Multiple sclerosis (MS) is frequently associated with cognitive impairment (CI), depressive symptoms and fatigue. Apart from being often neglected in routine neurological examination, these elusive clinical features of MS are also known for their poor association with structural damage in magnetic resonance imaging (MRI). Nevertheless, evidence from resting-state functional connectivity (rsFC) indicates that changes in functional brain networks, including abnormalities in graph-theory-based centrality measures, may provide the necessary surrogate markers for CI and enable objective differentiation from other clinically overlapping symptoms. These biomarkers, however, still meet some practical issues, such as non-trivial interpretability and limited applicability in clinical routine. Our recent contribution demonstrated that a single global scalar metric, the degree rank order disruption index (kD), could serve as a biomarker reflecting simultaneous focal increases and decreases in degree centrality throughout the brain, requiring less demanding interpretation than voxel-wise assessment. However, stronger correlations between cognitive processing speed or fatigue and rsFC were identified on the regional level (especially in the default mode network, basal ganglia, and cerebellum). This leads to hypothesis that focusing on the areas particularly involved in cognitive tasks or fatigue and possibly incorporating machine learning approaches or even time-resolved analysis may result in development of even more reliable biomarkers of CI. Hence, the aim of this project is to develop novel centrality-based indices and test them against the kD in predicting CI, fatigue and depression, using in part existing data in patients with MS, and subsequently to establish their prognostic value in a multi-centric prospective observational studies. If successful, the newly established biomarkers could serve as new endpoints for development of new therapeutic interventions and as a groundbreaking objective diagnostic tool to differentiate CI due to network damage from other treatable MS symptoms associated with reduced cognitive performance.

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