Reduced rich-club connectivity is related to disability in primary progressive MS. Issue 5 (September 2017)

• Record Type: Journal Article
• Authors: Stellmann, Jan-Patrick; Hodecker, Sibylle; Cheng, Bastian; Wanke, Nadine; Young, Kim Lea; Hilgetag, Claus; Gerloff, Christian; Heesen, Christoph; Thomalla, Götz; Siemonsen, Susanne
• Is Part Of: Neurology. Volume 4:Issue 5(2017)
• Publication Date: 2017-09
• Subjects: Neuroimmunology -- Periodicals; Neurology -- Periodicals; 616.8
• DOI: 10.1212/NXI.0000000000000375
• Languages: English
• ISSNs: 2332-7812

Abstract

Abstract : Objective: To investigate whether the structural connectivity of the brain's rich-club organization is altered in patients with primary progressive MS and whether such changes to this fundamental network feature are associated with disability measures. Methods: We recruited 37 patients with primary progressive MS and 21 healthy controls for an observational cohort study. Structural connectomes were reconstructed based on diffusion-weighted imaging data using probabilistic tractography and analyzed with graph theory. Results: We observed the same topological organization of brain networks in patients and controls. Consistent with the originally defined rich-club regions, we identified superior frontal, precuneus, superior parietal, and insular cortex in both hemispheres as rich-club nodes. Connectivity within the rich club was significantly reduced in patients with MS ( p = 0.039). The extent of reduced rich-club connectivity correlated with clinical measurements of mobility (Kendall rank correlation coefficient τ = −0.20, p = 0.047), hand function (τ = −0.26, p = 0.014), and information processing speed (τ = −0.20, p = 0.049). Conclusions: In patients with primary progressive MS, the fundamental organization of the structural connectome in rich-club and peripheral nodes was preserved and did not differ from healthy controls. The proportion of rich-club connections was altered and correlated with disability measures. Thus, the rich-club organization of the brain may be a promising network phenotype for understanding the patterns and mechanisms of neurodegeneration in MS.