Multiple Sclerosis (MS) is a neuroinflammatory disease of the central nervous system and is the leading cause of non-traumatic neurologic disability in the young adult population. The structural damage present in MS leads to a rearrangement in the communication between brain areas [1]. Furthermore, the general loss of synapses and neurons leads to a disruption in excitation inhibition balance [2]. In the first part of this project, we explore the hybrid functional magnetic resonance imaging (fMRI)-informed resting-state structural connectivity (rsSC) to simultaneously combine structural and functional information [3,4]. The sign of the entries in these hybrid matrices can be interpreted as a measure of excitation or inhibition between brain areas. We compute the rsSC matrices in a cohort of 13 healthy controls (HC) and 24 people with MS (pwMS) whose data was collected at the UZ Brussel hospital. In the second part, we explore the functional dynamics of the processed fMRI signals using the Leading Eigenvector Analysis (LEiDA) [5,6]. LEiDA performs temporal clustering to functional time series and allows the extraction of functional states that can help to identify differences in the time evolution of states between HC and MS. Our goal is to study the overlap of the identified states with the reference resting state networks (e.g., the Default Mode Network and the Somatomotor Network) [6] and investigate the switching behavior and time evolution of different brain functional states. For both approaches, we employ different types of brain atlases, namely atlases designed on structural features (structural atlases) or calculated from functional neuroimaging data (functional atlases). We found that the atlas{\textquoteright} choice is important in revealing significant differences in Excitation Inhibition Balance in the Default Mode and Somatomotor networks between the HC and MS groups studied and in the functional dynamical patterns of relevant time series.