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Neurocomputational mechanisms of modularity in human motor control

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Human motor behaviour is the outcome of the combined action of an extensive neural circuit that generates and controls movement and the mechanical properties of the human body. Fundamental to effective motor control are the interactions a) between different levels of the central nervous system (CNS) as well as b) between the neural motor drive and the musculoskeletal system. In this project, we will probe these interactions aiming to a) understand the principles governing motor coordination and b) elucidate the neural mechanisms implementing them. To answer these questions, we will develop a comprehensive analytical framework that quantifies neuronal and muscular interactions in task space. Ultimately, we will obtain insights into the hierarchical organisation of the human motor system and will be able to characterise the neural mechanisms that underlie movement execution. Crucially, the knowledge obtained from this project can have significant implications in a) health research by aiding to identify and rehabilitate alterations in motor patterns in movement disabilities (dyspraxia, spinal cord injury, etc.) and b) biomedical robotics to inform the control of neuroprosthetic limbs.


The main goal of this project is to understand how the central nervous system (CNS) interacts with the musculoskeletal system in order to execute effective movements. These interactions remain poorly understood primarily because of the lack of unifying methodology that allows their characterization at both the behavioural and neural levels. To address this problem, the successful candidate will develop novel algorithms using techniques from machine learning, information theory and/or network theory and couple them with large-scale neurophysiological measurements of neuromuscular activity during static and dynamic motor behaviours. We will first investigate how task variables are actually translated into “muscle synergies”, i.e. how different muscles are functionally coordinated in order to perform the task at hand. Then, we will delve into the mechanisms that implement the encoding and recruitment of muscle synergies in the CNS. Finally, we will infer the neural motor commands that drive muscle activity and assess to what extent they are shared across muscles and how this is modulated by the desired motor task.

Funding Notes

Candidates are encouraged to apply for the Emma and Leslie Reid Research Scholarship 2020 and/or Leeds Doctoral Scholarships 2020 - April Deadline. Please see phd.leeds.ac.uk for info

References

Ioannis Delis, Pauline Hilt, Thierry Pozzo, Stefano Panzeri, & Bastien Berret (2018), Deciphering the functional role of spatial and temporal muscle synergies in whole-body movements. Scientific Reports 8.

Ioannis Delis, Jacek P. Dmochowski, Paul Sajda, & Qi Wang (2018), Correlation of Neural Activity with Behavioral Kinematics Reveals Distinct Sensory Encoding and Evidence Accumulation Processes During Active Tactile Sensing. NeuroImage 175, 12-21.


Ioannis Delis, Stefano Panzeri, Thierry Pozzo & Bastien Berret (2014), A unifying model of
concurrent spatial and temporal modularity in muscle activity. Journal of Neurophysiology
111:675-693.

Cristiano Alessandro, Ioannis Delis, Francesco Nori, Stefano Panzeri & Bastien Berret (2013),
Modularity in motor control: from input-space to task-space perspectives. Frontiers in
Computational Neuroscience 7(43).

Ioannis Delis, Bastien Berret, Thierry Pozzo & Stefano Panzeri (2013), Quantitative evaluation
of muscle synergy models: a single-trial task decoding approach. Frontiers in Computational
Neuroscience 7(8).


How good is research at University of Leeds in Biological Sciences?

FTE Category A staff submitted: 60.90

Research output data provided by the Research Excellence Framework (REF)

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