Characterization of pathogenetically distinct phenotypes of MS by functional MRI imaging (fMRI), multi-focal electroencephalography (EEG) and translational neuromodeling

Principle investigator:   Prof. Dr. Klaas Enno Stephan

Project focus

We will focus in our project on model-based phenotyping of MS subgroups:

Computational phenotyping: Characterizing individual cognitive deficits (with a particular focus on dysfunctions of attention and executive control) in MS by Bayesian models of cognition that can be fitted to simple behavioural data.

Physiological phenotyping with fMRI and EEG: Developing (i) novel dynamic causal models (DCMs) for obtaining individual profiles of connectivity and synaptic parameters within neural circuits, and (ii) machine learning techniques that can exploit the physiological estimates provided by our models for computational diagnostics.

In brief, the idea is to use model-based estimates of individual physiological profiles wrt. synaptic processes (EEG), but also simpler fMRI-based connectivity fingerprints, for obtaining a multivariate and physiologically interpretable definition of MS subgroups.

We will primarily do methodological work, developing models for physiological phenotyping, primarily with EEG, and associated machine learning techniques described above. Additional work at the TNU (funded from other sources) will concern computational and fMRI-based phenotyping; we will make these advances available to the CRPPMS through collaborations.