Biology subjects

Kanzler, C. M.

Publications and source records attributed to Kanzler, C. M..

3 recordsLinked to original sources

bioRxiv2020

Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals

BackgroundAssessing arm and hand sensorimotor impairments that are functionally relevant is essential to optimize the impact of neurorehabilitation interventions. Technology-aided assessments should provide a sensitive and objective characterization of upper limb impairments, but often provide arm weight support and neglect the importance of the hand, thereby questioning their functional relevance. The Virtual Peg Insertion Test (VPIT) addresses these limitations by quantifying arm movements and grip forces during a goal-directed manipulation task without arm weight support. The aim of this work was to evaluate the potential and robustness of the VPIT metrics to inform on sensorimotor impairments in arm and hand, and especially identify the functional relevance of the detected impairments. MethodsArm and hand sensorimotor impairments were systematically characterized in 30 chronic stroke patients using conventional clinical scales and the VPIT. For the latter, ten previously established kinematic and kinetic core metrics were extracted and compared to conventional clinical scales of impairment and activity limitations. Additionally, the robustness of the VPIT metrics was investigated by analyzing their clinimetric properties (test-retest reliability, measurement error, and learning effects). ResultsTwenty-three of the participants, the ones with mild to moderate sensorimotor impairments and without strong cognitive deficits, were able to successfully complete the VPIT protocol (duration 16.6 min). The VPIT metrics detected impairments in arm and hand in 90.0% of the participants, and were sensitive to increased muscle tone and pathological joint coupling. Most importantly, moderate to high significant correlations between conventional scales of activity limitations and the VPIT metrics were found, thereby indicating their functional relevance when grasping and transporting lightweight objects as well as dexterous finger manipulations. Lastly, the robustness of three out of the ten VPIT core metrics in post-stroke individuals was confirmed. ConclusionsThis work provides evidence that technology-aided assessments requiring goal-directed manipulations without arm weight support can provide an objective, robust, and clinically feasible way to assess functionally relevant sensorimotor impairments in arm and hand in chronic post-stroke individuals with mild to moderate deficits. This allows better identifying impairments with high functional relevance and can contribute to optimizing the functional benefits of neurorehabilitation interventions. Retrospectively registered: clinicaltrials.gov/ct2/show/NCT03135093

neuroscience↗

bioRxiv2020

Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning

BackgroundA personalized prediction of upper limb neurorehabilitation outcomes in persons with multiple sclerosis (pwMS) promises to optimize the allocation of therapy and to stratify individuals for resource-demanding clinical trials. Previous research identified predictors on a population level through linear models and clinical data, including conventional assessments describing sensorimotor impairments. The objective of this work was to explore the feasibility of providing an individualized and more accurate prediction of rehabilitation outcomes in pwMS by leveraging non-linear machine learning models, clinical data, and digital health metrics characterizing sensorimotor impairments. MethodsClinical data and digital health metrics were recorded from eleven pwMS undergoing neurorehabilitation. Machine learning models were trained on data recorded pre-intervention. The dependent variables indicated whether a considerable improvement on the activity level was observed across the intervention or not (binary classification), as defined by the Action Research Arm Test (ARAT), Box and Block Test (BBT), or Nine Hole Peg Test (NHPT). ResultsIn a cross-validation, considerable improvements in ARAT or BBT could be accurately predicted (94% balanced accuracy) by only relying on patient master data. Considerable improvements in NHPT could be accurately predicted (89% balanced accuracy), but required knowledge about sensorimotor impairments. Assessing these with digital health metrics instead of conventional scales allowed increasing the balanced accuracy by +17% . Non-linear machine-learning models improved the predictive accuracy for the NHPT by +25% compared to linear models. ConclusionsThis work demonstrates the feasibility of a personalized prediction of upper limb neurorehabilitation outcomes in pwMS using multi-modal data collected before neurorehabilitation and machine learning. Information from digital health metrics about sensorimotor impairment was necessary to predict changes in dexterous hand control, thereby underlining their potential to provide a more sensitive and fine-grained assessment than conventional scales. Non-linear models outperformed ones, suggesting that the commonly assumed linearity of neurorehabilitation is oversimplified. clinicaltrials.gov registration number: NCT02688231

bioengineering↗

bioRxiv2019

An Objective Technology-based Assessment of Arm and Hand Sensorimotor Disability in Neurological Disorders

BackgroundDetailed assessments of upper limb disability are essential to understand and monitor sensorimotor recovery. Usually, multiple timeconsuming assessments are required to define a holistic sensorimotor profile of proximal (shoulder-elbow) and distal (wrist-hand) impairments and their impact on the capacity to perform activities. We propose and evaluate a novel physiologically-motivated computational framework for objectively assessing sensorimotor profiles in neurological patients using a single, rapid technology-based assessment involving goal-directed arm and hand movements. MethodsThe Virtual Peg Insertion Test (VPIT) was administered to 121 healthy and 80 neurological subjects. The framework provides 25 kinematic and kinetic metrics expected to describe 12 sensorimotor components representative of ataxia and paresis and their influence on task performance, as well as one overall disability measure. The feasibility (protocol duration), structural validity (factor analysis and correlations{rho} between sensorimotor components), concurrent validity (correlation with Action Research Arm Test; ARAT), and discriminant validity (comparing healthy controls and patients, and patients with different disability levels) were evaluated. ResultsThe median VPIT protocol duration was 16.5min in neurological patients. The sensor-based metrics could unambiguously be grouped into 12 mostly independent (median |{rho}|=0.14) components. Ten components showed significant differences between healthy and impaired subjects and nine components indicated clear trends across disability levels, without any ceiling effects. The VPIT overall disability measure and ARAT were moderately correlated ({rho}=-0.53, p<0.001). ConclusionsThis work demonstrates the possibility to rapidly, holistically, and objectively assess proximal and distal sensorimotor impairments and their influence on the capacity to perform activities with a single assessment.

neuroscience↗