Haptic Error Modulation Outperforms Visual Error Amplification When Learning a Modified Gait Pattern

Robotic algorithms that augment movement errors have been proposed as promising training strategies to enhance motor learning and neurorehabilitation. However, most research effort has focused on rehabilitation of upper limbs, probably because large movement errors are especially dangerous during gait training, as they might result in stumbling and falling. Furthermore, systematic large movement errors might limit the participants’ motivation during training. In this study, we investigated the effect of training with novel error modulating strategies, which guarantee a safe training environment, on motivation and learning of a modified asymmetric gait pattern. Thirty healthy young participants walked in the exoskeletal robotic system Lokomat while performing a foot target-tracking task, which required an increased hip and knee flexion in the dominant leg. Learning the asymmetric gait pattern with three different strategies was evaluated: (i) No disturbance: no robot disturbance/guidance was applied, (ii) haptic error amplification: unsafe and discouraging large errors were limited with haptic guidance, while haptic error amplification enhanced awareness of small errors relevant for learning, and (iii) visual error amplification: visually observed errors were amplified in a virtual reality environment. We also evaluated whether increasing the movement variability during training by adding randomly varying haptic disturbances on top of the other training strategies further enhances learning. We analyzed participants’ motor performance and self-reported intrinsic motivation before, during and after training. We found that training with the novel haptic error amplification strategy did not hamper motor adaptation and enhanced transfer of the practiced asymmetric gait pattern to free walking. Training with visual error amplification, on the other hand, increased errors during training and hampered motor learning. Participants who trained with visual error amplification also reported a reduced perceived competence. Adding haptic disturbance increased the movement variability during training, but did not have a significant effect on motor adaptation, probably because training with haptic disturbance on top of visual and haptic error amplification decreased the participants’ feelings of competence. The proposed novel haptic error modulating controller that amplifies small task-relevant errors while limiting large errors outperformed visual error augmentation and might provide a promising framework to improve robotic gait training outcomes in neurological patients

Assessing walking ability using a robotic gait trainer: opportunities and limitations of assist-as-needed control in spinal cord injury

Background: Walking impairments are a common consequence of neurological disorders and are assessed with clinical scores that suffer from several limitations. Robot-assisted locomotor training is becoming an established clinical practice. Besides training, these devices could be used for assessing walking ability in a controlled environment. Here, we propose an adaptive assist-as-needed (AAN) control for a treadmill-based robotic exoskeleton, the Lokomat, that reduces the support of the device (body weight support and impedance of the robotic joints) based on the ability of the patient to follow a gait pattern displayed on screen. We hypothesize that the converged values of robotic support provide valid and reliable information about individuals' walking ability.Methods: Fifteen participants with spinal cord injury and twelve controls used the AAN software in the Lokomat twice within a week and were assessed using clinical scores (10MWT, TUG). We used a regression method to identify the robotic measure that could provide the most relevant information about walking ability and determined the test–retest reliability. We also checked whether this result could be extrapolated to non-ambulatory and to unimpaired subjects.Results: The AAN controller could be used in patients with different injury severity levels. A linear model based on one variable (robotic knee stiffness at terminal swing) could explain 74% of the variance in the 10MWT and 61% in the TUG in ambulatory patients and showed good relative reliability but poor absolute reliability. Adding the variable 'maximum hip flexor torque' to the model increased the explained variance above 85%. This did not extend to non-ambulatory nor to able-bodied individuals, where variables related to stance phase and to push-off phase seem more relevant.Conclusions: The novel AAN software for the Lokomat can be used to quantify the support required by a patient while performing robotic gait training. The adaptive software might enable more challenging training conditions tuned to the ability of the individuals. While the current implementation is not ready for assessment in clinical practice, we could demonstrate that this approach is safe, and it could be integrated as assist-as-needed training, rather than as assessment.Trial registration: ClinicalTrials.gov Identifier: NCT02425332

Effects of age and gender on neural correlates of emotion imagery

Mental imagery is part of people's own internal processing and plays an important role in everyday life, cognition and pathology. The neural network supporting mental imagery is bottom-up modulated by the imagery content. Here, we examined the complex associations of gender and age with the neural mechanisms underlying emotion imagery. We assessed the brain circuits involved in emotion mental imagery (vs. action imagery), controlled by a letter detection task on the same stimuli, chosen to ensure attention to the stimuli and to discourage imagery, in 91 men and women aged 14-65 years using fMRI. In women, compared with men, emotion imagery significantly increased activation within the right putamen, which is involved in emotional processing. Increasing age, significantly decreased mental imagery-related activation in the left insula and cingulate cortex, areas involved in awareness of ones' internal states, and it significantly decreased emotion verbs-related activation in the left putamen, which is part of the limbic system. This finding suggests a top-down mechanism by which gender and age, in interaction with bottom-up effect of type of stimulus, or directly, can modulate the brain mechanisms underlying mental imagery

Common and different neural markers in major depression and anxiety disorders: A pilot structural magnetic resonance imaging study.

Although anxiety and depression often co-occur and share some clinical features, it is still unclear if they are neurobiologically distinct or similar processes. In this study, we explored common and specific cortical morphology alterations in depression and anxiety disorders. Magnetic Resonance Imaging data were acquired from 13 Major Depressive Disorder (MDD), 11 Generalized Anxiety Disorder (GAD), 11 Panic Disorder (PD) patients and 21 healthy controls (HC). Regional cortical thickness, surface area (SA), volume and gyrification were measured and compared among groups. We found left orbitofrontal thinning in all patient groups, as well as disease-specific alterations. MDD showed volume deficits in left precentral gyrus compared to all groups, volume and area deficits in right fusiform gyrus compared to GAD and HC. GAD showed lower SA than MDD and PD in right superior parietal cortex, higher gyrification than HC in right frontal gyrus. PD showed higher gyrification in left superior parietal cortex when compared to MDD and higher SA in left postcentral gyrus compared to all groups. Our results suggest that clinical phenotypic similarities between major depression and anxiety disorders might rely on common prefrontal alterations. Frontotemporal and parietal abnormalities may represent unique biological signatures of depression and anxiety

Increased task-uncorrelated muscle activity in childhood dystonia

Even if movement abnormalities in dystonia are obvious on observation-based examinations, objective measures to characterize dystonia and to gain insights into its pathophysiology are still strongly needed. We hypothesize that motor abnormalities in childhood dystonia are partially due to the inability to suppress involuntary variable muscle activity irrelevant to the achievement of the desired motor task, resulting in the superposition of unwanted motion components on the desired movement. However, it is difficult to separate and quantify appropriate and inappropriate motor signals combined in the same muscle, especially during movement

Rare ATG7 genetic variants predispose patients to severe fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver disorders and has a strong heritable component. The aim of this study was to identify new loci that contribute to severe NAFLD by examining rare variants

High resolution and contrast 7 tesla MR brain imaging of the neonate

IntroductionUltra-high field MR imaging offers marked gains in signal-to-noise ratio, spatial resolution, and contrast which translate to improved pathological and anatomical sensitivity. These benefits are particularly relevant for the neonatal brain which is rapidly developing and sensitive to injury. However, experience of imaging neonates at 7T has been limited due to regulatory, safety, and practical considerations. We aimed to establish a program for safely acquiring high resolution and contrast brain images from neonates on a 7T system.MethodsImages were acquired from 35 neonates on 44 occasions (median age 39 + 6 postmenstrual weeks, range 33 + 4 to 52 + 6; median body weight 2.93 kg, range 1.57 to 5.3 kg) over a median time of 49 mins 30 s. Peripheral body temperature and physiological measures were recorded throughout scanning. Acquired sequences included T2 weighted (TSE), Actual Flip angle Imaging (AFI), functional MRI (BOLD EPI), susceptibility weighted imaging (SWI), and MR spectroscopy (STEAM).ResultsThere was no significant difference between temperature before and after scanning (p = 0.76) and image quality assessment compared favorably to state-of-the-art 3T acquisitions. Anatomical imaging demonstrated excellent sensitivity to structures which are typically hard to visualize at lower field strengths including the hippocampus, cerebellum, and vasculature. Images were also acquired with contrast mechanisms which are enhanced at ultra-high field including susceptibility weighted imaging, functional MRI, and MR spectroscopy.DiscussionWe demonstrate safety and feasibility of imaging vulnerable neonates at ultra-high field and highlight the untapped potential for providing important new insights into brain development and pathological processes during this critical phase of early life

Robot-Aided Gait Assessment and Rehabilitation: an Assist-as-Needed Approach

According to the World Health Organization (WHO), around 1.1 billion people worldwide live with a form of disability, mostly affecting mobility. Limitations in walking affect independence, quality of life and lead to several secondary complications due to immobility. To provide suitable and targeted rehabilitation to patients, we require evidence-based treatments, trained health professionals and assessments to measure and track recovery. Current assessments of sensorimotor functions are hampered by issues such as limited sensitivity and objectivity that prevent their regular use in clinical practice and limit the benefit that the patients can obtain from rehabilitation. New technologies, such as robotic gait trainers, have become a real possibility for locomotor therapy after neurological injuries. Besides offering support and guidance to the movements, they allow creating standard protocols within the training sessions and measuring objective data with the integrated sensors. The long-term goal of this project is to develop a valid, reliable and sensitive assessment of walking activity in a robotic gait trainer that can be used in clinical practice to assess patients during every stage of rehabilitation. In this thesis, an algorithm allowing safe and objective assessment of patients’ walking ability was designed and implemented in a treadmill-based robotic gait trainer. This was tested and validated on a robotic test bench and in individuals after Spinal Cord Injury against established clinical scores. The algorithm consists of an assist-as-needed (AAN) controller that adapts the robotic support based on the patient’s ability to follow a reference gait trajectory displayed on a screen. The outcome measures are the body weight support and mechanical impedance (stiffness and damping) of the hip and knee robotic joints determined by the AAN algorithm in different gait phases. The construct validity of the AAN-based assessment was evaluated using a robotic test bench simulating known neuromotor impairments: the method was sensitive enough to capture differences in the simulated impairments and allowed the identification of confounding factors such as speed and gait-phase-dependent biomechanics. The AAN-based assessment was then evaluated in patients with Spinal Cord Injury. Each patient was assessed twice within a week to evaluate the reliability of the technique. The relationship between the AAN outcome measures and established clinical scores measuring walking ability was studied. A linear model based on only one variable (the robotic knee stiffness at terminal swing) was able to explain 74% of the variance in the 10-Meter-Walking-Test data in ambulatory patients. Adding the maximum isometric hip flexor torque as a second variable to the model increased the explained variance to >85%. The limited amount of data available from non-ambulatory patients prevented us to extend the findings to this population. Test-retest reliability was still too low for clinical application, since a change of 9.1% (between two different sessions) in the robotic knee stiffness at terminal swing would be required to indicate a significant change in walking ability. To increase the compliance of the controller to physiological deviations of the foot trajectory from the reference, we developed a “hybrid joint/end-point space controller with AAN”, combining the benefits of joint and end-point space robotic controllers. The adaptive hybrid controller shaped the end-point (i.e. the ankle) robotic stiffness according to the direction and the magnitude of the error performed at the level of the ankle. The resulting end-point force selectively counteracted certain foot position errors while leaving the robot compliant in other directions. While additional gravity compensation was needed to support a single severely impaired patient using this controller, we demonstrated the safety and feasibility of the hybrid controller in able-bodied subjects. Overall, we were able to design, develop and test a novel software for a treadmill-based robotic exoskeleton that can be used to quantify the robotic support required by a patient while performing gait training. The support determined by the algorithm provides information about the patient’s walking ability that could be used to assess the patient’s recovery, to provide motivation or to adjust the therapy accordingly. The adaptive characteristics of the software could enable a more challenging training environment that is tuned to the ability of the individual patients

Assessing walking ability using a robotic gait trainer: opportunities and limitations of assist-as-needed control in spinal cord injury

BACKGROUND Walking impairments are a common consequence of neurological disorders and are assessed with clinical scores that suffer from several limitations. Robot-assisted locomotor training is becoming an established clinical practice. Besides training, these devices could be used for assessing walking ability in a controlled environment. Here, we propose an adaptive assist-as-needed (AAN) control for a treadmill-based robotic exoskeleton, the Lokomat, that reduces the support of the device (body weight support and impedance of the robotic joints) based on the ability of the patient to follow a gait pattern displayed on screen. We hypothesize that the converged values of robotic support provide valid and reliable information about individuals' walking ability. METHODS Fifteen participants with spinal cord injury and twelve controls used the AAN software in the Lokomat twice within a week and were assessed using clinical scores (10MWT, TUG). We used a regression method to identify the robotic measure that could provide the most relevant information about walking ability and determined the test-retest reliability. We also checked whether this result could be extrapolated to non-ambulatory and to unimpaired subjects. RESULTS The AAN controller could be used in patients with different injury severity levels. A linear model based on one variable (robotic knee stiffness at terminal swing) could explain 74% of the variance in the 10MWT and 61% in the TUG in ambulatory patients and showed good relative reliability but poor absolute reliability. Adding the variable 'maximum hip flexor torque' to the model increased the explained variance above 85%. This did not extend to non-ambulatory nor to able-bodied individuals, where variables related to stance phase and to push-off phase seem more relevant. CONCLUSIONS The novel AAN software for the Lokomat can be used to quantify the support required by a patient while performing robotic gait training. The adaptive software might enable more challenging training conditions tuned to the ability of the individuals. While the current implementation is not ready for assessment in clinical practice, we could demonstrate that this approach is safe, and it could be integrated as assist-as-needed training, rather than as assessment. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02425332

An Adaptive and Hybrid End-Point/Joint Impedance Controller for Lower Limb Exoskeletons

Assist-as-needed (AAN) algorithms for the control of lower extremity rehabilitation robots can promote active participation of patients during training while adapting to their individual performances and impairments. The implementation of such controllers requires the adaptation of a control parameter (often the robot impedance) based on a performance (or error) metric. The choice of how an adaptive impedance controller is formulated implies different challenges and possibilities for controlling the patient's leg movement. In this paper, we analyze the characteristics and limitations of controllers defined in two commonly used formulations: joint and end-point space, exploring especially the implementation of an AAN algorithm. We propose then, as a proof-of-concept, an AAN impedance controller that combines the strengths of working in both spaces: a hybrid joint/end-point impedance controller. This approach gives the possibility to adapt the end-point stiffness in magnitude and direction in order to provide a support that targets the kinematic deviations of the end-point with the appropriate force vector. This controller was implemented on a two-link rehabilitation robot for gait training-the Lokomat®Pro V5 (Hocoma AG, Switzerland) and tested on 5 able-bodied subjects and 1 subject with Spinal Cord Injury. Our experiments show that the hybrid controller is a feasible approach for exoskeleton devices and that it could exploit the benefits of the end-point controller in shaping a desired end-point stiffness and those of the joint controller to promote the correct angular changes in the trajectories of the joints. The adaptation algorithm is able to adapt the end-point stiffness based on the subject's performance in different gait phases, i.e., the robot can render a higher stiffness selectively in the direction and gait phases where the subjects perform with larger kinematic errors. The proposed approach can potentially be generalized to other robotic applications for rehabilitation or assistive purposes