Ein datengetriebenes Optimierungsframework zur Verbesserung des Verständnisses von Anpassungen des neuromuskulären Systems an Gehirnpathologien

PIs: Prof. Dominik Göddeke, Prof. Miriam Schulte, Prof. Oliver Röhrle

@article{Maier2024,

title = {OpenDiHu: An efficient and scalable framework for biophysical simulations of the neuromuscular system},

author = {Benjamin Maier and Dominik G\"{o}ddeke and Felix Huber and Thomas Klotz and Oliver R\"{o}hrle and Miriam Schulte},

editor = {Elsevier},

url = {https://www.sciencedirect.com/science/article/pii/S187775032400084X},

doi = {https://doi.org/10.1016/j.jocs.2024.102291},

isbn = {1877-7503},

year  = {2024},

date = {2024-04-20},

journal = {Journal of Computational Science},

issue = {79},

pages = {102291},

abstract = {The versatile neuromuscular system, consisting of skeletal muscles and the nervous system, enables human to perform crucial everyday tasks. To investigate its functioning and dysfunctioning with computer simulations, highly resolved, multi-scale models are favorable, whose numerical solutions demand for high performance computing. We present OpenDiHu, a versatile, high-performance computing, open source software framework for detailed, systemic simulations of skeletal muscles and their recruitment mechanisms. OpenDiHu allows to solve a variety of multi-scale models, including 3D muscle mechanics, measurable electromyographic signals, action potential propagation in the muscle tissue, subcellular bio-chemo-electrical processes, and the neural drive to the muscle. All these components can be combined with a wide range of numerical solution schemes into comprehensive simulation setups for the entire system. Experiments on up to almost 27 000 cores demonstrate the efficiency and parallel scalability of OpenDiHu. This enables in silico experiments at very high spatial and temporal resolutions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Homs-Pons2024,

title = {Coupled simulations and parameter inversion for neural system and electrophysiological muscle models},

author = {Carme Homs-Pons and Robin Lautenschlager and Laura Schmid and Jennifer Ernst and Dominik G\"{o}ddeke and Oliver R\"{o}hrle and Miriam Schulte},

editor = {Wiley Online Library},

url = {https://onlinelibrary.wiley.com/doi/10.1002/gamm.202370009},

doi = {https://doi.org/10.1002/gamm.202370009},

year  = {2024},

date = {2024-03-31},

urldate = {2024-03-31},

journal = {GAMM-Mitteilungen},

abstract = {The functioning of the neuromuscular system is an important factor for quality of life. With the aim of restoring neuromuscular function after limb amputation, novel clinical techniques such as the agonist-antagonist myoneural interface (AMI) are being developed. In this technique, the residual muscles of an agonist-antagonist pair are (re-)connected via a tendon in order to restore their mechanical and neural interaction. Due to the complexity of the system, the AMI can substantially profit from in silico analysis, in particular to determine the prestretch of the residual muscles that is applied during the procedure and determines the range of motion of the residual muscle pair. We present our computational approach to facilitate this. We extend a detailed multi-X model for single muscles to the AMI setup, that is, a two-muscle-one-tendon system. The model considers subcellular processes as well as 3D muscle and tendon mechanics and is prepared for neural process simulation. It is solved on high performance computing systems. We present simulation results that show (i) the performance of our numerical coupling between muscles and tendon and (ii) a qualitatively correct dependence of the range of motion of muscles on their prestretch. Simultaneously, we pursue a Bayesian parameter inference approach to invert for parameters of interest. Our approach is independent of the underlying muscle model and represents a first step toward parameter optimization, for instance, finding the prestretch, to be applied during surgery, that maximizes the resulting range of motion. Since our multi-X fine-grained model is computationally expensive, we present inversion results for reduced Hill-type models. Our numerical results for cases with known ground truth show the convergence and robustness of our approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schmid2024,

title = {Postinhibitory excitation in motoneurons},

author = {Laura Schmid and Thomas Klotz and Oliver R\"{o}hrle and Randall K. Powers and Francesco Negro and Utku \c{S}. Yavuz},

editor = {PLOS Computational Biology},

url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011487#abstract1},

doi = {https://doi.org/10.1371/journal.pcbi.1011487},

year  = {2024},

date = {2024-01-19},

urldate = {2024-01-19},

journal = {PLOS Computational Biology},

volume = {20},

issue = {1},

abstract = {Human movement is determined by the activity of specialized nerve cells, the motoneurons. Each motoneuron activates a specific set of muscle fibers. The functional unit consisting of a neuron and muscle fibers is called a motor unit. The activity of motoneurons can be observed noninvasively in living humans by recording the electrical activity of the motor units using the electromyogram. We studied the behavior of human motor units in an inhibitory reflex pathway and found an unexpected response pattern: a rebound-like excitation following the inhibition. This has occasionally been reported for human motor units, but its origin has never been systematically studied. In non-human cells of the neural system, earlier studies reported that a specific membrane protein, the so-called h-channel, can cause postinhibitory excitation. Our study uses a computational motoneuron model to investigate whether h-channels can cause postinhibitory excitation, as observed in the experimental recordings. Using the model, we developed a method to detect features of h-channel activity in human recordings. Because we found these features in half of the recorded motor units, we conclude that h-channels can facilitate postinhibitory excitation in human motoneurons.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.3389/fphys.2023.1095260,

title = {Linking cortex and contraction\textemdashIntegrating models along the corticomuscular pathway},

author = {Lysea Haggie and Laura Schmid and Oliver R\"{o}hrle and Thor Besier and Angus McMorland and Harnoor Saini},

editor = {Frontiers in Physiology, Sec. Computational Physiology and Medicine },

url = {https://www.frontiersin.org/articles/10.3389/fphys.2023.1095260/full},

doi = {https://doi.org/10.3389/fphys.2023.1095260},

year  = {2023},

date = {2023-05-10},

urldate = {2023-05-10},

journal = {Frontiers in Physiology, Sec. Computational Physiology and Medicine },

issue = {Vol 14-2023},

abstract = {Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson’s disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons  and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chourdakis2022,

title = {preCICE v2: A sustainable and user-friendly coupling library},

author = {Gerasimos Chourdakis and Kyle Davis and Benjamin Rodenberg and Miriam Schulte and Fr\'{e}d\'{e}ric Simonis and Benjamin Uekermann and Georg Abrams and Hans-Joachim Bungartz and Lucia Cheung Yau and Ishaan Desai and Konrad Eder and Richard Hertrich and Florian Lindner and Alexander Rusch and Dmytro Sashko

and David Schneider and Amin Totounferoush and Dominik Volland and Peter Vollmer and Oguz Ziya Koseomur},

editor = {Open Research Europe},

url = {https://open-research-europe.ec.europa.eu/articles/2-51/v2},

doi = {10.12688/openreseurope.14445.2},

year  = {2022},

date = {2022-09-30},

urldate = {2022-09-30},

journal = {Open Research Europe},

abstract = {preCICE is a free/open-source coupling library. It enables creating partitioned multi-physics simulations by gluing together separate software packages.

This paper summarizes the development efforts in preCICE of the past five years. During this time span, we have turned the software from a working prototype -- sophisticated numerical coupling methods and scalability on ten thousands of compute cores -- to a sustainable and user-friendly software project with a steadily-growing community. Today, we know through forum discussions, conferences, workshops, and publications of more than 100 research groups using preCICE. We cover the fundamentals of the software alongside a performance and accuracy analysis of different data mapping methods. Afterwards, we describe ready-to-use integration with widely-used external simulation software packages, tests, and continuous integration from unit to system level, and community building measures, drawing an overview of the current preCICE ecosystem.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schmid2022,

title = {Spindle Model Responsive to Mixed Fusimotor Inputs: an updated version of the Maltenfort and Burke (2003) model},

author = {Laura Schmid and Thomas Klotz and Utku Yavuz and Mitchell Maltenfort and Oliver Roehrle},

doi = {10.36903/physiome.19070171.v2 },

year  = {2022},

date = {2022-01-27},

urldate = {2022-01-27},

journal = {Physiome},

abstract = {The muscle spindle model presented in Maltenfort and Burke (2003) calculates muscle spindle primary afferent feedback depending on the muscle fibre stretch and fusimotor drive. The aim of this paper is to provide an updated version of the model, which is now capable of replicating the originally published data. This is achieved by modifying the equations describing the modulation of the muscle spindle output in response to dynamic fusimotor drive. EDITOR'S NOTE V2: Showing manuscript and model files.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nehyat2020,

title = {POD-DEIM Model Order Reduction for the Monodomain Reaction-Diffusion Sub-Model of the Neuro-Muscular System},

author = {Nehzat Emamy and Pascal Litty and Thomas Klotz and Miriam Schulte and Oliver R\"{o}hrle},

doi = {https://doi.org/10.1007/978-3-030-21013-7_13},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22--25, 2018},

pages = {177--190},

publisher = {Springer International Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}