Linear Identification of Nonlinear Wrist Neuromechanics in Stroke

In many stroke patients, a motor cortex lesion alters motor control. Initially, paresis is most prominent, but then over time, joint stiffening and hyperreflexia may occur. How these different disorders develop over time is still unknown due to high system complexity. Secondary changes in the corticospinal tract, peripheral biomechanics and spinal reflexive system, may also occur. This thesis is part of the EXPLICIT-Stroke study (see Chapters 1, 2 and 3), a randomized, controlled trial that researches the effect of early therapy on post stroke recovery of the upper limb. Amongst other measurements, the EXPLICIT-Stroke study investigates post-stroke changes of brain function and corticospinal tract with fMRI and TMS, respectively. The work in this thesis aims to identify post stroke changes in peripheral biomechanics and the spinal reflexes of the wrist: wrist joint neuromechanics. Neuromechanics play an important role in the functioning of a joint. Inputs to the neuromechanical system are: neural input originating from supraspinal regions and externally applied rotation/torque. Neuromechanics therefore represent the translation from supraspinal input to muscle contraction and resultant joint rotation, torque and/or stiffness, and also describe the relationship between external perturbation and joint response. Joint impedance, the dynamic relationship between joint angle and resultant joint torque, was used to investigate joint neuromechanics. Neuromechanics can be split into: dynamics of passive soft tissues, voluntary muscle contraction and reflexive muscle contraction. Knowledge of changes in the underlying properties yields insight into the complex development of movement disorders and can eventually lead to targeted therapy. Measurement of impedance is achieved by external (motorised) angular perturbation of the joint whilst measuring the joint torque response. This is commonly supported by measurement of muscle activation: Electromyography (EMG). Joint neuromechanics are highly nonlinear. Although many nonlinear neuromechanical properties are known from literature, the effects of these nonlinear properties on joint impedance, and thus their functional and clinical relevance, have generally not been quantified. Commonly known examples of nonlinearity are increased resistance against movement in extreme angles of the range of motion and increased joint stiffness with muscle contraction. Due to nonlinearity, linearly observed neuromechanics depend on input, i.e., depend on measurement conditions. In line with the previous examples, joint stiffness depends on muscle contraction and joint angle. Therefore, understanding nonlinearity is essential for interpretation of joint impedance. Linear modelling and system identification methods allow for estimation of neuromechanical parameters. Use of these linear methods restricts measurement to small deviations in joint angle, angular velocity and muscle contraction. As normal movement often includes large deviations in angle, angular velocity and muscle contraction, such measurements do not describe the full range of interest in joint neuromechanics. Furthermore, comparison of subjects requires that they are measured in the same angles, angular velocities and contraction levels, such that observed differences between subjects are due to differences in neuromechanical properties, and not due to nonlinearity. For example, high joint stiffness in Chapter 9, was hypothesized to be caused by co-activation of the antagonistic muscle pair, i.e., the nonlinear system under a different contraction level (active state), and not caused by different peripheral neuromechanical properties.Biomechanical EngineeringMechanical, Maritime and Materials Engineerin

The gap between clinical gaze and systematic assessment of movement disorders after stroke

Background: Movement disorders after stroke are still captured by clinical gaze and translated to ordinal scores of low resolution. There is a clear need for objective quantification, with outcome measures related to pathophysiological background. Neural and non-neural contributors to joint behavior should be separated using different measurement conditions (tasks) and standardized input signals (force, position and velocity). Methods: We reviewed recent literature for the application of biomechanical and/or elektromyographical (EMG) outcome measures under various measurement conditions in clinical research. Results: Since 2005, 36 articles described the use of biomechanical and/or EMG outcome measures to quantify post-stroke movement disorder. Nineteen of the articles strived to separate neural and non-neural components. Only 6 of the articles measured biomechanical and EMG outcome measures simultaneously, while applying active and passive tasks and multiple velocities. Conclusion: The distinction between neural and non-neural components to separately assess paresis, stiffness and muscle overactivity is not commonplace yet, while a large gap is to be bridged to attain reproducible and comparable results. Pathophysiologically clear concepts, substantiated with a comprehensive and concise measuring protocol will help professionals to identify and treat limiting factors in movement capabilities of poststroke patientsBiomechanical EngineeringMechanical, Maritime and Materials Engineerin

Comprehensive neuromechanical assessment in stroke patients: Reliability and responsiveness of a protocol to measure neural and non-neural wrist properties

Background: Understanding movement disorder after stroke and providing targeted treatment for post stroke patients requires valid and reliable identification of biomechanical (passive) and neural (active and reflexive) contributors. Aim of this study was to assess test-retest reliability of passive, active and reflexive parameters and to determine clinical responsiveness in a cohort of stroke patients with upper extremity impairments and healthy volunteers. Methods: Thirty-two community-residing chronic stroke patients with an impairment of an upper limb and fourteen healthy volunteers were assessed with a comprehensive neuromechanical assessment protocol consisting of active and passive tasks and different stretch reflex-eliciting measuring velocities, using a haptic manipulator and surface electromyography of wrist flexor and extensor muscles (Netherlands Trial Registry number NTR1424). Intraclass correlation coefficients (ICC) and Standard Error of Measurement were calculated to establish relative and absolute test-retest reliability of passive, active and reflexive parameters. Clinical responsiveness was tested with Kruskal Wallis test for differences between groups. Results: ICC of passive parameters were fair to excellent (0.45 to 0.91). ICC of active parameters were excellent (0.88-0.99). ICC of reflexive parameters were fair to good (0.50-0.74). Only the reflexive loop time of the extensor muscles performed poor (ICC 0.18). Significant differences between chronic stroke patients and healthy volunteers were found in ten out of fourteen parameters. Conclusions: Passive, active and reflexive parameters can be assessed with high reliability in post-stroke patients. Parameters were responsive to clinical status. The next step is longitudinal measurement of passive, active and reflexive parameters to establish their predictive value for functional outcome after stroke.Biomechanical EngineeringElectrical Engineering, Mathematics and Computer Scienc

Loss of selective wrist muscle activation in post-stroke patients

Purpose: Loss of selective muscle activation after stroke contributes to impaired arm function, is difficult to quantify and is not systematically assessed yet. The aim of this study was to describe and validate a technique for quantification of selective muscle activation of wrist flexor and extensor muscles in a cohort of post-stroke patients. Patterns of selective muscle activation were compared to healthy volunteers and test-retest reliability was assessed. Materials and methods: Activation Ratios describe selective activation of a muscle during its expected optimal activation as agonist and antagonist. Activation Ratios were calculated from electromyography signals during an isometric maximal torque task in 31 post-stroke patients and 14 healthy volunteers. Participants with insufficient voluntary muscle activation (maximal electromyography signal <3SD higher than baseline) were excluded. Results: Activation Ratios at the wrist were reliably quantified (Intraclass correlation coefficients 0.77–0.78). Activation Ratios were significantly lower in post-stroke patients compared to healthy participants (p < 0.05). Conclusion: Activation Ratios allow for muscle-specific quantification of selective muscle activation at the wrist in post-stroke patients. Loss of selective muscle activation may be a relevant determinant in assigning and evaluating therapy to improve functional outcome.Implications for Rehabilitation Loss of selective muscle activation after stroke contributes to impaired arm function, is difficult to quantify and is not systematically assessed yet. The ability for selective muscle activation is a relevant determinant in assigning and evaluating therapy to improve functional outcome, e.g., botulinum toxin. Activation Ratios allow for reliable and muscle-specific quantification of selective muscle activation in post-stroke patients.Biomechatronics & Human-Machine Contro