthermore provide a unified methodological platform to bridge the divide between behavior, computation and neural implementation with promising clinical and technological implications (e.g. diagnostics, robotics, BMI).Open Acces