QTMODEM

Quantitative Motor Control for Decision Making


Abstract

The overall goal of this project is to provide a theoretical context encompassing motivation, motor control theory and motor decision-making. How does the brain relate incentive motivation to motor behaviour? How are these principles altered under Parkinson’s disease (PD)? The fact that three of PD’s major motor symptoms: bradykinesia, akinesia and hypokinesia are consistent with a lesser than normal motor invigoration, emphasizes the relationship between motivation and movement, strongly suggesting that PD may be reviewed as a disorder of motivation. This project is concerned with the two major quandaries impending to systematically test this hypothesis: the absence of a normative context relating motivation to motor control theory and the lack of an independent scale of incentive motivation. Therefore, we propose a novel unifying model called Quantitative Motor Control for Decision-Making (QTMODEM) to characterize motor processes as a function of incentive motivation for different kinds of movements. In other words, the QTMODEM intends to formalise the relationship between the selection of motor parameters and an independent metric of motivation derived from electro encephalography. We will first quantify this relationship with a series of psychophysics experiments with healthy participants by varying two factors: the nature of their movements and the participants’ level of incentive motivation. Second, we will perform experiments with PD patients to gain a quantitative insight into their dysfunctional relationship between motivation and movement statistics. Third, we will build the QTMODEM based on the dynamics of a trade off between motivation and motor costs, and optimal feedback control theory. Unlike previous models, we predict that the incorporation of motivation into the QTMODEM, will provide the proper normative context for the characterization of the dynamics between motivation and the selection of motor parameters, both under normal and PD conditions.

Individual Fellowship: Ignasi Cos, PI and Supervisor: Gustavo Deco

Funded by: European Union’s Horizon 2020 research and innovation programme, Marie Skłodowska-Curie Individual Fellowships (IF-EF)

Project reference: 656262