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

Abstract

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.

Spectral characterization of human leg EMG signals from an open access dataset for the development of computational models

“…The spectrum median frequency (MDF) of surface MUAP waveforms and EMG interference pattern signals were estimated, consistent with [ 8 ]. Specifically, the MDF is defined as the value that leads to the identity where P i is the value of the EMG power spectrum at the i th frequency bin, and M is the total number of frequency bins [ 40 ].…”

“…Large-scale neuromusculoskeletal computational models also use surface EMG signals for predicting neuromuscular responses, which may not be directly assessed through experiments with human subjects [ 7 – 11 ]. Specifically, surface EMG signals are simulated as an output to computational neural models, which reproduce motor control mechanisms of experimental conditions [ 8 , 12 – 15 ]. For validating and interpretating neural mechanisms simulated with these large-scale computational models, experimental and simulated surface EMG signals must be quantitatively compared [ 12 – 15 ].…”