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Lupus ; 30(2): 285-298, 2021 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-33307988

RESUMEN

OBJECTIVE: This work investigates network organisation of brain structural connectivity in systemic lupus erythematosus (SLE) relative to healthy controls and its putative association with lesion distribution and disease indicators. METHODS: White matter hyperintensity (WMH) segmentation and connectomics were performed in 47 patients with SLE and 47 healthy age-matched controls from structural and diffusion MRI data. Network nodes were divided into hierarchical tiers based on numbers of connections. Results were compared between patients and controls to assess for differences in brain network organisation. Voxel-based analyses of the spatial distribution of WMH in relation to network measures and SLE disease indicators were conducted. RESULTS: Despite inter-individual differences in brain network organization observed across the study sample, the connectome networks of SLE patients had larger proportion of connections in the peripheral nodes. SLE patients had statistically larger numbers of links in their networks with generally larger fractional anisotropy weights (i.e. a measure of white matter integrity) and less tendency to aggregate than those of healthy controls. The voxels exhibiting connectomic differences were coincident with WMH clusters, particularly the left hemisphere's intersection between the anterior limb of the internal and external capsules. Moreover, these voxels also associated more strongly with disease indicators. CONCLUSION: Our results indicate network differences reflective of compensatory reorganization of the neural circuits, reflecting adaptive or extended neuroplasticity in SLE.


Asunto(s)
Encéfalo/patología , Conectoma , Lupus Eritematoso Sistémico/patología , Adulto , Encéfalo/diagnóstico por imagen , Mapeo Encefálico/métodos , Estudios de Casos y Controles , Imagen de Difusión por Resonancia Magnética , Femenino , Humanos , Lupus Eritematoso Sistémico/diagnóstico por imagen , Masculino , Persona de Mediana Edad , Plasticidad Neuronal , Análisis de Regresión , Análisis Espacial
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