Biology subjects

Gassert, R.

Publications and source records attributed to Gassert, R..

8 recordsLinked to original sources

bioRxiv2024

Neural correlates of kinematic features of passive finger movement revealed by univariate and multivariate fMRI analyses

Finger movements are associated with a relatively large neural representation. Passive finger movement - which involves refraining from actively performing or resisting movement - is a robust approach to investigate the neural representation of kinesthesia and proprioception in the brain. While some studies have characterized the neural correlates of passive finger movement, they have relied solely on mass univariate analysis, potentially affecting result sensitivity. Additionally, limited consideration has been given to stimulus duration, a factor closely tied to some kinematic features (amplitude and velocity), which recently proposed modeling approaches now take into account. Here, we reanalyzed previously published data using univariate and multivariate analysis to understand how kinesthesia is neurally encoded in neurotypical subjects in two separate experiments. Systematic passive stimulation of the fingers was provided using an MR-compatible robot while functional magnetic resonance imaging data was recorded. Our analyses consisted of univariate and multivariate approaches, conducted separately for each kinematic feature and adjusted for stimulus duration, regardless of whether brain activation scales with it. We provide a detailed mapping of brain areas related to amplitude, velocity, and direction of passive finger movement, including sensorimotor, subcortical, and cerebellar areas. In general, multivariate pattern analysis was more sensitive than the univariate approach in identifying brain regions associated with passive finger movement. Our univariate analysis demonstrated that activity in sensorimotor and subcortical areas was higher for larger amplitudes and slower velocities, which opposes to the original studys results, likely due to our treatment of stimulus duration as a confounder specified as a parametric modulator. A novel result, we also demonstrated that brain activity in sensorimotor areas was higher for extension compared to flexion of passive finger movement. In terms of kinematic features, a larger neural representation was found for amplitude and direction compared to velocity of passive finger movement. This indicates that kinesthesia and proprioception may be more reliant on displacement than kinematic aspects of passive finger movement. While univariate analyses are limited in addressing spatial heterogeneity and subject-level variability, our multivariate analyses showed increased sensitivity in identifying brain regions encoding passive movement. Our findings may extend the knowledge of how the brain encodes physical movements and may help design neurorehabilitation strategies.

neuroscience↗

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↗

bioRxiv2018

Multifunction control and evaluation of a 3D printed hand prosthesis with the Myo armband by hand amputees

Upper limb amputations are highly impairing injuries that can substantially limit the quality of life of a person. The most advanced dexterous prosthetic hands have remarkable mechanical features. However, in most cases, the control systems are a simple extension of basic control protocols, making the use of the prosthesis not intuitive and sometimes complex. Furthermore, the cost of dexterous prosthetic hands is often prohibitive, especially for the pediatric population and developing countries. 3D printed hand prostheses can represent an opportunity for the future. Open 3D models are increasingly being released, even for dexterous prostheses that are capable of moving each finger individually and actively rotating the thumb. However, the usage and test of such devices by hand amputees (using electromyography and classification methods) is not well explored. The aim of this article is to investigate the usage of a cost-effective system composed of a 3D printed hand prosthesis and a low-cost myoelectric armband. Two subjects with transradial amputation were asked to wear a custom-made socket supporting the HANDi Hand and the Thalmic Labs Myo armband. Afterwards, the subjects were asked to control and use the prosthetic hand to grasp several objects by attempting to perform a set of different hand gestures. Both the HANDi Hand and the Myo armband performed well during the test, which is encouraging considering that the HANDi Hand was developed as a research platform. The results are promising and show the feasibility of the multifunction control of dexterous 3D printed hand prostheses based on low-cost setups. Factors as the level of the amputation, neuromuscular fatigue and mechanical limitations of the 3D printed hand prosthesis can influence the performance of the setup. Practical aspects such as usability and robustness will need to be addressed for successful application in daily life. A video of the tests can be found at the following link: https://youtu.be/iPSCAbd17Qw

bioengineering↗

bioRxiv2018

Performance Metrics for an Application-driven Selection and Optimization of Psychophysical Sampling Procedures

When estimating psychometric functions with sampling procedures, psychophysical assessments should be precise and accurate while being as efficient as possible to reduce assessment duration. The estimation performance of sampling procedures is commonly evaluated in computer simulations for single psychometric functions and reported using metrics as a function of number of trials. However, the estimation performance of a sampling procedure may vary for different psychometric functions. Therefore, the results of these type of evaluations may not be generalizable to a heterogeneous population of interest. In addition, the maximum number of trials is often imposed by time restrictions, especially in clinical applications, making trial-based metrics suboptimal. Hence, the benefit of these simulations to select and tune an ideal sampling procedure for a specific application is limited. We suggest to evaluate the estimation performance of sampling procedures in simulations covering the entire range of psychometric functions found in a population of interest, and propose a comprehensive set of performance metrics for a detailed analysis. To illustrate the information gained from these metrics in an application example, six sampling procedures were evaluated in a computer simulation based on prior knowledge on the population distribution and requirements from proprioceptive assessments. The metrics revealed limitations of the sampling procedures, such as inhomogeneous or systematically decreasing performance depending on the psychometric functions, which can inform the tuning process of a sampling procedure. More advanced metrics allowed directly comparing overall performances of different sampling procedures and select the best-suited sampling procedure for the example application. The proposed analysis metrics can be used for any sampling procedure and the estimation of any parameter of a psychometric function, independent of the shape of the psychometric function and of how such a parameter was estimated. This framework should help to accelerate the development process of psychophysical assessments.

neuroscience↗

bioRxiv2018

Enhancing Simulations With Intra-subject Variability for Improved Psychophysical Assessments

Psychometric properties of perceptual assessments, like reliability, depend on the stochastic properties of psychophysical sampling procedures resulting in method variability, as well as inter- and intra-subject variability. Method variability is commonly minimized by optimizing sampling procedures through computer simulations. Inter-subject variability is inherent in the population of interest and cannot be acted upon. In contrast, intra-subject variability introduced by confounds cannot be simply quantified from experimental data, as this data also includes method variability. Therefore, this aspect is generally neglected when developing assessments. Yet, comparing method variability and intra-subject variability could give insights on whether effort should be invested in optimizing the sampling procedure, or in addressing potential confounds instead. We propose a new approach to estimate and model intra-subject variability of psychometric functions by combining computer simulations and behavioral data, and to account for it when simulating experiments. The approach was illustrated in a real-world scenario of proprioceptive difference threshold assessments. The behavioral study revealed a test-retest reliability of 0.212. Computer simulations lacking intra-subject variability predicted a reliability of 0.768, whereas the new approach including an intra-subject variability model lead to a realistic estimate of reliability (0.207). Such a model also allows computing the theoretically maximally attainable reliability (0.552) assuming an ideal sampling procedure. Comparing the reliability estimates when exclusively accounting for method variability versus intra-subject variability reveals that intra-subject variability should be reduced by addressing confounds and that only optimizing the sampling procedure may be insufficient to achieve a high reliability. The new approach also allows accelerating the development of assessments by simulating the converging behavior of the reliability confidence interval with a large number of subjects and retests without requiring additional experiments. Having such a tool of predictive value is especially important for target populations where time is scarce, such as for assessments in clinical settings.

neuroscience↗

bioRxiv2018

Method for Improving Psychophysical Threshold Estimates by Detecting Sustained Inattention

Psychophysical procedures are applied in various fields to assess sensory thresholds. During experiments, sampled psychometric functions are usually assumed to be stationary. However, perception can be altered, for example by loss of attention to the presentation of stimuli, leading to biased data which results in poor threshold estimates. The few existing approaches attempting to identify non-stationarities either detect only whether there was a change in perception, or are not suitable for experiments with a relatively small number of trials (e.g., < 300). We present a method to detect inattention periods on a trial-by-trial basis to improve threshold estimates in psychophysical experiments using the adaptive sampling procedure Parameter Estimation by Sequential Testing (PEST). The performance of the algorithm was evaluated in computer simulations modeling inattention, and tested in a behavioral experiment on proprioceptive difference threshold assessment in 20 stroke patients, a population where attention deficits are likely to be present. Simulations showed that estimation errors could be reduced by up to 77% for inattentive subjects, even in sequences with less than 100 trials. In the behavioral data, inattention was detected in 14% of assessments, and applying the proposed algorithm resulted in reduced test-retest variability in 73% of these corrected assessments pairs. The algorithm complements existing approaches and can be adapted to sampling procedures other than PEST. Besides being applicable post-hoc, it could also be used online to prevent collection of biased data. This could have important implications in assessment practice by shortening experiments and improving estimates.

neuroscience↗