Validation of the NeuroFlexor method for obtaining the neural and intrinsic component of wrist hyper-resistance post stroke: Agreement with an EMG-driven optimization method and clinical scales

Half of the long-termed disabled stroke survivors experience increased hyper-resistance of the wrist. Discrimination between the two components of joint hyper-resistance, i.e. the neural reflexive and intrinsic tissue component, is important since the components require a different treatment method. To discriminate between the two components, objective methods are developed that make use of bio-mechanical modeling. This research aimed to address the agreement between a clinically easy applicable modeling method, the NeuroFlexor method, and a more comprehensive optimization method, which both objectively obtain the neural and intrinsic components of joint hyper-resistance. Furthermore, this research study addressed the agreement between the neural and intrinsic components obtained with the NeuroFlexor and ptimization method, and the external validation of the two components with clinical rating scales. Method NeuroFlexor based assessments and instrumented positional wrist perturbations were applied to chronic stroke survivors (n = 49) and healthy volunteers (n = 11). The neural and intrinsic components were estimated using the NeuroFlexor method, whose method is a force-relationship method, and a nonlinear electromyography driven wrist optimization model. The Modied Ashworth scale (MAS) was rated to all stroke survivors as clinical scale. Correlation analysis was conducted to nd the agreement between the components of both methods, and to nd the agreement between the MAS and the components. To analyze how well the neural and intrinsic components of both methods were able to predict the MAS, multiple regression analysis with a backward selection procedure was conducted for both methods separately and both methods together. On the healthy subjects, the optimization model was applied on the NeuroFlexor data to check differences in model structure.Results The neural components of both the NeuroFlexor method and optimization method had a strong correlation (r = 0.656), as well as the intrinsic components (r = 0.648). For both methods, the neural and intrinsic components were signicant estimators of the MAS, and the NeuroFlexor method and the optimization method were approximately equivalently able to predict the MAS (r^2 = 0.466 and r^2 = 0.519, respectively). For the multiple regression analysis with the intrinsic and neural component of both methods together, the neural component of the optimization method together with the intrinsic component of the NeuroFlexor method were more able to describe the MAS (r^2 = 0.605) than the two components of the methods separately. For healthy patients, the optimization model was not able to reliably estimate the two components from the NeuroFlexor data.Conclusion This study found evidence to support the use of the NeuroFlexor device for quantication of the neural and intrinsic components of wrist hyper-resistance post stroke. Further research is needed to establish the validity of the neural component of the NeuroFlexor and the intrinsic component of the optimization method