Disease activity in different treatment groups.

<p>Treatment groups as baseline of patients with EDA or NEDA one year later.</p

Change in disability after one year.

<p>The patient group as a whole had stable disability scores from baseline to follow-up. NEDA patients improved in disability, while the EDA patients showed a disability progression at one-year follow-up.</p

Patients in different treatment groups at baseline and follow-up.

<p>The arrows indicate change in treatment groups of patients from baseline to follow-up. Eight patients changed first line treatment during the period, not illustrated.</p

Annual gray matter atrophy rates.

<p>ANOVAs with Bonferroni-corrected post-hoc tests revealed that subcortical annual atrophy rates differed between patients with evidence of disease activity and healthy controls. Patients with no evidence of disease activity had similar atrophy rates as controls. Cortical atrophy rates were similar in all groups.</p

Baseline MRI characteristics of patients and controls.

<p>WM: white matter, GM: gray matter. The total neuroanatomical volumes, i.e. of both hemispheres combined, are presented. ANCOVAs were performed to test for differences in neuroanatomical volumes between the groups.</p><p>Baseline MRI characteristics of patients and controls.</p

Evidence of disease activity at one-year follow-up.

<p>54% of the patients were classified as NEDA after one year, while 46% of the patients showed either one or more evidences of disease activity.</p

Annual percent change of MRI volumes of patients and controls.

<p>Annual percent change for patients and controls. Independent samples t-tests and ANOVAs with Bonferroni-corrected post-hoc tests were used to test for differences between the groups. Significant differences in atrophy rates were identified between:</p><p>¹ RRMS and HC (p-value<0.001) and,</p><p>² EDA and HC(p-value<0.001).</p><p>Annual percent change of MRI volumes of patients and controls.</p

Multiscale networks in multiple sclerosis.

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype