Master's thesis: Modelling MEG-based resting-state functional connectivity to predict inter-individual differences in motor tasks.

This study is a contribution to the ERC HANDmade project (SH4,ERC- 2017-STG) directed by Prof. Betti (University "LaSapienza" of Rome), which investigates how natural hand usage shapes behaviour as well as intrinsic and task-evoked brain activity. In particular, the thesis work aims at modelling magnetoencephalography-based resting-state functional connectivity to predict inter-individual differences in motor tasks. It also investigates the possibility of finding a relationship between functional connectivity data at rest and muscular activation. Results indicate that individual differences in task-evoked functional connectivity can be related to individual differences in spontaneous cortical activity (at rest), but the same cannot be assessed with muscle activation. This could be due to the simplicity of the model or the features used to represent the data. Therefore, further tests should be done in the future to overcome these limitations and open novel opportunities for future developments of robotic-assisted technology and neuroprostheses, based on personalized educational training programs.

In codes.txt a brief explanation of the codes is provided.

Author: Anna Vettoruzzo

Supervisor: Prof. Giulia Cisotto

Co-supervisor: Prof. Viviana Betti and Dr. Ottavia Maddaluno