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98 results for “muscle dynamics”
Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article: </p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements generated with either character writing (PCR) or with 3D target reaching using reinforcement learning (RL).</p> <p>The overall structure of the data is:</p> <p>└── spindle_datasets<br> ├── pcr_dataset - Contains PCR synthetic training dataset<br> └── rl_dataset - Contains RL-generated synthetic training dataset</p> <p>The code to generate the PCR synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation</a></p> <p>The code to generate the RL-generated synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation</a></p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Simple muscle-lever systems are not so simple: The need for dynamic analyses to predict lever mechanics that maximize speed
<p><span>Here we argue that quasi-static analyses are insufficient to predict the speed of an organism from its skeletal mechanics alone (i.e. lever arm mechanics). Using a musculoskeletal numerical model we specifically demonstrate that 1) a single lever morphology can produce a range of output velocities, and 2) a single output velocity can be produced by a drastically different set of lever morphologies. These two sets of simulations quantitatively demonstrate that it is incorrect to assume a one-to-one relationship between lever arm morphology and organism maximum velocity. We then use a statistical analysis to quantify what parameters are determining output velocity, and find that muscle physiology, geometry, and limb mass are all extremely important. Lastly we argue that the functional output of a simple lever is dependent on the dynamic interaction of two opposing factors: those decreasing velocity at low mechanical advantage (low torque and muscle work) and those decreasing velocity at high mechanical advantage (muscle force-velocity effects). These dynamic effects are not accounted for in static analyses and are inconsistent with a force-velocity tradeoff in lever systems.</span> <span>Therefore, we advocate for a dynamic, integrative approach that takes these factors into account when analyzing changes in skeletal levers.</span></p>
Data from: Linking in vivo muscle dynamics to in situ force-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths
<p>Force-length (F-L) and force-velocity (F-V) properties characterize skeletal muscle's intrinsic properties under controlled conditions, and it is thought that these properties can inform and predict <em>in vivo</em> muscle function. Here, we map dynamic <em>in vivo</em> operating range and mechanical function during walking and running, to the measured <em>in situ</em> F-L and F-V characteristics of guinea fowl (<em>Numida meleagris</em>) lateral gastrocnemius (LG), a primary ankle extensor. We use <em>in vivo</em> patterns of muscle (tendon) force, fascicle length, and activation to test the hypothesis that muscle fascicles operate at optimal lengths and velocities to maximize force or power production during walking and running. Our findings only partly support our hypothesis: <em>in vivo</em> LG velocities are consistent with optimizing power during work production, and economy of force at higher loads. However, LG does not operate at lengths on the force plateau (±5% Fmax) during force production. LG length was near L<sub>0</sub> at the time of EMG onset but shortened rapidly such that force development during stance occurred almost entirely on the ascending limb of the F-L curve, at shorter than optimal lengths. These data suggest that muscle fascicles shorten across optimal lengths in late swing, to optimize the potential for rapid force development near the swing-stance transition. This may provide resistance against unexpected perturbations that require rapid force development at foot contact. We also found evidence of passive force rise (in absence of EMG activity) in late swing, at lengths where passive force is zero <em>in situ</em>, suggesting that history dependent and viscoelastic effects may contribute to <em>in vivo</em> force development. Direct comparison of<em> in vivo </em>work loops and physiological operating ranges to traditional measures of F-L and F-V properties suggests the need for new approaches to characterize dynamic muscle properties in controlled conditions that more closely resemble <em>in vivo </em>dynamics.</p>
Data from: Muscle force-length dynamics during walking over obstacles indicates delayed recovery and a shift towards more strut-like function in birds with proprioceptive deficit
<p>Recent studies of in vivo muscle function in guinea fowl revealed that distal leg muscles rapidly modulate force and work to stabilize running in uneven terrain. Previous studies focused on running only, and it remains unclear how muscular mechanisms for stability differ between walking and running. Here we investigate in vivo function of the lateral gastrocnemius (LG) during walking over obstacles. We compare muscle function in birds with intact (iLG) versus self-reinnervated LG (rLG). Self-reinnervation results in proprioceptive feedback deficit due to loss of monosynaptic stretch reflex. We test the hypothesis that proprioceptive deficit results in decreased modulation of EMG activity in response to obstacle contact, and a delayed obstacle recovery compared to iLG. We found that total myoelectric intensity (Etot) of iLG increased by 68% in obstacle strides (S 0) compared to level terrain, suggesting a substantial reflex-mediated response. In contrast, Etot of rLG increased by 31% in S 0 strides compared to level, but also increased by 43% in the first post-obstacle (S+1) stride. In iLG, muscle force and work differed significantly from level only in the S 0 stride, indicating a single-stride recovery. In rLG, force increased in S 0, S+1, and S+2 compared to level, indicating three-stride obstacle recovery. Interestingly, rLG showed little variation in work output and shortening velocity obstacle terrain, indicating a shift towards near isometric strut-like function. Reinnervated birds also adopt a more crouched posture across level and obstacle terrains compared to intact birds. These findings suggest gait specific control mechanisms in walking and running.</p>
Slow Chest Compression on Dynamic Hyperinflation, Dyspnea and Peripheral Muscle Deoxygenation in Patients With COPD
ClinicalTrials.gov study NCT02746536. IPD Sharing: YES. Countries: 1. Publications: 8.
Data from: Linking in vivo muscle dynamics to in situ force-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths
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Effects of endurance flight on mitochondrial physiology, lean mass dynamics and flight muscle morphology in blackpoll warblers
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Data from: Muscle force-length dynamics during walking over obstacles indicates delayed recovery and a shift towards more strut-like function in birds with proprioceptive deficit
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Simple muscle-lever systems are not so simple: The need for dynamic analyses to predict lever mechanics that maximize speed
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Data from: Tuning of feedforward control enables stable muscle force-length dynamics after loss of autogenic proprioceptive feedback
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Data from: Muscle–spring dynamics in time-limited, elastic movements
Muscle contractions that load in-series springs with slow speed over a long duration do maximal work and store the most elastic energy. However, time constraints, such as those experienced during escape and predation behaviours, may prevent animals from achieving maximal force capacity from their muscles during spring-loading. Here, we ask whether animals that have limited time for elastic energy storage operate with springs that are tuned to submaximal force production. To answer this question, we used a dynamic model of a muscle–spring system undergoing a fixed-end contraction, with parameters from a time-limited spring-loader (bullfrog: Lithobates catesbeiana) and a non-time-limited spring-loader (grasshopper: Schistocerca gregaria). We found that when muscles have less time to contract, stored elastic energy is maximized with lower spring stiffness (quantified as spring constant). The spring stiffness measured in bullfrog tendons permitted less elastic energy storage than was predicted by a modelled, maximal muscle contraction. However, when muscle contractions were modelled using biologically relevant loading times for bullfrog jumps (50 ms), tendon stiffness actually maximized elastic energy storage. In contrast, grasshoppers, which are not time limited, exhibited spring stiffness that maximized elastic energy storage when modelled with a maximal muscle contraction. These findings demonstrate the significance of evolutionary variation in tendon and apodeme properties to realistic jumping contexts as well as the importance of considering the effect of muscle dynamics and behavioural constraints on energy storage in muscle–spring systems.
Supplementary_Gene ontology defines pre-post- hatch energy dynamics in the complexus muscle of broiler chickens
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Dynamics of Muscle Mitochondria in Type 2 Diabetes (DYNAMMO T2D)
ClinicalTrials.gov study NCT02697201. IPD Sharing: NO. Countries: 1. Publications: 1.
Dynamic Evaluation of Ankle Joint and Muscle Mechanics in Children With Spastic Equinus Deformity Due to Cerebral Palsy
ClinicalTrials.gov study NCT02814786. IPD Sharing: NO. Countries: 1. Publications: 1.
The Relationship Between Q Angle and Quadriceps Muscle Activation on Dynamic Balance in Women
ClinicalTrials.gov study NCT04896190. IPD Sharing: NO. Countries: 1. Publications: 1.
Effectiveness of a Quick Release Dynamic Muscle-strengthening Program on Dynamic Stabilization of the Cervical Spine
ClinicalTrials.gov study NCT06545006. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Relationship Between Swallowing Dynamics and Suprahyoid Muscle Activity in Sarcopenic Dysphagia
ClinicalTrials.gov study NCT07198568. IPD Sharing: UNDECIDED. Countries: 1. Publications: 14.
Effect of Blood Flow Restriction Training to Muscle Strength, Dynamic Stability, and ACL Injury Prevention
ClinicalTrials.gov study NCT05951036. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Muscle Pain in Late Cold Water Immersion, Muscular Recruitment, Postural Control Dynamic and Sleep Quality
ClinicalTrials.gov study NCT02806609. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Muscle–spring dynamics in time-limited, elastic movements
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.