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Session: NeuroMuscular
Session starts: Thursday 26 January, 14:30
Presentation starts: 15:45
Room: Room 530

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Rafael Ornelas Kobayashi (University of Twente)
Antonio Gogeascoechea (University of Twente)
Massimo Sartori (University of Twente)

Abstract:
As the final common pathway of the central nervous system, alpha-motoneurons (MNs) are key for unravelling the neural mechanisms and adaptations underlying motor control, both in healthy and impaired individuals. Depending on the neurophysiological condition of an individual, including age, level of training, severity of motor disorder or neuronal lesion, pools of MNs may exhibit distinct neuro-anatomical properties and firing behaviours. For this reason, the ability to assess the subject-specific characteristics of MN pools is essential for understanding and controlling motor impairment and neurorehabilitation technologies. However, measuring the properties of complete human MN pools in vivo remains an open challenge. Therefore, this work proposes combining high-density electromyography decomposition, biophysical neuronal modelling and metaheuristic optimization into a novel neural-data driven framework. Briefly, this approach consists of decoding neural data from human MNs in vivo to drive a parameter optimization algorithm that fits biophysically realistic MN models to reproduce experimental spike trains. First, we demonstrate that this framework provides subject-specific estimates of MN pool properties from the tibialis anterior muscle on five healthy individuals. Second, we present a methodology for interpolating the MN properties of the entire pool, thereby enabling the creation complete in silico MNs for each subject. Third, we show that neural-data driven in silico MN pools reproduce the firing characteristics (i.e., recruitment time error < 0.01 s and discharge rate error = 0.208 Hz) of in vivo decoded MNs. Additionally, we show that this approach enables estimating muscle activation profiles (R² = 0.86 ± 0.04 with p-values < 0.005) during force-tracking tasks involving isometric ankle dorsi-flexion, at different levels of amplitude. This neural-data driven framework for estimating MN pool properties can open new avenues for understanding human neuromechanics and, particularly, MN pool dynamics, in a person-specific way. Moreover, it enables the creation of personalized computational models to develop neurorehabilitation therapies and motor restoring technologies according to each individual.