Brain connectome reorganization across the lifespan in relapsing-remitting multiple sclerosis

Normative modeling allows to assess individual MRI data against a normative reference cohort. Currently, however, mainly one-dimensional features such as cortical thickness (CTh) are considered, which neglect the complex network architecture of the brain. Here, we assess whether informing normative modeling with a complex feature reflecting complex brain network architecture can enhance personalized medicine for disorders affecting complex brain networks such as multiple sclerosis (MS).

We calculated normative trajectories of small-worldedness (SWI) and CTh across the lifespan from T1w data (456 RRMS, 84 HC). We investigated differences in RRMS and HC trajectories by evaluating the best-fit of different polynomial functions. Additionally, we calculated trajectories for clinical (EDSS scores) and neuropsychological (cognition/fatigue) variables in RRMS and studied a potential predictive value of network changes using Granger Causality Testing.

Cognition (via MuSIC composite) exhibited an inverse U-shaped quadratic trajectory, peaking mid-life before declining, akin to SWI. We found that in RRMS, small-worldedness follows a quadratic trend across the lifespan, while CTh exhibits a linear negative trend. In HCs, CTh followed a linear negative trend (β = -.012 mm/year, p < 0.001), mirroring patients. EDSS, similar to CTh, exhibited a linear accumulation throughout age. We found a Granger Causal relationship between small-worldedness and fatigue (observed F-value = 6.704), which lagged behind network changes.

We conclude that informing normative modeling with a complex feature reflecting complex brain network architecture can enhance personalized medicine for disorders affecting complex brain network structures such as MS. Additionally, we provide initial evidence that MS-fatigue might partly result from changes in network structure, which needs to be carefully evaluated in future studies.