Single-subject structural cortical networks in clinically isolated syndrome

Mult Scler. 2020 Oct;26(11):1392-1401. doi: 10.1177/1352458519865739. Epub 2019 Jul 24.


Background: Structural cortical networks (SCNs) represent patterns of coordinated morphological modifications in cortical areas, and they present the advantage of being extracted from previously acquired clinical magnetic resonance imaging (MRI) scans. SCNs have shown pathophysiological changes in many brain disorders, including multiple sclerosis.

Objective: To investigate alterations of SCNs at the individual level in patients with clinically isolated syndrome (CIS), thereby assessing their clinical relevance.

Methods: We analyzed baseline data collected in a prospective multicenter (MAGNIMS) study. CIS patients (n = 60) and healthy controls (n = 38) underwent high-resolution 3T MRI. Measures of disability and cognitive processing were obtained for patients. Single-subject SCNs were extracted from brain 3D-T1 weighted sequences; global and local network parameters were computed.

Results: Compared to healthy controls, CIS patients showed altered small-world topology, an efficient network organization combining dense local clustering with relatively few long-distance connections. These disruptions were worse for patients with higher lesion load and worse cognitive processing speed. Alterations of centrality measures and clustering of connections were observed in specific cortical areas in CIS patients when compared with healthy controls.

Conclusion: Our study indicates that SCNs can be used to demonstrate clinically relevant alterations of connectivity in CIS.

Keywords: Magnetic resonance imaging; clinically isolated syndrome; graph theory; gray matter; multicenter study; multiple sclerosis; structural cortical networks.

Publication types

  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain / diagnostic imaging
  • Cognition
  • Demyelinating Diseases* / diagnostic imaging
  • Humans
  • Magnetic Resonance Imaging
  • Neural Pathways / diagnostic imaging
  • Prospective Studies