Biology subjects

Müller, H.

Publications and source records attributed to Müller, H..

5 recordsLinked to original sources

bioRxiv2025

The Earlier You Know, the Smoother You Act

Intercepting a moving object with our hands requires that the moving object and the hand come into contact at some point along the objects trajectory. Hand movement must, therefore, be planned in advance so that the hand reaches a chosen spatial location along this trajectory at the correct moment in time. To enable such anticipatory behavior, the objects trajectory must be predicted based on sensory information collected before movement planning is concluded. Early completion of planning would allow for a smooth and economical movement; however, it also requires sufficiently accurate information about the objects movement to be available earlier. We investigate interception in the context of a complex, naturalistic motor task, toss juggling, to understand the extent to which proprioceptive and tactile information influence anticipatory movements of the catching hand. In our study, we compared solo juggling, where the efferent, tactile and proprioceptive information from the throwing hand is available during catch planning, with dyadic juggling, a functionally "deafferented" condition where no such information is available because the ball has been thrown by another person. Our results indicate that when internal and tactile information from the toss is absent, anticipatory behavior is greatly reduced. Experienced jugglers seem to rely more strongly on internal information than vision, allowing earlier planning and lower target uncertainty. In the dyadic condition, when internal information is absent, trajectory planning is delayed, leading to greater target uncertainty.

neuroscience↗

bioRxiv2023

Not only a matter of age: Machine learning-based characterization of the differential effect of brain stimulation on skill acquisition

Brain stimulation shows potential at enhancing cognitive and motor functions in humans. However, multiple studies assessing its effects on behavior show heterogeneous results, especially in healthy older subjects. We propose a new method to predict an individuals likelihood and the magnitude of the benefit from stimulation, based on the baseline performance of a sequential motor task, framed in the context of their age. Our results show a differential effect of stimulation, in which individuals with less efficient learning mechanisms benefit from stimulation, while those possessing optimal learning strategies resent a detrimental effect. Importantly, this differential effect was determined by ones ability to integrate task-relevant information at the early stages of training, and not the age. This study paves the way towards the personalized application of stimulation to maximize its effects, and constitutes the first steps to implement an individualized translational clinical intervention, based on the state of the neural system. TeaserAge notwithstanding, brain stimulation is most effective in deficient neural systems, while being detrimental to optimal systems Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/544579v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@bcfab0org.highwire.dtl.DTLVardef@ee55e9org.highwire.dtl.DTLVardef@14a7519org.highwire.dtl.DTLVardef@14de600_HPS_FORMAT_FIGEXP M_FIG C_FIG Main findingAnodal transcranial direct current stimulation (atDCS), applied over the hand representation of the motor cortex concomitant to the training of a sequential motor sequence, has differential effects as a function of the recipients ability to integrate task-relevant information at the early stages of training. Stimulation benefits individuals with seemingly less efficient learning mechanisms, enabling the rapid storage of the spatial coordinates of the motor sequence and an accelerated optimization of the accuracy of execution. In contrast, individuals possessing optimal learning mechanisms experience detrimental effects of stimulation, leading to drops in the accuracy of execution.

neuroscience↗

bioRxiv2022

Spatial and temporal muscle synergies provide a dual characterization of low-dimensional and intermittent control of upper-limb movements

Muscle synergy analysis is commonly used for investigating the neurophysiological mechanisms that the central nervous system employs to control muscle activations. In the last two decades, several models have been developed to decompose EMG signals into spatial, temporal or spatiotemporal synergies. However, the presence of different approaches complicates the comparison and interpretation of results. Spatial synergies represent invariant activation weights in muscle groups modulated with variant temporal coefficients, while temporal synergies are based on invariant temporal profiles that coordinate variant muscle weights. While non-negative matrix factorization (NMF) allows to extract both spatial and temporal synergies, temporal synergies and the comparison between the two approaches have been barely investigated and so far no study targeted a large set of multi-joint upper limb movements. Here we present several analyses that highlight the duality of spatial and temporal synergies as a characterization of low-dimensional and intermittent motor coordination in the upper limb, allowing high flexibility and dexterity. First, spatial and temporal synergies were extracted from two datasets representing a comprehensive mapping of proximal (REACH PLUS) and distal (NINAPRO) upper limb movements, focusing on their differences in reconstruction accuracy and inter-individual variability. For both models, we extracted synergies achieving a given level of the goodness of reconstruction (R2), and we compared the similarity of the invariant components across participants. The two models provide a compact characterization of motor coordination at spatial or temporal level, respectively. However, a lower number of temporal synergies are needed to achieve the same R2 with a higher inter-subject similarity. Spatial and temporal synergies may thus capture different levels of motor control. Second, we showed the existence of both spatial and temporal structure in the EMG data, extracting spatial and temporal synergies from a surrogate dataset in which the phases were shuffled preserving the same frequency content of the original data. Last, a detailed characterization of the structure of the temporal synergies suggested that they can be related to an intermittent control of the movement. These results may be useful to improve muscle synergy analysis in several fields such as rehabilitation, prosthesis control and motor control studies.

neuroscience↗

bioRxiv2022

stainlib: a python library for augmentation and normalization of histopathology H&E images

Computational pathology is a domain of increasing scientific and social interest. The automatic analysis of histopathology images stained with Hematoxylin and Eosin (H&E) can help clinicians diagnose and quantify diseases. Computer vision methods based on deep learning can perform on par or better than pathologists in specific tasks [1, 2, 15]. Nevertheless, the visual heterogeneity in histopathology images due to batch effects, differences in preparation in different pathology laboratories, and the scanner can produce tissue appearance changes in the digitized whole-slide images. Such changes impede the application of the trained models in clinical scenarios where there is high variability in the images. We introduce stainlib, an easy-to-use and expandable python3 library that collects and unifies state-of-the-art methods for color augmentation and normalization of histopathology H&E images. stainlib also contains recent deep learning-based approaches that perform a robust stain-invariant training of CNN models. stainlib can help researchers build models robust to color domain shift by augmenting and harmonizing the training data, allowing the deployment of better models in the digital pathology practice.

bioinformatics↗

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↗