In-silico neuro musculoskeletal model reproduces the movement types obtained by spinal micro stimulation

Kapardi, Mallampalli; Pithapuram, Madhav Vinodh; Rangayyan, Yashaswini Mandayam; Iyengar, Raghu Sesha; Singh, Amreek; Sripada, Sirisha; Raghavan, Madhusudan

doi:10.1016/j.cmpb.2022.106804

Cited by 7 publications

(8 citation statements)

References 34 publications

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“…This can result in new emergent behaviours and interactions, resulting in behaviours or outputs which need to be systematically validated. The modelling frameworks that combine spinal circuits, muscles and sensory organs already provide various coupling strategies and can be referred to for respective validation data (e.g., Stienen et al., 2007 ; Raphael et al., 2010 ; Dideriksen et al., 2015 ; Sreenivasa et al., 2015 ; Moraud et al., 2016 ; Kapardi et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…Thus, these parameters need to be adjusted to the applied scenario. The weights can be tuned empirically to obtain some desired motor output ( Teka et al., 2017 ; Kapardi et al., 2022 ) or by employing an optimisation algorithm ( Raphael et al., 2010 ). When considering populations of neurons, the connectivity rules can be based on the distance of the neurons to each other (e.g., Sreenivasa et al., 2015 ) or on stochastic distributions (e.g., Dideriksen et al., 2015 ).…”

Section: Mathematical Modelling Of the Corticomuscular Pathwaymentioning

confidence: 99%

“…A common example is to include α -motoneuron pools in skeletal muscle models to predict force generation as a function of α -motoneuron firing times or neural input (e.g., Röhrle et al., 2008 ; Farina et al., 2017 ; Sartori et al., 2017 ; Volk et al., 2021 ). Integrating afferent feedback from proprioceptive sensory organs within spinal cord neuronal circuits is another example of combining different components of the motor pathway to understand inherent behaviour, such as the relationship between spinal circuit connectivity and postural control (e.g., Stienen et al., 2007 ; Raphael et al., 2010 ; Elias et al., 2014 ; Dideriksen et al., 2015 ; Sreenivasa et al., 2015 ; Moraud et al., 2016 ; Aoyama and Kohno, 2022 ; Kapardi et al., 2022 ). By beginning to incorporate more physiologically realistic circuits further upstream to muscle, neuromusculoskeletal models can become more useful in answering questions about motor pathologies and motor control.…”

Section: Introductionmentioning

confidence: 99%

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Linking cortex and contraction—Integrating models along the corticomuscular pathway

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Mathematical Modelling Of the Corticomuscular Pathwaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Linking cortex and contraction—Integrating models along the corticomuscular pathway

et al. 2023

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“…The basic cause of skeletal muscle atrophy after SCI is the loss of excitatory afferent to motoneurons; this leads to the loss of motor innervation in the skeletal muscle 6,24 . In recent years, many studies have confirmed that spinal cord EES or ISMS could induce skeletal muscle contraction and limb movement 11,12,18 . However, there is still a lack of research relating to the specific mechanisms underlying spinal nerve stimulation in preventing neuronal apoptosis and muscular atrophy.…”

Section: Discussionmentioning

confidence: 99%

Effects of tail nerve electrical stimulation on the activation and plasticity of the lumbar locomotor circuits and the prevention of skeletal muscle atrophy after spinal cord transection in rats

Liu,

Chen,

et al. 2023

CNS Neurosci Ther

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IntroductionSevere spinal cord injury results in the loss of neurons in the relatively intact spinal cord below the injury area and skeletal muscle atrophy in the paralyzed limbs. These pathological processes are significant obstacles for motor function reconstruction.ObjectiveWe performed tail nerve electrical stimulation (TNES) to activate the motor neural circuits below the injury site of the spinal cord to elucidate the regulatory mechanisms of the excitatory afferent neurons in promoting the reconstruction of locomotor function.MethodsEight days after T10 spinal cord transection in rats, TNES was performed for 7 weeks. Behavioral scores were assessed weekly. Electrophysiological tests and double retrograde tracings were performed at week 8.ResultsAfter 7 weeks of TNES treatment, there was restoration in innervation, the number of stem cells, and mitochondrial metabolism in the rats' hindlimb muscles. Double retrograde tracings of the tail nerve and sciatic nerve further confirmed the presence of synaptic connections between the tail nerve and central pattern generator (CPG) neurons in the lumbar spinal cord, as well as motor neurons innervating the hindlimb muscles.ConclusionThe mechanisms of TNES induced by the stimulation of primary afferent nerve fibers involves efficient activation of the motor neural circuits in the lumbosacral segment, alterations of synaptic plasticity, and the improvement of muscle and nerve regeneration, which provides the structural and functional foundation for the future use of cutting‐edge biological treatment strategies to restore voluntary movement of paralyzed hindlimbs.

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“…Multiscale computational models of healthy human neuromuscular systems have evolved significantly over the years [1][2][3][4][5][6][7][8][9][10][11] and have been key to unravelling many aspects of the fine physiology that is involved in motor control. Particularly, studies based on such models have answered many questions on the functioning of pools of spinal cord neurons and how they help control muscle activity as a function of the descending drives from upper centres, the feedback from sensory receptors, and the dynamics of tendons and muscles.…”

Section: Introductionmentioning

confidence: 99%

Electrophysiological and functional signs of Guillain–Barré syndrome predicted by a multiscale neuromuscular computational model

Oliveira¹,

Watanabe²,

Kohn³

2022

J. Neural Eng.

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Objective. The diagnosis of nerve disorders in humans has relied heavily on the measurement of electrical signals from nerves or muscles in response to electrical stimuli applied at appropriate locations on the body surface. The present study investigated the demyelinating subtype of Guillain-Barré syndrome using multiscale computational model simulations to verify how demyelination of peripheral axons may affect plantar flexion torque as well as the ongoing electromyogram (EMG) during voluntary isometric or isotonic contractions. Approach. Changes in axonal conduction velocities, mimicking those found in patients with the disease at different stages, were imposed on a multiscale computational neuromusculoskeletal model to simulate subjects performing unipodal plantar flexion force and position tasks. Main results. The simulated results indicated changes in the torque signal during the early phase of the disease while performing isotonic tasks, as well as in torque variability after partial conduction block while performing both isometric and isotonic tasks. Our results also indicated changes in the root mean square values and in the power spectrum of the soleus EMG signal as well as changes in the synchronisation index computed from the firing times of the active motor units. All these quantitative changes in functional indicators suggest that the adoption of such additional measurements, such as torques and ongoing EMG, could be used with advantage in the diagnosis and be relevant in providing extra information for the neurologist about the level of the disease. Significance. Our findings enrich the knowledge of the possible ways demyelination affects force generation and position control during plantarflexion. Moreover, this work extends computational neuroscience to computational neurology and shows the potential of biologically compatible neuromuscular computational models in providing relevant quantitative signs that may be useful for diagnosis in the clinic, complementing the tools traditionally used in neurological electrodiagnosis.

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In-silico neuro musculoskeletal model reproduces the movement types obtained by spinal micro stimulation

Cited by 7 publications

References 34 publications

Linking cortex and contraction—Integrating models along the corticomuscular pathway

Linking cortex and contraction—Integrating models along the corticomuscular pathway

Effects of tail nerve electrical stimulation on the activation and plasticity of the lumbar locomotor circuits and the prevention of skeletal muscle atrophy after spinal cord transection in rats

Electrophysiological and functional signs of Guillain–Barré syndrome predicted by a multiscale neuromuscular computational model

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