Department Neuroscience
Principal investigator Dr. Ir. Patrick Forbes
E-mail address p.forbes@erasmusmc.nl
Website https://neuro.nl/research/forbes
Understanding motor control by decoupling the brain from the body
Supervisor: Lucas Mensink, l.mensink@erasmusmc.nl
Neural control of movement demands understanding of the body’s passive mechanics. For example, passive mechanisms such as (short-range) muscle stiffness contribute ~30-90% of the required load when standing upright. This variability results from dynamic factors such as thixotropy and viscosity arising from the binding and release of cross-bridges. Computational models describing these dynamic changes in passive muscle properties have been proposed. However, direct testing of passive properties in vivo is obscured by neural modulation. Our project aims to use nerve-blocking anesthesia to paralyze lower-limb muscles, disconnecting them from neural control. The focus is on characterizing the different mechanisms that dynamically affect passive ankle properties during standing balance and understanding the effect on control of the system.
Techniques
- Nerve-blocking
- Ultrasound
- Electromyography
- Musculoskeletal modelling
- Robotics
Further reading
Sakanaka, T.E., et al. (2018). PLoS One, 13(3), e0193850. DOI: 10.1371/journal.pone.0193850.
Tisserand, R., et al. (2022). PNAS Nexus, 1(4), pgac174. DOI: 10.1093/pnasnexus/pgac174.
Unlearning how to stand: probing human motor control through transforming standing balance
Supervisor: Matto Leeuwis, m.leeuwis@erasmusmc.nl
Human motor control must continuously adapt, both to abrupt perturbations and progressive changes. The central nervous system (CNS) maintains intended motion patterns by altering the control scheme and reweighting or transforming inputs. The CNS could even control standing balance when the relation between applied forces and whole-body angle was mirrored using a robotic balance simulator. Here, the relation between required ankle torque and sensory information was normal for proprioceptive signals but mirrored for vestibular and visual signals. Based on recent observations, we hypothesize that the CNS may be similarly able to control balance using transformed proprioceptive information. In this project, you will quantify human learning of sensorimotor transformations and test to what extent the CNS can transform different sensory inputs.
Techniques
- Robotics
- Musculoskeletal modelling
- Computational motor control
- Electromyography
- Sensory stimulation
Further reading
Forbes, P.A., et al. (2016). Journal of Neuroscience, 36(45) 11510-11520. DOI: 10.1523/JNEUROSCI.1902-16.