Cognitive function, disease burden and the structural connectome in systemic lupus erythematosus

Objective: To investigate brain structural connectivity in relation to cognitive abilities and systemic damage in systemic lupus erythematosus (SLE).
Methods: Structural and diffusion magnetic resonance imaging (MRI) data were acquired from 47 patients with SLE. Brains were segmented into 85 cortical and subcortical regions and combined with whole brain tractography to generate structural connectomes using graph theory. Global cognitive abilities were assessed using a composite variable g, derived from the first principal component of three common clinical screening tests of neurological function. SLE damage (LD) was measured using a composite of a validated SLE damage score and disease duration. Relationships between network connectivity metrics, cognitive ability and systemic damage were investigated. Hub nodes were identified. Multiple linear regression, adjusting for covariates, was employed to model the outcomes g and LD as a function of network metrics.
Results: The network measures of density (standardised ß = 0.266, P = 0.025) and strength (standardised ß = 0.317, P = 0.022) were independently related to cognitive abilities. Strength (standardised ß = -0.330, P = 0.048), mean shortest path length (standardised ß = 0.401, P = 0.020), global efficiency (standardised ß = -0.355, P = 0.041) and clustering coefficient (standardised ß = -0.378, P = 0.030) were independently related to systemic damage. Network metrics were not related to current disease activity.
Conclusion: Better cognitive abilities and more SLE damage are related to brain topological network properties in this sample of SLE patients, even those without neuropsychiatric involvement and after correcting for important covariates. These data show that connectomics might be useful for understanding and monitoring cognitive function and white matter damage in SLE.