Leveraging neurocomputational models to extract the intracerebral novel non-invasive marker of information processing speed in multiple sclerosis - Gaia Zin

Nonlinear dynamics, whole brain models, multiple sclerosis, cognition, magnetoencephalographic (MEG) data, resting state functional magnetic resonance imaging (fMRI), diffusion weighted imaging data (DWI), functional connectivity, structural connectivity In this interdisciplinary project, we will combine our expertise in dynamical systems, neurocomputational modelling, neuroimaging, and multiple sclerosis (MS) to develop a novel, non-invasive biomarker of information processing speed in MS. Such a marker would be of tremendous value to MS patients as it would not only enable us to better understand his/her cognitive functioning, as well as to further accelerate the development of novel treatments that are aiming more and more at remyelination. To achieve this goal, we will re-analyze an existing multimodal dataset, collected by the host co-supervisor Prof. J. Van Schependom in 100 people with MS and 50 controls, in different ways. First, we will optimize the construction of structural connectivity matrices for neurocomputational simulations (WP1). This work will follow along the lines of the work presented in (Manos et al., under review, 2023) of the home supervisor Prof. T. Manos. Next, the PhD candidate will move to Brussels to analyze functional connectivity in MS by using the available high-quality resting state magnetoencephalographic (MEG) data and collaborate closely with the host co-supervisor Prof. J. Van Schependom (WP2). Finally, in WP3, we will leverage the experience of Prof. T. Manos with The Virtual Brain to run simulations based on the optimized structural connectivity (WP1). By comparing the output of these simulations to the actually acquired data in terms of functional connectivity (WP2), we will be able to determine the model’s free parameters. One of these parameters is the average conduction delay, which can be easily recalculated as the average conduction speed. In this project, we hypothesize that the wide-spread demyelination in MS will lead to a decrease in the average conduction speed and that this decrease will be correlated with the most important cognitive test, the Symbol Digit Modalities Test. In summary, this interdisciplinary project optimally combines the expertise available at CYU and VUB and will provide an important, and novel outcome of the person with MS. Through the collaboration with UZ Brussel and the National MS Center Melsbroek and the large international clinical network in MS, this novel biomarker will be implemented across Europe.